rafmacalaba/data-use-mentions-extended huggingface dataset:
Croke et al. (2016) identified for their analysis of the impact of multiple-dose MDA on weight was considered for inclusion in our sample.<sup>20</sup>

# **3.3 Data extraction and choice of estimator**

Given the importance of statistical power, we sought to use the most precise unbiased estimator available. We also followed guidelines in the _Cochrane Handbook for Systematic Reviews of Interventions_ (Higgins and Green, 2011). We use the following principles for extraction of data and selection of estimates from included trials in our analysis below:

- i. If treatment effects are presented without standard errors, standard errors are calculated using other presented data (e.g., t-statistics, p-values, or 95% confidence intervals), where possible following the formulas provided in the _Cochrane Handbook_ (Higgins and Green 2011, section 7.7.3.3).

- ii. If results are reported in figures rather than in the text or in a table, _Web Plot Digitizer_ software (Rohatgi, 2015) is used to extract numerical estimates from the figures.

- iii. If key information on treatment impacts is missing from a paper (and cannot be derived from what is presented), original microdata (where available) is used to obtain relevant estimates. We also obtain information from trial authors in several cases, through either direct communication or thanks to the generosity of the Campbell Collaboration research team.

- iv. Where studies report multiple treatment impact estimates, we follow the standard in TMSDG and the medical literature of favoring unadjusted estimates. If studies

> 20 The Campbell Collaboration report just became publicly available.

17
apply at the cross-country level. What is far less understood are the factors that matter at the within-country sectoral or firm level, and especially the contribution of sector-specific inputs and policies related to trade. To our knowledge, only a few papers directly address the issue of micro-level determinants of export diversification. Goldberg, Khandelwal, Pavcnik & Topalova (2008) use firm-level data from India and find that access to new imported inputs—measured by tariff declines and the subsequent prices of intermediates—can account for an expansion of domestic firm product scope. Hausmann & Klinger (2008) apply a product-space methodology to argue that the specialization patterns of South African export sectors do not bode well for future exporting efforts.

> 1There is evidence that the first-order driver of export growth is the intensive rather than extensive margin (Besede ̆s & Prusa 2007), although this may vary according to income, with poorer countries more likely to diversify along the extensive margin (Cadot, Carr`ere & Strauss-Kahn 2007). Exports of existing products into new markets also accounts for a greater share of export growth than that of new products alone (Brenton & Newfarmer 2009).

2
**Table 7:** Effects of Turnovers on Public Goods Provision

||**Public Goods Index**|**Citizen**|**survey**|**Village head survey**|**Balance**|
|---|---|---|---|---|---|
||_Podes_2021|Access|Quality|Take Action|_Podes_2014|
||(1)|(2)|(3)|(4)|(5)|
|New village head|0.503<sup>_∗_</sup>|0.073<sup>_∗∗_</sup>|0.208<sup>_∗∗_</sup>|0.165<sup>_∗∗_</sup>|-0.058|
||(0.216)|(0.037)|(0.090)|(0.071)|(0.341)|
||[0.263]|[0.043]|[0.102]|[0.086]|[0.419]|
|Observations|378|8848|8846|443|375|
|Control mean|0.23|0.78|-0.028|0.76|0.018|
|Robust p-value|0.053|0.039|0.014|0.046|0.823|
|<br>MSE-opt. bandwidth|18.7|15.2|14.9|24.9|19.0|
|Effective obs.|161|3479|3427|241|161|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). Units of observation are villages in columns 1, 4 and 5, and citizens in columns 2 and 3.
26

# **6 Conclusions**

The starting point for the analysis in this paper was the observation that despite rapid growth in per capita GDP in the late 1990s, survey data shows that the drop in poverty nationally between 1992 and 2001 was small. A plausible explanation for this pair of facts is that poverty first increased during the period of economic stagnation that ended in 1995 and only declined once rapid growth was achieved in the second half of the decade. Consequently, recent growth has reduced poverty, even though the change observed between the two surveys shows only a small net drop in the fraction who are poor.

The simulated poverty trajectories in this paper support this view. Under a variety of scenarios, the simulations imply that poverty rates have followed an inverted U-pattern, increasing to over 40% or higher in 1994 and then dropping, down to below 36% in the 2000/01 survey. Variations of the analysis which take into account separate urban-rural growth rates show small increases in rural poverty in 1997 and 1998, when rural per capita income declined while urban per capita output grew, followed by declining rural poverty.

We employ both the Datt-Walker macro-micro projection technique and a modified “two survey” version of their method. In the Datt-Walker analysis there are instances where growth patterns diverged substantially from the general pattern. These are the simulations using Dar-specific growth data, aggregate household consumption estimates from the national accounts, and alternative urban/rural population growth rates. In all three cases, it is likely that the results are the consequence of rough approximations in the national accounts data.

