rafmacalaba/datause-encoder-data huggingface dataset:
<mark>interaction terms are not statistically significant (and are not shown in Table 7 for lack of space), except for that between childcare attendance with private center (Model 1 in Table 7). This interaction term is strongly statistically significant, and suggests that private childcare attendance has a stronger impact on maternal employment than public childcare. We find a lower effect from childcare attendance in communes which are far from town (Model 2), meaning that the effect is larger for communes closer to town. Possibly, employment opportunities as well as wages are higher in areas near towns, increasing the effect of childcare attendance on women’s employment.</mark> <mark>We also test the interaction of the childcare attendance and commune-level variables (availability of kindergartens in communes and access by car to villages).</mark><sup>18</sup> <mark>VHLSS contain commune-level data for rural areas but not for urban areas. Thus, we use the rural data sample to estimate the models, including interactions with commune-level variables. Both the interactions are negative, but not statistically significant. We also interact childcare and urban residence, but this interaction term is not statistically significant (results not shown).</mark> <mark>The effect of childcare attendance on maternal employment may depend on the opportunity costs of staying at home (i.e., not participating in the labor market) to take care of children. In Model 5, we test the interaction of childcare attendance with the mean wage of districts.</mark><sup>19</sup> <mark>The interaction is positive and highly significant, meaning that the effect of childcare use is greater in areas with higher wages. In Model 6, we include interaction between childcare use and per capita income for the district, but this interaction is not statistically significant at conventional levels. Income includes not only wages but also selfemployed income. Thus, the interaction between recourse to childcare and per capita income is positive but not statistically significant.</mark> <mark>Small children may receive care from older siblings and grandparents. Several studies show that informal childcare provided by grandparents can increase women’s labor supply (e.g., Dimova and Wolff, 2011; Li, 2017). Thus, we include interactions between childcare use and these variables (including the age and gender of the firstborn sibling, living with grandparents, household size, the proportion of elderly and children), but these interactions</mark>

> 18 Good roads are defined as roads passable by cars during all the previous 12 months.

> 19 Mean wages and per capita income at the provincial and district levels are obtained from Lanjouw et al. (2017).

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profiles are as expected, although we have to realize that it is choice of households, so we observe only revealed usage and not potential access (given costs, quality, ease, etc., many do not bother to seek access from formal financial institutions). Socioeconomic characteristics such as income, wealth and education play the largest roles in explaining access. Financial exclusion is thus often part of a broader exclusion in terms of education, jobs, formal training, etc. Households that use credit have a different profile from those with banking account and savings use, and the profile is more affected by income and wealth characteristics, as it tends to be the richer that borrow.

The comparison between the US and Latin America countries indeed shows some similarities between otherwise very different countries in terms of why people do not (want to) bank (Table 3). Conveniences, trust and savings are important considerations for households to seek financial services from banks. Unbanked households in the US and Mexico also display very similar characteristics, two countries otherwise at different levels of development, with the exception of home ownership (Table 4). The costs of being unbanked do vary considerably, however, as the alternative means are much fewer and more costly in Mexico. The costs in the USA for being unbanked are estimated to be only 2.5% of median income in the lowest income segment, whereas in Mexico they are estimated at 5% (Solo et al. 2004).

Although also weak and often neither comparable, some data on firm access to financing have more recently become available. Specifically, Table 5 reports the percentages of firms that say access to financing presents major or severe obstacles to the operation and growth of their business. The data come from the World Bank Investment Climate Assessments (ICA) that have been conducted in the last few years. About a quarter of the firms on average complain about the lack of external financing, with large variations though, from less than seven percent for Latvia and Lithuania, to more than 50 percent for several countries and a high of 60 percent for Brazil.

Somewhat similar to the households’ analysis is the answer to the question what are the unbanked firms? To the extent we know, profiles are as expected, with size of the firm 10
Next, we explore gender gaps. We find gender gaps in learning are positive on average with girls outperforming boys across nearly all regions as shown in Figure V by region. This points in the opposite direction of the gender gap for years of schooling which is negative on average.

