rafmacalaba/data-use-mentions-extended huggingface dataset:
>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C16. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Equality before the law (Peru)/Satisfaction with the way corruption is combatted (South Africa) indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ <!-- Start of picture text --> Peru<br><!-- End of picture text --> A1.1. FOSD: Counting Vector A1.2. Dominance Analysis <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>A1.3. SOSD: Social Sustainability Index A1.4. Rank correlations<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> 66
16

# Lending: Summary of Findings

Positive: Most of the Bank's PHN projects incorporate efforts to develop outreach activities both for service delivery and for information, education, and communication.

Most of these outreach efforts take place through government health bureaucracies, but there is also a substantial effort to enlist the help of communities and NGOs (although not the commercial sector) in these efforts.

Negative: Many projects have no quantitative, aggregate performance targets, and only a small minority of projects aims to specifically target the most disadvantaged people in the communities served.

Iluformation acquired about clients during project preparation is mainly quantitative; there is little understanding of the determinants of client behavior, especially in the health sector. The gap in qualitative information is generally not filled during project implementation, when the focus is on collecting routine service statistics, rather than inquiring into whether IEC and services appropriately meet client needs.

These two findings raise serious concerns about the effectiveness of the outreach efforts built into the projects. Are project services and IEC efforts likely to increase existing low levels of utilization and acceptance if they are designed in substantial ignorance of what clients feel and why they behave as they do? Can field workers be expected to be providing services to the most needy in their communities, when there is seldom a special effort to identify these clients and to monitor whether workers are reaching them?

Inconclusive but Drobably negative: A desk study of project plans cannot throw light on the degree to which project interventions are in practice changed during implementation to better meet client needs. However, very few project designs feature special institutional arrangements to encourage learning and adaptation during implementation. (It should also be noted that for the majority of projects where qualitative knowledge of clients is weak, there may be no valid basis on which to redesign interventions anyway.) Many project interventions--such as targeting, MIS development and improvement of supervision systems--potentially affect worker performance incentives, but it is not known how they have actually done so in practice. In
that “the evolution of the current crisis seems remarkably similar to emerging market crises of a decade ago.” Krugman (2009) also notes the role of self-financing in the partial recovery of Japanese firms after the turn of the century. In addition, while the Phoenix Miracles have largely been an emerging market phenomena (Calvo et al., 2004), more recent studies (e.g. Claessens, Kose, and Terrones, 2009; World Economic Outlook, April 2009) have documented a recovery in output ahead of a recovery in credit in recessions and credit crunches even in OECD countries.

# **_5.1 Output versus Credit Recovery – US Case_**

In Figure 6A, we present the macro statistics on US-GDP and credit markets since 2005. The GDP numbers are seasonally adjusted real GDP estimates from the Bureau of Economic Analysis. As a measure of credit, we use Bank Credit from the Federal Reserve Board’s statistical release H.8 on the Assets and Liabilities of Commercial Banks in the US. Bank Credit consists of the aggregate amount of assets held by all US commercial banks and includes both securities held by banks and loans and leases made by banks.<sup>20</sup> We use the December numbers each year from the H8 release which is in monthly data format. Figure 6A shows that both GDP and Bank Credit decline from 2008 to 2009 but begin to recover from 2009 to 2010. The drop in GDP from 2008 to 2009 was 2.63% whereas the change in GDP from 2009 to 2010 was 2.85%. Bank Credit dropped by 3.78% from December 2008 to December 2009 but showed a positive increase of 2.15% from December 2009 to December 2010.

Next, we look at US firm-level data to understand if we see similar effects in the corporate sector. Using annual financial statements from Compustat for US publicly listed companies, in Figure 6B, we present evidence on sales and short-term borrowing, aggregated across all firms.
global findings with novel descriptive statistics from the Sahel, demonstrating just how rapid urban growth over the last three decades has been.

Our results point to a number of implications for domestic and international policy makers. First and foremost, there is a need for better sharing of information between international, national, and local actors. Despite remarkable progress on the effects of climate change within expert literature, some of the most vulnerable communities on the frontlines of the changing climate are poorly informed about both looming disasters and slow-onset changes that will nevertheless impact everyday life. Early warning systems that alert local communities about impending climate events remain in short supply. Second, policy responses to climate change in many developing countries remain overly fragmented at a time when a coordinated approach that works across space and time is needed. For instance, the arrival of climate migrants in urban areas puts pressure on local land markets, many of which continue to be characterized by unregistered property rights. In this context, local attempts to facilitate registration of plots sometimes clash with national-level initiatives, clearly evidencing the need for better coordination.