In most cases, relative to the survey estimates, the simulations with the Datt and Walker method overestimate drops in poverty in rural areas and the nation as a whole, while underestimating the drop in poverty in the capital. This reflects the particular distributional incidence of growth (in the survey data) which is illustrated by the growth incidence curves. Our modified “two survey” method provides a close match between the simulation and the final year
as a whole. While manufacturing employment generally grew 17% from 1994 to 2005, employment in women-owned establishments in the unorganized sector grew 138%, roughly doubling the share of total activity accounted for by these establishments. As the second breakout for employment data highlights, this ownership growth is substantially stronger than even the employment growth for women in the unorganized sector, which expanded 26%. Our study analyzes in part the extent to which political reservations for women can account for this strong performance on the ownership margin.

Among the major states, those with the highest share of new proprietary businesses in the unorganized sector owned by women in 1994 are Karnataka, Tamil Nadu, Andhra Pradesh, and Kerala. Those with the lowest share of female entrepreneurs are Uttar Pradesh, Haryana, Maharashtra, Madhya Pradesh, and Rajasthan. Similar patterns hold across states when comparing overall business ownership rates by gender. All but one state (Sikkim) saw an increase in the share of new businesses owned by women during the period we study.

Women’s entrepreneurship and business ownership rates relative to men are highest among traditional and low-technology industries such as tobacco, paper, textiles, and wood products. These same industries comprise the industries in which most women-owned businesses are found in absolute terms. During our sample period, more than 90% of new female-owned businesses were found in 6 of 22 broad two-digit industries: textiles, tobacco, wood products, food products, furniture, and chemical products. At the two-digit level, nine industries saw an increase in the share of new businesses created by women between 1994 and 2005, with the largest increases for female entrepreneurship being among these traditional industries.

Our final data source is comprised of six rounds of the NSSO’s household-level Employment-Unemployment survey conducted from 1983 to 2009. The sampling frame for these surveys captures a representative sample of the Indian population, and aggregate figures are calculated based on the sampling weights provided with the data. The surveys lack the ownership fields required to study entrepreneurship, but they do contain
whether firm _i_ in country _c_ and industry _k_ is family-owned-familymanaged, _Lick_ is firm employment as reported in BvD Amadeus and Rule of Law _c_ is the WGI rule of law index (measured in standard deviations). The control vector _Xic_ includes the logarithm of GDP per capita and the logarithm of firm age, and _δk_ are 4-digit industry fixed effects. Standard errors are clustered at the country-level.

Columns (1) and (2) in Table II show that an improvement in contractual institutions is associated with a reduction in the probability of being family-managed - a 1 standard deviation increase in the rule of law index is associated with a 16.3% drop in the probability a firm is family-managed.

To test Prediction 2, that the effect of improving institutions on management mode is attenuated for large firms, we extend specification (8) by adding an interaction term between rule of law and firm size. Column (3) reports the result. The interaction term is positive confirming that the effect of contractual institutions on the management mode decision is attenuated for large firms.

13
been digitized, so we do not have access to that information. For that reason, the level and composition of income in tax records for that period is very different from the most recent 2011 - 2019 period.

Nonetheless, we do observe PIT filings and undistributed corporate profits for those years, and therefore can use the ratio of income in PIT filings + corporate income to total income in recent years to adjust total income the 2003-2010 period. We do not change directly the microdata for the 2003-2010 period. Instead, we adjust the g-percentiles used when merging tax records to survey data at the top. We proceed as follows:

1. We first create the g-percentiles used for the merging process with survey data, and compute for all years and g-percentiles the ratio between the sum of income in PIT filings plus undistributed corporate profits to total income. For 2011-2019, total income will include all forms of withholding that we do not observe in 2003-2010.

2. We then take the average value of these ratios across all periods, for each g- percentile. In Figure G1, we show the average adjustment factor, which we use to scale total income over 2003-2010, together with 95% CI of the mean to illustrate dispersion of the adjustment factor across years. What we observe is that for individuals just on the top 1%, the adjustment factor is on the order of 2 - 2.5 - since most of their income, as we observe in other years, is composed of wage income, we miss a lot of income in 2003-2010 when withholding of wages is not observed. As we move towards higher incomes, the adjustment factor slowly converges towards unit, consistent with the fact that at the very top most of the income is from undistributed corporate profits, which we observe across the entire period.

