rafmacalaba/data-use-mentions-extended huggingface dataset:
Vietnam, 2017).<sup>5</sup> The Formosa incident wreaked havoc on the livelihoods of local coastal communities residing in the four affected provinces, which happen to rely heavily on saltwater fishery. In May 2016, for the first time in history, the Vietnamese government announced a double-ban on both fishing activity and the processing and selling of seafood caught within 20 nautical miles of central Vietnam provinces, worrying that contaminated seafood in the region might not meet safety standards (VOA, 2016b). The ban would eventually last six months and was lifted in September 2016. However, all near-shore (within 20 nautical miles) deepwater fishing activity continued to be restricted, and would only eventually be lifted in May 2018, after series of inspections from the Health Ministry concluding that seafood caught in the area had met safety standards, and that marine resources had recovered (Vnexpress, 2018).

Figure 1 shows the map of Vietnam (divided into provinces) with a focus on the study area. The shaded provinces in central Vietnam are those directly affected by _Formosa_ : Ha Tinh, Quang Binh, Quang Tri, and Hue (from north to south). The location of Formosa steel plant is geo-coded and shown as the green asterisk on the southern tip of Ha Tinh province. The thick dashed line (in red) indicates the near-shore fishing-restricted zone demarcated by the government that was in effect for six months, between April and September 2016.<sup>6</sup> The thin dash line (in blue) indicates the Maritime Exclusive Economic Zone (EEZ) of Vietnam.

# **3 Data and Empirical Methodology**

## **3.1 Data and summary statistics**

We analyze the impact of _Formosa_ on fishery workers in central Vietnam using two main data sources. We collect labor-market information of fishery workers from the Labor Force Surveys of Vietnam (“LFS”) in 2015 and 2016. The second set of data comes from the Visible Infrared Imaging Radiometer Suite (“
accurately measuring caloric intake or TEE it seems clear that anthropometric outcomes (e.g. stunting, wasting, BMI and weight gain over time) are more reliable for the evaluation of adequate nutritional status. Estimates of TEE could also be useful inputs into quantitative models of structural and demographic change since “subsistence” food requirements may vary with mechanization and household composition. In addition, quantifying TEE is a useful contri– bution to debates about estimated caloric intake and nutritional outcomes given the scope for measurement error, measuring both calories in, calories out, and net caloric surplus can shed light on biases in measurement. Timeuse and anthropometric surveys are available for many developing countries, and the potential availability of multiple waves of time-use data in developing countries could be particularly useful for this type of exercise, as it may 18
this we can draw on estimates from the literature of these two elasticities. <u>Ravallion (2016b, Appendix) estimates the cross-country elasticity of the national poverty line to</u> the mean to be 0.52 (robust s.e.=0.04; n=598).<sup>15</sup> Using the estimates of the relative poverty gap index at the country level (calibrated to predicted national poverty lines) obtained by Ravallion <u>and Chen (2019), and also allowing for country fixed effects, we obtain an elasticity with respect</u> to the mean income or consumption of -0.56 (s.e.=0.08; n=144). Thus, the two effects of higher average income on the national poverty gap per capita (PG times the poverty line)—one on the — national poverty line and one on the PG itself essentially cancel out, suggesting that the income elasticity of the cost of eliminating poverty is likely to be close to zero.

> 14 PG is the aggregate proportionate distance below the poverty line (expressed as a proportion of the line and counted as zero for those above the line) per capita of the total population.

> 15 This is based on the compilation of national poverty lines across countries and over time from Jolliffe and Prydz <u>(2016)). Ravallion (2016b, Appendix) regressed the log of the national poverty line on the log of mean income</u> including country fixed effects.

10
35. Estache, A.. (2007) "Infrastructure and Development: A Survey of Recent and Upcoming Issues”, in Bourguignon, F., and B. Pleskovic, Rethinking Infrastructure for Development – Annual World Bank Conference on Development Economics, Global, pp47-82 36. ---------. 2006b. “PPI Partnerships vs. PPI Divorces in LDCs.” _Review of Industrial Organization_ 29(number): 3–26.

