rafmacalaba/data-use-mentions-extended huggingface dataset:
to private investment, and the degree of specialization, thus decreasing the rate of economic growth.

Public investment in resource-rich settings raises an important set of questions about the relationship between observed public expenditure levels and institutional quality. A significant strand of the policy advice suggests that oil and gas exporters should translate rents into investment for infrastructure. However, there is some evidence that this spending is often poor, in part because of the high volatility in resource revenues (Gelb and Grassman, 2010), and that institutional indicators for these settings tend to be lower (Leite and Weidmann, 1999). This presents a double bind: resource rich-settings have both rents that should be leveraged for greater public investment but also potentially relatively weaker institutional settings.

In the empirical literature, several studies have incorporated measures of corruption and institutional quality. Dal Bó and Rossi (2007) use a panel dataset of eighty electricity distribution firms from thirteen Latin American countries, and their regression results identify a robust negative relationship between corruption and firm efficiency. Haque and Kneller (2008) use a three-stage regression to show that corruption increases public investment, but lowers its rate of return on economic growth. Delavallade (2006) applies a three-stage least squares analysis to a panel of 64 countries from 1996 to 2001, and finds that higher corruption distorts spending away from social expenditures (health, education, and social protection) towards other public services, order, fuel, and energy. The author argues that social sectors may offer less opportunity for embezzlement. Cavallo and Daude (2008) use a system generalized methods of moments (GMM) estimator on a panel of 116 developing countries between 1980 and 2006 to test whether public investment crowds-out private investment. They find that there is generally a strong crowding-out effect, but this effect is reduced in countries with higher scores on the International Country Risk Guide (ICRG)’s index of Political Risk.<sup>9</sup> Another piece of the literature focuses on the institutional context in which public investment decisions are
countries that have survey data for at least two years.

We use firm survey data to construct our measure of access to finance and several control variables. A _ccess to finance_ is an indicator variable that equals one when a firm has a loan, overdraft, or line of credit, and zero otherwise. We prefer to use this objective measure of access

> 9 Beck et al. (2004) analyze how different aspects of the institutional and regulatory environment, as well as the ownership structure of the banking system, affect the impact of bank concentration on firms’ perceptions of financing obstacles.

> 10 The data are available at www.enterprisesurveys.org

5
underreporting is thus 9.3%, close to the estimate of Bendek & Lelkes (2011) for Hungary (9%-13%), an estimate of Kiełczewska et al. (2021) for Poland (6%), and the estimate of Paulus (2015) for Estonia (12%). Thus, Romania is not an outlier in the regional context.

**We use the ratio of median imputed tax income ("reported income") to median survey income ("true income") to measure tax (non)compliance.**<sup>**15**</sup> The more significant the difference between the median incomes, the higher the non-compliance with tax laws. This approach assumes that the survey income represents the "true" income earned, while the imputed tax income represents the income that individuals or companies report to the tax authorities. Using this ratio to measure tax compliance/noncompliance has several advantages. First, it provides a straightforward and easy-tocalculate measure that can be used to compare tax compliance across different groups or regions. Second, it is less affected by outliers or extreme values than other measures of income, such as the mean or total income. However, this approach also has some limitations. For example, it assumes that the survey data accurately captures the "true" income earned, which may not always be true. Additionally, the ratio may not capture all non-compliance with tax laws, such as underreporting deductions. We do not use the ratio between mean imputed tax income and mean survey income as the measure of tax (non) compliance because, as discussed above, the mean value of income in imputed data is driven upwards by top incomes not represented in the survey data.

**(measured at median income) seen in transport (32% gap), construction (26% gap), and accommodation and food services (18% gap)** . We report the tax (non)compliance across the
# **Appendix A1: Additional Tables**

# **Table A.1.1: ERSS Household and Demographic Characteristics**

||**2015/2016 Ethiopian**<br>**Socioeconomic Survey**|**2015 Ethiopian Agricultural**<br>**Labor Survey**|
|---|---|---|
|**Panel A: Household socio-dem**|**ographics**||
|Household size|6.19|5.61|
|Children aged 6-14|1.54|1.60|
|**Panel B: Head of Household**|||
|Gender (% Male)|0.75|0.89|
|Age|47.86|50.22|
|Years of schooling|2.34|3.92|
|Married|0.76|0.86|
|**Panel C: Children aged 6-14**|||
|Gender (% Male)|0.51|0.49|
|Age|9.93|10.14|
|Years of schooling|1.68|2.50|
|**Panel D: Child Labor**|||
|Labor force participation|0.52|0.52|

