rafmacalaba/data-use-mentions-extended huggingface dataset:
our method, consider a world in which all individuals ( _n, α_ ) choose an optimal hours worked _h_<sup>_∗_</sup> ( _n, α_ ) yet face labor supply frictions. Thus, each individual ( _n, α_ ) ends up working _h_<sup> ̃</sup> ( _n, α_ ) = _h_<sup>_∗_</sup> ( _n, α_ ) + _εn,α_ , where _εn,α_ is some deviation from optimal hours (it does not matter what causes this deviation from optimal hours).

Even with frictions that prevent individuals from working their optimal number of hours, we can still learn about the role of productivities vs. preferences in driving income inequality from labor supply elasticities. To solve the first issue that observed hours are not equal to optimal hours, suppose that we were able to elicit (via survey, for example) individuals’ true optimal hours worked _h_<sup>_∗_</sup> .<sup>13</sup> Productivity is equal to observed income ̃ divided by observed hours, _n_ = _z/h_<sup> ̃</sup> , and optimal income is given by optimal hours _h_<sup>_∗_</sup> multiplied by _n_ : _z_<sup>_∗_</sup> = _nh_<sup>_∗_</sup> = ( ̃ _z/h_<sup> ̃</sup> ) _h_<sup>_∗_</sup> .

> 13For example, the National Study of the Changing Workforce asks people how many hours they would prefer to work.

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standard deviations aggregated from monthly rainfall averages data. We use rainfall data in two ways: first, as a control for climate variability, and second, as a proxy for agricultural productivity variations, similar in spirit to Miguel, Satyanath, and Sergenti (2004).

Finally, we used Gridded Population of the World (GPW v4) data to construct data on district population counts. At a grid cell resolution of 30 arc-minutes (approximately 1 km at the equator), the GPW provides estimates of population count between 1990 and 2020 at a 5-year interval. We extracted these data at the district level and followed Hodler and Raschky (2014) to linearly interpolate the data to estimate population counts for the missing years, in turn allowing us to have population counts for the period 2000–2015.

> 16 We refrained from using mineral prices data from the Global Economic Monitor (GEM) of the World Bank because these do not cover all commodity categories in our sample, making the analysis less comparable. However, when we compare a similar set of minerals prices from the GEM and USGS, the overall correlation is 0.93 (unreported), suggesting that the use of US mineral prices is not unreasonable.

> 17 It is calculated as NDVI = (NIR - Red) / (NIR + Red), with NIR being near infrared (technical detail available at: <u>earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php,</u> accessed 18 Novermber 2014). Similarly to Andersson et al. (2015), we also use the MODIS Land Cover Type product (MCD12Q1) to extract the NDVI. These data are available for the period 2001–2012, and are provided at a 16day temporal resolution and a 250 m spatial resolution, making it possible to pair with other data at the district level.

> 18 For example, NDVI has been shown to be a good measure of vegetation greenness, primary productivity of vegetation, and leaf area index. NDVI is
Having tools or material for fishery, agricultural production and handicraft contributes to 12.2% of the inertia explained by the first component of the analysis.<sup>15</sup> Other items contribute to less than 10% of inertia each.

Using retrospective data on assets from the June survey, Figure 5 presents the asset index distributions before the earthquake, in February and in June. Using this index, we can notice that the inequality of household wealth (as measured by the Gini coefficient) has decreased after the earthquake due to higher losses among the wealthiest. This is particularly true among households living in camps (Gini is 0.2446 before the earthquake and 0.1970 in February). Then, between February and June, inequality of household wealth has increased—from 0.3267 to 0.3325 among non-camp households and from 0.1970 to 0.2183 among camp households.

