Productivity, Human Capital Formation Policy, and Redistribution:

Do Government Policies Promote Productivity?

Takayuki Sakamoto

Department of Policy Studies University of Kitakyushu

4-2-1 Kitagata, Kokuraminami-ku Kitakyushu, Japan 802-8577

E-mail: halosakamoto@gmail.com Phone: +81-93-647-6614

Abstract Productivity is an important determinant of national wealth and standards of living. Scholars have shown that different welfare production regimes pursue distinct human capital formation policies to promote productivity. But do those government policies actually promote the productivity of the economy? This paper analyzes whether such policies improve multifactor productivity in industrial democracies, after briefly presenting a human capital investment explanation for why they should help productivity. It finds that family and education policies promote productivity. While such policies with redistributive effects are productivity-enhancing, however, there is no strong evidence that direct redistribution itself (when simply and only measured as poverty or inequality reduction through taxes and transfers) promotes MFP growth, once other human capital formation policies are controlled for. Evidence of pro-productivity effects of ALMP is also not detected. Thus, the analysis finds reason for governments to pursue human capital policies to promote productivity and ultimately standards of living, but they should do so selectively by choosing appropriate policy tools. Keywords: human capital; public policy; education; growth; income distribution Word count: 8,274 An early version of this paper was prepared for the annual meeting of the American Political Science Association, New Orleans, 2012. The research was supported by Grants-in-Aid for Scientific Research, Japan Society for the Promotion of Science.

Productivity is an important determinant of national wealth and standards of living. Scholars

have shown that different welfare production regimes (WPRs) pursue distinct human capital

formation policies to promote productivity (Boix, 1998; Pontusson, 2005; Iversen and Stephens,

2008). But do governments’ human capital formation policies actually promote the productivity

of the economy? The literature has suggested, for instance, that Nordic social market economies

(SMEs) promote productivity by making human capital investments through public education

and vocational training. But whether those policies successfully promote productivity has not

yet been scrutinized. This paper analyzes whether such policies improve multifactor

productivity (MFP) in industrial democracies, after briefly presenting a human capital investment

explanation for why they should help productivity. In the explanation, governments can promote

human capital investments and productivity by reducing the costs of investments, and increasing

their net benefits with public policy. The empirical analysis examines the determinants of MFP

in manufacturing, service, and the total economy separately, as the dynamics of productivity may

vary across those sectors.

The analysis of this paper finds that some human capital formation policies—like family

and education policies—promote productivity. While such policies with redistributive effects

are productivity-enhancing, however, there is no strong evidence that direct redistribution itself

(when simply and only measured as poverty or inequality reduction through taxes and transfers)

promotes MFP growth, once other human capital formation policies are controlled for. Thus, the

analysis finds good reason for governments to pursue human capital policies to promote

productivity and ultimately standards of living, but they should do so selectively by choosing

appropriate policy tools.

2

I also consider whether it is actually WPRs as a whole—rather than individual policies—

that affect productivity growth. The results tentatively show that human capital formation

policies seem to have primary pro-productivity effects, and the effects of WPRs disappear when

those policies are controlled for (although WPRs are significantly associated with MFP growth

when entered without the policy variables). That is, at least according to the results, WPRs do

not seem to have additional pro-productivity effects on top of their education and family policies.

But this does not necessarily mean that WPRs do not matter, since those policies are some of the

defining characteristics of WPRs and they are correlated. When examined at the regime level

(when entered without the policy variables), Nordic SMEs that make large public human capital

investments successfully promote productivity in manufacturing and the total economy (also

continental SMEs, to a lesser extent), but LMEs with less public human capital investments also

succeed in promoting productivity in the service sector. Thus, there tentatively seem to be two

paths to high MFP growth, depending on sectors.

In the next section, I explain what productivity is. The third section reviews previous

explanations and findings. In the fourth, I offer a brief explanation for how governments’ human

capital formation policies can promote productivity. In the fifth, I present the empirical analysis

of the determinants of productivity, using data from 17 OECD countries. I then conclude.

MULTIFACTOR AND LABOR PRODUCTIVITY

Productivity is one of the most important factors determining the wealth of national economies

and their standards of living. Wealthy nations are wealthy because they can produce more output

from the same quantity of resources than less wealthy countries—wealthy nations are more

productive. Krugman (1997) writes, ‘Productivity isn’t everything, but in the long run it is

3

almost everything. A country’s ability to improve its standard of living over time depends

almost entirely on its ability to raise its output per worker’ (p. 11).

Despite its importance, productivity has received little analytical attention in political

science (a few exceptions are Iversen and Wren, 1998; Boyer, 2004; Kenworthy, 2004;

Pontusson, 2005). Productivity concerns political scientists, since it crucially affects economic

growth and improvements in living standards, which governments and citizens care about. It is

appropriate to study whether their human capital formation policies actually work as intended,

because governments pursue such policies with a view partly toward promoting productivity and

economic growth. This paper fills the gap in the analysis of the political economy of

productivity.

In contrast to productivity, GDP growth has received wide attention in political science

(just to name a few, Alesina, et al., 1997; Garrett, 1998; Clark, 2003; Pontusson, 2005).

However, productivity and GDP growth is conceptually and empirically different. On the

empirical side, when we compare OECD countries’ ranking orders between labor productivity

and per capita GDP growth rates, many countries move from one side of the OECD average line

to the other. For instance, Japan has higher-than-average labor productivity growth, but lower-

than-average GDP growth. So do Sweden and Germany. In contrast, the Netherlands,

Luxembourg, and Spain have lower-than-average labor productivity growth, but higher-than-

average GDP growth. A country can theoretically create GDP growth by just increasing labor

utilization (numbers of workers or hours worked), without improving productivity.1 Thus,

productivity growth deserves attention, separate from GDP growth. Below, I describe what labor

productivity and MFP are, how they differ, and what affects them.

1 Real GDP growth = labor productivity growth + labor utilization growth.

4

Labor Productivity

GDP per capita is a commonly used indicator of national wealth and standards of living, and its

growth that of improvements therein. Labor productivity growth, in turn, is an important

determinant of per capita GDP growth.2 It explains at least half of per capita GDP growth in

OECD countries in the 1990s (OECD, 2003).

Labor productivity is derived from a standard Cobb-Douglas production function with

constant returns to scale:

LAKY 1 (1)

where Y is output, K is capital input, is labor input, L A is MFP, and is elasticity of output

to labor. Dividing (1) by and rearranging, we get: L

1

L

KA

L

Y (2)

Y/L is labor productivity—the amount of output produced per hour worked, measured by

dividing real GDP (value added) by total hours worked. K/L is the capital-labor ratio—the stock

of physical capital per hour worked.

