rafmacalaba/datause-encoder-data huggingface dataset:
the higher skill occupations, but not necessarily a lower gender wage gap _within_ occupations.

If we look at the gender wage gap within occupations, we would expect the impact of trade to be a narrowing one. First, trade will lead to more competition and therefore less discrimination, as argued by Becker (1971). Second, increases in trade will drive up the relative demand for female labor because female labor supply tends to be more elastic than male labor supply, and because women are disproportionally represented in export-oriented sectors, at least in developing countries (Wood 1991). Hence, prima facie, we expect a negative relationship between globalization and the occupational gender wage gap.

Globalization can be measured along different dimensions, and here we look at trade as a percentage of GDP (in current prices) and the FDI net inflows as a percentage of GDP. Figures 3 and 4 show a negative cross-country relationship between these measures of globalization and the occupational gender wage gap. Similar results are found if trade is measured as a percentage of GDP in constant LCU.<sup>16</sup> Hence, these results suggest that trade and FDI inflows lower the occupational gender wage gap.

Instead of looking at the cross-sectional pattern one can also look at the time series pattern, by comparing ‘globalizing’ countries with ‘non-globalizers’. Dollar and Kraay (2001) compare the growth performance of countries with large increases in trade and significant declines in tariffs over the past 20 years with the growth performance of other countries. They used various definitions to label countries as ‘globalizers’. The interesting question here is whether these globalizing countries have also seen the greatest decrease in the occupational gender wage gap, that is the greatest narrowing of female and male wages?

Unfortunately our data are quite limited with respect to the globalizers as defined by Dollar and Kraay. This is especially so because we need to know the _change_ in the occupational gender wage gap between the 1980s and 1990s. The ILO October Inquiry provides only information on changes in the occupational gender wage gap for globalizers China and Peru. We therefore introduce a different breakdown, namely between the ‘fast trade group’ of countries and the ‘slow trade group’ of countries.

> 16 This is the measure used by Dollar and Kraay (2001).

15
of Brazil (see figure 1).<sup>6</sup> Argentina has increased the amount of resources committed to R&D over the past few years. Between 2007 and 2016, Argentina increased its total expenditures in R&D by 78 percent—measured in current US dollars in purchasing power parity (PPP) terms— and its R&D/GDP ratio by 15 percent (from 0.46 percent to 0.53 percent). These levels of growth are similar to Chile (16 percent), Mexico (16 percent), and Brazil (18 percent).

**Figure 1. R&D Expenditures, Argentina and Comparators, 2007–16 (percent of GDP)**

<!-- Start of picture text --> 2%<br>1.5%<br>1%<br>0.5%<br>0%<br>2007 2008 2009 2010 2011 . 2012 2013 2014 2015 2016<br>Argentina Brazil<br>Canada Chile<br>Mexico<br>Source: RICYT<br><!-- End of picture text --> However, Argentina lags behind regional peers in private R&D expenditures (figure 2) and does not seem to be catching up. The share of private R&D expenditures in total R&D expenditures fell by 21 percent between 2007 and 2016, reaching a level of merely 24 percent of total R&D. This suggests a large preponderance of public R&D in total innovation investments in Argentina, larger than in peer countries—and potentially the result of low returns to R&D in private enterprises.

**Figure 2. R&D Expenditures by Firms, Argentina and Comparators, 2007–16 (percent of total R&D)**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>2007 2008 2009 2010 2011 . 2012 2013 2014 2015 2016<br>Argentina Brazil<br>Canada Chile<br>Mexico<br>Source: RICYT<br><!-- End of picture text --> 6 Figures 1 to 3 contrast indicators on R&D and productivity in Argentina with a series of comparable countries. For these comparisons, we include one recent high-income country (Chile), two large Latin America economies (Brazil and Chile), and two countries belonging to the Organisation for Economic Co-operation and Development (OECD) (Canada and Chile).

4
17 Table A.6: Returns to Experience and Education for Women vs. Men, Robustness

