rafmacalaba/data-use-mentions-extended huggingface dataset:
sri lanka country risk profile 14 precipitation flood, meaning that there is a 2% chance of flooding damage in excess of this value in any given year.

The top panel of Figure 3 on the previous page illustrates the expected impact of a 2017-like flood event on individual incomes in Rathnapura district. The black outline indicates the regional income distribution as reported in HIES, while the red histogram illustrates the expected income distribution immediately following a 50year flood in the region. This distribution shows a mode around US$450 per person, with 10% of the district below the line. The bar on the per year, living poverty large right shows the number of people with incomes higher than US$2,000 per year; the poverty and subsistence lines are indicated by the dotted lines around US$300 and US$350 per year, respectively. Income losses resulting from this 2017-like event are expected to push some 11,300 individuals into income poverty in Rathnapura, and 5,700 below the subsistence income level, meaning they cannot meet basic needs.

At the national level and for the average year, we estimate that over 5,600 Sri Lankans face income poverty or subsistence for some amount of time every year due to precipitation flooding. However, large events can impoverish broad segments of the population when they occur. For example, the expected destruction from nationwide 50-year flooding could move nearly 34,000 individuals nationwide into transient income poverty or subsistence. Of these, the model indicates that just over a thousand will still be in poverty 10 years later. This suggests that disasters may have a long-term impact on the income and consumption of some Sri Lankan households.

# _Consumption poverty_

Income losses represent a complementary perspective to asset losses, and generate new insights into the costs of disasters in Sri Lanka. However, as a metric of disaster impacts, income losses may yet underestimate the link between poverty and disasters. First, they do not account for the reconstruction costs that households incur in rebuilding their assets after a disaster.
physical infrastructure. In fact, however, only a sub-set of schools benefited systematically from all actions what will be called from now on components 1358.

To assess the probable impact of the PARE program we consider a number of experiments based on the following question: What would have been the program's historical performance if it had been implemented as envisaged without faults or delays. We construct counterfactual experiments based only on those schools, which received all of the main components of the program. Before going into the analysis, it's important to mention that the information available posed important constraints for building a panel data set.

Table 3 shows the distribution of students by school type in the sample. Our analysis will focus on schools located in rural and native communities, the two most disadvantaged groups in the population with the lowest educational attainment, poorest test performance and highest incidence of school desertion. At the margin, the supplemental actions provided by the program should have the greatest impact amongst this population. Table 4 shows the resulting samples for analysis considering that, for the reasons already noted, we concentrate our attention on a sub-set of these schools -- those which benefited integrally from the program.

**Table 3. Distribution of students by school type,** 1992.

|**CWapa**|**s Guetrem**|**Fidalgo C**|**Oxam**|**SA,**<br>|**cta:**|IVichoacan:|T|
|---|---|---|---|---|---|---|---|
|LIban<br>396|<br>107|257|357||1,119|<br>361|1,480|
|**Rual**<br>200|<br>202|175|239||816|<br>208|1,
After pre-processing, the Data Generating Group extracted the relevant remote sensing data for the LSMS-ISA households based on the ten data extraction methods for all nine remote sensing sources. This generated time series data sets of daily precipitation or temperature from January 1, 1983 until December 31, 2017. For each country in each of these years, a growing season was defined based on FAO recommendations.<sup>7</sup> And so, for each of the 17 LSMS-ISA country-wave household data sets, this generated 90 remote sensing weather data sets (six precipitation sources + three temperature sources _×_ ten extraction methods). The time series weather data sets include daily observations and the unique household identifiers made part of the publicly available LSMS-ISA data. Data sets were named and labeled `x0, ..., x9` for each extraction method, `rf1, ..., rf6` for each precipitation data source, and `tp1, ..., tp3` for each temperature data source. These 1,530 blinded time series data sets were then shared, via a secure server, with the Data Analysis Group.