While global in its scale, our work leaves important related questions to future research. For one, we do not know with sufficient precision how exactly drought-induced urban growth looks. Our data from the Sahel that we discuss in section 2 suggests that climate migrants first settle at the outskirts of growing cities, building informal settlements that likely become formalized over subsequent decades. Whether this speculation is correct and whether it generalizes beyond the Sahel remains a task for future research. Second, although extant literature makes it hard to gainsay the fact that slow-onset events cause population movement, the data limitations inherent in the global scope of our study do not allow us to show it directly. In addition, we still know far too little about how drought-induced migration differentially affects individual households and their members. In the same vein, wide gaps remain in our understanding of how climate migrants adjust to their new conditions once they arrive in rapidly growing cities, what coping mechanisms they
quantitative case study approach (Abadie and Gardazebal, 2003; Abadie et al., 2015). Synthetic control and related estimation techniques for quantitative case studies have the advantage of minimal data requirements while providing a disciplined and transparent approach for the analysis of policies with aggregate data. Moreover, recent developments in synthetic control and related techniques have been shown to be more adaptable than the canonical form (Abadie, 2021) and capable of recovering causal effects where the assumptions of canonical synthetic control may not be met.

The analysis uses annual national economy-level panel data over the period 1980-2018, 8
**|
|---|---|
|High income<br>34.5|33.9|
|Upper middle income<br>8.4|49.1|
|Lower middle income<br>2.8|40.5|
|Lowincome<br>1.4|42.7|

The data for control variables was collected from different sources. Data on the structural and cyclical characteristics of national economies comes from the World Bank’s World Development Indicator Database<sup>9</sup> and UNCTAD-STAT;<sup>10</sup> the figures on education are sourced from Barro & Lee (2013);<sup>11</sup> those on the real effective exchange rate (REER) are taken from Darvas (2012);<sup>12</sup> and metal- and oil-price figures are sourced from the IMF’s Primary Commodity Prices database.<sup>13</sup> Referring to the original datasets will provide further details on the methodology and sources used. Table 4 shows income-level-related heterogeneity across countries for the specified variables.

> 8 Available at <u>http://econ.worldbank.org/projects/inequality</u>

> 9 Available at <u>http://data.worldbank.org</u>

> 10 Available at <u>http://unctadstat.unctad.org</u>

> 11 Available at <u>http://www.barrolee.com</u>

> 12 Available at <u>http://bruegel.org</u>

> 13 Available at <u>http://www.imf.org</u>

8
district-of-birth characteristics ( _Zd_ ) and the indicator for belonging to the most exposed cohorts in order to alleviate concerns that other district-of-birth characteristics and not INPRES intensity could explain the results we find.<sup>13</sup> Standard errors are clustered at the district of birth of mothers.

Another potential threat to identification is related to the migration of mothers. In our data, mothers’ district of residence in 2013 is limited to the nine rural districts in the Indonesia ECED data. Thus, we may be concerned that migration from high INPRES birth districts to our nine districts reflects particularly aspirational individuals (i.e., those likely to obtain more education and to experience upward geographic mobility) whereas migration from low INPRES birth districts to our nine districts selects on particularly low-aspirational individuals (i.e., those likely to obtain less education and to experience downward geographic mobility). While we cannot directly rule out this possibility, our analysis of migration patterns among mothers in our data suggests that this is unlikely to be a major threat to validity. Figure 6 presents the mobility pattern across districts by whether the district experienced low intensity or high intensity INPRES. Each dot is a birth district, sorted by levels of Human Development Index (a measure of overall development). Birth districts that are more (less) developed are on higher (lower) values of the y-axis. For each birth district, we calculate the median HDI of the district where mothers are residing in 2013. The difference between this median 2013 HDI and the birth district HDI (x-axis) indicates the level of mobility experienced by mothers coming from each birth district. The figure clearly shows similar and consistent migration patterns across low and high intensity INPRES districts: mothers born in higher HDI districts experience larger downward mobility, while mothers born in lower HDI districts experience larger upward mobility. This suggests that differential migration patterns between low and high intensity INPRES districts are unlikely to bias our results.

We expand the empirical model above to investigate the effect of maternal education
Kab. Gresik Kab. Bangkalan Kab. Sampang Kab. Pamekasan Kab. Sumenep Kota Kediri Kota Blitar Kota Malang Kota Probolinggo Kota Pasuruan Kota Mojokerto Kota Madiun Kota Surabaya Kota Batu<br><!-- End of picture text -->

In response to data that show a high degree of variance in gender gaps in education in Indonesia, a team at the World Bank conducted a subnational research sample study to examine issues of gender gaps throughout the country and across different indicators, as well as issues related to students with disabilities. This study focuses primarily on the social inclusion issue of gender, while also referencing issues faced by children with disabilities—an issue that is garnering greater attention in Indonesia through the ratification of national policies, as well as international frameworks.<sup>8</sup> Findings from field visits are coupled with reviews of the literature related to school enrollment, student achievement, early marriage, literacy, labor force participation, and other studies on gender and disability in Indonesia, to determine how national trends are exhibited at local levels and what gaps in gender equality are masked by the national figures.

