rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 8.B. Financial markets during global recessions

## A. Capital flows

## B. Credit

<!-- Start of picture text --> Percent of GDP Index, t-1 = 100<br>50 120<br>1975 1975 1982<br>40 1982 1991 2009<br>110<br>1991 Average<br>30 2009<br>Average 100<br>20<br>90<br>10<br>0 80<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>C. Equity prices D. House prices<br>Index, t-1 = 100 Index, t-1 = 100<br>1975 1982<br>140 105<br>1991 2009<br>Average<br>100<br>120<br>95<br>100 1975 1982<br>90 1991 2009<br>Average<br>80 85<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>E. Financial conditions F. Inflation<br>Percent Percent 1975 1982<br>2 1991 2009 Average 16 1991 2009<br>Average<br>1 12<br>0 8<br>-1 4<br>-2 0<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br><!-- End of picture text --> Sources: Bank for International Settlements; Bloomberg; Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Equity prices, house prices, financial conditions, and inflation are weighted by GDP in U.S. dollars. Credit, equity prices, and house prices are index numbers equal to 100 one year before year “t” (i.e., t-1 = 100). For details, see Appendix 2.

38
# **3. Data**

The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS).<sup>4</sup> The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015 - were conducted using mobile phones.<sup>5</sup> The original sample comprised 501 respondents (51% Male, 49% Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako,<sup>6</sup> refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2% per wave.

We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location. It should also be noted that the respondent was selected randomly among household members above the age of 18 (split between men and women) to obtain a good representation of the (subjective) opinions of male and female adults. As a consequence, and unlike more typical welfare surveys, the respondent often was not the male household head or spouse. Additional information on the data methodology can be found in (EtangNdip, Hoogeveen, & Lendorfer, 2015).

> 4 All data from this survey can be downloaded from <u>http://www.gisse.org/pages/enquete-continue-sur-les-deplacesrefugies/enquete-sur-les-deplaces-et-les-refusgies.html</u> and from: <u>http://bit.ly/2nsxSd6.</u> 5 More precisely, the subsequent interviews were conducted in August 2014, September/October 2014 and then from November 2014 every month. More information on high frequency panel data using mobile phone interviews can be found in (J. Hoogeveen, Croke, Dabalen, Demombynes, & Giugale., 2014) and (Dabalen et al., 2016). 6 It should be noted that the definition of IDPs used in this survey is different from the one adopted from the UNHCR. This agency considers as IDPs also people displaced in the northern part of the country.

5
# **Table C.29: Role model analysis: Effects on time use**

## **Panel A: Women’s time use (Hours)**

|||Last wor|kday||Last|non-workda|y|
|---|---|---|---|---|---|---|---|
||(1)<br>Domestic<br>work|(2)<br>Leisure|(3)<br>Sleep|(4)<br>Work|(5)<br>Domestic<br>work|(6)<br>Leisure|(7)<br>Sleep|
|exposed|0_._08|0_._03|_−_0_._16 *|_−_0_._05|_−_0_._16 *|0_._12|0_._09|
||(0_._08)|(0_._08)|(0_._10)|(0_._18)|(0_._10)|(0_._14)|(0_._10)|
|N|465|465|465|465|488|488|488|
|Non-exposed mean|3_._17|1_._57|6_._85|9_._80|4_._58|3_._27|8_._22|
|Non-exposed SD|0_._97|0_._98|1_._08|1_._20|1_._06|1_._80|1_._31|

## **Panel B: Husband’s time use (Hours)**

|||Last workd|ay||Last|non-workd|ay|
|---|---|---|---|---|---|---|---|
||(1)<br>Domestic<br>work|(2)<br>Leisure|(3)<br>Sleep|(4)<br>Work|(5)<br>Domestic<br>work|(6)<br>Leisure|(7)<br>Sleep|
|exposed|0_._05|_−_0_._19 ***|0_._03|0_._16|0_._20***|_−_0_._11|_−_0_._20 *|
||(0_._08)|(0_._07)|(0_._08)|(0_._18)|(0_._08)|(0_._08)|(0_._10)|
|N|414|414|414|414|449|449|449|
|Non-exposed mean|0_._98|2_._59|7_._31|9_._93|2_._04|4_._19|8_._86|
|Non-exposed SD|0_._64|1_._32|0_._75|1_._37|0_._94|1_._99|0_._93|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory and enumerator dummies.

