rafmacalaba/data-use-mentions-extended huggingface dataset:
almost 45% of children being stunted, i.e. their HAZ scores are under 2 standard deviations from the WHO median.<sup>16</sup> Boys are more likely to suffer from growth retardation than girls. Regional differences are also significant. While more than half of the children in the northern part of the country are stunted; only 34% of children suffer from acute growth retardation in the southern regions (see Table 2 for more details).

# Figure 3

Children also score below the international mean for WHZ, although the mode of the distribution is closer to the international standard. Acute malnutrition affects 17% of children, with no discernible difference between boys and girls. Differences in weight-for-height z-scores are also observed between the northern, central, and southern regions of Nigeria (see left panel of Table 1). While 11% of children are wasted in the relatively rich southern region, almost a quarter of them suffer acute malnutrition in the north.

# Tables 1 & 2

# **Climate and Weather Data**

In the most recent DHS surveys the location of sampled communities is geo-referenced using GPS technology.<sup>17</sup> These data allows us to construct a precise history of climate for each DHS cluster. For this purpose, we combine the geographic location of clusters with detailed weather data from a large weather station network spanning from the mid-1980s to 2008, the year of the last DHS. The location of the weather station network is displayed in Figure 4. The geographic coverage of the network is fairly good except for the central east and western part of the country.

# Figure 4

> 16 To have a sense of the magnitude of these figures, for a three years old child a 1 standard deviation would be equal to approximately 3.75 centimeters difference in height.

> 17 In order to maintain confidentiality of respondents, the DHS randomly displaces the GPS coordinates of each cluster. Rural clusters contain a minimum of 0 and a maximum of 5 kilometers
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1), the coefficient of c is -0.096 (0.064) at the mean (one SD above the mean) of female labor participation rate. Thus, higher female labor force participation FF thus facilitates female entrepreneurship, and could change the intra-industry FDI effects from crowding out to crowding in female entrepreneurship **_._** In contrast, better female education is associated with higher female

> 10 Both variables come from the World Development Indicators.

> 11 To the extent that the rule of law may also level the playing field, especially for women, we have also examined whether the FDI effects depend on a country’s level of rule of law. We have not found it to matter much.

16
preintervention sample, but is clearly more satisfactory in dealing with the counter-cyclical margins issue.

# **(6) Data**

Several data sets are utilized in the empirical analysis. The daily price data come from the Department of Agricultural Marketing (DAM) unit of Ministry of Agriculture. The Trading Corporation of Bangladesh (TCB) also reports daily price data from major urban centers. We compared daily price data from both sources. The price trends in both data sets are nearly 24
Table 4: Additional checks

||||Hours w|orked|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Households|with susceptib|le individual|s||||
|PM 2.5|-0.152***||-0.188***|-0.161**||-0.167***|
||(0.053)||(0.052)|(0.062)||(0.049)|
|Contemp.||-0.042|||||
|PM 2.5||(0.046)|||||
|Log(PM 2.5)|||||-7.478***<br>(1.746)||
|Observations|2,167|2,245|2,167|1,817|2,167|2,167|
|R-squared|0.436|0.422|0.430|0.425|0.429|0.202|
|B. Households|without susce|ptible individ|uals||||
|PM 2.5|-0.019||-0.038|-0.053||-0.027|
||(0.063)||(0.053)|(0.044)||(0.035)|
|Contemp.||0.004|||||
|PM 2.5||(0.045)|||||
|Log(PM 2.5)|||||
median clearance time and in clearance times at the 75<sup>th</sup> and 90<sup>th</sup> percentiles of the distribution. Reductions in the latter two statistics indicate a compression of the clearance time distribution, and thus reduced uncertainty about the time required for customs clearance.<sup>5</sup> We also investigate the effect of program adoption on the inspection rate. Reductions in clearance times, uncertainty or inspection rates (along with any other operational benefits) are likely to reduce (unobserved) trade costs and thus might allow participating firms to increase their imports. We therefore investigate changes in adopting firms’ monthly imports.