**Figure 5. Distribution of wealth (directly affected areas)**

<!-- Start of picture text --> Asset Index Density, Non-Camp Households Asset Index Density, Camp Households<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Asset Index Asset Index<br>June (Gini=0.3325) June (Gini=0.2183)<br>February (Gini=0.3267) February (Gini=0.1970)<br>Before Earthquake (Gini=0.3346) Before Earthquake (Gini=0.2446)<br>kernel = epanechnikov, bandwidth = 4.6918 kernel = epanechnikov, bandwidth = 3.0994<br>.05<br>.02<br>.04<br>.015<br>.03<br>.01<br>.02<br>.005 .01<br>0 0<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA
offerings such as electronic payments and mobile banking remain largely unexploited, particularly when compared to peers like Thailand and Malaysia.

In terms of physical access to outlets, Sri Lanka also enjoys high levels of banking penetration, with bank branch density at 17 per 100,000 adults, compared to the South Asia regional average of 10.2. Interestingly, countries like Malaysia (10.2) and Thailand (11.7) do not fare much better than even the South Asian average when it comes to physical branch penetration. However, they are far ahead in terms of digital financial inclusion<sup>15</sup> (the upper middle-income average was at 62 percent) which is a more efficient and flexible mode of banking compared to physical financial access. Overall, when considering Sri Lanka’s level of financial inclusion, there is evidence to show that ease of physical access to accounts has not translated into full financial inclusion, particularly with respect to usage.

# **3. Data and Methodology**

Information from the recently concluded National Financial Inclusion Survey (NFI survey) for Sri Lanka is used in our analysis, given its national coverage and data quality.<sup>16</sup> The NFI survey was initiated by the CBSL and the International Finance Corporation. It was the first such focused survey on financial inclusion for Sri Lanka. The data indicators capture Sri Lankans’ behavior relating to saving, borrowing, making payments, and managing risks, as well as with respect to financial capability.

# **3.1. NFI Survey Data**

The survey data are based on face-to-face interviews with 4,791 nationally representative and randomly selected Sri Lankans over 18 years of age, stratified by geographic areas. Fifty-six percent of the sample respondents were female, and 83 percent lived in rural areas. The age of respondents varied between 18 and 97 years, with a mean of 45 years.

> 15 “Digital financial inclusion” can be defined broadly as digital access to and
different segments of households, with consumers who differ in average income and therefore differ in willingness-to-pay for preschool education. The private provider optimally chooses between a high-price strategy, serving consumers with high willingness-to-pay only, and a low-price strategy, serving consumers from both segments. An expansion of public supply has ambiguous effects on private enrollment, depending on the difference in willingnessto-pay across consumer segments and on the relative size of each segment. Crowding-out effects of more public provision are less likely when the differences in willingness-to-pay across consumer segments are relatively large.

Our paper clearly relates to the more general literature on crowd-out effects of government funding, in particular the strand of the literature dealing with crowd-out effects of public provision of private goods. However, the existing empirical research has mainly been devoted to health care markets. Cutler and Gruber (1996) and Gruber and Simon (2008) analyze the extent to which public health insurance crowds out private insurance, while Cohen et al. (2013) study crowd-out effects of public providers in the US market for outpatient substance abuse treatment. In each case, sizable crowd-out effects are identified.

Among the relatively few empirical studies on child care markets in this strand of the literature, recent contributions include Bassok et al. (2014a) and Bassok et al. (2014b), who find evidence of significant crowding-out of private providers as a result of increased public provision in the child care markets in Oklahoma and Florida, respectively. Cascio and Schanzenbach (2013) also find evidence of crowd-out effects from the expansion of public preschool in Oklahoma and Georgia, particularly for high-income families. Similarly, Cascio (2009) finds evidence of sizable crowd-out effects using US census data over five decades.<sup>4</sup> These mixed findings for the US and Brazilian settings can be rationalized in the context of our model. As noted above, crowding-out effects of more public provision are less likely when the differences in willingness-to-pay across consumer segments are relatively large. These differences are expected to be generally larger in the Brazilian
in the top 100 list. The shareholder data structure was obtained from Bankscope.

> 6 In Latin America, we examine international bank operations in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, and Uruguay. In Eastern Europe, we focus on Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovenia, Slovak Republic, and Turkey.