**Table C.30: Role model analysis: Effects on other outcomes**

||Economi|cally active|Slapped o|r beaten by husband|Hiding money|
|---|---|---|---|---|---|
||(1)<br>Wife|(2)<br>Husband|(3)<br>Ever|(4)<br>In last 5 months|(5)|
|exposed|_−_0_._02|_−_0_._03|0_._03|0_._02|0_._07|
||(0_._02)|(0_._02)|(0_._05)|(0_._02)|(0_._04)|
|N|488|449|482|482|488|
|Non-exposed mean|0_._97|0_._93|0_._30|0_._02|0_._12|
|Non-exposed SD|0_._16|0_._25|0_._46|0_._13|0_._33|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The nonexposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory and enumerator dummies.

92
greater damage from climate shocks than manufacturing and services firms. Weaker performing firms are more affected by natural disasters in India and Indonesia (Pelli et al. 2023, Xie 2022). In the aftermath of large storms, capital gets reallocated towards more productive firms (Pelli et al. 2023) and industries with a larger comparative advantage (Pelli and Tschopp 2017). In the US, smaller firms and less-productive establishments were less likely to survive being damaged by Hurricane Katrina (Basker and Miranda 2017). Given this evidence suggesting that climate shocks have more adverse impacts on smaller or less productive firms, it is also important to understand which types of firms are most exposed to shocks, a question which has not been examined systematically in the existing literature.

The remainder of this paper is structured as follows: Section 2 describes the data and methodology used for the meta-analysis. Section 3 presents the results and Section 4 concludes.

# **2. Data and Method**

# **2.1 Data**

We use data from multiple sources to analyze the relationship between flooding and extreme heat and relative wealth, proxied by the Relative Wealth Index (RWI). A similar dataset for firms is constructed using the most recent Indian Economic Census.

**Relative wealth.** The Relative Wealth Index, developed by Meta’s Data for Good team, uses a combination of machine learning algorithms, satellite data, ground survey data, and other publicly available datasets to estimate the wealth distribution at granular spatial resolution. Each RWI data point represents the center of a 2.4 km by 2.4 km square. It uses cross-sectional household-level data from the nationally representative Demographic and Health Survey from multiple countries linked to additional data such as satellite imagery (Chi et al. 2022). The Demographic and Health Survey (DHS) is a series of nationally representative surveys conducted in multiple countries, including South Asian countries.

**Firm size in India.** We use the most recent cross-sectional firm-level data from the Sixth Economic Census of India, conducted in 2013, which captures information for over 58 million non-agricultural firms across India, including employee counts for each firm (Government of India

5
already accounted by the fact that total income taxes collected by the government – which approximately match income taxes paid by the richest quintile – are lower. Similarly, as the poorest quintile pays on average only 1.2 percent of total income taxes, the bias from informal activity is also likely to be very small.

Figure 6 summarizes taxation by income quintile. On average, in Latin America the tax categories we consider generate revenues equivalent to 15 percent of GDP: therefore, although lower, revenues are close to the ones in the UK (19 percent) and in the US (16 percent). To be sure, actual government revenues are higher, especially in the United Kingdom (36 percent) and in the United States (25 percent) where other forms of taxation that cannot be captured by household surveys or cannot be easily attributed to specific income quintiles – such as corporate taxes – generate large government revenues. In Latin America, our estimates also miss all government revenues from natural resources exploitation, and in all countries they do not consider contributions to pension systems. Nonetheless, to the extent that the taxes we consider are the ones that most directly affect individuals, they will be the ones that will mostly influence redistributive politics.

