rafmacalaba/datause-encoder-data huggingface dataset:
and other sectors in the market economy, as well as between the market economy and the unpaid household economy where women are the main workers (Fontana and Wood 2000). For instance, if trade leads to increasing occupational segregation, or a reduction in leisure time for female workers, women may be less motivated to pursue a life-long career, thereby increasing the gender gap.

Only a few country studies have looked at the impact of trade on the gender wage gap. Black and Brainard (2002) find that increased competition through trade did contribute to the relative improvement in female wages in concentrated relative to competitive industries in the United States between 1976 and 1993, suggesting that trade may benefit women by reducing firms’ ability to discriminate. García-Cuéllar (2000) and Artecona and Cunningham (2002), using data on Mexican wages, find that the gender gap decreased more in industries that were more affected by trade (in terms of import licenses’ reductions or import penetration) than in other industries. Berik, van der Meulen Rodgers and Zveglich (2004) analyze the impact of competition from international trade on the gender wage gap in Taiwan and South Korea between 1980 and 1999, and find that greater international competition in concentrated sectors was associated with larger gender wage gaps, contradicting Becker’s theory.<sup>2</sup> Fontana and Wood (2000) use a computable general equilibrium model for Bangladesh to simulate the gendered effects of changes in trade policies and capital flows. They find that the gender wage gap narrows with a rise in foreign capital inflows and an increase in exports, as long as exports are female-led.

This study analyzes the rarely-used ILO October Inquiry to do a _cross-country_ study of the impact of globalization on the gender wage gap, and provides evidence on the impact of trade and FDI net inflows across many countries. The ILO October Inquiry is the most far-ranging survey of wages around the world, and contains information on the gender wage gap in 161 narrowly-defined occupations in more than 80 countries for

> and Gender Initiative Any responsibility for remaining errors that remain rests with the author.

> 2 There are also a number of country studies looking at the impact of market structure on the gender gap. Although these studies do not focus on trade, they are suggestive in the sense that trade can have an impact on the gender wage gap through changes in market structure. Black and Strahan (2001) have shown that deregulation in the banking industry in the United States was associated with a significant improvement in the relative wages of women. Hellerstein _et al_ . (2002) found that enterprises that have weak market power tend to pay women and men more nearly equal wages, while firms that are large relative to the market tend to discriminate.

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relatively short time periods of 5-year averages. In order to account for the cyclical reversion to the trend, they include in the standard growth model (3) the output gap at the start of the period: where _s=5_ and ln _yit_ − _s_<sup>−ln</sup> _yitT_ − _s_ is the output gap. They also add a time specific effect<sup>μ</sup> _t_<sup>, and</sup> a country-specific effect<sup>η</sup> _i_<sup>. Controlling for the initial output gap allows them to improve the</sup> regression fit and avoid overestimating the speed of transitional convergence. The output gap in the regression is given by the difference between (the log of) potential (trend) and actual GDP per capita around the start of the period. The trend output is obtained using the BaxterKing filter for each country in the sample (Baxter and King, 1999).

The inclusion of the initial output gap in the growth regression model is contentious because with the steady state shifting over time there is no stable long-run trend that can be identified using the filter. We therefore forecast real per capita GDP growth using a pure cross section version of the regression model (4). The model includes all variables representing convergence factors and structural policies mentioned earlier, and only one variable in the category of stabilization policy (inflation). These are the most pertinent to growth over a long time span and the most commonly found in the empirical growth literature.

In this paper we use Loayza _et al._ ’s panel data and panel estimates to forecast real per capita GDP growth for the set of all developing countries for the period 2005-14. We do so without having to forecast each of the steady state growth determinants in _X_ .<sup>14</sup> Only for the set of 35 developing countries in Loayza _et al._ ’s panel data (the panel contains 55 developing and 23 developed economies), we quantify the growth effects of individual growth

> 14 We can do this because the growth model is estimated using averages of the underlying growth determinants in the five year prior to the last year of the current period. Kraay and Monokroussos (1999) use this approach

13
The emergence of China and India on the global economic stage has understandably been the subject of much discussion in international media, business and policy circles. The nearly 9 percent annual rate of real per-capita GDP growth that China has averaged over the last quarter century is unprecedented. And with an average growth rate of GDP per-capita of nearly 4 percent per annum since 1981, India’s “takeoff” seems less than spectacular only in comparison with China’s.