Taking natural logarithms and differentiating with respect to time yields:

LK

LK

A

A

LY

LY

/

)/()1(

/

)/(

(3)

Defining , , and , we get: Y/Ly Aa K/Lk

k

k

a

a

y

y

)1( (4)

Thus, labor productivity growth is a function of growth in MFP and in the capital-labor ratio.

From (4), we can derive MFP growth as:

2 See footnote 1.

5

k

k

y

y

a

a

)1( (5)

Labor productivity measures how efficiently labor (combined with the stock of physical capital

available per worker and technology) converts inputs into outputs. The capital-labor ratio

contributes to labor productivity, because a higher ratio means that each worker has more

physical capital (e.g., machinery and equipment) to work with, so can produce more output.

Thus, physical capital investments promote labor productivity. Increases in the capital-labor

ratio (capital deepening) account for about 45% of labor productivity growth in the recent decade,

and MFP growth explains the rest (OECD, 2007). Thus, labor productivity growth is an

important determinant of per capita GDP growth, and MFP is that of labor productivity growth

(and of per capita GDP growth).

MFP

MFP is, approximately, a measure of technological progress. It reflects the efficiency with

which all inputs are converted into outputs. In the presence of technological shifts, the same

amount of labor and capital inputs can produce more outputs. It is measured as the part of GDP

that cannot be explained by all other inputs combined, like labor and capital. As such, it is a

residual concept, but despite the definitional fuzziness, it is an indispensable component of

analysis of economic output and growth in economics.

What promotes MFP? Though the following factors are not an exhaustive list and are not

cleanly separable, for purposes of concise description, we can think of 1) improvements in

human capital; 2) technological advances; and 3) efficiency gains from other sources.

First, high skills and knowledge possessed by workers facilitate innovation and the

absorption and diffusion of technological advances. Human capital development, in turn, is

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promoted by education, vocational training, and the like.3 Second, technological advances

promote MFP. They can come in embodied or disembodied form. Embodied change is

advances in the quality of capital inputs in the forms of new products, machinery, equipment,

and designs. Disembodied change includes innovations in: management, organization, or

production procedures; scientific knowledge; or spillovers from inputs. High human capital also

facilitates these changes, since it is well-trained, creative scientists, engineers, managers, and

workers that generate, adopt, or implement the changes.

Third, efficiency gains can come from other sources, such as circumstances, institutional

environment, or policies that increase competition or the incentives of workers and firms to

generate innovation or adopt and diffuse new technologies. Competition is generally considered

to promote productivity, as it encourages firms and individuals to innovate and adopt new

technologies in order to survive competition. Product market regulations and employment

protection are argued to be unconducive to productivity, since they restrict competition, the exit

and entry of firms in the market, and labor resource reallocation and adjustments to new

technologies and change in market demand.

As we have seen, not all MFP growth is from improvements in human capital. Yet,

human capital is important for both hard and soft technologies to materialize their pro-

productivity potential, because even advanced technologies may not help productivity much,

unless workers and firms who use them figure out innovative or skillful ways to take advantage

of them to raise efficiency. For instance, economists believe that it took the IT revolution years

3 Skills and knowledge also directly improve the quality of labor and contribute to MFP, unless

they are explicitly renumerated and incorporated into a measure of labor inputs in which case

their contribution is captured by labor inputs.

7

to start showing its productivity effects because it took workers and firms time to put it to

productive use (Krugman and Wells, 2005).

PREVIOUS EXPLANATIONS AND FINDINGS ABOUT THE EFFECTS OF POLICY

Political-economic analyses that empirically study the effects of policy on productivity are not

many. But in the available literature, more work has been done on the role of product market

regulations and employment protection legislation (EPL), which researchers consider to

negatively affect productivity by suppressing competition, innovation, adoption of technology,

adjustments to change in market and demand, and resource allocation efficiency. Nicoletti and

Scarpetta (2003) empirically find that product market regulations generally slow down MFP

growth, but that productivity benefits from deregulation in manufacturing are larger when

countries are farther from the technological frontier. By contrast, Amable et al. (2010) argue that

the effect of regulation on innovation can be positive, when industries are close to the

technological frontier (their dependent variable is innovation).4

Meanwhile, OECD (2007) and Bassanini and Venn (2007) show that stringent EPL

reduces productivity growth (cf. Amable et al., 2010). They also find that minimum wages

increase productivity levels either because they give better incentives to invest in training or

because of substitution of high-skilled workers for low-skilled ones; and that parental leave

increases productivity probably because it encourages women’s labor market participation and

enables them to make use of previous investments in firm- or industry-specific skills.

There is also research that suggests an impact of inequality on productivity. Though

inequality itself is not a policy, it becomes relevant because it can affect human capital

4 Amable et al. point out that the relationship between regulation and innovation is not as clear as

suggested by the literature, theoretically and empirically.

8

investments by individuals, and because most governments conduct some level of redistribution

to reduce inequality. Aghion et al. (1999) theoretically argue that in the presence of capital

market imperfections, individuals’ endowments (family wealth) determine their investments in

education, and the unequal distribution of wealth reduces aggregate productivity and output

growth, because the poor’s limited borrowing capacity restricts their investment opportunities in

education and their marginal productivity of investment is relatively high due to decreasing

returns to individual capital investments. If their theoretical conjecture is correct, one might

expect government redistribution (reducing inequality) to be conducive to productivity growth.

Conversely, one can also theoretically conceive negative effects of equality when it is achieved

by government redistribution. Redistribution through income tax may create an incentive

problem and reduce the incentive to invest in human (or physical) capital, lowering growth.5 To

the best of my knowledge, there have not been empirical investigations to test the effect of

redistribution on productivity growth. This paper tests it.

Though it is not about the effects of policy, there is empirical research that examines the

relationship between education (human capital) and productivity or economic growth. Perotti

(1996) argues that income equality boosts secondary school enrollment (his proxy for investment

in education), which in turn leads to higher economic growth. Englander and Gurney (1994) and

de la Fuente and Donenech (2000) show that secondary school enrollment and years of schooling

5 Though not about productivity growth, empirical evidence is mixed about the relationship

between inequality and economic growth. See, for instance, Alesina and Rodrik (1994) and

Perotti (1996) for a negative association; Arjona et al. (2001) for no association; Forbes (2000)

for a positive association.

9

boost productivity.6 These studies find positive impacts of education on productivity or

economic growth, but their independent variables are the stock of human capital measured by

educational attainment or school enrolment and do not allow one to test whether governments’

education policy affects productivity growth (human capital stocks can be a result of other

factors as well as public policy). So I test the role of education policy in the empirical analysis.

In political science, a few studies have hinted at a link between human capital policy and

productivity (Boix, 1998; Pontusson, 2005; Iversen and Stephens, 2008), but it has not been put

to an empirical test.