As with many customs reforms, evaluation of the IHC reform is complicated by a number of factors. First, randomized treatment is virtually impossible to implement given the operational demands of customs and other border agencies, so only _ex post_ evaluation methods are feasible. Second, financial and other costs associated with participation in the IHC program mean that relatively few Serbian importers began to use the program during the period of our data. Moreover, the participating firms differ substantially from the typical importing firm in the size and composition of their import bundle, and in the moments of the distribution of the time needed to clear their import shipments. The underlying heterogeneity of trade outcomes across treated and untreated units present important challenges for the evaluation of many trade facilitation reforms. Third, the trade outcomes we study have complex dynamics that are quite problematic for conventional impact evaluation tools such as difference-in-differences (DiD) estimators.<sup>6</sup> These challenges are daunting for any empirical methodology, but mitigated by the synthetic control method proposed by Abadie et al. (2010). We believe the method is well-suited for evaluation of many customs reforms. The method can exploit the high-quality administrative data that exists in the customs environment. In particular it can capture, and exploit, the complex dynamics observed in the outcome variables of interest. The method also offers more
approximately twice as big with a reduction of the odds of dying by 15.8%. The results for improved water infrastructure are similar, with a reduction of the odds of dying by 4.6% for improved wells or springs and 15.8% for piped water into the household. The estimated effects for the included control variables appear consistent with our prior assumptions both regarding the expected sign and magnitudes of coefficients. In terms of its protective effect, the single most important factor appears to be mother’s education, with odds ratios ranging between 0.89 for primary and 0.41 for tertiary education.

Even though the data set used in this paper is substantially more restricted in terms of temporal and spatial coverage than a previous paper of us (Günther and Fink, 2010),<sup>16</sup> the estimates reported in Table 5 look overall very similar to our prior estimates. With the more restrictive coding rules applied in this paper, the marginal effects of piped water and flush toilets are slightly larger; the difference in the estimated coefficients is, however, not significant.

# **4.2 Aggregate Mortality Effects and Contribution to MDG 4**

Having estimated an empirical relation between water and sanitation infrastructure and child mortality, we can compute predicted mortality under the counterfactual assumption that all children in our sample are provided with access to improved water and sanitation. Given that we distinguish between three levels of water and sanitation, we consider two alternative scenarios: In the first scenario, we assume that all children are provided with access to basic improved water and sanitation. In the second scenario, we assume that all children are provided with access to the highest quality water and sanitation infrastructure, i.e. water pipes and flush toilets in their households.

To see how these assumptions translate into changes in mortality, we first predict mortality under the true (observed) distribution of water and sanitation access, which, by construction, yields current mortality rates. In a second
As noted above, because MDB participation in syndicated lending and public-private partnerships is well documented in global datasets, there is emerging empirical research in those areas. In contrast, to our knowledge, systematic information on MDB guarantees is not as readily available.

- **_Dive_** deeply to study the effects of a subset of projects. The monitoring and evaluation frameworks should be built into these projects while they are being designed. Attention should be paid to the types of data that must be collected to demonstrate the effects of projects on gender and poverty outcomes, which will often require representative household surveys. To identify the causal effects of such projects, approaches such as staggered program roll-outs and randomized controlled trials (RCTs) could be considered.

- **_Design_** upstream policy interventions for sector reform and to improve the enabling environment to complement PCM investments. This should be done intentionally and in

> 21 See https://ida.worldbank.org/en/financing/ida-private-sector-window/what-is-ida-private-sector-window

> 22 See https://www.worldbank.org/en/news/press-release/2023/06/22/world-bank-group-intensifies-focus-on-private- <u>sector-launches-effort-to-scale-investment-in-emerging-markets</u>

45
here again that parental schooling (education) in our analysis is a summary measure of the socioeconomic status of the family a child is born into, and thus capture any and all correlated family and neighborhood factors that aect children's educational attainment.

Given that we have the full count census data, our main estimation sample (treatment group: 1968-1972 and comparison group: 1957-1962) gives us 2,048,164 father-child pairs where household heads are the fathers, and the children were living in the household at the time of the census. We calculate the years of schooling based on the education level a respondent has completed. A comparison shows that the children exposed to Inpres schools and their fathers have more education than comparison cohorts and their fathers (see Table 1).

The census also reports information on birth district and province of an individual. We match this birth district information with the Inpres school construction intensity data, which was graciously provided to us by Esther Duo. The school intensity data was originally reported as the number of schools per 1000 children in a district. We use a normalized measure of the Inpres intensity by dividing the number of schools by the highest number of schools received by a district.<sup>27</sup> This normalization implies that the estimated coecients can be interpreted directly as the eects for the districts with the highest treatment intensity.

We use built-up density in a district in 1975 for estimating the selection equation for father and children's coresidency. We implement inverse probability weighting to correct the biases in the estimates due to sample truncation arising from coresidency. The source of built-up data is the Global Human Settlement Layer (GHSL) (Pesaresia et al. (2015)). The built-up data are at 300 meters by 300 meters grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the total built-up area at the district level.