<!-- Start of picture text --> Enrollment<br>5<br>0<br>-5<br>-10<br>Learning<br>70<br>50<br>30<br>10<br>-10<br>-30<br>-50<br>Gender Gap<br>East Asia & Pacific Europe & Central Asia LAC MENA North America SSA<br>Gender Gap<br>East Asia & Pacific Europe & Central Asia LAC MENA North America SSA<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE V<br><!-- End of picture text --> Gender Gap – Enrollment versus Learning (2000-2010), By Region _Notes:_ LAC refers to Latin American and the Caribbean; MENA refers to the Middle East and North Africa; and SSA refers to Sub-Saharan Africa. The gender gap takes the difference of female and male enrollment or learning. A positive gender gap indicates females do better and vice-versa. _Source:_ Primary enrollment rates are from Lee and Lee (2016). Learning estimates are taken from our database.

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9

|Afghanistan|59,711|128|383,951|
|---|---|---|---|

Source: UNHCR 2016 Data on return from EU countries compiled by Eurostat is more comprehensive but does not distinguish between the return of economic migrants – which is plausibly the larger number ‐ and refugees, and therefore represent at most an upper bound on the return of refugees. Even so, the number of returnees – whether forced or voluntary – is small except for Germany and the United Kingdom which house a large population of economic migrants, and, in the case of Germany, of large numbers of asylum seekers from countries in the Balkans.

**Table 2: Types of Return from EU Member States in 2015**

||Persons<br>returned|Voluntary<br>return|Enforced<br>return|Type<br>of<br>return<br>unknown|
|---|---|---|---|---|
|Germany|55,340|‐|‐|55,340|
|UK|50,590|‐|‐|50,590|
|France|18,245|5,920|12,325|0|
|Greece|14,390|‐|‐|14,390|
|Spain|13,315|2,355|10,960|0|
|Poland|12,930|12,080|850|0|
|Sweden|9,830|7,285|2,545|0|
|Netherlands|8,620|‐|‐|8,620|
|Hungary|5,975|210|5,765|0|
|Belgium|5,835|3,310|2,525|0|
|Austria|5,275|‐|‐|5,275|
|Italy|4,670|1,015|3,655|0|
|Finland|3,365|‐|‐|3,365|
|Denmark|2,655|170|2,480|0|
|Romania|1,995|1,810|180|0|
|Croatia|1,940|1,250|690|0|
|Cyprus|1,840|‐|‐|1,840|
|Lithuania|1,720|‐|‐|1,720|
|Czech Rep.|1,715|‐|‐|1,715|
|Slovakia|1,230|670|560|0|
|Latvia|1,030|695|340|0|
|Slovenia|840|90|110|640|
|Bulgaria|735|180|555|0|
|Luxembourg|720|545|175|0|
|Portugal|610|240|370|0|
|Estonia|560|475|85|0|
|Malta|465|285|180|0|
|Ireland|365|115|250|0|
- 3 -

In particular in Africa, these constraints often tend to be severe. In the region, about 70 percent of the total rural population do not have access to any all-weather road (Roberts et al., 2006). In Liberia, about 40 percent of rural residents must spend more than 2 hours to walk to the nearest health facility (Kruk et al. 2010). Moreover, the road condition is generally poor in rural Africa. In Uganda, 79 percent of paved roads are in good condition, but 75 percent of unpaved roads are in poor condition. In Kenya, about 40 percent of tertiary roads are in bad condition (World Bank, 2016).

Demand-side constraints further aggravate rural people’s health access already limited by the above-mentioned supply-side difficulties. In Africa, rural residents are generally much poorer than urban residents. Thus, holding everything else constant, the rural demand for health services is lower in rural areas. Jovanovic et al. (2003) show that subscription to health insurance is lower in rural areas. Because of the lack of proper insurance, rural residents are less likely to use emergency medical services (Fan et al., 2011). This is a particularly important concern in Africa where poverty is concentrated in rural areas. Many studies are supportive of the importance of health insurance to improve people’s health care access in developing counties (see, e.g., Spaan et al., 2012). This means that financing is a critical issue for poor people.