7 While it is possible that our sample selection might introduce certain biases, we believe these are unlikely to be relevant for at least two main reasons. First, since our sample covers the large majority of foreign banks operating in the countries considered, we are confident that limiting our sample to the top 100 banks will have little impact on the findings. Second, focusing on Latin America and Eastern Europe is justified by the fact that foreign presence in other parts of the world (most notably in Asia) has been very small until very recently.

8 Branches operating in a country as a branch of a top 100 subsidiary incorporated in a developing country were coded as a subsidiary. The top 100 bank is under no legal obligation to honor those branches liabilities in excess of their developing country subsidiary.

9 We eliminated Colombia from our sample because Colombian legislation does not allow the entry of foreign bank branches.

10 There were only 5 cases in our sample in which a bank in the top 100 list had an equity participation below 50 percent, so we believe that imposing the 50 percent ownership rule when identifying subsidiaries is not likely to bias our estimations. In other words, our sample does not exclude many cases of equity participations below the criteria used to identify subsidiaries.

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and coincides with our representative agent model which is based on the national income accounts data).

> 53 We have also shown that if we are only interested in results at the decile level (as opposed to household results), running a model with thousands of households and then aggregating results to deciles (as we have done in table 4.2) does not appear to make much difference compared with aggregating the households to ten households first and then running the model with ten households. See Rutherford, Tarr and Sheopotylo (2005) for details regarding this section.

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agricultural trade policy that encourages private investment, and investments in public goods that encourage productivity growth (World Bank, 2019).

**Figure 2: Poverty among agricultural households in 2014**

<!-- Start of picture text --> 70.0<br>60.0<br>50.0 24.0<br>36.7<br>40.0<br>22.0<br>30.0<br>20.0<br>37.6<br>24.5 22.9<br>10.0<br>0.0<br>East Center West<br>Poor Non-poor<br>% of agriculturral households<br><!-- End of picture text --> _Source:_ 2014 Timor-Leste Survey of Living Standards Notwithstanding the role that agriculture can potentially play in reducing poverty in Timor-Leste, the country has one of the least developed agricultural production systems in the world. All subsectors are dominated by subsistence systems and by farmers who are very risk averse (Denning 2013; World Bank 2017). Only 30 percent of the country’s arable land is currently under crop production and grazing, and the sector’s productivity has been very low. Agricultural productivity as measured by the value of crop output per hectare in Timor-Leste is well below that of other small island developing states and has fallen below the average for other low-income, food-deficit countries in recent years (Figure 3a). Also, agricultural value added per worker has been falling since 2012 (Figure 3b). The estimated 64,000 metric tons of maize and 65,000 metric tons of rice produced in 2015 are 30 percent below the five-year average and well below the country’s estimated requirements (245,000 metric tons) of these staples (FAO statistics). The country’s cereal yield is also among the lowest in the East Asia and Pacific region (UN Women and World Bank 2018)
**TABLE A.1. Data Description and Sources**

|Variable|Description & Source|N|Mean|SD|
|---|---|---|---|---|
|SMig_ij,t_|Stock of emigrants of educational category “high” aged 25+ born in country_i_and<br>living in OECD country_j_in_t_(2000 or 1990) divided by stock of residents of<br>educational category “high” in country_i_in year_t_. Stock of emigration and stock of<br>“”|<br>3052|0.0246|0.1909|
||residents of educational category high from Docquier, Marfouk and Lowell (2007).||||
|RelGDP_ij,t_|<br>Log of GDP per capita of country_j_minus log of GDP per capita of country_i_in year<br>2000. GDP data from Penn World Table Version 6.2.|<br>3052|1.4360|1.2890|
|RelTFP_ij,t_|Log of TFP (measure TPF_K06) per capita of country_j_minus log of TFP of country<br>_i_in year 2000. UNIDO World Productivity Database, Isaksson (2007).|1983|0.7860|0.7628|
|RelWage80_ij,t_|<br>Log of wage in 80th percentile of country_j_minus log of wage in 80th percentile of<br>country_i_. Wage data from Occupational Wages around the World Database.|1247|1.2650|1.4945|
|RelWage90_ij,t_|<br>Log of wage in 90th percentile of country_j_minus log of wage in 90th percentile of<br>country_i_. Wage data from Occupational Wages around the World Database.|1247|1.1810|1.3953|
|RelWage80to50_ij,t_|<br>Log of wage in 80th percentile of country_j_minus log of wage in 50th percentile of<br>country_i_. Wage data from Occupational Wages around the World Database.|1247|
the labor content of exports. The original source of the former is typically national labor force surveys. These are often not easily reconcilable with national account data, which are the source of compensation of employees data. This discrepancy is particularly acute at the sectoral level also due to the different sectoral classifications of the two sources of data, ultimately making the labor and jobs content of exports data not strictly comparable at the sectoral level.<sup>9</sup> However, this method allows both the labor and jobs content of exports to be consistent with the national account and labor force survey data, respectively.