37. Estache, A. and M. Fay. 2010, “Emerging issues in infrastructure”, in D. Leipziger, ed. , Globalization and Growth: Implications for a Post-Crisis World, Commission on Growth and Development,pp151-193 38. Estache, A., V. Foster and Q. Wodon, 2002. "Maling Infrastructure Reform Work for the Poor: Policy Options Based on Latin America's Experience," ULB Institutional Repository 2013/43979, ULB -- Universite Libre de Bruxelles.

39. ---------. 2009. “Corruption and Insfrastructure Services: An Overview.” Editorial comments. _Utilities Policy_ 17(number): 153–55.

40. Estache, A. and L. Wren-Lewis. 2009. “Towards a Theory of Regulation for Developing Countries: Following Laffont's Lead.” _Journal of Economic Literature_ XLVII(3).

41. Fay, M. and M. Morrison. 2006. “Infrastructure in Latin America and the Caribbean— Recent Development and Key Challenges.” Directions in Development Series, World Bank, Washington, DC.

42. Ferraz, C. and F. Finan. 2007 “Exposing Corrupt Politicians: The Effects of Brazil’s Publicly Released Audits on Electoral Outcomes.” IZA Discussion Paper 2836, Brazil.

43. Fiva, J. H., and J. Natvik Gisle, 2009. “Do Re-election Probabilities Influence Public Investment?” CESIFO Working Paper 2709.

44. Flyjberg, B. 2008. "Public Planning
The same trend is seen in Ethiopia, where Chinese investment made up 11.5% of the countries’ total FDI inflows in 2000-2005, and rose to top place in 2006-2011 contributing 25% of total FDI in the country. In Zambia, too, Chinese investment held a more fluctuating but generally significant place in total FDI during the same period.

**Figure 6: The Percentage of Chinese Investment of Total FDI in Ethiopia and Zambia, 2000-2011**

<!-- Start of picture text --> Ethiopia Zambia<br>55.60%<br>40.00%30.00% 25.00% 29.00% 32.00% 24.00% 27.00% 26.00% 60.00%40.00% 31.27% 23.60%<br>20.00% 11.50% 20.00% 13.80% 17.26% 11.24% 8.26%<br>10.00%<br>0.00%<br>0.00%<br><!-- End of picture text -->

# _Source: Shen and Zhai_

In Ghana, Liberia and Rwanda, Chinese investment is proportionally much less significant – except occasional spike years seen in each country, the average Chinese/Total FDI ratios linger at low levels, less than 1% in Liberia, 1.8% in Rwanda, and 3.5% in Ghana.

**Figure 7: The Percentage of Chinese Investment of Total FDI in Ghana, Liberia and Rwanda, 2000-2011**

<!-- Start of picture text --> Ghana Liberia Rwanda<br>36.50%<br>59.03% 30.00%<br>80.00% 40.00% 40.00%<br>60.00% 30.00% 30.00%<br>40.00% 5.23
1

# <u>Abstract</u>

This paper uses a large household data set from Guatemala to analyze how the receipt of internal remittances (from Guatemala) and international remittances (from the United States) affects the marginal spending behavior of households on various consumption and investment goods. Contrary to other studies, this study finds that households receiving remittances actually spend less at the margin on consumption – food and consumer goods and durables – than do households receiving no remittances. Instead of spending on consumption, households receiving remittances tend to spend more on investment goods, like education, health and housing. The analysis shows that a large amount of remittance money goes into education. At the margin, households receiving internal and international remittances spend 45 and 58 percent more, respectively, on education than do households with no remittances. These increased expenditures on education represent investment in human capital. Like other studies, this paper finds that remittance-receiving households spend more at the margin on housing. These increased expenditures on housing represent a type of investment for the migrant as well as a means for boosting local economic development by creating new income and employment opportunities for skilled and unskilled workers.
in frequency between 1960 and 2020. Figure 1 presents the number of disaster episodes categorized by severity during the last 60 years. Between the periods 1960-79 and 2000-2020, the number of disaster episodes has almost tripled. Disaster episodes associated with an official declaration of a state of emergency (level iii) or a call for international assistance (level iv) account for a small proportion of the total number of disasters in a given year, with the former still occurring every year and the latter rarely seen in most years. These observations are generally true for all eight types of disasters, with the share of severe disasters (levels iii and iv) relatively higher in some years for wildfire and droughts. See Appendix, Figure 6 for the relative share of episodes across regions and figure 7 for a visual breakdown of disaster episodes by disaster group.