|Group:|(|1) Male||(2|) Fema|le|(3) M|ale - Fe|male|
|---|---|---|---|---|---|---|---|---|---|
|**_Panel A:_ Returns to Experience**|HIC|MIC|LIC|HIC|MIC|LIC|HIC|MIC|LIC|
|1. Baseline|4.1|1.9|2.2|3.0|1.5|2.1|1.1|0.4|0.1|
|2. Decadal Cohort FE &_≥_2 Yrs of Data|3.5|1.8|1.4|2.5|1.1|0.8|1.0|0.7|0.6|
|3. Important Events FE &_≥_2 Yrs of Data|3.4|1.7|0.6|2.3|0.7|-0.5|1.1|1.0|1.1|
|4. Industry (10) FE|3.9|1.9|2.3|2.9|1.6|1.9|1.0|0.3|0.4|
|5. Occupation (10) FE|3.9|1.7|2.0|2.8|1.4|1.9|1.1|0.3|0.1|
|6. Industry (10) FE & Occupation (10) FE|3.9|1.7|2.0|2.8|1.4|1.9|1.1|0.3|0.1|
|7. Excluding Self-Employed Individuals|4.2|1.9|2.2|3.2|1.5|1.7|1.0|0.4|0.5|
|8. Female Non-Empl. Rate_<_54%|4.1|2.1|2.2|3.2|1.5|2.0|0.9|0.6|0.2|
|9. Female Non-Empl. Rate_<_44%|4.2|2.0|2.3|3.2|1.7|2.1|1.0|0.3|0.2|
|10. Diff Female - Male Non-Empl_<_25%|4.1|2.0|2.3|3.1|1.6|2.2|1.0|0.4|0.1|
|11. Diff Female - Male Non-Empl_<_22%|4.2|2.0|2.3|3.2|1.6|2.2|1.0|0.4|0.1|
|12. Diff Female - Male Non-Empl_<_13%|4.5|2.0|2.3|3.5|1.3|2.0|1.0|0.7|0.3|
|13. HH Head/Spouse & Child Costs 1 Year|3.3|1.4|1.6|2.4|1.1|1.9|0.9|0.3|-0.3|
|14. HH Head/Spouse & Child Costs 2 Years|3.3|1.4|1.7|2.5|1.2|1.6|0.8|0.2|0.1|
|**_Panel B:_ Returns to Education**|HIC|MIC|LIC|HIC|MIC|LIC|HIC|MIC|LIC|
|1. Baseline|9.4|7.9|8.5|11.1|9.0|9.9|-1.7|-1.1|-1.4|
|2. Decadal Cohort FE &_≥_2 Yrs of Data|9.1|7.8|8.8|10.8|8.8|10.5|-1.7|-1.0|-1.7|
|3. Important Events FE &_≥_2 Yrs of Data|9.0|7.7|8.7|10.7|8.7|10.4|-1.7|-1.0|-1.7|
|4. Industry (10) FE|8.7|6.6|6.8|10.0|8.1|7.7|-1.3|-1.5|-0.9|
|5. Occupation (10) FE|6.0|5.3|6.1|6.1|5.9|7.4|-0.1|-0.6|-1.3|
|6. Industry (10) FE & Occupation (10) FE|6.0|5.4|6.1|6.2|5.9|7.3|-0.2|-0.5|-1.2|
|7. Excluding Self-Employed Individuals|9.7|7.0|8.2|11.6|8.1|10.1|-1.9|-1.1|-1.9|
|8. Female Non-Empl. Rate_<_54%|9.5|9.8|8.7|11.2|10.1|9.9|-1.7|-0.3|-1.2|
|9. Female Non-Empl. Rate_<_44%|9.1|10.4|9.3|11.4|9.9|10.9|-2.3|0.5|-1.6|
|10. Diff Female - Male Non-Empl_<_25%|9.4|9.8|9.1|11.1|9.9|10.7|-1.7|-0.1|-1.6|
|11. Diff Female - Male Non-Empl_<_22%|9.6|10.0|9.3|11.4|9.8|10.9|-1.8|0.2|-1.6|
|12. Diff Female - Male Non-Empl_<_13%|10.2|6.9|8.7|11.9|8.6|9.4|-1.7|-1.7|-0.7|
|13. HH Head/Spouse & Child Costs 1 Year|9.4|8.6|9.1|11.5|9.5|10.5|-2.1|-0.9|-1.4|
|14. HH Head/Spouse & Child Costs 2 Years|9.4|8.6|9.1|11.5|9.6|10.4|-2.1|-1.0|-1.3|

_Notes:_ This table shows the returns to experience and the returns to education by gender, as well as the gender gaps, when implementing various robustness checks. See text for details.
# Heterogeneous technology and panel data: The case of the agricultural production function

# **<u>Introduction</u>**

The focus of empirical analysis of agricultural production functions, similar to that of production functions in general, has changed over the years.<sup>1</sup> Following the work of Cobb and Douglas (1928), research centered on questions about the efficiency of the factor markets. Research interest subsequently shifted to issues related to changes in factor demand, leading to interest in the elasticity of substitution, to factor augmentation, to a search for the proper algebraic form, to issues related to aggregation, and more recently to issues related to the variability in income and in productivity growth. The statistical aspects of the analysis were affected by the recognition of the endogeneity of the inputs, raised by Marschak and Andrews (1944), and by the methods of accommodating this complication in the estimation. This led to the use of panel data, to the dual approach to the estimation, and to the use of instrumental variables.