The Data Analysis Group then processed each of the time series weather data sets using a user-written Stata package `wxsum` which is available via Github. This package processes daily precipitation or temperature data and outputs up to 22 different weather metrics. See Table 4 for a complete list of weather metrics used in the analysis. These weather metrics from each of the 1,530 weather data sets were then merged to the relevant country-wave LSMS-ISA data set using the unique household identifier (90 weather data sets per country-wave data set). All countrywave data sets containing the production data and the weather metrics from each remote sensing source and extraction method were then appended to create a single panel data set covering all countries, waves, remote sensing sources, and extraction methods. Table 5 summarizes the scope of the resulting data.

The Data Analysis Group then conducted all of the analysis on the blinded data set
treatment on the treated (ToT) effect by instrumenting participation in the 4P program with the random assignment to the treatment group at barangay level. We present the results in the Appendix.

# 7. Results

## 7.1. Balance

We take advantage of the data collected in 2008 for an impact evaluation of the 4P program to test whether randomization was carried out successfully. We can identify households in our sample in these data, which includes a rich set of characteristics of households where our respondents resided in 2008, including characteristics of the household head, spouse of the household head, access to social services, housing conditions and asset ownership. Of 48 available indicators, we find statistically significant differences in only three. 4P beneficiary households are less likely to be headed by a person with no

> 10 https://ridie.3ieimpact.org/index.php?r=search/detailView&id=923.

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this data set display variations of property rights both in cross section and time series reflecting the differential timing of many decentralized reforms. The rich variation in property rights and the size of the data set allow us to examine in detail the determinants of property rights such as management turnover, profit retention rates, production decisions, financing arrangements, and wage control rights.

We find that the property rights configurations were in general consistent with the implications of a principal-agent model in which the government had at least three goals: profitability (or tax revenue), control over firms, and inter-firm equality -- through bailing out firms in financial troubles, and collecting heavier taxes on well-performing firms. The property rights structure also reflected government attempts to deal with the information problem relying on the rationality of SOEs: it designed high-powered incentives -- that is, higher firm-level pay sensitivity -- for efficient firms, which accepted this type of contract because they expected high revenue associated with their high productivity, and it used low-powered incentives and more centralized control for inefficient firms, which chose this type of contract in anticipation of low productivity.

# **II. The Data and the Decentralized Reforms**

This research uses _A Survey of State Enterprises: 1980--1989,_ a retrospective survey conducted by the Chinese Academy of Social Science in 1991.2 The data set is a balanced one: no firms were dropped out during the 10 years. The survey questionnaires were sent out to 800 state enterprises. Valid responses of 769 firms, located in 21 cities in four provinces of China (Sichuan, Jiangsu, Jilin, and Shanxi), were returned. This data set was not designed to be a random sample of the state enterprises: large firms were over2This data set has been used by many other authors, including Groves et al., "Autonomy and Incentives in Chinese State Enterprises," _Quarterly Journal of Economics,_ 109(1) (1994); "China's evolving managerial labor market," in _Journal of
were unsatisfactory, dowries would not rise, though the number of single or divorced<sup>women might.</sup> Thus, providing opportunities for women outside marriage and the marriage market would significantly improve their well-being by allowing them to leave an abusive husband, by finding a way of "bribing" him to stop the abuse, or by presenting a credible threat that<sup>achieves</sup> the same objective. In more specific terms, the main opportunities for women outside the marriage market would be in the labor market. Policies that invest in the hulman capital of women could make a difference but would be more effective if job opportunities<sup>for women were</sup> also provided. Thus, income-generation programs and micro-lending strategies<sup>focused on</sup> women could be of great value.

