rafmacalaba/data-use-mentions-extended huggingface dataset:
2011a. _Doing Business 2011: Making a Difference for Entrepreneurs_ . Washington, DC: World Bank.

- ———. 2011b. ―Enterprise Surveys’ Database.‖ www.enterprisesurveys.org/.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in

- Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

46
data disaggregated by gender from all destinations in the same subregion as the destination country for which data are lacking. Using the same notation as in the previous section, assume that _Mo,d,t_ is the aggregate migrant stock from origin country _o_ to destination country _d_ in year _t_ and that _Wo,d,t_ is the female migrant stock for the same origin-destination pair in the same year _t._ The ratio of female migrants to male migrants is denoted as  _o,d,t_ , which is given by  _o,d,t = Wo,d,t /Mo,d,t ._ For example, imagine that in a given decade, the gender splits of emigrants from Uruguay in Scandinavian countries are known, except for Sweden. In this situation, it is assumed that the ratio of female migrants to male migrants from Uruguay to Sweden is the ratio of female migrants to male for all of the other Scandinavian countries (Denmark, Finland, Norway) in that decade. Formally, this can be stated as where _G_ is the destination region (the Scandinavian countries except Sweden), _o_ is the origin country (Uruguay), and _d_ is the destination country (Sweden). Once this proportion  _o,d,t_ is calculated, it can be multiplied by the total number of migrants _Mo,d,t_ to Sweden to calculate the number of female migrants. There is considerable variation in the balance between male and female migration from Uruguay to Scandinavian countries other than Sweden (Denmark, Finland, Norway) during the 1990 census round (table A4). On average, however, 47 percent of Uruguayan migrants are men and 53 percent are women. In the 1990 census, Sweden records 2,640 migrants as originating from Uruguay. Then 1,390 (0.53*2,640) of these migrants are women and 1,250 (0.47*2,640) are men.

# **{Table A4 here}*
for only 17 districts.<sup>11</sup> A second and equally important limitation of district malaria data arises from the fact that we have to use district ...xed e¤ects to control for the relevant time invariant unobserved factors, and thus it is not possible to use district level malaria data for identi...cation. Drawing on the insights from the literature on Malaria in tropical countries, we devise a way to exploit the DSD level variations in historical malaria prevalence. We use interactions of district level malaria data with DSD level exogenous characteristics that are likely to be informative about malaria susceptibility and transmission.

The district level malaria prevalence data are, by de...nition, average of the malaria prevalence across di¤erent DSDs in a district. Our approach to constructing instruments is based on the idea that if we can ...nd indictors of susceptibility to and transmission of malaria in a DSD in a district, the interaction of district level malaria intensity with the DSD level ‘malaria susceptibility indicators’ will provide us with a measure of di¤erences in historical malaria intensity across di¤erent DSDs. The ‘malaria susceptibility indicators’ can be thought of as the DSD speci...c weights one needs to recover the historical DSD level malaria prevalence from the available district level malaria estimates. Since the Anopheles mosquito is the carrier of malaria parasites (especially Plasmodium Vivax and Plasmodium Falciparum), we use geographic features that a¤ect the ease with which mosquitos can survive and multiply in a DSD as indicators of malaria susceptibility of a DSD. The literature on malaria transmission and prevalence identify a number of such geographic features including water body and elevation. Water body can be a fertile breeding ground for Anopheles mosquitos, but mosquitos ...nd it di¢ cult to survive in high altitude. We use the proportion of land above 1000 feet elevation, and proportion of land under water in a DSD as indicators of malaria susceptibility of a DSD. Thus the
<!-- Start of picture text --> lait = Shymkent city<br>3 400000 w= Astana Almaty city<br>$irs)o2#300000350000 m@m NorthTurkestanEast Kazakhstan Kazakhstan<br>25 250000 m Pavlodar<br>Ss a Mangystau<br>200000 m Kyzylorda<br>88 150000 m Kostanay<br>@ Karaganda<br>2£ 100000 = Zhambyl<br>50000 mm Western Kazakhstan<br>m Atyrau<br>fe} = Almaty<br>NDORnRWABDOANM STN ON © wAktobe<br>sssesessesegeseqgsgsgsaNAAN ANNNAN NAAN AWS BAKmola<br><!-- End of picture text --> <!-- Start of picture text --> Annual growth rate of registered entities for 2009-18 (%)<br>0 2 4 6 8 10<br>hs<br>BSNwaeCE). _—————<br>wakeyodaith _—_—_—_—_—_—_—_—_—_=_=____=<br>Western Kazakhstan ———<br>Karaganda SS<br>Aktobeooo} —<br>Al ayat <<<br>matyZanyci SS —=—<br>‘Akmola —<br>North Kazakhstan ———<br>TurkestPavlod a nr ——==<br>Kostanay<br>East Kazakhstan =<br><!-- End of picture text --> <!-- Start of picture text --> 300,000 OtherServi<br>m OtherService<br>Arts<br>% 250,000 = Health<br>S = Education<br>s TM Government<br>2 200,000 m AdminService<br>o lm TechnicalService<br>g 450,000 m RealEstate<br>o , @ Finance<br>ha micT .<br>5 100,000 m Accommodation<br>2 | Transport<br
2</sup> to relate it to household information based on the Vietnam Household Living Standard Surveys (VHLSS) for 2010, 2012, and 2014. Benefiting from the panel

