rafmacalaba/data-use-mentions-extended huggingface dataset:
# **MEXICO: HUMAN CAPITAL EFFECTS ON WAGES AND PRODUCTIVITY**

## **I. INTRODUCTION**

Human capital is a stock of skills produced by education, experience and training (Welch, 1970; Mincer, 1989). The empirical evidence on the links between human capital, on the one hand, and productivity and/or wage growth on the other, is strong.

Human capital is viewed not only as an investment, but also as a factor of production. Numerous studies using worker and firm level data have shown that more educated and/or trained individuals are more productive. In a work environment that is rapidly changing due to technological advances, the cognitive abilities for workers to process new information become increasingly important and thus command higher wages, and in turn higher incomes (Welch, 1970; Mincer, 1989; Tan and López-Acevedo, 2002; Hellerstein and Neumark, 2004). Moreover, highly educated workers have a comparative advantage with respect to the adjustment and implementation of new technologies, by being able to adopt them more easily or faster than non-educated workers. For this reason, the productivity of highly educated workers, relative to lesseducated workers, is greater (Bartel and Lichtenberg, 1987, Tan and López-Acevedo, 2002; Hellerstein and Neumark, 2004).

Similarly, there is limited, but growing, empirical literature on the link between human capital and a firms’ performance (Koning, 1994; Ravenga, 1995; Batra and Tan, 1995; Barrett and O’Connell, 1998; World Bank, 1998a, 1998b, 1999, 2000, 2001a, 2001b; Dearden, Reed, and Van Reenen, 2000). Using panel data, several studies have demonstrated the positive impact of education and training on productivity (Nielsen and Rosholm, 2002; Batra and Tan, 1995; Dearden, Reed, and Van Reenen, 2000).

In addition, extensive literature has been compiled in the closely related field of human capital and its effects on workers’ wages. A near-consensus
effect than automating plants from the increased demand for inputs, although in this case the backward linkage effect on employment is positive also for automating plants. The coefficient of exposure to automation is again positive and significant.

As the sample in columns (2) and (3) is smaller than that in table 5 (given the availability of plant level input data), to check that this is not driving the change in the _ETR_ coefficient, in column (4) we replicate the result of table 5 (column 1) but over the smaller sample. The coefficient of exposure to automation is negative and significant and even larger in absolute term than in table 5, thus lending further support to the hypothesis that the negative _ETR_ coefficient on employment is driven by the positive vertical linkages. The results are robust also to using directly the exogenous instruments in an OLS regression so as to avoid having to instrument two endogenous variables (column 5).

The improved performance of plants induced by automation does not only result in increased demand for goods, but also of services. This is documented in column (6) which uses services’ expenditure as dependent variable. The positive and significant coefficient suggests that plants adopting robots spend additional resources on professional services, which include R&D and marketing consulting.

Table 7: Plant level impact of robots on domestic inputs’ expenditures and employment: vertical linkages.

|VARIABLES|(1)<br>Domestic<br>inputs|(2)<br>Employment|(3)<br>Employment|(4)<br>Employment|(5)<br>Employment|(6)<br>Services|
|---|---|---|---|---|---|---|
|ETR _×_ secondary|0.064***|0.028**|0.018**|-0.014**|-0.
share|0.21|0.37|
|Index of labor market strength|0.09|0.11|
|<br>Index of input market strength|-1.71|0.24|
|Index of output market strength|0.04|0.06|
|Index of labor market strength, female-owned businesses|0.08|0.17|
|Index of input market strength, female-owned businesses|-1.79|0.29|
|Index of output market strength, female-owned businesses|0.02|0.03|
|Establishment characteristics, unorganized services:|||
|Total female-owned establishments in district-industry|152|801|
|Total employment in female-owned establishments in district-industry|216|1,041|
|Total male-owned establishments in district-industry|1,773|5,651|
|Total employment in male-owned establishments in district-industry|2,569|7,628|
|<br>Female business-ownership share|0.09|0.21|
|Female entrepreneurship share|0.11|0.24|
|<br>Index of labor market strength|0.10|0.11|
|Index of labor market strength, female-owned businesses|0.10|0.15|