To conclude, in this case-study we have demonstrated that violence can<sup>be used as an</sup> instrument to redistribute resources. The example we focus on is the highly publicized<sup>link</sup> between the marriage market and wife-abuse in the Indian sub-continent. lTo<sup>examine this, we</sup> employ an approach that combines the usual methods of economics with ethnographic<sup>techniques</sup> borrowed from anthropology. We construct a non-cooperative bargaining and<sup>signaling model of</sup> dowries and domestic violence which is informed by the ethnographic evidence. Predictions from this models are tested with survey data gathered from the same population examined<sup>in the</sup> in-depth interviews. We find that for the most part the econometric evidence is consistent<sup>with</sup> the theory. In Probit-IV estimates of violence we find that husbands<sup>are mrore likely to beat their</sup> wives when the wife's family is rich because there are more resources<sup>to extract and the returns</sup> are greater. We
# **III Data and empirical model**

## **Sectoral aid data**

Knowledge of the intended purpose of aid is crucial to obtain an accurate estimate of the degree of fungibility. Therefore, the use of sectorally disaggregated aid in this paper constitutes a marked improvement over previous studies that lack complete information on the purposes for which aid is given. Fiscal response models (FRMs) typically focus on the effect of aggregate aid on a recipient’s budget and evaluate aid as being fungible if it is diverted away from public investments or developmental expenditures (e.g., Heller, 1975; Franco-Rodriguez et al., 1998; Feeny, 2007).<sup>7</sup> Early fungibility studies (McGuire, 1982, 1987; Khilji and Zampelli, 1991, 1994) distinguish between military and economic aid and evaluate how these types of aid affect public military and non-military expenditures. Other studies (Feyzioglu et al., 1998; Swaroop, Jha, and Rajkumar, 2000; Devarajan et al., 2007) attempt to investigate aid at the sectoral level but are only able to disaggregate concessionary loans; thus, the omission of sectoral grants may influence their results. In this body of literature, Pack and Pack (1990, 1993, 1999) are the only studies that employ a comprehensive sectoral disaggregation of foreign aid by focusing on countries whose recipient governments report both public expenditures and aid received in a disaggregated form.<sup>8</sup> In addition, several recent studies (Chatterjee, Giuliano, and Kaya, 2007; Pettersson, 2007a,b) have used sectorally disaggregated aid data from the OECD’s Creditor Reporting System (CRS), as described in OECD (2002), to study fungibility.<sup>9</sup> The CRS database disaggregates foreign aid according to a number of dimensions, most importantly the sector or purpose of aid, but has two main disadvantages. First, the CRS data are incomplete. Only some
cost than conventional household surveys.

CDR analysis can play a vital role by filling the spatial and temporal gaps left by traditional research methods. By making inferences based on cellular network usage, CDR analysis can reliably project the evolution of poverty dynamics over a specific timeframe. Unlike censuses and household surveys, CDR analysis is quick and relatively inexpensive and can be performed by a small team of statisticians using records that are already collected by Mobile Network Operators (MNOs).

Guatemala offers a prime example of the limits of traditional data collection. The country’s most recent Population and Housing Census, dates from 2002, and all national poverty data are derived from just four household surveys conducted over the past 25 years. For example, the most recent 2014 household survey ( _Encuesta Nacional de Condiciones de Vida_ , ENCOVI) covered

**3**
# **5.2 Credit Card Data**

Our partner bank shared administrative data on credit card transactions for all customers in the sample. These data allow us to measure spending and borrowing behavior of all customers in our sample for 12 months prior to the intervention and 3 months after the intervention. The dataset contains detailed records of all credit card transactions that occurred during this time period, which include the transaction amount, description, vendor name, and spending category code.<sup>11</sup> The credit card data also include information about outstanding balances and repayment, which we use to measure consumers’ willingness to take on debt. Importantly for our purposes, each transaction in the data contains the standardized Merchant Category Code (MCC), a 4-digit identifier that classifies a business by the types of goods or services it sells. The MCC makes it possible to assign each transaction to a specific spending category. Importantly, for the goal of our analysis, the MCC allows us to distinguish between spending on durable, nondurable, tradable, and nontradable goods. To classify spending into durable versus nondurable goods, we follow the standard categorization used in the literature (see Aaronson, Agarwal, and French, 2012; Agarwal and Qian, 2014; Ganong and Noel, 2019; Chetty et al., 2020). For example, some durable spending items include apparel, consumer electronics, and furniture. To the best of our knowledge, no other paper has used MCCs to classify credit card spending into tradable and non-tradable expenditures. We therefore created our own categorization by manually inspecting each individual MCC and classifying it as tradable or nontradable. In our classification, all codes for services are assigned to the nontradable category, whereas codes for goods are assigned to the tradable category if those goods are typically imported or exported. For example, some tradable spending items include apparel and consumer electronics.