> 1 This number is based on the national poverty line, which is at is $3.49‐a‐day in 2011 PPP terms. By the $1.90‐a‐day poverty line, the extreme poverty rate was 3% in 2014.

> 2 These are third‐level administrative subdivisions, equivalent to a sub‐district.
2050, the simulation suggests that it will have increased by more than 60 percent (to almost 3 years). These simulations (illustrated in Figure 7) make clear the complementarity between the reforms: combining them leads to substantially larger marginal impacts than either reform alone.

# 6. Discussion

Recent estimates suggest that the quality of human capital can explain a dominant share of world income differences (Jones 2014; Malmberg 2017) in part through its effects on economic growth (Hanushek and Woessmann 2015; World Bank 2018). Thus, the fact that many children in low-income countries learn little from attending school may be one of the most pressing development challenges. In this analysis, we focus on one component of the education production function—teachers’ knowledge of the subject they are teaching. While a growing literature has shown that teachers matter, much less is known about the link between specific teacher characteristics and student learning (Glewwe and Muralidharan 2015). Here we show that teachers’ content knowledge, or lack thereof, is an important part of the reason why primary school students in Sub-Saharan Africa are already well behind the expected curriculum—and their peers in other parts of the world—after only a few years of schooling. This implies a loss of potential human capital for numerous cohorts of students.

Our results have implications for both research and policy. Regarding the former, our results strongly suggest that research on how to improve teacher content knowledge should be a priority—there are few well identified studies on how to improve the quality of pre-service training (Glewwe and Muralidharan 2015). Moreover, we show that the marginal gain from collecting matched student and teacher data for one additional grade is large. Specifically, with additional data on teacher knowledge in other lower primary grades, one can recover the structural parameters of interest under the assumption that the cumulative education production function is fully characterized by two parameters, α and γ.

Our simulation results suggest that reforms that ensure that upcoming cohorts of teachers have greater mastery of the content
# **Appendix**

## **A.1 Summary statistics: Regression data**

This section presents summary statistics on the data used in the regression analysis.

## **A.2 Matching communities between 1996 and 2011 census**

As explained in Section 4, the geographical definitions for communities changed between the 1996 and 2011 census. In this section we explore how different strategies of matching communities over time affects our estimates. In our main analysis, we match 2011 communities to 1996 by nearest centroid. It is possible that many communities from 2011 match to the same community in 1996. When this is the case, the groups of 2011 communities are aggregated into one unit. Additionally, it is possible that a number of communities from 1996 find no matches, in which case they are dropped from the sample.