In both China and India, this growth has been accompanied by substantial—in the case of China, dramatic—reductions in the aggregate incidence of absolute poverty measured in terms of income or consumption. Figure 1 displays the trends for the two countries over the period from 1981 to 2001.<sup>1</sup> The headcount rates of poverty are calculated on as comparable a basis as is currently feasible with the data available. The poverty line is the World Bank’s dollar-a-day global standard of about $32.74 per month at 1993 Purchasing Power Parity. China started this period with the higher poverty rate, but this soon changed.

**_Figure 1: Growth and poverty reduction in China and India, 1981–2003_**

<!-- Start of picture text --> 1400 70<br>China<br>1200 60<br>India per-capita GDP<br>1000 poverty rate 50<br>800 40<br>600 China poverty rate 30<br>400 20<br>India<br>200 10<br>per-capita GDP<br>0 0<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>day)<br>% of population poor (below $-a-<br>Per-capita GDP (constant 2000 USD)<br><!-- End of picture text -->

**_Source_** Poverty measures from Chen and Ravallion (2004).

However, concerns are being expressed about the distributional impacts of the growth processes in both countries. The domestic debate about growth-promoting reforms has become increasingly contentious. It is widely felt that the gains from growth have been spread too unevenly, with some segments of the population left behind in relative and even absolute terms. Yes, the two giants are awakening from their slumber, but they are still only partially awake, as segments of their societies remain (relatively and absolutely) dormant. This unevenness has shown up as rising income inequality by conventional measures in both countries. These developments in turn have led some to question the sustainability of growth.

1 At the time of writing, only preliminary data were available for India for 2004/05 and this data point is not plotted in Figure 1. The preliminary data suggest that the overall trend poverty rate in India indicated in Figure 1 has been maintained, although it has not accelerated (in percentage points per year) since the early 1990s.

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the bank recapitalizations because the central bank issued securities to offset some of the crisis-related liquidity credits it made.<sup>19</sup> Table 2 Increases in Government Domestic and Total Debt<sup>a</sup>

|1995-2004<br> (Changeinpercent ofGDP)<br> <br>|
|---|
|DomesticDebt<br>Total Debt|
|Argentina<br>42.2<br>78.7|
|Turkey<br>35.5<br>18.1|
|Indonesia<br>29.7<br>30.3|
|Egypt<br>27.0<br>10.3|
|Thailand<br>24.8<br>23.8|
|Uruguay<br>22.0<br>74.5|
|Brazil<br>19.9<br>26.8|
|Colombia<br>19.5<br>29.6|
|Mexico<br>b<br>18.3<br>8.7|
|Morocco<br>17.3<br>-24.9|
|Slovak Rep.<br>17.0<br>9.1|
|Poland<br>16.1<br>-2.6|
|India<br>15.2<br>3.1|
|China<br>14.8<br>7.4|
|Peru<br>10.0<br>13.3|
|Malaysia<br>9.1<br>-7.5|
|Czech Rep.<br>7.1<br>-0.9|
|Pakistan<br>3.6<br>4.2|
|Philippines<br>3.5<br>5.5|
|Bangladesh<br>3.4<br>-3.3|
|Venezuela<br>3.2<br>-30.0|
|Tunisia<br>2.8<br>0.5|
|Russia Fed.<br>-7.1<br>-26.5|
|Chile<br>-9.5<br>-10.7|
|Hungary<br>-36.1<br>-68.8|

a Includes central bank debt.

b Figures start in 1993. Mexico's crisis began at end-1994. The rise in domestic debt from 1995 to 2004 was 16.9 percentage points, total debt declined by 16.2 percentage points.