This paper casts light on the nexus of transport connectivity, medical supply sustainability and people’s health care service access. As discussed, these factors are interdependent on one another. To estimate the system of equations and solve the endogeneity problem, the paper relies on the three-stage least squares (3SLS) model (Zellner and Theil, 1962). One equation examines people’s demand for health care services, and another represents the quality of the services determined by transport connectivity and other factors. The data come from Madagascar where rural transport connectivity is extremely limited, because of low road density as well as the poor condition of the rural road network. Detailed health facility databases are combined with spatial road condition data.
# **4 Concluding Remarks**

The World Bank remains the leading intellectual institution in development and has been a world leader in a number of important areas, including poverty measurement, delivery of social services, impact evaluation, measurement of development outcomes, international trade and migration. It has also been a leader in generating survey data on important dimensions of development including Living Standard Measurement Surveys (LSMS); enterprise surveys (also known as Investment Climate surveys), and Purchasing Power Parity measurement through the International Price Comparison Project.

Bank research is resolutely oriented toward policy. Researchers play a crucial role in both learning from past policies and thinking critically about future policies. Without research, the conceptual foundations for policy making would be weak; there would be very little new knowledge or data to inform policy decisions; there would be little or no innovation; and we would know too little about what succeeds and what fails in the fight for development and against poverty. Without research and critical inspection to measure failure and success, deeply flawed policy orthodoxies would persist and successful policies would be dropped for the wrong reasons.

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deportation. As a result, migrant populations may be grossly undercounted in censuses, unless proper corrective procedures are implemented.

# **_Population registers_**

Population registers are continuous reporting systems used to enumerate the resident population of a particular area, which typically and historically corresponds to a municipality, parish or police precinct. Popular in many parts of Europe – especially the Benelux, Scandinavian and Baltic countries – population registers record the names and addresses of the residents as well as their key demographic variables, such as births, deaths and changes in marital or residential status. A legal requirement typically exists to register and notify any alterations in status. As such, they may be used to record both internal and international migration, and provide detailed up-to-date demographic and socioeconomic information. This makes them a rich, and often fairly exhaustive, source of (migration) data, although the incentive to keep records updated varies across individuals and subgroups of the population. In- and outflows can be tracked with much greater frequency and depth. Although the extent of register data is also rather limited, the continuous updating of the information makes registers more appropriate for tracking migration and for nuanced research questions in comparison with census data.

Population registers, unfortunately, are not widely available outside Europe, and suffer from a higher degree of heterogeneity across countries in terms of registration criteria as well as data scope and quality. The Nordic countries typically implement impressively accurate registers, while the precision of those from Southern Europe is far 6
changes in mean income. The household survey provides additional information on income dispersion in each group, which is assumed fixed across simulations.

# 3.1 A Simple Micro-Accounting Method

Macro-RHG models can be relatively easily connected with a household income survey to compute poverty and inequality indicators over a sample of actual households and not only over group-specific means as in the standard Macro-RHG framework. The advantage of this method is that now a uniform intra-group distribution is not assumed but instead use is made of the distribution observed for the sample of actual households. In general this method follows five steps:<sup>3</sup>

1. Classify the available sample into the categories of households distinguished by the macro model (using information on the main source of income of the household head, for instance).

2. Retain from the macro model nominal growth rates in per capita consumption or disposable income induced by a shock for all categories of households.

3. Apply these growth rates separately to the per capita disposable income or consumption expenditure of each household in the household survey. This provides absolute income or consumption expenditure levels following the shock.

4. Adjust poverty lines (expressed in monetary units) using changes in consumer prices given by the macro model (possibly separately for the rural and urban sectors, or even separately for each household group). Then, using the new absolute nominal levels of income and consumption for each group, calculate standard income distribution measures such as the headcount index, the poverty gap, and the Gini coefficient.