A common assumption in much of the work was that of a homogeneous technology, implying that a common production function generated observations used in the analysis. In reality, firms face the practical problem of choosing which technology to employ jointly with inputs so that technology is heterogeneous in that there is more than one function associated with the data. In this paper we examine the consequences of this extension in estimating an aggregate agricultural production function using panel data, where the underlying functions are all of the Cobb-Douglas form. We model the technology choice as conditional on predetermined variables referred to as state variables. This approach provides a different view of the empirical results of most of the aforementioned subjects. The root of the difference is the recognition that the observations in the sample represent moves between functions as well as movements along a given function. The empirical formulation allows for the dependence of parameters of the 1 For the development of the work on agricultural production functions, see Mundlak (2001).
sectors by reducing their cost of raising funds abroad. Guiso _et al_ (2004) argue that domestic financial market development still matters for growth even in economies that are highly integrated to world capital markets. Their example shows that domestic financial market development is still important to a country like Italy that has been fully integrated for the last 140 years. On the other hand, the under-development of domestic financial markets in developing countries could explain their excess external savings, the capital flow reversal and low interest rates in the United States. The direction of capital flows observed in the global economy is the result of weaker enforcement of financial contracts and shallow domestic capital markets that raises the cost of financing due to inefficiencies in channeling resources from savers to domestic investment. This prevents emerging market firms to borrow internationally to finance domestic investment, and limits the ability of EMEs to produce financial assets for global savers (Mendoza, Quadrini and Rios-Rull, 2007; Caballero, Farhi and Gourinchas, 2008; Smith and Valderrama, 2009a,b; Valderrama, 2008).

Our paper complements existing evidence that more intense financial linkages may lead to deeper domestic financial markets after controlling for the legal and institutional framework of the country. Chinn and Ito (2006) evaluate the linkages between capital account openness and financial development for a sample of 108 countries from 1980 to 2000. They find that financial openness contributes to deepening the stock markets in countries beyond a specific threshold level of legal and institutional development. Ito (2006) carries out an analogous exercise focusing on Asian economies and finds that the impact of financial openness on the depth of stock markets is enhanced in countries with lower levels of corruption and higher levels of rule of law and of bureaucratic quality. Baltagi _et al._ (2009) use four different datasets since the 1980s to test the linkages between financial openness and financial development. They find that: (a) trade and capital account openness may have a significant impact on financial development in countries that are relatively closed, and (b) trade and financial openness may be substitute mechanisms of promoting financial development rather than complements as suggested by Rajan and Zingales (2003).

Our research improves upon the evidence presented in Chinn and Ito (2006) and Ito (2006) by: a) presenting evidence for a sample period which is much larger than their dataset, b) presenting panel data evidence with outcome measures of financial openness that capture the degree of financial openness instead of the absence of restrictions on cross-border asset trade, c) instrumenting for financial openness with external instruments recommended by the recent 5
results in three treatment subgroups—those who overestimated their household’s position on the income distribution, those who underestimated their household’s position, and those for whom their prior was confirmed. Note that households that responded to the inequality module had previously been interviewed for the CPHS several times. Hence, they were familiar with the scope and credibility of the survey.

After the treatment, the module collected information on beliefs about inequality. Respondents were asked, _“There can be a big gap between the rich and the poor in the country. Do you think this gap is a problem for society?_ ”, with responses coded yes or no. They were also asked, _“Do you think the government should do something to reduce this gap between the rich and the poor?”,_ with responses coded yes or no.<sup>6</sup> These questions mirror the survey-based measures of beliefs about inequality that have been extensively used by scholars (Alesina, Stantcheva, and Teso 2018; Cruces, Perez-Truglia, and Tetaz 2013; Hoy and Mager 2020; Kuziemko et al. 2015).

We use Equation (1) to estimate the average effect of the treatments on responses to these two questions. We estimate this average effect separately for the three treatment subgroups mentioned above. As a robustness check, we also estimate a version of Equation (1) that excludes all controls.

that excludes all controls. (1) i 0 1T T i 2 i 3 T i i ii ii ii ii<sup>+ XX</sup> ii<sup>γγ+ εε</sup> ii YY = ββ + ββ TT + ββ TTRRT RR + ββ SST Here, i is the outcome for respondent _i_ in district _j_ in state _k_ ; T ii ii<sup>is a vector of binary</sup> variables indicating the respondent’s state; YY i is a binary variable that equals one if the SST ii household was in a rural area; i includes controls for religion, caste and household size; and TTRRT RR ii T T i denotes treatment assignment, with XX 1 being the quantity of interest. ii TT ββ

> 6 The full set of questions is in the supplementary materials.