# (4) Empirical Strategy and Estimating Equations

Our empirical strategy follows closely the approach
Monetary Fund– International Financial Statistics (IMF-IFS) database.

_Source_ : International Monetary Fund– International Financial Statistics (IMF-IFS) database.

Net foreign assets and/or liabilities are seen as the reflection of a country’s external position and trade and investment with the rest of the world. Central banks traditionally define NFA as the change in net foreign assets as the current account balance. This definition is a bit narrow and more of a short-term nature. A country’s external sector also involves portfolio investments, direct investments as well as debt instruments. This broader context is more reliable. It is captured in the definition proposed by Lane and Ferretti (2001), which relies on two identities. The first identity is _NFA_ = _FDIA_ + _EQA_ + _DEBTA_ + _FX_ – _FDIL_ – _EQL_ – _DEBTL_ (1) where _FX_ = foreign exchange reserves, _FDI_ = the stock of direct investment, _EQ_ = portfolio equity investment, _DEBT_ = debt, _A_ = assets, and _L_ = liabilities.

5
change over time. As a result, ∆w��� �0 , ∆w��� �0 and �w� �w������ ��w� �w���� . We � � impose this restriction and solve for �w �w ���: We then estimate the long-run equilibrium wage differential between matched workers across country pairs. As illustrated above, the equilibrium differential is a function of the “deep” parameters that capture the responsiveness of capital and labor mobility on wages. Deepening economic integration, changes in policy, and a host of other factors may affect the long-run differential. Since the differential may also be affected by changes in the domestic wage structures, we also analyze how the differential changes in the context of changes in the domestic wage structure.

# **III. Empirical Results**

The discussion of the econometric results has three parts. Prior to presenting the data and results concerning the convergence of wages across and within countries, the following section covers the analysis of international convergence of GDP per capita.

# **A. Stylized Facts: Latin American Wage Convergence in Context**

Using GDP per capita data<sup>4</sup> for 169 countries, Figure 1 shows the standard deviation and mean pairwise differences for all countries. The figure shows divergence between countries’ GDP per capita over the 1990-2008 period. Starting with the 2008 financial crisis, we observe income convergence. This pattern would be consistent with the higher-income countries pulling ahead

> 4 The data are PPP-valued GDP per capita in constant (2011) dollars from the World Development Indicators.

10 | P a g e
The Global Findex data is particularly well-suited to measure the gender gap in access to finance as the definition of account penetration includes only individual or joint accounts and not the use of someone else’s account. The use of savings and credit refers to individual behavior as well. This is in contrast to other demand-side data on financial behavior that measure account penetration, savings behavior and credit use at the household level.<sup>8</sup> In contrast, individual-level indicators allow us to directly measure women’s control over their assets, an important component of economic empowerment.

# **_2. 2 Descriptive Statistics_**

We start our analysis by documenting ownership and use of financial services around the world by gender. In particular, we provide descriptive statistics on three key dimensions of financial services: account ownership (and barriers to account ownership), savings and credit.

All reported country, regional, and income-group statistics are weighted using individual weights provided by Gallup<sup>9</sup> and by country-level adult population.

# <u>2.2.1 Accounts</u>

According to the Global Findex data approximately half of adults worldwide report having an account at a bank, credit union, cooperative, post office or microfinance institution (MFI). Not surprisingly, there is enormous variation in the use of financial services between high income and developing economies: account penetration is close to universal (90 percent) in high-

> 8 For instance, the European Bank for Reconstruction and Development (EBRD) _Life in Transition Survey (LITS)_ includes the question: “Do you or anyone in your household own: A Bank Account.”

> _9_ All country-level averages use individual weights that are provided by Gallup to ensure a nationally representative sample. First, base sampling weights are constructed to account for oversamples and household size. Second, poststratification population statistics are used to weight the data by gender, age, and, where reliable data are available, education or socioeconomic status
origin is a strong predictor of whether a country has credit information sharing institutions. Both creditor rights and the legal origins dummies are available for 133 countries.

Finally, we include controls for the ownership structure of the banking sector in 2001: the assets shares of government and foreign owned banks. Similarly to our measures of banking competition and concentration these variables come from the GFDR 2013 database. Appendix A includes more information on the variables used in the analysis and the data sources.