As we have seen, with the exceptions of some labor and product market policies, the

productivity effects of government policy have not been empirically examined. Such empirical

analysis is what this paper pursues. The next section explains the theoretical basis for public

policy’s effects on productivity through human capital investments.

HUMAN CAPITAL INVESTMENT PERSPECTIVE

Individuals get education and training necessary to maximize their utility. They will get

education and training up to a point where the marginal costs of them equal the marginal benefits

of doing so. The net benefits perceived by individuals determine their decisions about skill

investments.

The perceived net benefits differ across individuals. Individuals differ in their financial

resources and socioeconomic conditions. In a free market without government intervention,

these factors are largely determined by individuals’ family and environment.

6 Research also reports a robust relationship between cognitive skills measured by test scores and

economic growth (OECD, 2010b; Hanushek and Woessmann, 2012).

10

For individuals from low-income families, the costs of education are relatively high (i.e.,

the costs constitute a larger share of their income), because their ability to finance education is

more limited than individuals from wealthy families (limited borrowing capability) (Aghion et

al., 1999).7 The higher costs reduce the net benefits of education for individuals with less wealth.

As a result, they underinvest in education and skills, which in turn leads to less human capital

and lower productivity.8

However, income distribution and education in the real world do not take place in the

total absence of government intervention. Governments engage in redistribution to ease

inequality and poverty. They also provide public education, family support, job training, and

other services to improve the quality of citizens’ lives or to fill the gap left by the market, albeit

to varying degrees. Some governments also use these policies as a strategy to promote human

capital formation and the competitiveness and growth of their economy. Other governments

leave more of these to market provision and private (individual) self-help.

Governments can help promote human capital investments by individuals in two related

ways. One is by reducing the costs of investments and increasing their net benefits by using

policies in education, redistribution, welfare, and the labor market. The other is by creating a

social environment (with policies and institutions) where human capital investments pay and/or

individuals believe it. The second serves to increase the net benefits of education understood by

individuals.

Reducing the Costs of Human Capital Investments and Increasing Net Benefits

7 See also Galor and Zeira (1993). 8 I here ignore countervailing disincentives for high- and middle-income households; i.e., the

negative effects on human capital investments and productivity of disincentives caused by the

relative absence of inequality.

11

Policies of redistribution, public education, welfare, and labor market programs can reduce the

costs of education/training and boost their net benefits, thereby correcting for underinvestment in

skills and promoting human capital formation and productivity.

In education, governments can provide a free or low-cost high-quality public education,

directly lowering the costs of education. In redistribution, they can increase the incomes of low-

income households and reduce the relative costs of human capital investments as a share of

disposable incomes. Various welfare programs providing cash transfers and in-kind services can

directly or indirectly increase the incomes of low-income households and reduce the relative

costs.9

Likewise, ALMP—job training, placement services, employment incentives, work

experience programs, and job creation—reduces the costs of skill formation by directly

providing job training or indirectly supporting employment or job experiences. It can also

enhance job seekers’ prospects of landing a job and thereby increase the benefits of skill

formation perceived by individuals (it helps them believe that they can gain employment or

better jobs and be better off, if they invest in education/training). Passive labor market programs

such as unemployment benefits may also reduce the costs of education/training by providing the

unemployed with time and financial means to invest in skills without worrying about earning a

living, although they may also work against skill investments and productivity if they create

work disincentives.

9 There is some indirect evidence to suggest that redistribution encourages education and better

academic and wage outcomes—the OECD (2010a) reports that individual income tax

progressivity and unemployment benefits are correlated with a smaller influence of parental

socioeconomic background on children’s academic achievement and their wages.

12

In addition to their effect of reducing skill investment costs, education and job training

directly improve the quality of labor and contribute to productivity.

Institutional Incentive Structure, Information, and Beliefs

The incentive structure, information, and beliefs also affect individuals’ skill investments. As for

information and beliefs, individuals make decisions under incomplete information. They can

have inaccurate beliefs about the net benefit of skill investments and consequently may make

sub-optimal decisions. If so, it is important that individuals have accurate information, so that

they do not incorrectly discount or overestimate the benefits of investments. Society or

government can help individuals correctly calculate the net benefit and make appropriate

decisions. If an educational system is of high quality and successfully keeps students, parents,

and citizens informed of the value of education, individuals do not have to be misinformed about

or to underestimate the net benefits of education. A high-quality education or training system

may also be able to better nurture or discover talents in individuals.

The easier it is to receive education/training and to see their benefits, the easier skill

investments are, and the more common they will become in society. Low-cost high-quality

public education, employment-enhancing labor market programs, and family support10 help

create an environment where people feel more positively about skill investments.

Such a system works better if there is a well-functioning constellation of labor market

policy and institutions, where individuals can find suitable or better jobs with additional

education or training. If finding a job were difficult, they would not make investments. In this

10 For instance, widely available low-cost daycare makes it easy for parents to go to school or

receive job training (on top of the productivity benefit that high quality daycare prepares children

for better academic achievements and eventually improves the quality of labor).

13

sense, placement services and job incentives or creation programs should help because they

improve job prospects.

Regimes

In contrast to the view that individual policies affect productivity, one could also posit that it is

actually the whole constellation of WPRs as a regime that affects productivity. The varieties of

capitalism (VoC) and WPR literature and their predecessors may suspect such a regime effect

(e.g., Boix, 1998; Garrett, 1998; Hall and Soskice, 2001). In this view, individual policies (and

institutions) are constitutive parts of a regime that complement each other and that may not

produce outcomes in isolation. In the empirical analysis below, the presence of such regime

effects is also tested to gauge whether it is policies or regimes that affect productivity.

My hypotheses can be summarized as follows:

H1: Widely-available, low-cost, high-quality public education boosts MFP, as it promotes human

capital investments by reducing the costs of education and increasing its net benefits (and as it

improves the quality of labor);

H2: Family support policy boosts MFP, as it encourages human capital investments by reducing

the costs of skill formation and increasing its benefits;

H3: ALMP boosts MFP, as it promotes skill investments by reducing the costs of job training

and increasing the net benefits (and as it improves the quality of labor);

H4: Redistribution boosts MFP, because poverty and income inequality suppress MFP by

increasing the relative costs of skill formation and discouraging human capital investments, and

redistribution eases poverty and inequality;

H5: R&D promotes MFP by facilitating technological advances and innovation;

14

H6: WPRs as a whole (not individual policies) affect MFP. Nordic SMEs with large public

human capital investments should have high MFP growth. Continental SMEs with lower

investments should have lower MFP growth than Nordic ones. LMEs with still less investments

should have yet lower growth.