> 3For instance, damage amount data is available for 46 percent of high income countries but only 18 percent of low income countries.

6
, based on household data from a large intercensus.

We compare these 2015 benchmark estimates with five sets of municipal poverty estimates. The first is direct sample estimates taken from the 2014 survey. We generate the second, third, and fourth sets of estimates by combining the 2014 sample with geospatial indicators and estimating a household-level “unit-context” model of welfare, a sub-area model of poverty rates, and an arealevel model of poverty rates. The fifth and final set of estimates are the official poverty estimates

> 6 In other words, municipalities are the level at which we wish to estimate poverty. This is below the level of representativeness of the survey.

> 7 https://dataforgood.facebook.com/dfg/tools/relative-wealth-index

5
5!5 substitutes, wages, and the kind of work involved. Research in - this sense is synergistic investigating many factors altogether tells us more than a number of partial investigations could. One possibily highly productive route to easier applications of such a research approach would be the use of existing institutional frameworks and data resou.:ces, and one of the most suitable institutions is the joint WHO/UNICEF Nutrition Support Program being run in 18 countries (WHO/UNICEF (1986)). The quality and spread of the JNSP varies greatly from country to country, but in the more successful cases, such as Tanzania, much of the basic individual nutritional and agricultural data is systematically collected on a regular basis. This accumulation of long-run data sets could form a core on which to build more complete studies as suggested (WHO/UNICEF (1986) pg 50-55). Two general areas of research are suggested by the dyadic framework, as applied specifically to the context of African countries. Both of these topics are treated at the level of individual women and child pairs, and so must also be seen ultimately within the context of historical changes and policies at the level of the whole society.

One concerns breastfeeding. Breast feeding has been identified as thae sine qua non of factors in the integrative approach to nutrition and family planning Lamptey and Sai (1985), Katz (1977) pp 235-236), related as it is to both post-partum amenorrhoea (p.p.a.) and infant and maternal nutrition. Sufficient clinical research has been done in gaining a fuller understanding of the physiological relationships between the duration, intensity and
These three steps generate various quantifications of the intuitive features presented (including strength and diversity of contacts) as well as other measures (intensity and distribution of usage over space and time, and mobility). For each feature, we also add an indicator for whether that individual is missing that feature. Altogether, there are approximately 5,500 features with variation.

# **4. PREDICTION AND RESULTS**

A first question is how individual features correlate with default. Table 2 presents the single variable correlation with default.

Characteristics traditionally available to lenders are not very predictive. Demographic features (gender and age) have very low correlation with repayment (magnitudes between 0.04 and 0.07). Having a credit bureau record has a small negative correlation with repayment (-0.02). For individuals with records, the most predictive feature is the summary score (-0.072; lower is better), and the fraction of debt lost (- 0.046). That individual credit bureau features are only slightly predictive suggests that predicting repayment in this setting is a difficult problem.

Individual features derived from mobile phone usage have slightly higher correlations, ranging up to 0.16. But mobile phone usage data are richer, so there are many more features of behavior that can be included in a model. Many features measure similar concepts, so the table presents broad categories, and the correlation of one top feature within that category. Correlated features include the periodicity of usage (top correlation -0.16), slope of usage (0.13), correlations in usage (0.11), and variance (-0.10). The table highlights particular features that perform well in isolation, including the slope of daily calls sent, and the number of important geographical location clusters where the phone is used. Next, we consider using multiple features together to predict repayment.