**Figure 6: Taxation of the richest and poorest income quintiles.**

<!-- Start of picture text --> 0.8<br>Taxation (poorest quintile)<br>0.6 Taxation (richest quintile)<br>0.4<br>0.2<br>0<br>BOL USA MEX GRB NIC COL HND PER ARG<br>Taxation (% GDP per capita)<br><!-- End of picture text --> Overall, in Latin America, the share of taxation paid by the richest income quintile averages 61 percent. This remains significantly higher than the share paid by the richest quintile in the United Kingdom (43 percent), and similar to what the richest quintile contributes in the United States (58 percent). In contrast, the poorest income quintile brings to the government much lower revenues: as Figure 6 shows, on average the richest income quintile pays 22 times more taxes than the poorest quintile. This remains close to the ratio of the United States (19 times), and much higher than the one of the United Kingdom (6 times).

There are however large variations in taxing behavior: in Nicaragua, for instance, the richest quintile pays in taxes an amount equal to 10 times what the poorest quintile pays, 14
indexes. In these two cases, the ranking is consistent across indexes and easier to use for the purpose of aid allocation.

Is this ranking related to the actual distribution of EU social funds? Table 3 shows the indicative allocation of cohesion funds across the eight countries considered and the rank, which can be compared to the rank in Table 2. In first and last position we find Romania and the Czech Republic and this matches perfectly with all indicators in Table 2. The IL index which better approximates the rank of the actual distribution of funds is _IL_<sup>_progA_</sup> , where all ranks are the same, except for Slovakia and Estionia.

Table 3: Indicative allocation of Cohesion Fund, 200713 (2007 prices, in % GDP)

|Country<br>Cohesion|Fund (%GDP)|rank|
|---|---|---|
|Czech Republic|6.69|8|
|Estonia|7.17|5|
|Hungary|8.69|2|
|Latvia|7.32|4|
|Lithuania|8.02|3|
|Poland|7.13|6|
|Romania|18.94|1|
|Slovakia|7.11|7|

_Source: our elaboration on European Union (2007) and Eurostat for GDP_

# **6 Ranking poor countries for North-South transfers**

Table 4 summarizes poverty and income lever indexes together with the country ranks obtained by computing each index for eight African countries based on the Global Income Distribution Database (GIDD). This is a database constructed by the World Bank that homogenized a number of standard variables including income and consumption across household consumption surveys worldwide.<sup>3</sup> The welfare variable used is household equivalent consumption at Purchasing Power Parity (PPP) in USD where the equivalence scale is the square root of household size. To compute the indexes, we define as poverty line _z_ the World Bank official poverty line of 1.25 USD/day PPP. Similarly to what we have

> 3For a full description of the data see Ackah, Bussolo, Hoynos, and Medvedev (2008)

23
|SwampReclamation Project|
|---|
|SwampReclamation Project|
|SwampReclamation Project|
|SwampReclamation Project|
|Smallholder Coconut Development Project|
|Smallholder Coconut Development Project|
|Smallholder Coconut Development Project|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Nucleus Estates and Smallholders Project(05)- NES Project(05)|
|Small Enterprise Development Project(02)- SEDP Project(02)|
|Small Enterprise Development Project(02)- SEDP Project(02)|
|UniversityDevelopment Project|
|UniversityDevelopment Project|
|UniversityDevelopment Project|
|UniversityDevelopment Project|
|Power Project(10)|
|Power Project(10)|
|Power Project(10)|
|Urban Development Project(04)|
|Urban Development Project(04)|
|Urban Development Project(04)|
|Urban Development Project(04)|
|Seeds Project(02)|
|Seeds Project(02)|
|Seeds Project(02)|
|Seeds Project(02)|
|Seeds Project(02)|
|Seeds Project(02)|
|Irrigation Project(17)|
|Irrigation Project(17)|
|Irrigation Project(17)|
|Irrigation Project(17)|
|Irrigation Project(17)|
|Irrigation Project(16)|
|Irrigation Project(16)|
|Irrigation Project(16)|
|Irrigation Project(16)|
|Irrigation Project(16)|
|Nucleus Estates and Smallholders Project(06)- NES Project(06)|
|Nucleus Estates and Smallholders Project(06)- NES Project(06)|
|Nucleus Estates and Smallholders Project(06)- NES Project(06)|
|Nucleus Estates and Smallholders Project(06)- NES Project(06)|
_29_ _Ways Out of Poverty - Diffiusing Best Practices and Creatinz Capabilities_ entrepreneurship. Small farmers are more likely to invest in risky ventures<sup>when they</sup> have a way of insuring themselves.