In any case, much of the growth of government domestic debt in the crisis countries studied here seems to have occurred as a result of bank recapitalizations. However, these recapitalizations were often well after the crisis began and not always in 19 Note that liquidity credits are included in the estimated costs of the crises, but they do not increase government domestic debt. During crises, central bank debt increases during crises only from sales of debt to tighten monetary policy or debt issues to support banks.

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TABLE A16: Impact of leaders’ exposure on intermediary outcomes, by baseline social interactions about ECD

||(1)<br>Nutrition|(2)<br>Stimulation|(3)<br>Health|(4)<br>Micronutrients|(5)<br>Proteins|(6)<br>Attitudes|(7)<br>Hygiene|
|---|---|---|---|---|---|---|---|
|ITT Leader|-0.14|-0.04|-0.06|-0.19<sup>_∗∗_</sup>|-0.23<sup>_∗∗∗_</sup>|-0.08|-0.14<sup>_∗_</sup>|
||(0.10)|(0.06)|(0.06)|(0.08)|(0.09)|(0.07)|(0.08)|
|Social (Had interactions about ECD at baseline)|0.05|-0.06|-0.01|0.03|-0.04|0.06|0.02|
||(0.08)|(0.09)|(0.08)|(0.07)|(0.10)|(0.09)|(0.08)|
|ITT Leader X Social|0.01|0.13|0.13|0.06|0.01|0.18<sup>_∗_</sup>|0.06|
||(0.10)|(0.11)|(0.08)|(0.09)|(0.11)|(0.10)|(0.09)|
|Observations|2062|2062|2073|2059|2062|1970|2068|
|ITT leader + ITT leader X Social|0.13|0.35|0.21|0.10|0.07|0.29|0.34|

_Note:_ Social is a binary variable indicating that the household had talked to at least one other community member about ECD in the week before the baseline survey. At baseline, households were asked whether they had talked to other community members about ECD practices in the last 7 days. This includes interaction about ECD practices with the health promoter, pre-school teacher, primary school teacher, other teachers, family members, neighbours, or elected leaders in the village. About half of the households had talked to at least one other community member about ECD practices. All outcome variables are standardized using the mean and standard deviation of the control household. The first 6 columns show ITT estimates on indices aggregating information regarding early childhood investments, as reported by the caregiver. Column 7 aggregates information regarding hygiene of the child, as observed by the enumerator. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 64
**Figure 2.2 From "Twin Surpluses" To "Twin Deficits" for Country Groups in MENA Region**

<!-- Start of picture text -->
1. Fiscal balance  2. Current account<br>(Percentage of GDP) (Percentage of GDP)<br>15<br>30<br>GCC OEC OIC 25 GCC OEC OIC<br>10<br>20<br>5<br>15<br>10<br>0<br>5<br>-5<br>0<br>-5<br>-10<br>-10<br>-15<br>-15<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text -->

Sources: International Monetary Fund, World Economic Outlook Database October 2017; and World Bank Staff calculations. Note: Current account balance of country groups are average current account balance across member countries w eighted by gross domestic products. GCC: Gulf Cooperation Council countries. OEC: Oil exporting countries other than GCC countries. OIC: oil importing countries. Forecast Starting 2017.

Even if many MENA countries, especially oil exporters, appear to have relatively low levels of debt, there are important contingent liabilities related to state-owned enterprises—such as non-performing loans—that suggest an escalation of public debt. With little ability to increase spending to embark on the kind of transformational agenda that is required to address the socio-economic challenges they face, MENA countries will have to mobilize private sector financing.

# **Infrastructure needs and challenges**

The infrastructure needs in the region are large. They are estimated to be around US$100 billion annually. Most of the needs are in electricity generation and transportation, followed by water and sanitation, and information and communication technologies.