# **3. Global patterns of wages in exports**

## **3.1. Global patterns of labor value added across income groups and regions**

We start by discussing global patterns of total wages linked to exports, followed by some cross-country comparisons.<sup>10</sup> We examine the labor content of exports both in terms of their direct and indirect value added.<sup>11</sup> The labor content of exports is measured as nominal values in US$ (denoted LVAX), and as a share of gross exports (LVAX share), which is a measure of the labor intensity of a country’s or sector’s exports. We also use our measures of the labor content of domestic output (denoted LVAD).

**Error! Reference source not found.** shows that the global share of total labor value added in the value of gross exports (LVAX share) has been in constant decline since 1995. In 1995, the total (direct plus indirect) value of wages was $46 for each $100 of gross exports and in 2011 the value was $40.<sup>12</sup> The direct value

> 9 To check this we compute the sectoral average wage for laborers by dividing the total compensation of employees by the total number of employees. For several country-sector pairs this
labor market changes were contemporaneous with rising food prices, they are worth taking seriously as possible confounding factors. Given my estimation strategy, however, NREGA will only affect the results insofar as the local expansion of the program was systematically related to the change in the agricultural price index or, rather, to the instrumental variable for this change.

Based on 7-day employment recall information in the NSS-EUS, I compute the population weighted mean of the fraction of days per week spent in public works employment (both NREGA and other) for rounds 61 and 66.<sup>29</sup> Including the 2004-09 change in this public works employment variable (∆ _PW_ ) in regression (8) results in no appreciable changes in my estimates of _γ_ (compare cols. 5 and 7 of Table 3). Of course, the coefficient on ∆ _PW_ does not necessarily reflect the _causal_ impact of NREGA or any other public works employment program in India on rural wages; this specification merely serves as a robustness check.

# **4 Food Prices and Welfare**

## **4.1 Welfare elasticities**

Now consider a rural household embedded within the economy sketched out in Section 2. It has an endowment of farmland and of manual labor that it can supply to its own farm (insofar as it has land) or to other farms, or to firms in the services or manufacturing sectors. Household income is simply _y_ in equation (5) with aggregate endowments replaced by household endowments. Indirect utility is a function of this income and of prices, _PM , PS,_ and _Pj_ , _j_ = 1 _, ..., c_ . Following the conventional derivation, the percent change in

> 29This is essentially the same variable considered by Imbert and Papp (2012). Across districts, the mean days per week of public-works employment reported in 2004-05 was 0.0022, increasing to a still rather minuscule 0.0144 days per week in 2009-10. Note, however, that NREGA employment is
# **5. Data**

This study uses quarterly data from the National Employment Survey _“Encuesta Nacional de Ocupación y Empleo”_ of Mexico, a rotating panel of households. There are two periods of implementation (ENE: 2000-2004) and (ENOE: 2005-2009). It is nationally representative but, strictly speaking, the ENE survey had an adequate frame only for the urban population. The data includes a rotating panel at the individual and household level (2000-2009)<sup>6</sup> .