We summarize both MCC categorizations in Table 1. As there are thousands of individual MCCs, we report summary statistics using standard
Evidence from a Market-Oriented Approach in Kenya. Available at SSRN 3969797.

Stoeffler, Q., Carter, M., Guirkinger, C., & Gelade, W. (2022). The spillover impact of index insurance on agricultural investment by cotton farmers in burkina faso. The World Bank Economic Review, 36(1), 114140.

“UNESCO Institute for Statistics” _United Nations Educational, Scientific and Cultural Organization._ 2020. <u>http://data.uis.unesco.org/Index.aspx</u> UN (2019). Mutilations Génitales Féminines & Mariage d’Enfants. https://www.unicef.org/burkinafaso/media/2221/file/Rapport%20sur%20les%20mutilations%20g%C3% A9nitales%20f%C3%A9minines%20et%20le%20mariage%20des%20enfants.pdf United Nations Development Programme, Human Development Report (2021). "Gender Development Index." Retrieved from http://hdr.undp.org/en/content/gender-development-index-gdi..

<mark>Vaillant, J., Koussoubé, E., Roth, D., Pierotti, R., Hossain, M., & Falb, K. L. (2020). Engaging men to transform inequitable gender attitudes and prevent intimate partner violence: a cluster randomised controlled trial in North and South Kivu, Democratic Republic of Congo.</mark> _<mark>BMJ Global Health</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(5), e002223.</mark> Van den Bold, M., Dillon, A., Olney, D., Ouedraogo, M., Pedehombga, A., & Quisumbing, A. (2015). Can integrated agriculture-nutrition programmes change gender norms on land and asset ownership? Evidence from Burkina Faso. _The journal of development studies_ , _51_ (9
of benefits of improved forecasting and early warning: reduced asset losses, reduced human losses and improved economic productivity. In a first step, he uses data from studies on Europe, the OFDA/CRED International Disaster Database (CRED 2015) and the Copenhagen Consensus on Human Life’s definition estimation of life value (DALY – Value of Disability Adjusted Life Year; Copenhagen Consensus Center 2015) to estimate the benefits of forecasting and early warning in the three benefit categories in Europe.

Hallegatte (2012) then uses a benefit transfer approach to apply these estimates to LICs and MICs; the benefit of forecasting in Europe in percentage of gross domestic product (GDP) corresponds to the potential total benefit of European forecasting quality in LICs and MICs:

- The reduction in asset losses due to increased forecasting quality is estimated to be between 0.0003 and 0.017 percent of GDP. From that it is inferred that improving forecasting services to European standards would lead to avoided asset losses between US$ 300 million and 2 billion per year in LICs and MICs.

- Using data on fatalities due to weather disasters from the OFCA/CRED EM-DAT database, the average annual risk of death due to extreme weather in LICs and MICs is 7.5 per million. After

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# **3. Impact of the Japanese earthquake on supply chain reconfiguration**

In this section, we study how the 2011 earthquake impacted imports from Japan and whether it led to more diversification or reshoring of production.