The empirical analysis below will test the reduced form of the hypotheses. It will test the

relationships between the above policies and MFP growth—not the policies’ effects on the

amount of human capital investments or the stock of human capital.

DATA ANALYSIS

Data

I analyze industry-specific data from 17 OECD countries from 1990 to 2006, to examine the

relationships between government policies and MFP growth.11 I analyze three sectors—

manufacturing, service, and the total economy (‘service’ here is market service and does not

include the public sector). The starting year is set to 1990. The length of the time-series is

limited by the availability of consistent time-series cross-national data on policy variables, as

many of the data only start sometime during the 1980s or 1990s (education, 1992-; family

support, 1980-; ALMP, 1985-; EPL, 1985-).

Dependent Variable

The dependent variable is MFP growth (the first difference of natural logs, ∆lnMFP).12 I use EU

KLEMS Database (November 2009 Release) for MFP data (see Timmer et al. (2007) for a

description of the database).

11 They are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland,

Italy, Japan, the Netherlands, Portugal, Spain, Sweden, the United Kingdom, and the United

States. Luxembourg, New Zealand, Norway, and Switzerland are excluded for lack of MFP data. 12 11 /)log()log( ttt YYYY

15

The statistical test using EU KLEMS’ MFP represents a strong test for the productivity

effects of human capital investment policies, since the effects of educational attainment

(measured by degrees), gender, and age (job experience) are already removed from the measure

of MFP. Even if human capital investment policies have positive effects on productivity through

their effects on educational attainment or on job training, such effects do not count as the

policies’ positive effects on productivity. The productivity effects of educational attainment and

job experience are attributed to labor inputs (L).

With this measure of MFP, many of actual improvements in the quality of human

capital—e.g., what workers actually learn and what quality of skills and knowledge they achieve

as a result of education and vocational training—are not captured by labor inputs. The

productivity effects of these and other unmeasured improvements are captured by MFP.

The statistical test using the EU KLEMS’s data is a strong test, also because the data do

not include Norway (a high-productivity economy with large public spending on human capital

formation) and New Zealand (a low-productivity economy with small public spending). If the

exclusion of the two countries has any effect on the results, it will be against the pro-productivity

effects of human capital formation policies.

Independent Variables

The estimation of the impact of policies on MFP here uses public spending levels or ratios for

most independent variables. These variables should ideally be measured also by their

substantive properties or quality. But coding such data for quantitative cross-national analysis is

16

not immediately easy and needs to await future projects. The independent variables are the

following.13

Family, Univ, PrimarySec, Preprimary, ALMP, RedisPoverty, and RedisGini are policy

variables that are hypothesized to affect MFP growth. Family is public spending on family

support, such as child allowances, childcare support, income support during parental leave, and

single parent payments (real spending per head (US$ in PPPs) in natural logs). Univ is public

spending on university education as a percentage of GDP. PrimarySec and Preprimary are

similarly public spending on primary-secondary and pre-primary education.

ALMP is public spending on ALMP, such as job training, employment services including

counseling and vocational guidance, youth measures, direct job creation, and employment

incentives and subsidies (real spending per head (US$ in PPPs) in natural logs).

RedisPoverty and RedisGini are measures of redistribution by government. RedisPoverty

measures redistribution in terms of poverty reduction (gross minus net poverty rates divided by

gross rates). RedisGini measures redistribution in terms of reduction in income inequality (gross

minus net Gini coefficients of working-age household income as a percentage of gross Gini

coefficients).

R&D is gross government domestic expenditure on R&D as a percentage of GDP. R&D

spending in general is expected to positively affect MFP by promoting technological advances

and innovation, but there is a question as to whether all spending—public and private—is

equally conducive. A possibility is that public spending may not be as efficient as private

13 I tested the left-party control of government as an independent variable, but subsequently

dropped it, as it was not significant.

17

spending or may crowd out private spending. So I will also test total R&D spending including

private funding.

The original raw data for all these spending/economic independent variables are from

OECD.Stat (http://stats.oecd.org).

Economic Controls

EPL is a measure of strictness of EPL.14 In the literature, EPL is suspected to be unconducive to

productivity, because it can curb competition, innovation, and adoption of new technologies,

retard the reallocation of human and other resources, and/or impair the ability of firms to adjust

to changes in market demand and technology. Restrictive EPL can make hiring and firing

difficult. Innovation and productivity growth are importantly driven by the turnover of firms and

workers, and EPL can restrict the turnover (OECD, 2003). In addition, with secure employment

thanks to EPL, workers can potentially have less incentive to maximize work efforts or to invest

in skill acquisition.

On the other hand, EPL may also serve a productive function, as job security and tenure

enabled by EPL may improve employers’ and employees’ incentives to invest in training and

skill acquisition. So its overall effects may depend on how these effects stack up against each

other. Empirical evidence is mixed (OECD, 2007).

The lagged level of real GDP per capita (US$ PPPs) in natural logs is also entered to

control for a convergence (catch-up) effect (GDPcapita).15

14 The overall summary indicator of the OECD Indicators on Employment Protection (Version 1,

1985-2008). 15 I use the lagged level of per capita GDP, instead of its initial level, since the latter (time-

invariant) creates perfect collinearity with country FEs. For robustness check, I estimated pooled

18

All independent variables are lagged by one year. It is natural to think that lags of

educational spending (e.g., primary/secondary school spending) should be longer. But I

arbitrarily opted not to use longer lags for mainly two reasons. First, we do not know the real lag

structure, and the issue is complicated, considering that the first and last years of

primary/secondary schooling are already 11 years apart. Second, the time-series for educational

spending is 15 years, and longer lags would sacrifice the number of years available for analysis.

Later in the empirical section, I will also examine the effects of WPRs as regimes. LME

is a dummy variable for LMEs (Australia, Canada, Ireland, Japan, the United Kingdom, United

States), Nordic for Nordic SMEs (Denmark, Finland, Sweden), Continental SME for continental

SMEs (Austria, Belgium, France, Germany, the Netherlands), and Others for Italy, Portugal,

Spain.

Estimation

I estimate both pooled OLS and fixed effects (FEs) models. All estimation uses panel-corrected

standard errors (PCSEs) to correct for panel heteroskedasticity and contemporaneous correlation

of errors (Beck and Katz, 1995). The results of random effects (REs) models are not reported

(except when pooled OLS and FEs produce conflicting results), as the Breusch and Pagan

Lagrange multiplier test and Hausman test reject REs.

I estimate both pooled OLS and FEs models to compensate for the potential problems of

each method. The reason is as follows. Pooled OLS leaves open the possibility of omitted

variable biases. The omitted variable problem can be avoided by using FEs models. But FEs

models use only within variance, and do not utilize between variance. When variables have

OLS and REs models with the latter. The results did not change. If anything, the significance of

the human capital investment variables becomes stronger.