A second matching strategy employs the use of cartographic data for both censuses. We calculate the areas of community polygons from 2011 inside 1996 community polygons. If over 75% of the area of a 2011 community is contained in a single 1996 community, then it is matched to that community. As before, it is possible that several 2011 communities match to a single 1996 community. Additionally, it is possible that communities from both years find no match or do not reach the threshold and are dropped.

For robustness, in Table A.2 below we present the two most common specifications presented in the paper: including 1996 controls and municipality fixed effects, and also including 2011 controls for each community matching strategy. The strategies described above are denoted Centroid and Area _>_ 75%, respectively. The first two columns correspond to the initial estimates presented in columns 3 and 4 of Table 2. Noticeably, the point estimates do not change significantly across the various community matching strategies. In addition, they remain quite stable even when restricting the sample to unique matches between censuses (columns 5 and 6).

## **A.3 Additional robustness checks**

In
for a monetary reward, redeemable upon return to temporary Voluntary Consulting and Testing (VCT) sites where results where provided. The VCT sites were set up such that all respondents' homes are within ve kilometers distance from at least one site. Approximately 70 percent of those tested chose to pick up their results. In 2006 and 2008, rapid blood tests were adopted, eliminating the time delay between testing and provision of results.

For my analysis, I am using a subsample of married women in their rst marriage, who did not become pregnant from a relationship with men other than their future husbands prior to marriage. I restrict the sample to women in unbroken rst marriages because I abstract from modeling any decisions related to marriage or partnership. I also restrict the sample to include only women who were interviewed in at least one of the 2006 and 2008 rounds. Data collected in these rounds include family rosters, containing information on all children of respondents, and the elicitation of subjective expectations. I exclude women who were born before 1960, because the last age of fertility in the model is assumed to be 44 and 2004 is the earliest year included for fertility outcomes. After excluding additional women for missing information, the estimation sample consists of 1006 women.

Tables 1 to 4 and Figures 1 and 2 provide descriptive statistics for the variables used in the analysis. The average age of women in the sample is 26.6 in 2004. 29 percent of the sample resides in Balaka (south), 35 percent in Mchinji (center), and 36 percent in Rumphi (north). The median number of years of schooling is 5. The women in Balaka have the lowest schooling levels with 31 percent of women never having attended school and only three percent having attended some secondary school. The women in Rumphi have the highest schooling levels, with 99 percent of women ever having attended school and 26 percent some secondary school. The average age of marriage in the overall sample is 17.5 and is similar across regions.
out of six cases in China. In those four cases, the speci fi cation tests support both the need for instrumental variables and the validity of the instruments adopted,

> 51Detailed lists of the instruments used are shown in the notes to Tables 19-21. They are based on industryregion averages of some of the endogenous variables and, in China, on the fraction of plants that have been restructured into shareholding companies and the average share of government-owned capital. In Brazil, we also use industry-level tariffs, import penetration rates and export shares taken from Muendler (2003a) and Muendler (2003b).

25
therefore suggests that households were using the stimulus money to repay their existing debts, in a direct reallocation of how household funds were being spent.

If the effect on savings is not significant, however, what can explain the significant rise in consumption in urban areas? One possibility is that households experienced greater confidence in the economy as a result of the government stimulus program, and increased their spending as a result. Table 7 examines, in a fixed-effects panel framework, the monthly effects on the share of households in the community that felt the economy was improving (based on the data available in the SES, described in Figure 4). Although data on household perceptions are only available for the first six months in 2008 and 2009, we find a significant pattern of lower confidence in urban areas by mid-2008, with a rise in confidence by early 2009. We see a similar pattern in rural areas, although the effects post-2008 are stronger in urban localities. The data, despite their limitations in coverage across years, therefore seem to indicate that higher consumption may have stemmed from greater confidence in the economy.