## 3.1 _Impact on imports from Japan_

As a first exercise, we investigate whether importers less dependent on Japan as a source behaved differently from importers more dependent on Japan in the aftermath of the 2011 earthquake. Specifically, we rely on the following specification: J i i i i i i (1) JJ ii ii ii J ln�YY �= αα + θθ + σσ + ββ II(H HHh SSha aa J JJ 2011 ) + εε where i are country ’s imports of HS 6-digit product from Japan for the 15 largest auto producing JJ countries or the 15 largest exporters of final electronics. ii<sup>7,8</sup> The analysis focuses on the largest auto YY HH kk producing countries as those are the countries where lead firms located their production facilities. The countries are selected based on 2010 motor vehicle production data from the Organisation Internationale des Constructeurs d'Automobiles (OICA). As there is no comparable production data for electronics, we select the largest exporting countries of final electronics based on the 2004-2010 data.<sup>9</sup> i ) is an indicator variable taking value 1 if the importer is dependent on Japanese suppliers in the period preceding the earthquake (more than ii percent of total imports in a II(H HHh SSha aa J JJ 2011 product line between 2004-2010, where can take value of 15, 10 and 5). The specification also includes xx i , i and i , which are respectively the importer-sector, importer-time and sector-time fixed effects. xx We expect the coefficient of interest, , to be negative if the shock led to a greater drop in reliance on αα θθ σσ ββ 7 See Table A1 in Annex for the list of countries in the sample.

8 We use product level
58.4|0.5%|58.9|63.6|0.8%|
|Ghana,1998–2005|56.1|60.1|1.0%|49.5|51.3|0.5%|52.3|54.2|0.5%|
|Moldova,2001–10|78.8|81.8|0.4%|73.1|68.1|-0.8%|76.4|73.5|-0.4%|
|Nepal,1996–2003|57.9|61.1|0.8%|55.1|55.0|0.0%|57.0|58.6|0.4%|
|Peru,2004–10|68.3|69.2|0.2%|51.5|51.2|-0.1%|55.0|50.8|-1.3%|
|Romania,2001–09|82.2|84.8|0.4%|60.9|||71.9|67.1|-0.9%|
|Thailand,2000–09|74.2|77.8|0.5%|56.9|53.6|-0.7%|61.4|65.6|0.7%|

<mark>Source: Author’s calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA, and National Household Surveys.</mark> 27
<br>9%<br>6%<br>3%<br>0%<br>Low Income Low Middle Income Upper Middle Income<br>Cost of Equity RoE<br><!-- End of picture text --> In sum, the analysis of the match between risk and returns and the stability of the returns contributes to explain the major slow down in capital flows relatively well for most regions and all income groups. The only exception may be Africa where the huge wedge between return on equity and cost of equity is reflected in the low absolute levels of investment but not in the somewhat unexpected increase in the flows of investments observed over the last three years.<sup>18</sup> Overall, only 32 of the 120 companies covered by this sample enjoyed an RoE larger than their CoE during the post Asia crisis period, implying that two thirds had bad experiences and would probably be reluctant to disburse or even commit significant new investments. This is particularly true for the rail and water sectors since thye represented only 25% the successful companies. The rest were in energy and ports.

> 18 World Bank (2003), PPI database

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intervention **15,** Consequently, it may not be the case that all public services have **income** elasticities greater than unity. Further, this will depend not only on income growth but also changes in the relative prices of public and private goods. Courakis et al (1993) using time series data for Greece and Portugal distinguish between the components of **16** public expenditure, namely transfers, consumption and investment . They relate these shares to relative prices of public expenditure, permanent income, population and a nominal demand variable and find significant differences in responses across expenditure types. They find that income elasticities greater than unity only hold in the case of transfer expenditures in Greece and consumption expenditures in Portugal. For investment outlays income elasticities were no different from unity. Relative prices were found to have a negative influence on various components of public expenditure particularly for government consumption in Greece. The importance of including the relative price of government goods and services has always been noted in studies of demand for public goods (e.g., the median voter model) in the public finance and public choice literature **17*** To our knowledge, our paper is the first that includes such a relative **18** price term as a determinant of government size for a large cross-country data set

- 15Think, for example, of telecommunications where technological change has shifted parts of the industry away from natural monopoly, facilitating multiple provision and changing with it the regulatory functions of government.

- **16** This approach, of course, requires appropriate deflators, something lacking for most developing countries and which effectively stops us from systematically separating out the components of public spending.

7 Borcherding and Deacon (1972), Buchanan (1977), Borcherding **(1985),** Mueller (1989).

**18** See also, Lybeck (1986) which concentrates on 12 OECD countries.

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