19

small within variance and most of their variance is between variance, the results of FEs models

can be unreliable. If one wants to avoid missing any potential effect of between-variance-

dominant variables (which happen often in time-series cross-national analysis), it is advisable to

check the results of pooled OLS (and REs, but REs are rejected by specification tests). In the

models reported below, while the F test rejects the null of no fixed effects, individual country

FEs are not significant except for a couple of countries, which seems to suggest paying attention

also to pooled OLS results.

The estimation reported here does not correct for autocorrelation, as autocorrelation is not

a problem. In LM tests, none of manufacturing equations and the FEs models for service and the

total economy show any significant autocorrelation. The service and total-economy models

without FEs show small autocorrelation (.23 and .15). So I checked the results, using Prais-

Winsten estimation with common AR(1) error process. The results remain the same, except for

ALMP which turns from significant to marginally insignificant. Overall, autocorrelation is not

likely to be a problem, since the dependent variable enters as a first difference of natural logs,

and first differencing usually eliminates unit root concerns and removes most autocorrelation.

I ran many regressions, adding and dropping different variables in different combinations,

to check the stability of coefficients and significance. The reported results are those that are

relatively stable across different specifications, unless otherwise noted.16

16 The reason I enter human capital investment policy spending in levels rather than in change is

what the existing literature suggests. Previous empirical studies have repeatedly failed to find a

positive correlation between growth in human capital and productivity or economic growth (e.g.,

Benahabib and Spiegel, 1994; Islam, 1995, 2003; Mason et al., 2009). The coefficient on human

capital growth in these studies has consistently turned out either negative or insignificant. The

finding of no or negative effect is robust to different specifications and measurements of human

20

The Results

Table 1a presents the results of the baseline models. The results suggest that public policy plays

a significant role in MFP growth in manufacturing, market service, and the total economy (with

variations across policies and sectors).

To begin with, family support spending boosts MFP growth in manufacturing, service,

and the total economy. The coefficient is all positive and mostly significant.17 Since family

support (unlike education and job training) does not directly act on individuals’ skills and

knowledge, the results tentatively suggest that it promotes productivity by making human capital

investments easier for families and their children by, for instance, improving their financial

means. Or family support like paid parental leave and childcare may give parents the incentive

for sustained skill investments and facilitate human capital accumulation by making it easy to

continue their careers while raising children. It could also be possible that high-quality childcare

provided by governments benefits children’s education and careers later and eventually

contributes to productivity.

-- Tables 1a and 1b about here --

University education spending also significantly boosts MFP growth across all sectors.

The pro-productivity result of this spending is the most stable of all variables. The results of

capital (see, e.g., Benahabib and Spiegel, 1994; but for positive findings, see Hanushek and

Woessmann (2012) who use cognitive skills as a proxy for human capital). However, studies

commonly have found a positive and significant correlation between the level of human capital

and productivity growth. These patterns are also replicated in my results. When I enter them in

change, most of the human capital investment variables turn out negative and insignificant. 17 Family in pooled OLS is also significant in the total economy and service, when the lagged

level of per capita GDP is replaced with the initial level of per capita GDP. In REs models, it is

significant in manufacturing and service.

21

primary/secondary school spending are less stable. It is positive and significant in manufacturing

and service in FEs. But in pooled OLS, it is indistinguishable from zero in the two sectors, and

negative and significant in the total economy (this is the only time PrimarySec is significantly

negative). In REs, it is insignificant in all sectors (results not shown).

As the education spending variables are correlated with each other, I also ran the

regressions without Univ (results not shown). PrimarySec becomes positive and significant in all

three sectors in FEs, though in pooled OLS, it is insignificant in all sectors. In REs, it is positive

and significant in manufacturing, and positive but insignificant in service and the total economy.

When entered only with the economic controls, PrimarySec is positive and significant in both

FEs and pooled OLS (with the exception of service in pooled OLS where it is insignificant).

Since these estimates may be biased by omitted variables, we should not attach too much

meaning, but considering all, the productivity effect of primary/secondary spending may still be

positive. But we need more research to find the real answer.

There is little evidence that pre-primary school spending promotes MFP growth (not

reported). It is positive and significant only in service in FEs, and becomes negative and

significant in pooled OLS. With university spending removed, it is indistinguishable from zero

in all models. In models only with the economic controls, it is all positive but significant only in

service in FEs. So there is a possibility that it may promote MFP in service, which would be

consistent with the view that general skills nurtured in early childhood education facilitate

economic performance in the service sector (Pontusson, 2005). But evidence is weak for

preprimary education.

So, there is much evidence that public spending on university boosts MFP growth.

Primary/secondary school spending may also boost it. But there is little evidence that pre-

22

primary spending affects growth (with the possible exception of service). Research has hinted at

beneficial effects of early childhood education in promoting workers’ cognitive skills (hence,

human capital and productivity by implication), income equality, competitiveness in ICT, and

students’ academic achievement at later stages (e.g., Pontusson, 2005; Iversen and Stephens,

2008; OECD, 2010a). But the results here suggest, at least, that the sheer amount of spending on

pre-primary school is not associated with MFP growth. More empirical tests using better

measures capturing the quality and quantity of education are necessary. It may be the type,

quality, or accessibility of pre-primary education that matters. Also, we may need to use longer

lags for pre-primary spending, as much time lapses from pre-primary education to a professional

life (longer lags were difficult to execute in this paper, as this variable’s time-series is short). Or

positive effects of pre-primary spending may be absorbed in those of primary/secondary or

college education. Further research is required.

The results of ALMP do not support our human capital investment hypothesis. ALMP is

negative and significant in service and the total economy. The negative sign is robust to

different specifications (pooled OLS, FEs, and REs). Only when all other human capital

investment variables and R&D are removed, does it become positive and significant only in

pooled OLS in manufacturing and the total economy (not reported).18 The lack of positive

results is noteworthy in light of a literature suggesting beneficial effects of ALMP on various

economic performance measures (e.g., employment, labor market adjustments, adoption of

technologies) (e.g., Pontusson, 2005; OECD, 2006, 2007; Nelson and Stephens, 2012).

ALMP encompasses different programs with varying objectives and effects (public

placement services, job training, activation, and employment incentives), and it may be that not

18 In REs, it is positive and insignificant in all sectors.

23

all programs are designed to improve human capital. So I replaced ALMP with public spending

on job training which should work more directly on productivity if it ever does (Training, Table

1b). But it was insignificant in all sectors.19

To further check the robustness of the negative sign of ALMP and job training, I ran the

regressions with an interaction term between ALMP (and training) and the SME dummy to

explore the possibility that ALMP boosts MFP only in a supportive or coherent institutional

environment, à la the VoC literature (results not shown). The results confirm the lack of

evidence for a positive effect of ALMP. Both the interaction term and the marginal effect of

ALMP conditional on SMEs are negatively signed, and the marginal effect is even significant in

the total economy and manufacturing in pooled OLS and REs (i.e., ALMP has a more negative

effect on MFP in SMEs than LMEs).20 The results are similar for job training.