3. We then use these adjustment factors to scale up total income in each g-percentile in the period 2003-2010. Note that we do not scale them to the average _level_ of income observed in 2003-2010 - we simply
Venezuelan migrants to native workers by computing ratios for all variables included in the table. The main takeaways are the following. Relative to their population density, Venezuelan migrants are over-represented in three occupations: outsourced workers, domestic employees and private-sector employees. Except for private-sector employment, these occupations are characterized by low wage levels (on the basis of the data for Ecuadoran

> 16The EPEC survey also contains a more traditional occupation variable. However, the variable is too detailed and cannot be aggregated as it lacks numerically coded categories. For example, the top 10 occupations only account for 4% of the workers in the survey.

13
# **Box 1. Improving Remittance Data in Mexico**

Mexico’s official data indicates that remittance inflows increased from US $8.8 billion in 2000 to US $17.2 billion in 2004, an increase of 95%. According to Mexico’s central bank, this increase in remittance inflows reflects major improvements in the way remittances are reported by financial institutions to Mexico’s central bank, rather than a major increase in the number of Mexican migrants working in the USA, which increased during the same period by 22%.

**<u>Chart 4. Remittance Inflows to Mexico from the USA</u>**

<!-- Start of picture text --> 20 $20,000<br>New regulation to<br>improve remittance<br> statistics<br>10 $10,000<br>0 $0<br>2000 2001 2002 2003 2004<br>Migrant Population Remittances<br>Remittances in US Million Dollars<br>Mexican Migrants in USA in Millions*<br><!-- End of picture text -->

- *It refers to migrants born in Mexico and currently living in the USA.

Sources: CONAPO and Central Bank of Mexico.

Before 2002, only commercial banks reported their remittance transactions to Mexico’s central bank. On October 28, 2002, Mexico’s central bank issued a new regulation requiring all money transfer companies to register at the central bank and report the value of their remittance transactions on a monthly basis. As shown in Chart 4, after the new reporting system was introduced, remittance statistics started to see a rapid growth. While other factors may also have influenced the increase in remittance inflows, the improvement in data collection practices has largely contributed to it.

Moreover, at the international level it is also important to address some misclassification problems that may cause either sub- or over-reporting of remittance inflows. For example, are funds borrowed by a migrant in
of rural spouses determines who is earning income from cash crop agriculture through increased control over household land, with women’s outcomes being more sensitive to self-esteem levels (Botea et al., 2021).

# **3. Sample Data and Empirical Strategy**

# **3.1 Data set construction**

We started by compiling data from all baseline surveys conducted by the World Bank’s Africa Gender Innovation Lab (GIL) which included any measures of socio-emotional skills and economic outcomes for both men and women. This approach yielded a database of 10 studies in 8 countries (Benin, the Republic of Congo, Côte d’Ivoire, Ghana, Kenya, Mozambique, Nigeria, and Togo) conducted between 2013 and 2020. To broaden the scope of our analysis, we added data for the two African countries included in the World Bank’s STEP Skills Measurement Program – Ghana and Kenya, as well as data from the Future of Business Survey, which was a co-product of Facebook, OECD and World Bank (which includes respondents from Angola, Benin, Botswana, Côte d’Ivoire, Cameroon, Ethiopia, Ghana, Kenya, Mozambique, Nigeria, Senegal, Tanzania, Uganda, South Africa, and Zambia). Appendix Table A2 summarizes the database of studies included in our sample.

Since each study used different scales and measures of skills, it was necessary to align the data under a common framework. We utilized a framework developed by GIL as part of its larger research agenda to examine the importance of socio-emotional skills for women’s economic outcomes in Sub Saharan Africa. To establish this framework, GIL worked with psychologists and used existing literature to develop a list of skills that are malleable, span the range of socio-emotional skills while mapping to existing frameworks<sup>5</sup> , can be used to examine gender differences, and include higher-order categories that allow for separate treatment arms to rigorously unpack the causal influence of particular socio-emotional skills. We appended the data and mapped each
-0.216***|0|
|Access to electricity|-0.017***|-0.077***|0|-0.243***|-0.302***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

# **Table A10: Identifying the $3.20 poor based on covariates of poverty** _India 2011/2012_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|0.000|-0.002***|0|-0.034***|-0.036***|0|
|Asset index|0.005***|0.006**|0.6341|-0.210***|-0.208***|0.6341|
|Literacy|0.003|-0.002|0.3235|-0.247***|-0
5 2.2. **Colombia’s education system and policy response.** The education system in Colombia comprises one year of preschool, five years of primary education, four years of lower secondary education and two years of upper secondary education. In 2014, 87% of the schools in Colombia were public and out of those, 78% were located in rural areas (OECD, 2016). All children between five and fifteen years old are legally required to attend preschool plus nine years of compulsory basic schooling. However, it is estimated that 20% of the students do not continue studying beyond primary school (OECD, 2016), and only 65% of boys and 77% of girls complete lower secondary education (Radinger et al., 2018).