5. Compare the post-shock poverty and income distribution indicators with the baseline values to assess the impact of the shock on the poor.

> 3See on this approach the detailed description in Agénor, Izquierdo and Fofack (2003). A similar method is followed by Löfgren, Robinson and El-Said (2002), and Coady and Harris (2001).

7
to simplify the comparison with services. For instance, the manufacturing sample survey in the year 2005-06 is referred to as the 2006 survey. Likewise, we use 2010 in the tables and text to refer to the years 2009-10 for ASI data and years 2010-11 for NSSO data. For services, we always refer the surveys by their start year.

# **_Sample Preparation_**

Our descriptive work primarily compares manufacturing and services during the overlapping period of 2000-2010. The raw data have 155,513 ASI observations and 408,002 NSSO observations. For the ASI, we drop establishments that are not “open,” excluding plants that were closed or non-operational when surveyed, were deleted due to deregistration, and were out of coverage (that is, belonging to defense, oil storage, etc.). Next, we drop ASI establishments that do not belong to the manufacturing sector. Such industries may relate to mining and quarrying, fishing and aquaculture, or the services sector. Finally, we flag observations with null, missing, negative, or extremely large values of output, output per worker, and employment. At this stage, those states that are not surveyed by ASI are also flagged from the NSSO surveys. These include the states of Arunachal Pradesh, Mizoram, Sikkim, and the Union Territory of Lakshadweep. We also flag observations that have blank state codes. In the end, we drop observations that have any one of the flags described above. These drops result in a final sample of about 93,000 for the organized sector and 400,000 for the unorganized sector for the three survey years under consideration.

The NSSO surveys many more establishments in the services sector, about 800,000 during our period of study. We drop some very small industries: NIC 61 (Inland, sea and coastal water transport), 73 (Research on natural sciences, engineering, social sciences and humanities), and 90 (Sanitation services). We also drop NIC industries 37 (Recycling) and 50–52 (Wholesale and retail trade, repair of motor vehicles & household goods) due to inconsistent coverage across survey years. The latter exclusions substantially reduce the included establishment counts.

In our policy analyses, we also exclude plants from small and conflict states. These include the Andaman and Nicobar Islands, Dadra & Nagar Haveli, Daman & Diu, Jammu & Kashmir, Tripura, Manipur, Meghalaya, Mizoram, Nagaland and Assam. In some analyses, we drop observations for plants with negative value added, missing raw materials value and missing values of fixed assets. Lastly, we also drop any observation in the services sector surveyed from the states of Arunachal Pradesh, Lakshadweep, Mizoram and Sikkim because they are not covered by the organized manufacturing survey, with which we seek to compare trends. The latter step impacts less than 1% of our sample. We are left with about 352,000 observations corresponding to the year 2001, 186,000 from 2006, and 109,000 in the year 2010. These correspond to 421 districts in each year. In the regression analysis at the district level, we additionally drop a small number of observations that lack district identifiers.

# **IV. Empirical Strategy**

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large infrastructure projects and improved the quality of existing infrastructure assets, which contributed to their strong pre-pandemic growth performance.

Across the region, advances in infrastructure for information and communication technology (ICT) and connectivity, primarily reflecting an unprecedented increase in mobile phone subscriptions, have helped move millions of households out of extreme poverty, particularly in rural areas (Bahia et al. 2020; World Bank 2021i).

By contrast, progress in power infrastructure has been far more limited, with power shortages and blackouts continuing to constrain economic activity across the region, especially in South Africa. Only about one-half of households have access to electricity in SSA compared to over 90 percent worldwide. Deterioration in the quantity and quality of power infrastructure has increased the need for investment in renewable energies. These have the potential to improve access to electricity while addressing climate change challenges.