Why no positive result for ALMP? There are several possibilities. First, Pontusson

(2005, p. 128) writes that during high unemployment periods, ALMP tends to lose its active side

and turn into income support for the unemployed under a different name. In such cases, ALMP

may not be particularly productivity-enhancing. Some high ALMP spenders have high

unemployment and low MFP (Belgium, Denmark). Relatedly, some countries with large ALMP

spending also have large passive unemployment spending and low MFP growth (Belgium in all

sectors and Denmark in manufacturing). It may be that large passive spending cancels out the

productivity benefits of ALMP spending, and these countries weaken the association between

ALMP and MFP growth in the sample. To check for these possibilities, I ran the regressions

excluding Belgium and Denmark. But the results do not change. Of course, the combination of

19 In REs, it is negative and insignificant in all sectors. 20 ALMP remains negative and significant also when using a sub-sample consisting of only

SMEs.

24

large spending on both unemployment and ALMP does not automatically mean low MFP: some

countries have large spending on both, and their MFP growth is high (Finland, Sweden, and to a

lesser extent, the Netherlands).

Second, ALMP here is simple spending levels and does not take account of qualitative

properties of ALMP programs, such as type, quality, accessibility, and effectiveness. Quality

may be more important than quantity. But this is difficult to immediately test in the absence of

such cross-national data, and needs to await the construction of a dataset. Third, ALMP may not

have any pro-productivity effect left over after controlling for other human capital policies.

Last but not least, ALMP may simply not improve MFP growth. Although it is not about

productivity effects, there is micro (individual) level research on the impact of ALMP on

employment. Such research shows that ALMP’s impact varies considerably across individual

programs and is not always clear-cut, and some of them are ineffective (e.g., Cahuc and

Zylberberg, 2006; Card et al., 2010; but see Nelson and Stephens (2012) for macro data showing

that ALMP spending increases employment). The results are also sensitive to different

performance measurements, presumed time horizons for program effects, and the duration of

programs themselves. Considering these mixed employment results and the robustness of

ALMP’s negative coefficient for productivity effects in this paper, little to negative or mixed

effect of ALMP on MFP growth is possible. More complexity may underlie the relationship

between ALMP and productivity. There is also the possibility that if ALMP successfully

promotes employment as it is intended to, increased employment can lead to lower productivity

because it increases the share of low-skill workers among all employed workers (the composition

effect), while this is less likely because the composition effect is controlled for in the EU

KLEMS data. Further research is needed for an answer.

25

We do not get clear results with RedisPoverty. I estimated many specifications, dropping

and adding other policy variables, but the direction of the coefficient flips between FEs and

pooled OLS and across sectors, and it is not always significant. The results in Table 1a suggest

that redistribution (poverty reduction) is associated with lower MFP growth in manufacturing

and the total economy, according to the FEs results, and it is not statistically distinguishable from

zero in the other models.21 In other models with an interaction term mentioned below,

redistribution almost always pushes down MFP growth, when significant. So there is some,

though weak, indication that redistribution may slow down MFP growth, when measured simply

and only by the size of poverty reduction.

When the equations are stripped down to just RedisPoverty and the economic controls

(when the other human capital variables are not controlled for), it becomes positive and

significant in pooled OLS in manufacturing and the total economy (still negative in FEs but

significant only in service, results not shown), although these estimates may be biased by omitted

variables. On the whole, we need caution in interpreting these weak, unstable results and should

not attach more meaning than is warranted. But if we were to take care not to dismiss potentially

significant and useful information, the interpretation would be: redistribution in the broad sense

of the term—including poverty reduction as well as redistributive elements in other human

capital investment policies (partly since they are not controlled for in the stripped down

equations), and when estimation also uses the cross-national variance—may facilitate human

capital investments and productivity growth, but in the presence of family support and education

policies (when we control for them), there is not much additional productivity benefit from

redistribution itself (measured only by poverty reduction), or it may even have negative effects.

21 It is also insignificant in REs models.

26

It may be that, compared to some other better targeted programs such as public education, only a

portion of redistribution (transfers) is spent on human capital investments by households, and as

a result, transfers are less effective in promoting human capital formation and productivity than

other targeted programs. But this is just a speculation based on unstable results, so further

research is needed for clearer evidence and firmer answers.

There is still the possibility that redistribution (poverty reduction) is effective when used

with other human capital investment policies. To explore this possibility, I entered an interaction

term of redistribution with ALMP, job training, public education, and family support,

respectively. In short, there is little evidence of positive interaction effects of redistribution with

any of the other human capital investment policies. The respective interaction term is almost

always negative and insignificant (with a few exceptions of a negative and significant

coefficient). Further, judging from the sign, magnitude, and significance of the coefficients for

the constitutive and interactive terms, it is also unlikely that RedisPoverty has a positive marginal

effect conditional on any of the other human capital investment policies.22

The results of RedisGini (inequality reduction) are weaker than RedisPoverty: it is mostly

not significant, but in manufacturing (only in pooled OLS), it is negatively associated with MFP

growth (not reported). When entered just with the economic controls, it is positively associated

with growth in the total economy (significant only in pooled OLS). Overall, there is no strong

evidence that redistribution (when measured simply and only as poverty or inequality reduction

through taxes and transfers) itself is conducive to MFP growth. But these are only two of the

22 I also checked the interaction of redistribution and SMEs to explore the possibility that

redistribution boosts MFP growth only in a fitting institutional environment. There is little

evidence to show either way. The interaction term flips its sign and is mostly not significant.

27

many ways of operationalizing and measuring redistribution. Other operationalizations and

measures need to be tried before reaching firmer conclusions.

Government R&D spending is positive and significant in manufacturing and the total

economy in pooled OLS, indicating it promotes MFP growth. In FEs, the coefficient is negative,

but statistically not distinguishable from zero, except in the total economy. We cannot say which

results—pooled OLS or FEs—to trust. But with caution, we lean toward the positive results of

pooled OLS. Pooled OLS uses both between and within variance, and government spending,

including R&D, tends to have more between variance than within variance (whereas FEs only

use within variance and cannot tell whether cross-national difference in government spending

leads to different cross-national MFP performance). Further, both REs and between effects

models show that government R&D is positive and significant in manufacturing and the total

economy (results not shown). Per capita R&D spending (public + private) produces similar

results (not shown). It is positive and significant in manufacturing and the total economy in

pooled OLS. In FEs, it is negative but never significant.