7
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Table 1: Summary of key PSPF indicators, 2003/04**

|**Covered working population**|**Unit**|**Civil servants**|**Teachers**|
|---|---|---|---|
|Number of men employees|1,000|50.212|94.211|
|Number of women employees|1,000|19.492|47.221|
|Number of employees|1,000|69.704|141.432|
|Average age of men employees|1,000|39.66|36.03|
|Average age of women employees|1,000|37.80|33.03|
|Average age of employees|1,000|39.14|35.03|
|Average annual salary of men|1,000|2,862.3|2,176.3|
|Average annual salary of women|1,000|2,701.2|1,851.4|
|Average annual salary|1,000|2,817.4|2,067.9|
|**Covered retiredpopulation**||**Civil Servants**|**Teachers**|
|Number of men retirees|1,000|12.155|8.297|
|Number of women retirees|1,000|1.367|2.765|
|Number of retirees|1,000|13.521|11.062|
|Average age of men retirees|1,000|64.5|70.1|
|Average age of women retirees|1,000|62.6|68.0|
|Average age of retirees|1,000|64.3|69.6|
|Average annual pension of men /1|1,000|2,301.7|1,945.3|
|Average annual pension of women /1|1,000|1,801.1|1,198.3|
|Average annualpension /1|1,000|2,248.5|1,758.6|
|**Other indicators**||**Civil Servants**|**Teachers**|
|System dependency ratio /2|%|29.1|8.2|
|Estimated total expenditure /3|UGS mln|71,180|48,831|
|Of which:||||
|_exp. for old-age pensioners (regular pensions)_|_UGS mln_|_30,358_|_19,054_|
|_exp. for old-age pensioners (CPG payments)_|_UGS mln_|_25,000_|_25,000_|
|_exp. for survivors (regular pensions)_|_UGS mln_|_5,145_|_870_|
|Other payments (non-CPG gratuities, etc.) /4|UGS mln|10,677|3,907|
|Total expenditure as % of GDP|%|0.59|0.40|
|Average old agepension replacement rates /5|%|79.7|83.4|
|**Memo Items**||||
|GDP|UGS mln|12,121,|000|
|Population|1|23,443,|052|

**Notes** : /1 this includes only annuities from reduced and full pensions; /2 number of all beneficiaries divided by the number of active employees; /3 due to be paid in 2003/2004 excluding eventual arrears; /4 MPS experts’ estimates; /5 average pension divided by the average covered wage (i.e., the wage received by active teachers or civil servant employees).  Notice that this refers to only the reduced pension and excludes the CPG: i.e., 1/3 of full pension for the first 15 years of retirement. **Source** : MPS, MOFPED.

The system dependency ratios are very low and would suggest that if the PSPF were to become a contributory scheme, it could become financially balanced with a relatively low contribution rate. However this first observation is not supported by a more careful analysis of the generosity of the scheme and long term trends in key indicators.

The system is rather generous by international standards. Individuals can retire at the early age of 45, vesting periods of only 10 years are considered low, the benefit accrual