Political acceptability and economic efficiency may thus best be helped<sup>by designing</sup> forms of insurance appropriate for a particular economy. For example,<sup>large-scale</sup> unemployment insurance can be afforded by advanced economies. Smaller<sup>insurance</sup> schemes are practical in poor countries. This would include micro-finance<sup>programs,</sup> which are mostly not used to fund investment but to help deal with temporary<sup>inability to</sup> pay for consumption and which spread default risk among small groups<sup>of people.63</sup>

**The New Swedish Model.** The general picture that thus emerges<sup>fordesirable</sup> government policy might be termed the "New Swedish Model". Before<sup>the1990s,</sup> Swedish economic policy was often looked at as a "third way" between<sup>capitalismand</sup> socialism. After the economic crisis of the early 1990s Sweden and<sup>several other</sup> Scandinavian countries have remodeled themselves. The cornerstones of<sup>the current</sup> model are:

- prudent macro-economic policies and low inflation;64

- a highly competitive environment for firms with few barriers to entry and growth and minimum red tape (figure 16); and

- a strong social safety net.

<!-- Start of picture text --> Figure 1i: Lower levels of regulation in Sweden<br>Number of procedures Number of days it takes Index of formalism in<br>needed to to court enforcement<br>register a business register a business<br>Svveden OECD Ird'lSwede OECD irdn! Sweden OECD bIl'<br>Average Average Average Average Average Average<br>Note: The index of formalism measures substantive and procedural statutory intervention in judicial cases at lower<br>level civil trail courts.<br><!-- End of picture text --> Source: Doing Business Database As countries advance, the complexity and average size of institutions,<sup>be they firms or</sup> government agencies or other forms of organizations, increases.<sup>The shareof</sup> government in total economic activity also increases, not because government<sup>interferes</sup> with production decisions and the ecology of firms, but because of the<sup>growthof transfer</sup> payments that lie behind the social safety net of advanced societies,<sup>whichin turn</sup>

> **63** Morduch, 1999.

> **64** Dollar and Kraay, 2000.
could explain the negative coefficient in the ENI and the Pew dataset.<sup>9</sup> Or more educated individuals might have fewer ties to their home country. We have attempted to control for this using time spent away from the home country, and desire to return home, but this may not fully capture the strength of the ties. We also do not have data on whether migrants are repaying family for loans, for example for education. One additional key issue is that our use of crosssection data does not yield any information about economic shocks that affect either the migrant or the family.

Table 6 examines the extensive margin. More educated migrants are less likely to remit anything in the baseline specification, but this is not statistically significant. We find that the negative effect of education on the decision to remit anything is strengthened by the inclusion of different sets of covariates. The coefficient on education (measured by university degree) is negative and significant once any covariates are included. The alternative measure of education, years of schooling, is not statistically significant. The intensive margin result (Table 7), that once the decision is made to remit, more educated migrants remit more, again appears to be driven by the income effect. Adding only family variables to the baseline specification reduces the coefficient on university education by approximately 3%, but it remains highly significant. However, if only income variables are added to only the baseline specification the coefficient becomes statistically insignificant, with approximately the same point value as the full specification with the full set of covariates.