As for the economic controls, there is no strong stable evidence for EPL; the sign flips

and significance changes across different models, including many other models I estimated to

check sensitivity. Lagged per capita GDP is negative most of the time, as expected from the

convergence effect, but is significant only one-third of the time in these and other models I have

tried.

Do WPRs Matter in MFP Growth?

Before concluding that policies matter, we need to consider a distinctive possibility that it is

actually the type of WPRs—rather than individual policies—that matters to productivity growth.

Explanations such as VoC, WPRs, and coherent regimes (Garrett, 1998) hint at the effects of

28

such regimes as a whole on economic performance. So I use dummy variables for WPRs to

estimate if some regime has higher MFP growth than others. These regime dummies create

collinearity with country dummies, which makes it impossible to keep all the necessary variables

in an FEs setting. So, only the results of pooled OLS is reported.

-- Table 2 about here --

Models 1-3 in Table 2 show the results of the regressions only with regime dummies and

the economic controls. Thus, they are the results, not controlling for human capital formation

policies. The excluded dummy is LME, so that is the reference regime. In manufacturing, both

Nordic and continental SMEs (that make larger public human capital investments) have

significantly higher MFP growth than LMEs (that have smaller public human capital

investments).

In market service, Continental SME is negative and significant, suggesting their MFP

growth is lower than LMEs’. Nordic is also negative but not significant. But when Nordic and

continental SMEs are combined together as ‘All-SMEs’, their MFP growth is significantly lower

than LMEs (results not reported). In the total economy, Nordic is positive and significant.

Continental SME is also positive and not far from significance (p=.126). When combined, All-

SMEs is positive and significant against LMEs (p=.074).

All in all, Nordic SMEs have higher MFP growth than LMEs in manufacturing and the

total economy. Continental SMEs’ growth in the two sectors may not be as high as Nordic

SMEs’, but probably higher than LMEs’. If trusted, these results for the two sectors are

consistent with the expectations by the human capital investment explanation—Nordic SMEs

with large public human capital investments perform best, and continental SMEs with not as

29

large investments as Nordic SMEs but larger investments than LMEs perform less well than

Nordic SMEs but better than LMEs.

In service, in contrast, LMEs have higher MFP growth than SMEs despite small public

human capital investments, results not exactly consistent either with the human capital

explanation or with other researchers’ view that LMEs’ economic growth depends (rather than

on productivity growth) on labor utilization (Pontusson, 2005) and/or on the abilities of firms to

hire workers at low wages (Iversen and Wren, 1998), in that LMEs, too, achieve high

productivity, depending on sectors.

So is it regimes or policies that matter? It is difficult to estimate their individual

contribution to MFP growth by entering both regime and policy variables in the same equations,

since the latter are some of the defining characteristics of the former and they are correlated. We

should note that the estimates on the regime dummies in the presence of the policy variables in

the same equations do not mean what they meant in their absence in Models 1-3 of Table 2. In

the presence of the policy variables, the coefficients on the regime dummies are estimates of

their effects when all the effects of the policy variables are removed from them (controlled for).

‘Nordic SMEs’ without their family, education, and labor market policies are not extremely

useful from the standpoint of human capital formation policies.

For lack of a better option within the scope of this paper, however, I entered both the

regime dummies and policy variables in the equations to see whose statistical associations

survive (Models 4-6). As it turns out, the policy variables except Family retain their statistical

significance reported in Table 1a, but the regime dummies are no longer significant, except

Others (Italy, Portugal, Spain) and Continental SME in service. Though not reported, Family is

also significant and positive in the total economy and service in FEs models (though three

30

country dummies drop out due to perfect collinearity). Thus, the results of the policy variables

are more stable than those of the regime dummies. Though further research is required on this

issue, we may tentatively say that it is human capital formation policies that seem to have

primary productivity effects, or that a large portion of productivity effects of regimes is through

their human capital formation policies. But again, since the regimes are tautologically those that

have those distinct human capital formation policies (as well as other institutional and policy

factors), anything conclusive needs to await further research.

DISCUSSIONS

This paper started with the question, ‘Does governments’ public policy promote productivity

growth?’ I suggested a study of the role of public policy in productivity growth from the

perspective of human capital investments. The empirical results suggest the following: Family

support policy seems effective in improving MFP growth. So does public education, particularly

university education. Family support and education seem to be a promising avenue to

governments’ efforts to promote productivity. If trusted, the results also suggest that these

policies may enable governments to promote both productivity and equality, since they also tend

to counter inequality (Pontusson, 2005; Iversen and Stephens, 2008).

Contrary to our expectations, ALMP (or job training) is not positively associated with

MFP growth, and we even detect a negative association. Likewise, we fail to find stable

evidence of pro-productivity effects of direct redistribution (when measured only by poverty or

inequality reduction through taxes and transfers). These results contradict the hypotheses

generated from the human capital investment perspective. Some possible reasons for the results

have been mentioned in the previous section, and I will not repeat them here. In future research,

we first need to find out whether these policies are really not productivity-enhancing and, if not,

31

why they are unconducive to productivity. For redistribution, for example, an obvious

possibility is that the operationalization or measurement of redistribution used here (the size of

poverty or inequality reduction through taxes and transfers) is not appropriate for estimating the

productivity effects of redistribution. This narrow measure of redistribution misses many aspects

and components of redistribution in general, so our results do not necessarily mean that

redistribution does not help productivity growth. Other operationalization and measurement

should be tried before drawing conclusions.

Another possibility is that while redistribution has productivity benefits, its efficiency

costs outweigh the benefits. Or the particular way industrial governments have gone about

redistribution may not have been well-designed to aid productivity growth while minimizing

efficiency costs. More research is required to find the answer.

The results of this paper also suggest another area where more research is necessary. The

productivity benefits of family and education policies are detected in all three sectors, including

market service. However, in the regime level analysis where those policies are not controlled for,

LMEs have higher MFP growth in service than SMEs. While there is a possibility that regimes

do not matter once policies are controlled for, we also cannot eliminate the possibility that they

do matter (particularly when divergent human capital formation policies are a definitional

characteristic of the regimes). If they matter, the LMEs result is an indication that governments

may be able to promote MFP growth without large public investments in human capital,

depending on circumstances, at least in the service sector. Economists generally attribute high

productivity growth in the U.S. service sector to high ICT use which, according to them, is

facilitated by high competition (due to low regulations) and resulting low prices and to better

incentive structure. Growth is high particularly in sectors with high ICT use or high-skill

32

workers (OECD, 2008), and market service is one of the most intensive users of new technology

and skilled labor (Inklaar et al., 2008).23 This may suggest that governments potentially have

two ways to achieve high MFP growth (depending on sectors): one is public investments in

human capital, and the other is competition. The market solution to high productivity may be

only specific to the service sector. If so, we need to understand why the market solution

produces high productivity in service.