Notes: Descriptive statistics taken from National Sample Statistics. Output index values are multiplied by 10 for presentation.
households irrespective of the period analyzed. Our preferred measure highlighting the full extent of exposure to shocks comes from our 1-month incidence measure since it prompts for each one of 16 different types of shocks. Here we find that 60% of households in the treatment vs. 61% in control (p-value = 0.711) have had a shock exposure in the past month.<sup>31</sup> We present analyses of recovery and coping mechanism using shock exposure in the past year since this allows us to better reflect on the ability of the TUP program to strengthen households’ resilience to shocks over the medium term.

First, we find that the TUP program improves the ability of UP households to partially or fully recover from the shocks. As Table 9 shows, treatment households are 19.7 percentage points, or 53% (p-value =

> 0.025) more likely to have at least somewhat recovered from shocks occurred in the past year, compared to

> 31Exposure appears lower in the past 12 months (26% for treatment vs. 30% in control, p-value = 0.225), although this is, in part, a function of the questionnaire structure which first asked whether the household experienced any shock before asking for specific types of shock, likely underestimating shock incidence. While this means the absolute levels may be affected, there is no reason to believe this would affect responses differentially between the control and treatment groups.

31
. The ‘skewness-adjusted’ z-score is calculated on this set of values.

A third approach is to use the ordinal rank of the absolute error within the entire distribution of absolute errors within the indicator (again consistent with Duggan and Morton, 2004). We rank all errors according to size, give each a corresponding rank, and divide these ranks by the maximum rank within the distribution of absolute errors within the indicator. The rank-proportion scores are therefore free of distributional assumptions. They can be interpreted as the percentile of error made by an official relative to other in our data set.

# **Measuring the incentive environment**

To construct our management indices, we followed the best-practices laid out in the World Management Survey literature (such as in Bloom and Van Reenen, 2007; Bloom et al., 2012). Management interviews were ‘double blind’ so: (i) managers were not told in advance they were being scored or shown a score grid; (ii) enumerators were given no information on the organization’s performance. Only managers (directors) were enumerated the World Management Survey and the measures of organizational management practices 38
# **7. References**

- Amiti, Mary, and Jozef Konings. 2007. "Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia." _American Economic Review_ , 97(5): 1611–1638.

- Artuc, Erhan; Shubham Chaudhuri; and John McLaren 2007 “Trade Shocks and Labor Adjustment: Theory.” NBER Working Paper 13463 http://www.nber.org/papers/w13463

- Artuc, Erhan; Shubham Chaudhuri; and John McLaren 2010 “Trade Shocks and Labor Adjustment: A Structural Empirical Approach” _American Economic Review_ , 100(3) (June): 1008-45.

- Baldwin, Richard (ed.) 2009 The Great Trade Collapse: Causes, Consequences and Prospects A VoxEU.org Ebook available at <u>http://voxeu.org/index.php?q=node/4297.</u>

- Bernard, Andrew; Raymond Robertson; and Peter K. Schott 2010 “Is Mexico a Lumpy Country?” _Review of International Economics_ 18(5) (November): 937–950.

- Bergin, Paul, Robert Feenstra, and Gordon Hanson. 2009 “Offshoring and Volatility: Evidence from Mexico’s Maquiladora Industry.” _American Economic Review 99_ : 1664-71.

- Claessens, Stijn; Hui Tong; and Shang-Jin Wei 2011 “From the Financial Crisis to the Real Economy: Using Firm-level Data to Identify Transmission Channels” NBER Working Paper No. 17360 National Bureau of Economic Research.

- Davidson, Carl; Steven Matusz, and Andrei Shevchenko. 2008 “Globalization and Firm-level Adustment with Imperfect Labor Markets” _Journal of International Economics_ 75(2): 295-309.