> 7 Calculated using Susenas 2018.

> 8 Social inclusion is defined by the World Bank as “the process of improving the terms for individuals to take part in society” and “the process of improving the ability, opportunity, and dignity of people, disadvantaged on the basis of their identity, to take part in society” (World Bank, 2013; Inclusion Matters: The Foundation for Shared Prosperity).

4
# <u>Performance in Practice: Evidence from the Cases</u><sup>39</sup>

This section analyzes the performance of the ARAs in three broad categories of indicators: revenue collection, compliance management, and taxpayer services. The focus on compliance and taxpayer services is important to round out the picture provided by looking at collection indicators, since collections are only partially determined by tax agencies.

# _Revenue Collection and Efficiency_

Most ARAs have been successful in the most basic of their tasks: raising revenues.<sup>40</sup> SUNAT increased total tax revenues<sup>41</sup> from 8.4% of GDP in 1991 to 12.3% in 1998, during which time tax policy reforms included the rationalization of the tax structure and the reduction of many tax rates (more recent data show collections falling to about 11% and then recovering to nearly 12% in 2003).<sup>42</sup> SENIAT increased total revenues from 5.9% of GDP in 1994 to 8.5% in 1998, though the increase was due in part to the introduction of a new sales tax. SUNAT and SENIAT also increased their revenues in real terms over the 1990s. Mexico’s SAT, which benefited from the least autonomy, increased revenues only marginally from 1996-1997 (see Figure 2).

> 39 For more data and analysis, see the appendix.

> 40 All data sources are national tax agencies, unless otherwise noted. Latin American data from Taliercio (2000).

> 41 All data for the Latin American cases exclude customs revenues, unless otherwise noted. All data for the African cases include customs revenues, unless otherwise noted.

> 42 For a more recent analysis, see Mann (2004).

27
as a result of labor shortages, rice production declined by 12 percent at the national level, with more precipitous declines (up to 20 percent) in the most affected counties such as Lofa and Margibi; cassava production, which is less labor and input intensive than rice, supposedly experienced a 5 percent drop at the national level.<sup>7</sup> Our study stands apart from this work by providing the first econometric evidence on the Ebola epidemic’s impact using nationally representative household survey data combined with administrative records on EVD mortality at the district level.

More broadly, past literature on the economic impact of infectious disease focuses on long-term consequences of early life shocks operating through the mechanism of human capital formation and, ultimately, through the labor market (Almond 2006; Bleakley 2007, 2010; Baird et al., 2016). There is also a small economics literature focusing on aggregate labor supply effects of epidemic disease (e.g., Young 2005; Voigtländer and Voth 2012). To our knowledge, the mechanism explored in this paper -- fear of contagion -- is novel, albeit relevant only to virulent epidemics, where the (perceived) likelihood of contagion from proximity to an infected individual is significant.

The remainder of the paper is organized as follows: Section 2 describes the data and the context of the EVD outbreak in Liberia. Section 3 details the empirical strategy used to assess the Ebola crisis’ impacts on labor, agricultural production, and consumption. Section 4 presents the main findings. Section 5 briefly summarizes the paper.

> 7 Using a market-chain analysis approach, FAO (2016) shows that the Ebola crisis disrupted the collection and transportation of agricultural production to consumption areas, which in turn reduced farmers’ incomes (higher costs of inputs and lower negotiating power with collectors whose numbers decreased and producer were forced to sell at lower prices than expected).

4
> The evidence also indicates that access to historical credit information such as business credit bureaus is positively related to the presence of factoring (Klapper 2005). Under certain circumstances factoring can also work well even in weak domestic information environments if the receivables are from obligors located in strong information environments. For example, the receivables of an Estonian firm whose customers are located in Germany may qualify for factoring because the factor can efficiently assess the creditworthiness of

> 13 There are some efforts to expand the use of this lending technology in developing nations.  The World Bank and Fair, Isaac have recently formed a joint venture to determine the feasibility of developing a pooled data small business credit scoring model using data from Brazil, Columbia, and Mexico (Miller and Rojas 2004).

15
between 1982 and 1986 were not of school age in 1999. We compared their exposure to that of those born between 1977 and 1981 in war and non war districts.