Transport infrastructure development has also been limited. In many countries, only a small proportion of the road network is paved, and railway development is broadly inadequate. Higher-quality transportation infrastructure will be key to boosting intraAfrica trade, fostering the development of regional supply chains, and enhancing SSA’s integration into the global economy. The African Continental Free Trade Agreement (AfCFTA) could catalyze the modernization of SSA transportation networks and facilitate cross-country cooperation on large intra-regional transportation projects. For example, the implementation of AfCFTA could increase demand for intra-Africa freight by more than a quarter, which would require substantial improvement to road and rail connectivity in SSA (UNECA 2022).

The region’s annual infrastructure investment needs are estimated at over 9 percent of GDP—the largest among all EMDE regions and nearly four times estimated current infrastructure spending in SSA (figure 18.A; Fay et al. 2019; Rozenberg and Fay 2019). The gap between needed and actual investment reflects insufficient funding for new projects, limited private sector participation, and inefficient spending on the operation and maintenance of infrastructure assets.

Many of the region’s economies rely on official external funding sources—multilateral and bilateral—to help finance investment in infrastructure. Official development finance, led by the World Bank and the African Development Bank, has increased appreciably and is supporting transport and water and sanitation investments in a number of countries. China has also emerged as a major bilateral source of infrastructure finance, increasingly so in the energy sector, particularly hydropower.

Private sector participation in infrastructure investment has also increased recently following a large decline in the mid-2010s. Private participation in 2020 accounted for nearly one fourth of infrastructure funding commitments compared to just 3 percent on average in 2016-17, with a large share of the investments going to the telecom, energy, and transport sectors (ICA 2022).
Table 3: Effect of SNAP-benefit changes on real sales

|Specifcation|(1)<br>OLS|(2)<br>IV|(3)<br>IV|(4)<br>IV|(5)<br>IV|(6)<br>IV|(7)<br>OLS|(8)<br>IV|
|---|---|---|---|---|---|---|---|---|
|VARIABLES||||Log|real sales||||
|Log benefts per population|-0.0702*|0.0318|-0.0338|-0.0661|-0.0448|-0.0402|0.0617*|0.00597|
||(0.0404)|(0.150)|(0.0770)|(0.0866)|(0.108)|(0.106)|(0.0324)|(0.109)|
|Log housing price|||0.110*|0.0228|0.0316|0.0357|0.0665|0.0412|
||||(0.0607)|(0.0518)|(0.0553)|(0.0564)|(0.0403)|(0.0566)|
|Log unemployment rate||||-0.135***|-0.137***|-0.139***|-0.141***|-0.137***|
|||||(0.0298)|(0.0307)|(0.0306)|(0.0288)|(0.0297)|
|Log population||||-0.149|-0.156|-0.162|-0.153|-0.142|
|||||(0.165)|(0.166)|(0.165)|(0.117)|(0.126)|
|Log average wage||||0.00271|0.00219|0.00112|0.000677|0.00234|
|||||(0.0395)|(0.0394)|(0.0394)|(0.0395)|(0.0395)|
|Log tobacco tax|||||-0.00514|-0.00541|-0.00298|-0.000174|
||||||(0.00777)|(0.00773)|(0.00608)|(0.00747)|
|Log SNAP average gross income||||||-0.0333|-0.0193|-0.0239|
|||||||(0.0236)|(0.0172)|(0.0212)|
|Log SNAP average rent||||||0.00501|-0.000600|0.000703|
|||||||(0.00959)|(0.00920)|(0.00890)|
|Observations|382560|382560|382560|382560|382560|382560|382524|382524|
|R-squared|0.962|0.962|0.962|0.963|0.963|0.963|0.963|0.963|
|Prob _>_ F|0.089|1.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|7970|7970|7970|7970|7970|7970|7970|7970|
|Number of clusters|48|48|48|48|48|48|48|48|
|First stage F-stat||2.578|13.996|17.857|20.655|20.682||20.194|
|Energy controls|||||||X|X|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Store and period fixed effects are included. Energy controls refer to annual state-level total energy, electricity, LPG, and natural gas quantities consumed and prices for the residential sector.

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