Infrastructure traditionally has been financed, and managed, by governments with little private sector involvement. Because budgets are tighter, there will be underinvestment in maintenance of publicly owned and operated infrastructures. Interestingly, this is what led to the privatization of infrastructure in the United Kingdom in the 1980s, among other places (see Estache and Fay, 2007).

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taxes and tax administration, as well as regulations in the areas of business licensing, labor, foreign exchange, environment, fire and safety. Crime is directly affected by the Corruption obstacle and Policy Instability is affected by Corruption, Infrastructure, and Anticompetitive behavior<sup>9</sup> . The dashed double-headed arrows between Policy Instability and the following variables – Crime, Inflation, Taxes and Regulation, and Judicial Efficiency – indicate that the direction of orientation between Policy Instability and these variables changes between patterns.

The output also shows that relations between the obstacles themselves is quite complex and there are multiple relations in the DAG between the various business environment obstacles.<sup>10</sup> Since the main focus of this paper is to determine the business environment obstacles that have a direct effect on growth, we do not dwell on the interactions between the different variables and leave it for future work. Hence, rather than start at the bottom and focus on the farthest variable, which is likely to have a very diluted impact on firm growth, we start at the top of the agenda and focus on the variables with direct effects which are likely to have the biggest impact on growth. Most importantly, the DAG analysis also identifies only Financing, Crime and Policy Stability as having direct effects on firm growth, as suggested by specification 11 of Table 2. As discussed in Section II, the analysis identifies direct effects after conditioning on all subsets of the other variables. This suggests that in regression analysis, Financing, Crime and Policy Instability will always have significant coefficients irrespective of the subsets of other obstacles included in the regression. Thus, these are binding constraints, and policies that relax these constraints can be expected to directly increase firm growth.

14
McKenzie, David (2018) “Statistical Power and the Funnel of Attribution”, Development Impact blog, January 8 https://blogs.worldbank.org/impactevaluations/statistical-power-and-funnel- <u>attribution.</u>

- McKenzie, David and Anna Luisa Paffhausen (2018) “Small Firm Death in Developing Countries”, _World Bank Policy Research Working Paper no._ 8236.

McKenzie, David and Christopher Woodruff (2014) “What are we learning from business training evaluations around the developing world?”, _World Bank Research Observer_ , 29(1): 48-82 Smith, Sheryl Winston, and TJ John Hannigan (2015) “Swinging for the fences: How do top accelerators impact the trajectories of new ventures?”, <u>http://druid8.sit.aau.dk/druid/acc_papers/5ntuo6s1r5dvrpf032x24x5on5lq.pdf</u> Wagner, Rodrigo (2016) “Does Feedback to Business-Plans Impact New Ventures? Evidence from a Field Experiment”, Available at SSRN: http://ssrn.com/abstract=2766566 Yu, Sandy (2016) “How do Accelerators Impact High-Technology Ventures?”, Mimeo. UC Berkeley 31
# 1. Introduction

Regions and countries compete for foreign investment because it is perceived to have several bene fi ts for the host economy (Caves, 1996). For example, foreign fi rms often are associated with better technology, which may spill over to domestic fi rms<sup>1</sup> . Moreover, their jobs are perceived to be good in the sense that they require more quali fi cations, offer more training or pay higher wages. These reasons have been used to justify regional or national industrial policy in order to secure foreign investment.