- Davidson, Carl; Fredrik Heyman, Steven Matusz, Fredrik Sjoholm, and Susan Zhu, 2010 "Globalizaton and Imperfect Labor Market Sorting" University of Nottingham Research Paper 2010/30.

- Dix-Carneiro, Rafael 2010 “Trade Liberalization and Labor Market Dynamics”, mimeo, Princeton University.

- Eaton, Jonathan, Sam
female workers to the employer. The higher cost results in lower equilibrium discrimination and therefore higher female employment. This effect is likely to be more pronounced in countries and industries where domestic competition is low to begin with. Thus, we predict that the relationship between exporting and female employment is larger (more positive) when domestic competition is low. The prediction is unlikely to hold if exporting activity is a mere proxy for other correlated variables such as the age of the firm, industry to which the firm belongs, firm-size, etc. Our empirical results confirm this prediction. Other predictions that we test for include how the strength of the relationship between exporting and female employment depends on the dependency of sectors on female relative to male workers, social attitudes towards women’s work, gender disparity in laws in the area of employment, and the law and order situation proxied by the quality of the functioning of courts.

Fourth, the firm-level survey data we use provide information on several firm characteristics and the quality of the business environment. This allows us to control for several firm and industry characteristics that may spuriously affect our main results.

Our results show a positive, statistically significant and quantitatively large relationship between the export orientation of the firms and female employment. A conservative baseline estimate indicates that the share of female workers in a firm is higher by about 16 percentage points for firms that export all their output compared to firms that do not export at all. This is a large difference given that the mean share of female workers is about 29 percent. Furthermore, the positive relationship between female employment and exporting activity is much stronger (more positive) in countries with higher competitive pressure in international markets relative to domestic markets, in industries that rely more on female workers, in countries where social attitudes towards women’s work are more favorable, in countries where laws discriminate less against women’s 4
USD)<sup>_⋄_</sup>|947|1267.11|164.07<sup>_∗∗_</sup><br>(77.72)|
|**Labor inputs**||||
|Household hired labor|947|0.63|0.02<br>(0.03)|
|Quantity of hired labor<sup>_⋄_</sup>|947|28.85|1.37<br>(2.49)|
|Total household plot hours in last week<sup>_⋄_</sup>|2113|219.58|-8.46<sup>_∗_</sup><br>(4.91)|
|Total household plot hours in last week/ha<sup>_⋄_</sup>|2113|21.21|-1.51<sup>_∗∗_</sup><br>(0.58)|

Note: *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals, respectively. Agricultural input usage only collected for half the sample. _⋄_ indicates variables that have been winsorized at the 5% level. All regressions control for whether household head completed primary school, number of boys between 6 and 16 years old, and stratum fixed effects.

22
neighborhoods in Hyderabad were randomly selected for the opening of a microfinance institution Spandana, which used the canonical group lending model and targeted women who may not necessarily be entrepreneurs. Spandana also targeted the “poor, but not the poorest of the poor” (Banerjee et al. 2015b). For our main analysis, we use data from the first wave of the household surveys, conducted about 15–18 months after Spandana openings in 52 neighborhoods where Spandana was opened to make sure our participants and nonparticipants come from the same local markets (which has been noted to improve PSM performance, as discussed in the previous section). Since the microfinance program was targeted to females in the range of 18–59 years old, the data was collected only on households that have at least one eligible woman in the household.