- 12 -
Policy Research Working Paper 8788

# **Abstract**

The formal private sector has a key role to play in fostering growth and reducing unemployment in South Africa— strengthening its performance is therefore critical. This paper looks at firm behaviour, firm entry and exit, job outcomes, and productivity dynamics using firm-level administrative data for South Africa. It is the first paper to benchmark employment and productivity dynamics against various comparator countries for which similar analysis has been undertaken. The paper finds that South Africa has an aged private sector with low firm dynamism and characterized by large firms that hold a large share of employment and revenue, although they are not as productive as micro firms and pay lower wages on average. The paper also finds that job creation is concentrated predominantly in incumbent firms, which are old and large, and job creation from entry and exit is negligible. The static and dynamic productivity decompositions raise a concern that although productive efficiency is gained, it is at least in part at the expense of labor. Large firms are not exploiting economies of scale, and particularly unproductive large firms may drive the weak performance of the private sector. Relatively high wages in South Africa could be partly explained by the inefficient use of labor and negative correlation between productivity and size. Likewise, these larger firms could be responsible for the negative direct impact on jobs of firms raising productivity.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at raterido@worldbank.org, Ayanda.Hlatshwayo@treasury.gov.za, Duncan.Pieterse@treasury.gov.za, and Andre.Steenkamp@treasury.gov.za.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
||Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Finance Obstacle (0-4 scale)|Severity level of access to finance as an<br>obstacle to firms’ day to day operations. The<br>severity level is defined on a 0-4 scale as: no<br>obstacle (0), minor obstacle (1), moderate<br>obstacle (2), major obstacle (3), and a very<br>severe obstacle (4).<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Overdraft Y:1 N:0|Dummy variable equal to 1 if the firm has<br>overdraft facility and 0 otherwise.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Inflation (CPI, annual %)|Annual percentage rate of inflation based on<br>the consumer price index. Values of the<br>variable used are for the fiscal year covered by<br>the Enterprise Survey. In cases where the fiscal<br>year spanned more than one calendar year, the<br>inflation figure is the weighted average over<br>the calendar years covered, where the weights<br>used are the proportion of firms covered in the<br>calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|Primary Enrollment (Gross rate)|Gross enrollment rate in primary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross primary<br>enrollment rate values for the fiscal year<br>covered by the Enterprise Survey. In cases<br>where the fiscal year spanned more than one<br>calendar year, the enrollment figure is the<br>weighted average over the calendar years<br>covered, where the weights used are the<br>proportion of firms covered in the calendar<br>year. In the second step, annual values were<br>taken from the first step and averaged over the<br>three years prior to the (final) year covered by<br>the Enterprise Survey. Three years average<br>was taken to avoid too many missing values of<br>primary enrollment rate.<br>Source: World Development Indicators,<br>World Bank.|
|Industry fixed effects|A set of dummy variables indicating the<br>industry to which the firm belongs. Industry<br>grouping here is at the 2-digit ISIC Rev. 3.1|

39
Table C3: Robustness on Controls: Drivers of Total Debt Inflows, All Sectors - Advanced Economies (Quarterly AHKS data, missing filled from Public Sources)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|log(VIX_t−_1)|-9.101<sup>_∗∗∗_</sup>|-4.154|-3.733|-11.27<sup>_∗∗∗_</sup>|-11.45<sup>_∗∗∗_</sup>|-2.690|-5.111|
||(2.676)|(2.294)|(2.260)|(3.134)|(3.138)|(1.592)|(2.618)|
|GDP Growth_it−_1|0.506<sup>_∗∗∗_</sup>|0.402<sup>_∗∗_</sup>|0.539<sup>_∗∗∗_</sup>|0.526<sup>_∗∗_</sup>|0.275<sup>_∗∗_</sup>|0.285|0.276<sup>_∗∗_</sup>|
||(0.179)|(0.150)|(0.185)|(0.189)|(0.122)|(0.138)|(0.133)|
|FFR_t−_1||3.199<sup>_∗∗∗_</sup><br>(0.871)||||5.404<sup>_∗∗∗_</sup><br>(1.266)|3.397<sup>_∗∗∗_</sup><br>(0.977)|
|Yield Curve_t−_1|||-3.892<sup>_∗∗∗_</sup>|||3.016<sup>_∗∗_</sup>|1.101|
||||(1.109)|||(1.087)|(1.337)|
|TED Spread_t−_1||||4.422||-4.517|-2.554|
|||||(2.988)||(2.428)|(2.639)|
|Observations|1127|1127|1127|1127|1127|1127|1127|
|_R_<sup>2</sup>|0.065|0.105|0.086|0.069|0.100|0.112|0.114|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|TimeTrend|||||Yes||Yes|

Sample is from 2002q4-2014q4, countries as listed in Appendix A.4. Total Debt is the sum of Portfolio Debt and Other Investment Debt inflow data, constructed by AHKS as described in Section 2. Dependent variables are expressed as a percentage of GDP. VIX is the implied volatility of S&P 500 index options. GDP growth is calculated as a year-on-year percentage growth. FFR is the effective US Federal Funds Rate, lagged one quarter. Yield Curve is the difference between 10 year US Treasury constant maturity rate and 3 month US Treasury constant maturity rate, lagged one quarter. TED Spread is the difference between the 3 month US dollar LIBOR rate and the 3 month US Treasury rate, lagged one quarter. FFR is the effective US Federal Funds Rate, lagged one quarter. Yield Curve is the difference between 10 year US Treasury constant maturity rate and 3 month US Treasury constant maturity rate, lagged one quarter. TED Spread is the difference between the 3 month US dollar LIBOR rate and the 3 month US Treasury rate, lagged one quarter. Errors are clustered at the country level. ** p _<_ 0.05, *** p _<_ 0.01