# **8. Lessons Learned: What Matters Most**

The crash of global financial markets in 2008 caused a ripple effect on economic demand and growth worldwide. Export-oriented economies were hit particularly hard by this financial crisis-induced demand shock, and governments stepped in quickly with broad-ranging stimulus programs to lessen the negative effects on households from rising unemployment and borrowing costs. To better understand the role that stimulus policy might play in softening the effects of these shocks, we have examined the nationally-representative data from Thailand, an export-dependent economy where a large-scale stimulus program was introduced in 2009. Using monthly data spanning 2006-2010, we use a dynamic lagged dependent model to examine the potential role of government stimulus on household consumption, income, borrowing or debt repaid. To address simultaneity of changes in government spending and household outcomes, we construct a community-level synthetic panel and 26
# _Implications of the Divergence and Concentration of Export Structures_

It appears from the above that there are several inconsistencies in the region’s trade patterns that need further examination. First, there appears to be a significant divergence<sup>15</sup> between products that are exported to the region and to the rest of the world. While primary products dominate intra-regional trade, manufactured products dominate exports to the rest of the world. India is the only exception, having achieved some symmetry in exports with the region and the rest of the world. This is mainly a consequence of the restrictive trade regimes particularly in India, Bangladesh and Pakistan despite measures to reduce them in recent years.<sup>16</sup> Another explanation appears to be India’s growing efficiency in a range of agriculture as well as manufactured products (discussed further, below). The ability to increase regional exports is contingent on the degree to which dynamic exports<sup>17</sup> are incorporated in the regional export mix.

Secondly, the exports of most member countries (with the exception of India) seem to be confined to a few products, that is, a small number of products seem to accumulate a significant share of exports revenues. The likely success or failure of any proposed regional trade arrangement is contingent on the range of products prospective members have the capacity to export or import. If the members export a wide range of diversified goods, this is a positive factor. If their exports are concentrated, it will limit the prospects of increasing regional trade.<sup>18</sup> Table 7 presents a summary of the divergence and concentration estimates to further examine the two propositions, above. The summary is derived from raw data in annex Table A3. The data are for products that

> 15 Divergence is defined as the departure of intra-regional exports from extra-regional exports in major product categories. 16 See Pigato et. al and Pitigala et al (2001) for more details on trade reforms
24 components matters for households receiving international remittances in Indonesia. In other words, estimations ignoring the selectivity part of the model would be biased.

Table 11 takes the coefficients from Tables 7 to 10 and calculates the estimated and counterfactual marginal budget shares for the five categories of expenditure for each type of household. This table also shows the overall Average Treatment Effects on the Treated (ATT), which averages the ATT for all households receiving remittances in 2007 and compares it to the counterfactual of what would have happened if these households did not receive remittances in 2007.<sup>18</sup> Three of the ATT results in Table 11 (column 7) are noteworthy. First, compared to a counterfactual situation in which they did not receive international remittances in 2007, households receiving remittances in 2007 spend more at the margin on one key consumption good: food. Households receiving remittances in 2007 spend 8.5 percent more at the margin on food than what they would have spent on this good without the receipt of remittances. Second, compared to a counterfactual situation in which they did not receive international remittances in 2007, households receiving remittances in 2007 spend less at the margin on one important investment good: housing. Households receiving remittances in 2007 spend 39.1 less at the margin on housing than what they would have spent on this good without the receipt of remittances. Finally, compared to a counterfactual situation in which they did not receive international remittances in 2007, households receiving remittances in 2007 spend more at the margin on education, but this result is not statistically significant.

# 8.  Conclusion

This paper has used data from a large, panel household survey in Indonesia to analyze
a 3.4% increase in haven deposits corresponds to around 0.075% of GDP (i.e. 2.2%*3.4%) and the implied average leakage rate is approximately 7.5% (i.e. 0.075%/1%). A simple simulation that weighs countries by their historical share of aid disbursements and accounts for crosscountry differences in the ratio of haven deposits to GDP, implies a leakage rate of around 5% in aggregate disbursements. These modest leakage rates represent a lower bound in the sense that they only include aid diverted to foreign accounts and not money spent on real estate, luxury goods etcetera.