Existing empirical work comparing foreign and domestic fi rms has shown that foreign fi rms have a more educated workforce and pay higher wages than domestic fi rms. But these fi ndings may at least in part be driven by selection of foreign investment into certain fi rms or sectors. For example, consider the fi nding that foreign ownership is associated with higher wages. This may be driven by wages increasing after the foreign acquisition, or by foreigners buying domestic fi rms that pay above-average wages. In this paper, I use panel data on Portuguese fi rms to provide evidence on the type of domestic fi rms bought by foreigners and on the effect of foreign acquisitions on skill composition and wages. I fi nd that foreigners “cherry pick” domestic fi rms, choosing those fi rms with an educated workforce and higher wages. Moreover, these fi rms are already very similar to the group of existing foreign fi rms. Following the foreign acquisition, I fi nd no signi fi cant changes in the educational composition of the workforce. There is evidence that average wages increase following the foreign acquisition but changes are smaller than cross-sectional estimates of this The positive wage premium for foreign fi rms is a stylized fact for developed and developing countries. Feliciano and Lipsey (1999), using industry-level data for the US for the period 1987-1992, fi nd a differential in favor of foreign fi rms that is larger for services (9-10 percent) than for manufacturing (5-7 percent). For UK manufacturing establishments, Girma et al. (1999) fi nd that foreign fi rms pay, on average, about 5 percent higher wages than domestic fi rms, even when sector, establishment size and productivity are controlled for. For Mexico

> 1For example, Dimelis and Louri (2001), Haskel et al.(2002) and Aitken et al.(1996) fi nd evidence consistent with positive spillovers to the host economy.

2
# **II. Data and Measurement**

The main data sources for this paper are the World Bank’s Enterprise Surveys (WBES) in around 80 countries. The WBES data are collected by the World Bank to benchmark the investment climate in developing countries across the world and to understand the determinants of firm performance and behavior. In each country the survey was based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the non-agricultural private economy in the country.<sup>6</sup> Stratification was based on three criteria: the sector of activity, firm size, and geographic location.<sup>7</sup> For each country in the sample, we use the most recent survey available. Consequently, this paper focuses on explaining cross-country differences in firm performance, but by controlling for industry effects we will focus on differences within specific industries. With our primary focus on the manufacturing sector, we will mainly use the manufacturing sample of around 12000 firms. The number of countries in sub-Saharan Africa is 32 (see the Appendix for the list of the SSA countries).<sup>8</sup> Typically the stratified sampling yields 100 to 1000 firms per country.

The WBES includes questions on various aspects of the business environment, including infrastructure, regulation burdens, corruption, and access to finance. The availability of these indicators about the business environment allows us to simultaneously control for various aspects of the business environment. The survey has both objective and subjective measures of the

> 6 Thus wholly state-owned firms are not in the sample.

> 7 See Ayyagari, Demirguc-Kunt, and Maksimovic (2011) for more detailed description of the WBES.

> 8 In the data set, Ghana is included but we exclude it because we suspect the quality of the data is too poor—for instance, its labor productivity is almost always 400 log points higher.  We note that the data period for Ghana, 2007, was around the period that Ghana experienced currency rebasing.  We thank Margaret McMillan for pointing this out.

6
The fixed and random effects estimates described above can be biased due to measurement error, omitted variables, and reverse causality. The concern about reverse causation is justified, considering that our measure of remittances refers to balance of payment statistics that largely cover flows transferred through the formal financial system. Thus, it is conceivable that remittances may grow over time simply because financial development in the recipient countries allows banks to play a greater role in the remittance transfer process. Furthermore, biases might also occur because of common omitted variables driving the behavior of both remittances and financial development. Finally, measurement error, which is known to plague balance of payment statistics on remittances, will also likely bias our estimates.

We conduct a number of different estimations to address the concerns outlined above. First, we separately conduct estimations for the most recent period (1995-2003), because the potential for measurement error should be smaller in this period, since remittance statistics are likely to have improved over time. Second, we conduct estimations including time dummies to mitigate the concern for omitted relevant regressors. Third, we try to address the potential bias due to reverse causality by conducting estimations lagging regressors and, separately, by using lagged values of the regressors as instruments in a GMM dynamic framework à la Arellano and Bover (1995).

Two equations, (2) and (3), are estimated as part of the dynamic system GMM estimates ’ _FD_ i,t= γ _FD_ i,t-1+ β 1 _Rem_ i,t + β 2 _X_ i,t+ α i+ ui,t (2) In equations (2) and (3), the use of instruments is required to deal with the likely endogeneity of the explanatory variables (most notably, remittances) and with the fact that in 12