The data cover almost 10 million individuals from 2000 (Q2) to 2009 (Q2) between 15 and 65 years old<sup>7</sup> in 291 municipalities across the country<sup>8</sup> . We observe whether a specific individual changes _SS_ status (provided by formal employment) over consecutive periods. We also observe whether the individual is covered by _SS_ through the spouse or directly through his or her job. At the household level we have an average of 100,000 households per period. Figure 1a shows the quarterly trend of the share of individuals with _SS_ and households covered by _SS_ . Formality exhibits an upward trend more so after the first quarter of 2005. Figure 1b shows the trends of the shares of population by their labor market status. There is a drop in the share of wage employees without SS and other informal employment at around the fourth quarter of 2004 but for the most part the shares are stable.

<!-- Start of picture text --> Figure 1a. Share of Households and Individuals with Social Security Figure 1.b. Shares of Population by Labor Market Status<br>2000q1 2001q3 2003q1 2004q3 2006q1 2007q3 2009q1<br>date<br>2000q1 2001q1 2002q1 2003q1 2004q1date2005q1 2006q1 2007q1 2008q1 2009q1 wage-employed with ss self-employed<br>wage-employed without ss not in labor force<br>share individual with ss share household ss covered other informal employment<br>source: Labor Surveys source: Labor Surveys
Antipov & Pokryshevskaya 2012). In assessing the housing sector, many of these authors contrast the predictive performance of ML algorithms with standard regression techniques. Across the board, the authors find that RF (significantly) outperforms parametric estimation techniques in terms of accuracy and predictive power.

2.4 Data Gap: EMDEs Are Largely Absent in Property Price Analyses This overview on existing studies and data sources within the realm of house prices points to five major gaps that we try to address with this paper: First, there is a striking data gap in the availability of house prices, particularly for EMDEs. Most existing property price compilation efforts concentrate on developed economies, publish data only in indexed format, and do not report underlying actual house prices. This may be attributed to the fact that national sources, such as central banks or statistical offices on which these indices base their data, do not collect, report, or publish property price data. Further, as underlying data to these indices are very country- and context-specific, they fit the purpose of monitoring price changes over time within a specific country but do not facilitate cross-country assessments. Some countries, for instance, only consider prices for family homes in the capital while other countries use flats in urban areas for the index. The same applies for prices: some report the transaction prices while others draw on listed prices, while yet others average prices (cf. BIS database; Mack & Martínez-García 2011). Mack & Martínez-García (2011), who collate publicly available national sources to build a database of (nominal and real) house prices for developed economies, acknowledge this flaw outlining that the main contribution of their database is “sorting out the existing data by country, selecting the most _similar_ series and documenting the differences across countries to clarify the extent to which international sources can be made comparable for empirical analysis purposes” (Mack & Martínez-García 2011: 3). Achieving comparability across countries with the existing data sources is almost impossible.

9
it were observed data; for example, counting the percentage of households with imputed consumption below the poverty line would provide a biased estimate of poverty (as it ignores the error term that separates observed consumption from predicted consumption).

Exploring the time variation in estimated poverty rates, possibly disaggregated by sub-regions, can also be a powerful instrument to _understand the causes of poverty using the newly obtained panel data_ . Suppose for example that the researcher has (as for Morocco) 48 points in time and also representative data for 20 regions. This would amount to a panel of 960 observations that can be used to study the determinants of poverty when combined with regional quarterly macro and micro data. Such a wealth of poverty points is rare in poverty studies and could substantially expand the poverty work that is routinely undertaken in country poverty assessments.

Another possible application is poverty _forecasting_ . Using the most recent LFSs, Morocco today can count on 12 years of quarterly statistics for a total of 48 points in time. This time series can be used to produce poverty forecasts for the future using dynamic panel data models. Note that one will have to account for the fact that one is dealing with imputed data and not observed data in order to obtain consistent estimates of the parameters of the panel data model.

It is also possible to use the imputation-based poverty estimates for _simulations of policy reforms and economic shocks_ . For example, one could simulate how an increase in the unemployment rate or an increase in female labor force participation might affect the poverty rate.

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