While the leakage estimates reported above are averages for those countries with annual aid from the World Bank above 2% of GDP, we show that leakage rates exhibit a strong gradient in aid-dependence, both within this sample and beyond. On the one hand, lowering the threshold to 1% of GDP (sample of 46 countries), we cannot reject the null hypothesis of no leakage. This suggests that the average leakage rate across all aid-receiving countries is much smaller than the estimates obtained from the main sample, which account for less than 10% of all World Bank aid. On the other hand, raising the threshold to 3% of GDP (sample of 7 countries), we find a higher leakage rate of around 15%. This pattern is consistent with existing findings that the countries attracting the most aid are not only among the least developed but also among the worst governed (Alesina and Weder, 2002) and that very high levels of aid might foster corruption and institutional erosion (Knack, 2000; Djankov et al., 2008).

While the comprehensive deposit dataset employed in the main analysis is restricted and subject to confidentiality requirements, we also study publicly available series recently released by the BIS. This allows us to study deposits in some individual havens: we find that bank accounts in Switzerland and Luxembourg contribute significantly to the
, 2020). Following previous studies, survey clusters are used as a proxy for reference networks (Blakely, 2000; Kelly et al., 2018; Storey & Kaggwa, 2009; Uthman et al., 2011; Vyas & Heise, 2016).

The analysis focuses on Colombia, a country with a long-standing conflict and home to the second largest internally displaced population in the world. It uses three rounds of Demographic and Health Surveys (DHS) data for the 2005-2015 period to examine the extent to which gender norms that limit women’s access to reproductive health, economic opportunities, and mobility, and norms that tolerate violence against women, and endorse patriarchy become less traditional among 2
hundreds of meters of elevation difference for each grid cell. We summarize the pixel mean ( _rugged_ ) and maximum ( _rugged_ _~~m~~ ax_ ) of this ruggedness index evaluated over the entire administrative areas as well as over populated areas ( _rugged_ _~~p~~ opmask_ and _rugged_ _~~m~~ ax_ _~~p~~ opmask_ ). Populated areas are defined as areas with any population in the Landscan 2012 population model (described in population count and density section below). Figure A2.1 (right panel) shows the spatial variation of the log of _rugged_ .

# **Agriculture:**

Agriculture and arable land can indicate higher economic growthpotential, or conversely, imply a lack of urbanization and infrastructure. Agricultural activities provide evidence of economic activity, which in turn may indicate a presence of water infrastructure. In order to measure agricultural land-use we use the Global Hybrid dataset (0611-2012 V2) produced by Fritz et al. (2015), which estimates the percentage share of land used for agriculture within a one square kilometer pixel. Expert assessment of five existing global land cover products along with national and subnational crop statistics as inputs provides the likelihood of a pixel indicating agricultural land use. By mul-

> 12Large scale (1:10 million) vector data are available for download from: http://www.naturalearthdata.com/downloads/

> 13Similarly, Verdin et al. (2007) derive slope measures at 30-arc-seconds derived from the 3-arcseconds SRTM data.

40
. From the Orbis data set, we find that the median liabilities of firms are around Mex$350 million, with median assets and revenues of around Mex$1.1 billion and Mex$800 million, respectively. We construct _AvgExposureEnergym,Aug14_ , a measure of banks’ exposure to the energy price shock at the municipality level, as the average exposure to the energy sector in August 2014, weighted by loan value, of the banks serving municipality _m_ .<sup>11</sup> Given that lending tends to be local (Degryse and Ongena 2005), this measure allows us to capture variation in the exposure of firms to banks that were more affected by the shock.

Finally, panel E shows two statistics at the state-quarter level. The first one is the state GDP, which is measured as an index normalized for each state to its level in January 2014. The second variable, _AvgExposureEnergys,Aug14_ , corresponds to the average exposure to the energy

> 10 These maturities do not include revolving loans.

> 11 In Mexico, there are 2,448 municipalities, with an average population of around 400,000 people.

9