# **5. Conclusions**

This paper answers the question “Do more educated migrants remit more?” using micro level data. Our approach has the key advantage over other papers in this literature (Faini, 2007 and Niimi, et al. 2008) in that we are able to link the remittance decision of the migrant with their education level and therefore answer this question directly. In contrast, cross-country macroeconomic analyses which relate the amount of remittances received at a country level to the share of migrants with tertiary education are able at best to tell us whether countries which

> 9 An alternative explanation may be that the high-earning highly educated are less likely to respond to surveys. Survey methods which draw a sample from areas which are known to have a high concentration of migrants (e.g. the Pew survey) or from sampling locations where migrants tend to congregate (e.g. the NiDi surveys) are particularly likely to miss highly educated high-income individuals who may be living in areas where there are less of their countrymen.

15
first, by using a "poverty map" to assign resources to small geographic areas, and then conducting an informal on-site assessment of the "poverty" of the community requesting a project.<sup>5</sup>

# 3. The data set

In 1997, Peru was divided into 13 administrative regions, 24 departments, 194 provinces, and 1812 districts (Webb and Femrndez Baca 1997, p. 112). Regions and departments are administrative arms of the central government, which appoints a governor for every region. Provincial and district-level mayors are chosen at municipal elections every three years. Data on FONCODES expenditures, socio-economic characteristics, and voting patterns is available at different levels of geographic aggregation.

FONCODES itself keeps monthly records on the number of projects and aggregate amounts spent in each district. Three points are worth noting about these data. First, because expenditures are recorded in the month in which a project is _approved,_ there is a lag of about two weeks before _disbursement_ of the first installment of funds, and of several months before the second (and final) disbursement. Second, only expenditures on projects are included, and not administrative costs or expenses for general overhead. Third, district-level information on the distribution of expenditures is only available for the cornmunitybased projects. There is no information on the allocation formulas or the actual distribution of the special projects.

The Peruvian National Statistical Institute (INEI) has estimated population size, the proportion rural, and a host of socio-economic indicators at the district level. Most of these indicators are based on a population and housing census conducted in 1993. The one important exception is the rate of chronic malnutrition, which is based on a census of height and weight amongst schoolchildren also conducted in 1993.

The Jurado Nacional de Elecciones, the body which supervises elections in Peru, keeps records on electoral outcomes for elections held since 1990. Some of these data have also been compiled and published (Tuesta 1994, Jurado Nacional de Elecciones). Results are generally available for all provinces in the country, and for all districts in Metropolitan Lima.

The level of aggregation used for the analysis in this paper was dictated by theoretical considerations and the availability of data. Section 4 uses national aggregates of FONCODES expenditures to analyze the relationship between the timing of expenditures and the electoral calendar. Sources of funding and the administration of the community-based and special projects are different, so the analysis is broken down by project type. FONCODES has assigned resources to individual provinces since 1993, and to individual districts since 1996. Section 5 uses a province-level data set to analyze the

> 5 Note, however, that even without political interference actual expenditures in a given province would rarely be equal to allocations because of the demand-drive **n** ature of the program.

5
Table 1 Exact Solutions for the Nucleolus, Shapley-Value, and Egaliatarian Imputations

|x|Nucleolus<br>Imputation||Shapley-Value<br>Imputation||Egalitarian<br>Imputation|
|---|---|---|---|---|---|
|x,<br>xy<br>Ny<br>yy,|1/3 + (e).+ €4— 2ey/3e,2,<br>1/3 +(2+ ea— 2€ 4M 3e\23<br>1/3 + (e,4+ €2;— 2€,)/3e,.,<br>1|=<br>>ors<br>s|1/3 + (éyat @)4- 2@53)/6**2**<br>j 5<br>1/3 + (é.+3 — 2€ 1)/6 y94<br>1/3 + (e,3+ @23— 2e,3)/6e)45<br>1|=<br>2ors<br>s|1/3<br>3<br>3<br>1|
||||‘7<br>=|=<br>ae<br>:|C|