We have a total of 3,318 households in the main sample. We construct our outcome variables following Banerjee et al. (2015b) as monthly adult-equivalent expenditures, adjusted for inflation. Because the distributions of expenditures in rupees have significantly long right tails, we use log transformation on all expenditure variables. To ensure observations with zero expenditures are not dropped, we add one to all zero values before taking logs. Table 1, panel A reports summary statistics for the outcome variables. Specifically, we have data on total consumer expenditures, total nondurable expenditures, total durable expenditures, “temptation goods” (defined as meals outside of home, alcohol, and gambling), health and education (total education expenditures and education fees), expenditures on festivals, and home repairs (the questionnaire only asked to report home repairs above 500 Rs).<sup>14</sup> Table 1, panel B reports shares of expenditure categories as a percent of total expenditures. We first calculate shares of each category as a percent of total for each household and then report mean, median, etc., across all households. The average durable expenditures are only about 6%, while nondurable are 94
account. For example, it would be a mistake to consider countries with low stock-market capitalization as financially backward if, for instance, they allocate a large amount of credit to private enterprises through the banking.

Using the methodology outlined in the previous section, two indices are presented to measure the degree of a country’s financial development. They were constructed in a way such that comparisons over time and across countries are possible. Each index consists of indicators that quantify different aspects of the performance of the financial system.” The first index is closely linked to the development of the banking sector. The second is related to stock markets. In order to obtain information about long-term financial development from the following financial indicators, it is important to focus on their long-run trends. Their temporary fluctuations, which may be rather large, are mostly determined by the business cycle and one-time events, such as the privatization of public enterprises.”'

# 1. Banking sector development

- The-first element to consider in assessing the development of the banking sector is the depth of a nation’s financial system relative to the size of its economy. This can be measured by quasi-liquid liabilities of the financial system relative to GDP, that is, (M3-M1)/GDP.” Currency and highly liquid demand deposits (M1) are excluded because they are rather volatile and do not represent longterm investments in financial intermediaries. Another element to consider is the participation of private institutions in providing credit. This is important because private institutions have clearer incentives to . mobilize savings toward profitable projects, exert corporate control, and facilitate risk diversification. To proxy for the participation of private institutions in the provision of credit, this paper uses the ratio

2° Detailed data for the period 1985-95 on these and all other indicators presented in the paper are given in the appendix. 21 Of course, the decomposition of any economic variable into its permanent and transitory components is far from trivial. No formal decomposition technique is used in this paper.

*2 The end-of-year nominal
the lion-share of the “rich” that are missing or whose incomes are understated in the HIECS arguably reside in either Cairo or Alexandria, and (ii) the real estate markets are most developed in Cairo and Alexandria such that the coverage and the quality of the house price data are highest for these two cities (henceforward we will refer to these as districts).

Table 2 provides some basic statistics on the number of observations available to us. For the house price databases we only counted observations above the median house price value (which practically coincides with the mode of the house price density). Since we are interested in the top tail behavior of the house price distribution, we do not use the lower house price values.

|sub-group|Database<br>Betak-online<br>Bezaat|HIECS|
|---|---|---|
|Cairo|5772<br>8475|1289|
|Alexandria|1293<br>2012|767|
|Urban Egypt||6935|

Table 2: Number of observations used The following sections proceed with the empirical application that combines the household expenditure survey and the house price data. A validation of our methodology in a controlled setting where only the survey data are used can be found in the Annex.

# **4.1 Pareto tail index estimated on income survey data**

This subsection presents first estimates of the Pareto tail index of Cairo’s and Alexandria’s income distributions by using household survey data only. These estimates will serve as a reference point. Under the assumption of Pareto distributed top tails we have that: 1 _− F_ 2( _y_ ) = � _τy_ � _−θ_ . Rearranging terms yields: If this assumption holds true, a plot of log ( _y_ ) against _−_ log (1 _− F_ 2( _y_ )) should reveal a linear relationship with a slope parameter equal to<sup><u>1</u></sup>
in the patterns of governance structures. On the

> 67 Thus the ordering of classes is different from that in Tables 2a and 2b, which followed LCA's somewhat arbitrary ordering. 68 Bolivia has the highest estimated shadow-economy presence among the six countries studied in this paper (Medina and Schneider, 2019). Since the WBES use representative samples of formally registered firms, the population of firms from which the sample is drawn in Bolivia may have a higher share of large firms than the populations in other countries, among other potential differences across the populations of firms that may be relevant for firms' governance structures. These differences due to variation in the prevalence of shadow economy, and thus variation in the share of the economy that the WBES represents, cannot be a complete explanation either however, since Peru, where the prevalence of governance structures differs markedly from Bolivia, follows Bolivia closely in the estimated rate of the shadow economy.