# **4. Empirical strategy**

In order to test empirically whether lower competition and concentration in the banking market are associated with a higher probability of a credit bureau emerging, we estimate the following cross-country equation Bureaui = α+ βCompetitioni+ γConcentrationi + δXi + εi. (1) In this equation, i indexes countries, the outcome variable Bureaui is equal to one if the country has a credit bureau and equal to zero otherwise, Competitioni is our measure of bank competition (log minimum capital requirement), Concentrationi is our measure of banking concentration (asset share of the largest three banks), Xi is a vector of country control variables and εi is the error term. We estimate the equation through a Probit regression with robust standard errors.

The country control variables Xi include several characteristics that could be correlated with both our explanatory variables of interest (competition and concentration) and the emergence of a credit bureau. As discussed in section 3, these variables are GDP per capita, the credit to GDP ratio, measures of the institutional environment, legal origin dummies, the asset share of government-owned banks and the asset share of foreign-owned banks.

In line with hypothesis 1 stated in section 2, we expect the coefficient on competition (β) to be positive. Banks in countries with a higher minimum capital requirement, i.e. with higher entry 12
--|---|
||Modeled|Model-predicted|Survey-measured|
|By gender||||
|Female|17.4|2.8|6.4|
|Male|20.0|7.9|8.7|
|By age group||||
|below 25|19.4|6.8|7.1|
|25-45|18.3|3.7|6.6|
|above 45|19.8|8.4|9.4|
|By nationality||||
|Georgian|18.8|5.6|7.6|
|Azeri|22.6|17.2|12.8|
|Armenian|26.9|12.4|12.5|
|Other|16.2|2.0|3.2|
|Total|19.0|5.9|7.8|

Sources: CFUWBES waves 1 and 2. Notes: Jobs lost in the table are reported as cumulative. The cumulative jobs lost by the time of CFUWBES2 is predicted by aggregating modeled jobs lost in CFUWBES 1 and the modeled incremental jobs lost from R1 to R2.

These patterns are not consistent with those seen for the larger group of all private sector workers according to the GHFPS. In that data, 22.9 percent of men had lost their job between March and December 2020 due to COVID (and were not working in December 2020) in contrast to 40.8 percent of women (Table 5-19). More

> 51 The CMMHH results must be considered tentative, because Palestinians and Syrians each represent a very small share of pre-COVID PFPS workers (approximately 3 percent each), and sample sizes for the CMMMHH were even smaller, making these statistics somewhat unreliable.

38
not vanish (i.e. makes a positive contribution to total inequality) while the between-group inequality component will tend to infinity (when _μ_ 2 tends to infinity as _λ →_ 0 while _s >_ 0).

# **2.2 An alternative to top income data: Challenges**

As stated in the previous section, DB-2 (the top income database) typically takes the form of tax record data. These data have at least two advantages: (1) it directly observes realized incomes (which makes the estimation of _F_ 2( _y_ ) or any income statistics such as inequality among top earners rather straightforward), and (2) it provides a count of the number of top income earners, which makes for

> 6Hence it is expected that any efforts made to fix the top tail of the income distribution by bringing in complementary data (top income database) will be rewarded the most by the Theil index.

6
TFP, a similar measure to SVAR-identified technology, has been found to be uncorrelated across countries, while unadjusted measures of TFP are correlated (Huo et al., 2020; Imbs, 1999). Structural VARs point to the presence of cointegration between TFP in the United States and other economies but with slow and limited spillovers (Mandelman et al., 2011; Miyamoto and Nguyen, 2017). In a broader dataset, utilization-adjusted U.S. TFP has been found to have spillover effects on TFP growth in other advanced economies but only at very gradual rates (Adler et al., 2017). Using data for patents and R&D spending as proxies for productivity-enhancing technology adoption, some cross-country spillovers have been identified (Keller, 2010). Finally, in a factor modeling framework, TFP growth has been found to be one of the most important correlates of common developments in G7 GDP growth (Crucini et al., 2011) and a GDP factor estimated for a broader range of 117 economies (Abate and Serven, 2019).

An alternative and growing strand of the literature has highlighted the role of slow technological diffusion between leading and lagging firms across advanced economies (Organisation for Economic Co-operation and Development, 2015; Andrews et al., 2015; Cirera and Maloney, 2017). Long lags in the adoption and intensity of use of new technologies have been found to explain

> 14See, for example, Francis et al. (2017), Francis et al. (2019), and Kose et al. (2003).

20