The Colombian government has implemented several initiatives to facilitate the integration of Venezuelan children in public schools. For example, Decree 1288 of 2018 simplified the process for Venezuelans to validate their educational through standardized tests. This policy was designed with the objective of allowing migrant children to enroll in school grades according to both their age and prior academic achievement. In addition, also since 2018, the Colombian government allowed Venezuelan children to attend public schools regardless of the immigration status of their households.

Unfortunately, before 2018 (the end of our sample period, limited by data availability) the Colombian government did not keep systematic track of school enrollment by student nationality. However, our administrative individual-level dataset does distinguish between Colombian and foreign students, and crucially, according to the 2018 population census almost 9 out of 10 settled foreigners in Colombia are Venezuelans.<sup>6</sup>

> 6If we compute this share at the municipality level the average is 0.74 and the standard deviation 0.29. This suggests that the biggest cities in population, such as Bogota, have a somewhat smaller share.
largest dividends for women, households, and communities – has become increasingly urgent. While this is globally relevant, it is particularly important in Sub-Saharan Africa, which has both the lowest ranking on the Human Development Index and on the Gender Inequality Index (UNDP, 2018). Moreover, as gender-related data collection efforts expand, there is growing demand for how to capture empowerment in a meaningful way in cross-country surveys without being costprohibitive.

In this paper, we build on a classical conceptualization of empowerment which has been discussed across literatures (e.g., Batliwala, 1994; Freire, 1990; Rowlands, 1995), but not operationalized as a common survey measure. Specifically, we argue that women “taking power” (declaring higher decision-making ability for themselves than husbands report about them, when both are separately asked about intrahousehold decision-making) versus the case when they are “given power” (the husband reports higher decision-making for his wife than she does for herself) captures an important dimension of empowerment, prominent in theoretical work but not as well addressed in the empirical empowerment literature.

Using recent Demographic and Health Survey (DHS) data for more than 70,000 couples across 23 countries in Sub-Saharan Africa, we examine patterns of power and their relationship to empowerment proxies. We find that there is substantial contention about decision-making power across households, and that this power is multifaceted. Grounding ourselves in empowerment theory, we compare scenarios where the woman takes power versus is given power, and contrast this with the most studied case of no intra-household contention regarding women’s agency.

3
,<sup>2</sup> which is leading to the increased popularity of studying FDI in a gravity context. A recent example of this is Kox and RojasRomagosa (2020) which uses a structural gravity model of FDI to estimate the potential impact of preferential trade agreements (PTAs) on the bilateral FDI stocks and flows between the countries signing.

> 2 Examples include the US Bureau of Economic Analysis, the OECD’s BMD4 database and EUROSTAT’s BPM6 data sets.

5
Policy Research Working Paper 10781

# **Abstract**

Economic zone programs are generally pursued to improve firms’ performance within discrete areas by removing the constraints firms face. Whether or not they succeed in doing so is an empirical question. This paper capitalizes on a unique survey of firms within and outside zones in South Asia to assess the effects of zone programs on firms’ performance in exports, investment, employment, and productivity. Adopting a propensity score matching approach and district-level fixed effects, the paper explores four questions: whether zones support firm performance; whether the type of zone makes a difference, which kinds of public support services matter more, and whether firms inside zones grow faster. The results show that (i) being inside a zone positively affects foreign direct investment and employment, (ii) the effects across zone types are mixed, (iii) infrastructure and trade facilitation play a greater role in firm performance than fiscal incentives and governance facilities, and (iv) firms inside zones grow faster.

This paper is a product of the Finance, Competitiveness and Innovation 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/prwp. The author may be contacted at rgalal@worldbank.org.

_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
03)|(0.05)|(0.05)|
|High Mobility|0.00|0.01<sup>_∗_</sup>|0.00|-0.00|0.05|0.03|
||(0.04)|(0.01)|(0.00)|(0.00)|(0.03)|(0.03)|
|_β_1+_β_2 (High Mobility Efect)|0.26<sup>_∗∗∗_</sup>|0.36<sup>_∗∗∗_</sup>|0.58<sup>_∗∗∗_</sup>|0.18<sup>_∗∗∗_</sup>|0.26<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
|_β_1+_β_2 = 0 (_p-value_)|0.00|0.00|0.00|0.00|0.00|0.01|
|RI p-values _β_1|0|0|0|0|0|0|
|RI p-values _β_2|.96|.58|.9|.21|.51|.4|
|Low Mobility Control Mean|.34|0|0|0|.17|.16|
|High Mobility Control Mean|.35|0|0|0|.27|.21|
|Obs|983|981|983|983|968|968|

_Notes_ : High Mobility refers to a binary variable for above median mobility index, which averages the z-scores from mobility variables (a)-(c) in table 1. * p _<_ 0.1, ** p _<_ 0.05, ***