39
_FVAX_ and _DVAR_ lagged. All level variables in natural logarithms. WIOD sample values in constant terms.

Focusing on the OECD sample, a 10 percent increase of GVC integration as a buyer increases domestic value added by 0.7 percent (column 1). Using the WIOD sample, we similarly observe that a 10 percent increase of GVC integration as a buyer leads to domestic value added gains of 0.4 percent (column 3). This is in line with our results in the next sections where robustness exercises using WIOD largely confirm the estimates obtained with OECD data.

GVC integration as a seller also substantially increases domestic value added (columns 2 and 4). Note that the elasticity is higher than those of all other control variables, while it was smaller when using the

> 8 For the country and industry coverage of the data set, see appendixes 2 to 4.

18
the causal impact of trade on economic growth, with the inclusion of additional regional fixed effects of trade intensity, ranges between 0.395 and 0.65 percent.

Our finding is also robust to the addition of institutional quality as a control variable. Controlling for institutional quality has been a major criticism of Frankel and Romer (1999). Rodrik, Subramanian, and Trebbi (2004) indicated that Frankel and Romer’s (1999) results suffer from omitted variable bias due to excluded determinants of growth, specifically institutional quality. We therefore controlled for institutional quality, measured by the polity2 indicator per Aisen and Veiga (2013).<sup>35</sup> The results indicate that both IV1 and IV2 present significant and positive coefficients of 0.52 percent and 0.71 percent respectively (Table A14). These results are not significantly different from the baseline estimates, indicating the robustness of our finding to the addition of institutional quality as a control variable. It is important to note that, given the low variance of the polity2 indicator, country and time fixed effects likely already account for institutional quality.

This study also uses a GMM estimator as a further robustness check. The credibility of the results of the GMM estimator depends on the outcomes of diagnostic tests for overidentification and serial correlation, as presented in Table 4. In each specification, the number of instruments is lower than the number of countries. In our case, the Hansen test, which diagnoses the problem of overidentification, shows that the additional instruments associated with the system GMM estimator are valid. The two tests for serial correlation show evidence of first order autocorrelation since the null hypothesis of no autocorrelation is rejected using the AR (1) test. However, the AR (2) test cannot reject the null hypothesis and provides evidence of no second order autocorrelation. Therefore, the results of the diagnostic tests indicate that the GMM model is valid.

> 35 The Polity2 variable measures the quality of institutions. It provides a score ranging from
.331)|(0.330)|(0.316)|(0.319)|
|_R_<sup>2</sup>|0.876|0.877|0.877|0.878|0.880|0.880|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|672|672|672|672|672|672|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 21
***|0.017|0.056*|0.027|
|_CSeason_2_|0.067**|0.029|0.030|0.018|0.046|0.028|

_source: author's calculations using GUS and IFS data_ In the first version of the model, domestic credit (DC) was used instead of _M2_ to reduce the danger of multicollinearity. However, even though of all monetary aggregates, _DC_ is the one least closely correlated with wages, its correlation coefficient is still relatively high (0.68) and poses serious risk that the estimates of the coefficients' standard errors will be distorted. Therefore, two additional regressions were carried out, each including only one of the two correlated variables. Second and third column in Table 4 present the output of regressing inflation on the set of explanatory variables including money _(M2)_ and wages, respectively.

> **23** Number of stars beside a coefficient or a statistic indicates the level of significance: no stars -insignificant at up to 10% level, * - significant at 10%, ** - significant at 5% and *** - significant at 1%.