# **<u>Predicting Repayment</u>**

Our features are
We use the following data from different countries for our research. To run the econometrics model with panel data, we collect data through years 2006 to 2016.

- United States: data within the United States are mainly from U.S. Census Bureau. According to the literature, the most important variables include property tax revenue, GDP, population, public finance (i.e. federal transfers<sup>5</sup> ), property tax rate, household size, and governance quality.<sup>6</sup> U.S. data have the most variables and the best quality among the data we have collected.

- Canada: data are mainly collected from Statistics Canada, the statistics department of the government. Main variables include property tax revenue, GDP, population, public finance (i.e. federal transfers), and household size. Besides the United States, Canada is the only country that provides clear federal transfer data publicly online.

- Australia: Australian Bureau of Statistics has the main variables we need, including property tax revenue, GDP, population, and household size.

- Chile: variables for Chile are also property tax revenue, GDP, population, and household size, the same as Australia. And they are collected from the National Statistics Institute.

- OECD: data on OECD countries are all from the official OECD website. Variables are the same as for Australia and Chile, including property tax revenue, GDP, population, and household size.

# **IV. Methodology**

Most of the previous research simply use pooled Ordinary Least Squares (OLS), which is very likely to cause endogeneity (e.g. omitted variable bias), due to the different environments and situations in different countries (or states/provinces within a country). Since there exist some structural differences between countries, they are not consistently comparable using pooled OLS model. Therefore, we utilize Fixed Effects model<sup>7</sup> to estimate
|**Table 1: Business Service Provider Survey Data**|All Pro|viders|Me|ans by Provid|er Type|
|---|---|---|---|---|---|
||Mean|S.D.|HR|Accounting|Marketing|
|_Provider Characteristics_||||||
|Age of firm (years)|11.5|8.5|11.4|14.2|8.8|
|Has more than one branch|0.38|0.49|0.37|0.52|0.24|
|Number of full-time professionals|10.9|16.8|10.9|13.1|8.6|
|Annual sales (000s of USD)|148|357|85|173|162|
|Number of customer per month|29.6|47.0|28.3|42.1|17.8|
|Daily rate (USD)|285|692|210|233|387|
|_Quality information_||||||
|Offers money-back guarantee|0.02|0.15|0.00|0.00|0.06|
|Will re-do work if not satisfied|0.53|0.50|0.60|0.43|0.58|
|Share of staff with tertiary degree|0.73|0.25|0.72|0.75|0.70|
|Share of staff with skill certification|0.39|0.25|0.37|0.45|0.34|
|Belongs to a professional organization|0.84|0.37|0.82|0.97|0.71|
|_Main way of getting customers_||||||
|Word of mouth|0
validate the equality of pre-intervention trends between both sets of schools. However, the authors reject the equality of trends in the pre-period when schools that received benefits during the first year of operation of the intervention are included in the treatment group. These schools were improving outcomes more rapidly in the pre-PEC years than comparison schools which suggests that any PEC evaluation is likely to suffer from self-selection bias, at least on its initial stages – that is, better performing schools had a higher probability to apply for and receive benefits in the first years of program operation. The authors find that the program reduced dropout rates by 0.27 percentage point but find no impact on failure rates.

Skoufias and Shapiro (2006) build on the standard difference-in-difference methodology in Murnane et al. (2006) to evaluate PEC on school averaged repetition, failure and dropout rates over the first three years of PEC benefits. Their approach combines difference-in-difference techniques and propensity score matching. In this way, each PEC school is matched to a set of comparison schools with similar observed characteristics and located in similar communities, rather than to all comparison schools. However, because they only have data on two periods, the authors are forced to assume that pre-intervention trends between treatment and comparison schools were the same. Their findings suggest that participation in PEC decreases dropout rates by 0.24 points, failure rates by 0.24 points and repetition rates by 0.31 points.