Different countries generate productivity growth in different circumstances and in

different combinations of policies. The mechanisms of growth generation and the role of

government policies should be uncovered further.

23 R&D spending is also high in LMEs.

33

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Table 1a. Determinants of MFP Growth (Dependent variable = ∆lnMFP) Manufacturing Market Service Total Economy (1) (2) (3) (4) (5) (6) FEs Pooled OLS FEs Pooled OLS FEs Pooled OLS

RedisPovertyt-1 -0.00152* 1.58e-05 -0.000555 -6.07e-05 -0.000620** 0.000133 (0.000878) (0.000228) (0.000380) (0.000105) (0.000293) (8.41e-05)

lnFamilyt-1 0.0305* 0.00293 0.0249*** 0.00688** 0.0174*** 0.00215 (0.0177) (0.00600) (0.00918) (0.00269) (0.00606) (0.00270)

Univt-1 0.0460** 0.0196** 0.0243*** 0.00962** 0.0230*** 0.0100*** (0.0184) (0.00904) (0.00898) (0.00446) (0.00659) (0.00313)

PrimarySect-1 0.0196* -0.00376 0.0108* -0.00436 0.00451 -0.00579*** (0.0118) (0.00627) (0.00576) (0.00335) (0.00388) (0.00188)

lnALMPt-1 -0.0153 -0.00990 -0.0171** -0.00631* -0.00867 -0.00467* (0.0157) (0.00610) (0.00710) (0.00377) (0.00536) (0.00273)

lnR&Dt-1 -0.0447 0.0364*** -0.0119 0.00421 -0.0304*** 0.0123*** (0.0277) (0.00887) (0.0122) (0.00406) (0.00907) (0.00276)

EPLt-1 0.0118 0.00596 0.00868* -0.000195 0.00389 0.000999 (0.00909) (0.00424) (0.00458) (0.00194) (0.00348) (0.00143)

lnGDPcapitat-1 -0.0354 0.0188 -0.0374* 0.00469 -0.0479*** -0.00271 (0.0535) (0.0226) (0.0224) (0.00961) (0.0159) (0.00761) Constant 0.210 -0.149 0.276 -0.0445 0.413*** 0.0450 (0.512) (0.233) (0.217) (0.103) (0.155) (0.0761) Observations 186 186 186 186 186 186 R-squared 0.339 0.197 0.349 0.129 0.417 0.216 Panel-corrected standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 1b. Determinants of MFP Growth, Regressions with Job Training (Dependent variable = ∆lnMFP) Manufacturing Market Service Total Economy (1) (2) (3) (4) (5) (6) FEs Pooled OLS FEs Pooled OLS FEs Pooled OLS

lnTrainingt-1 -0.00807 0.000277 -0.00240 -0.000318 0.000719 0.000523 (0.00908) (0.00314) (0.00458) (0.00199) (0.00365) (0.00137) Observations 186 186 186 186 186 186 R-squared 0.339 0.188 0.331 0.113 0.409 0.203 Panel-corrected standard errors in parentheses. Training is entered in place of ALMP in the baseline models of Table1a. Only relevant variables are shown. *** p<0.01, ** p<0.05, * p<0.1

Table 2. Determinants of MFP Growth, Regime Regressions (Dependent variable = ∆lnMFP)

Manufacturing Service Total

Economy Manufacturing Service Total

Economy (1) (2) (3) (4) (5) (6)

Pooled

OLS Pooled

OLS Pooled

OLS Pooled

OLS Pooled

OLS Pooled

OLS Nordic 0.0195** -0.00546 0.00598* 0.0131 -0.00650 0.00566 (0.00993) (0.00400) (0.00330) (0.0180) (0.00848) (0.00749) Continental SME 0.0154* -0.0113** 0.00518 0.0155 -0.0144*** 0.00157 (0.00905) (0.00474) (0.00338) (0.0139) (0.00545) (0.00603) Others 0.00110 -0.0168*** -0.00447 -0.00711 -0.0208*** -0.00591 (0.0111) (0.00582) (0.00441) (0.0146) (0.00708) (0.00535)

RedisPovertyt-1 -7.22e-05 3.87e-05 0.000159* (0.000264) (0.000124) (9.53e-05)

lnFamilyt-1 0.00236 0.00383 0.000876 (0.00718) (0.00333) (0.00300)

Univt-1 0.0193** 0.00971** 0.00887** (0.00974) (0.00450) (0.00348)

PrimarySect-1 -0.00154 -0.00492 -0.00598*** (0.00663) (0.00400) (0.00227)

lnALMPt-1 -0.0168** -0.00588 -0.00688** (0.00723) (0.00381) (0.00297)

lnR&Dt-1 0.0262*** -0.000789 0.00761** (0.00895) (0.00596) (0.00328)

EPLt-1 -0.00681 0.00254 -0.00304* 0.00521 0.00704** 0.00202 (0.00493) (0.00244) (0.00165) (0.00666) (0.00292) (0.00244)

lnGDPcapitat-1 -0.00579 0.00879 -0.0160*** 0.0112 0.0132 -0.00232 (0.0196) (0.0112) (0.00589) (0.0235) (0.0101) (0.00724) Constant 0.0800 -0.0849 0.170*** -0.0443 -0.126 0.0563 (0.201) (0.115) (0.0602) (0.250) (0.107) (0.0731) Observations 287 287 287 186 186 186 R-squared 0.058 0.062 0.091 0.212 0.180 0.234 Panel-corrected standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Appendix Descriptive statistics

Variable Obs. Mean Std. Dev. Min Max

∆lnMFP in manufacturing 287 0.0163 0.0328 -0.0710 0.1473

∆lnMFP in service 287 0.0019 0.0195 -0.0867 0.0824

∆lnMFP in the total econom 287 0.0039 0.0128 -0.0318 0.0566

RedisPoverty 232 59.45 17.94 16.03 87.50

RedisGini 232 27.32 8.65 12.20 43.24

lnFamily 288 5.96 0.76 3.21 7.01

Univ 253 1.30 0.48 0.32 2.71

PrimarySec 245 3.58 0.61 2.54 5.00

Preprimary 247 0.367 0.204 0.002 0.936

lnALMP 287 5.11 0.69 3.76 6.47

lnTraining 287 3.85 0.88 0.36 5.49

lnR&D (by government) 324 0.66 0.20 0.25 1.22

lnR&D (total) 342 1.95 0.78 0.39 4.17

EPL 340 2.00 1.01 0.21 4.10

lnGDPcapita 340 10.12 0.20 9.44 10.56