The results in table 7 show that cohorts who were not supposed to be of primary school age during the conflict years but born in conflict affected districts do not show a different primary school completion rate as compared to the same cohorts born in unaffected districts. This gives support to the fact that our identifying assumptions are satisfied.<sup>25</sup>

# **5. Empirical results**

# **5.1. School attendance in 2001**

Table 8 shows the results for the impact of the two channels of violence exposure in 1999 on school attendance in the 1999/00 and 2000/01 academic years. As discussed previously, we are mostly interested in looking at the effects of the violence on school attendance in the post-conflict academic year (i.e. 2000/01). Table 8 reports the estimates from a fixed effect model applied to our ̳ _ad hoc’_ panel dataset derived from the TLSS2001. For sake of simplicity, we have estimated a linear probability model. The analysis is disaggregated by gender and age groups. The results highlighted in bold are our main coefficients of interest.

The results show a negative and significant impact of displacement on school attendance in 2001 for the overall sample. The impact on girls is larger than that on boys but the difference is not statistically significant (panel A, table 8). We observe also that household displacement is associated with more substantial decreases in primary school attendance among younger individuals. This effect is stronger for girls (columns 4-6). Among older individuals, boys seem to be more affected than girls. We do not find any statistically significant effect of house destruction on primary school attendance in 2001 (panel B, table 8), in line with the descriptive analysis in section 4.2.

The impact of these two channels of violence exposure
– Table A.22 Heterogeneous treatment effects on women’s empowerment and well-being

||Had an IGA|at baseline|Married a|t baseline|Consumptio|n at baseline|TCLP par|ticipation|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Treatment|(2)<br>IGA _×_<br>Treatment|(3)<br>Treatment|(4)<br>Married _×_<br>Treatment|(5)<br>Treatment|(6)<br>Conso _×_<br>Treatment|(7)<br>Treatment|(8)<br>TCLP _×_<br>Treatment|
|**Women empowerment**|-0.035|-0.271|-0.234**|0.241**|-0.011|-0.049|-0.108|0.015|
||(0.040)|(0.180)|(0.103)|(0.110)|(0.076)|(0.088)|(0.091)|(0.112)|
|**Women fnancial index**|0.363***|1.024|0.764***|-0.468**|0.232***|0.233*|0.678***|-0.414|
||(0.080)|(0.891)|(0.230)|(0.235)|(0.086)|(0.140)|(0.244)|(0.269)|
|**Women
|0.6|-13.8|-13.2|25.2|7.1|-7.5|-0.4|
|4|47.3|1.0|-13.1|-12.1|26.4|2.2|-5.0|-2.8|
|5|45.7|1.1|-10.3|-9.2|23.2|6.5|-8.0|-1.5|
|6|45.2|1.8|-12.5|-10.7|21.4|6.5|-6.2|0.3|
|7|44.4|1.5|-11.5|-10.0|19.2|7.0|-6.4|0.6|
|8|43.2|1.7|-10.7|-9.1|19.2|5.1|-5.3|-0.2|
|9|43.0|-1.1|-9.3|-10.4|19.6|1.2|-0.8|0.4|
|10|40.7|-2.0|-5.6|-7.5|21.8|1.2|-2.2|-1.0|

Source: Authors’ own estimate based on NSSO data.

In summary, we observe two broad changes: first, a significant drop in FLFP is observed between 2004-05 and 2011-12, which wiped out any increase between 1993-94 and 2004-05. This phenomenon is distinctly visible across all age groups, socio-economic groups and across groups with different levels of educational attainment. Second, the phenomenon is more pronounced in rural than in urban areas
# **3 Data**

We use three sources of data. Baseline data come from the household survey administered to all households in March and April 2016. Outcome data come from records collected by our field team stationed at the NBS branch during the intervention and from NBS administrative data.

Panels A and B of Table 1 use data from the 2016 survey to compare the characteristics and savings-related behaviors of existing account holders who were assigned to the three different treatments in the transfer experiment of 2015. Appendix Table A3 contains the definition of variables. We report the p-value of the joint test of equal means across all three categories in column 5, and we do not expect (and do not observe) differences in time-invariant characteristics such as age and gender of the respondent, that could not have changed as a result of treatment. The p-value of the F-test that all characteristics in panel A are jointly zero is 0.480. Despite the differential attrition, we take comfort in the fact that account holders in the treatment and control group appear to be similar.

In panel B, households that received the transfer did not expect more withdrawals in the next three months than those who did not receive the transfer (p-value is 0.227). Past and predicted usage for both samples is too limited to make the basic account worthwhile given the fee structure. According to their expected use in the three months after the baseline survey, existing account holders would save on average MK 1,067 (USD 5.80 according to the 2016 PPP exchange rate) even after accounting for the cost of the ATM card if they planned to keep it for four months. Panel B of Table 1 also reports account usage six months after the 2015 transfer, using administrative data. We find that individuals who received large transfers (either in cash or via direct deposit) made significantly more transactions (pvalue is 0.022). Although the overall mean is low, individuals who received the large transfer were also more likely to correctly report