Loera (2005) and Loera and Cazares (2005) use achievement and coverage data over school years 2001-02 to 2003-04 and report a statistically significant positive correlation of 0.90 point between student test scores and PEC benefits. Bando (2010) exploited the exposure to the program across cohorts and schools over time; Bando finds that the program impacted decreasing failure and drop6
||(0.0011)|<br>|
|GDP per capita|<br>||||-126.3||
|<br>(relative to US average)|<br> <br>||||(128.0)|<br>|
|Forest coverage|||||-225.6|*|
|<br>(% of state area)|<br> <br>||||(130.9)|<br>|
|Year FE|||||YES||
|Constant|38.5<br>*<br>-89.8|*|-14.8||283.0||
||(21.1)<br> <br>(49.9)||(30.3)||(172.3)||
|N (state-years)|911<br> <br>900||900||833||
|R-squared|0.00<br>0.03||0.04||0.07||

_Note: Pooled OLS, standard errors clustered on state level in parenthesis. Significance levels: *** 0.01, ** 0.05, * 0.10. Source: authors_

# 4.2. Evidence from Bangladesh

This study also shows that the resilience of an energy system to natural shocks ultimately is closely associated with its overall stability and service reliability. Even in the absence of natural shocks, electricity consumers in Bangladesh are used to unreliable service. According to the 2012 Enterprise Survey conducted by the World Bank (World Bank, 2018), 73% of firms report experiencing regular power outages, with an outage frequency of 85 times per month and a duration of 70 minutes on average. In addition, a significant share of the population still does not have access to electricity. In 2016
or 32 percent of total principals in East Java.

Two districts in East Java were selected for visits for this study, namely Bangkalan and Sampang, both of which are located on the island of Madura, which was noted by several interviewees during field visits as an area of the country that has been “left behind” in education. In 2018, the average GPI for NERs in Bangkalan at senior secondary is 0.97 and, among the population aged 50 years and over, women are 24 percent less literate than men.<sup>12</sup> In Sampang, the average GPI for NERs at the three different school levels is also 0.97, and there is a 15-percentage-point gender gap in literacy in the 50-years-and-over age group.

> 9 BPS (2018). _Statistik Indonesia 2018_ : https://www.bps.go.id/publication/2018/07/03/5a963c1ea9b0fed6497d0845/statistik- <u>indonesia-2018.html</u>

> 10 Literacy rate is the percentage of population who can read and write simple sentences.

> 11 Overview of Education and Cultural Data 2017/18.

> 12 Susenas 2018.

6
# **Data**

We applied this method to recent household consumption data from Kenya. Kenya’s source for official poverty estimates is the Kenyan Integrated Household Budget Survey (KIHBS). The two last rounds were implemented in 2005/6 and 2015/16. The 2005/6 round used a representative sample of households in Kenya stratified by 7 provinces and 69 districts split into urban and rural. The sample size of the cleaned data set includes 12,695 households in 1,338 clusters. The 2015/16 round used a representative sample of households in Kenya stratified by 47 counties split into urban and rural. The sample size of the cleaned data set includes 21,585 households in 2,387 clusters. For both surveys, data collection was carried out over a period of 12 months.

The 2015/16 round was accompanied by a CAPI pilot implementing the rapid approach. The pilot used the same sampling frame as the 2015/16 round and interviewed up to an additional 6 households in the same clusters, resulting in a sample size of 12,662 households (as not all clusters and all intended households were interviewed due to non-response).<sup>8</sup> The questionnaire was derived from the KIHBS 2015/16 questionnaire but was considerably simplified across all modules. Specifically, the consumption module was administered according to the rapid approach. We are using the data set that was constructed with 5 food and 5 non-food items in the core (selected based on the highest consumption share in KIHBS 2005/6), with the remaining 128 food and 76 non-food items partitioned into 3 optional modules.<sup>9</sup> Thus, the expected time saving was about 30 percent.

In the remainder of the paper, we use KIHBS 2015/16 with the full consumption module as a benchmark. The previous round of 2005/6 is used to define the core items, and for two of the alternative approaches it is used to adjust the poverty line and