**Source:** Authors’ analysis based on an agricultural household survey conducted in 2015 and the 2015/2016 Ethiopian Socioeconomic Survey (ERSS)

27
***|
|||||(1.49)|(1.50)|
|||Controls|Vil. FE|Vil. FE|All|
|||Observations|887|887|887|
|||R-squared|0.274|0.355|0.421|
|||FTest||0.000|4.63e-07|

Note: Skill proxies obtained through village informant (CHW), scored on scale from 1 (low) to 3 (high). The right side of the table presents the correlation of the three questions asked to the village informant with the improved index of the correponding skill, which the question intended to proxy. In the regressions, the dependent variable is the average rank of maize yields calculated over the 4 seasons (short rain 14 to long rain 16). Controls include education, literacy, gender, age and age squared of the farmer, land and cattle ownership, size and quality of the house, household size, whether the farmer is the household head, household head’s gender, village fixed effects and enumerator-assignment fixed effects. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 <!-- Start of picture text --> Extraversion Agreeableness onsciennous: Neuroticism Openness Average<br>All 0.42 0.56 0.43 0.27 0.34 0.40<br>Kenya C08 above 0.49 0.44 0.47 0.30 0.28 0.40<br>Sample median<br>oeow 0.60 0.50 0.44 0.35 0.27 0.43<br><!-- End of picture text -->

|||Extraversion|Agreeableness|onsciennous:|Neuroticism|Openness|Average|
|---|---|---|-
..|..|..|..|
|<br>_(Oxford tracker)_|(00528)|(00520)|||||(00554)|(00546)|||||
|<br>Stringency Index|.<br>..|.<br>..|-0.126**|-0.121***|-0.108***|-0.133***|.<br>..|.<br>..|-0.115**|-0.114**|-0.0828**|*<br>-0.124**|
|<br>_(Oxford tracker)_|||(0.0488)|(0.0437)|(0.0307)|(0.0456)|||(0.0513)|(0.0464)|(0.0280)|(0.0482)|
|_Regional Dummies_<br>Dummy: Sub-Saharan|3.911*|4.896**|5.132**|5.172**|1.906|4.734**|4.838**|5.843***|6.192**|5.958***|2.393*|5.713***|
|<br> Africa (SSA)|(2.001)|(2.019)|(2.243)|(2.032)|(1.369)|(2.012)|(2.099)|(2.121)|(2.358)|(2.157)|(1.249)|(2.124)|
|Observations|89|89|78|89|79|89
these two indicators in addition to level regressions. As discussed above, the data set includes a limited number of low-income countries due to the lack of sectoral data for electricity consumption. Despite a large number of observations for advanced economies, the variation in growth of both value added and electricity consumption is primarily driven by other income groups in countries.

> 11 The IEA provides electricity consumption data until 2016, but PW9 data is limited to 2014 and earlier. The study uses electricity consumption data until 2016 whenever possible, but the estimation results rely on PW9 covariates and include data until 2014.

9
It is worth noting that many countries only adjust for food prices with spatial deflators, neglecting housing costs. However, this approach can lead to a significant underestimation (overestimation) of poverty in urban (rural) areas, as housing costs tend to be higher in urban areas compared to rural areas.<sup>24</sup> To address this, we incorporate housing prices into the spatial deflators. While capturing accurate spatial variations in non-housing non-food prices can be challenging due to limited information on product specifications and unit values in market price surveys, housing characteristics and price information are typically available in the HBS data. Therefore, we estimate a housing price index using the HBS data and incorporate it into the spatial deflators.<sup>25</sup> The updated spatial deflators for 16 SSA countries clearly demonstrate that the cost of living is higher in urban areas, particularly in dense urban areas, compared to rural areas (Figure 2).

Figure 2: Cost of living index across urban versus rural areas in 16 SSA countries <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>1.4 1.4 1.4<br>1.2 1.2 1.2<br>1 1 1<br>.8 .8 .8<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: DOU: Degree of urbanization. DB: Dartboard. The cost-of-living index is prepared as a spatial deflator for each country. It is normalized to 1 for each country overall. WorldPop 250m is used for both the DOU and DB methods.

# **4.3 Analysis with the new global dataset**

To examine the relationship between household welfare and population density, we conduct regression analysis using household consumption expenditures as a proxy measure
- _income_ = a country’s per capita GDP (USD at 2000 prices) in natural logarithms from the World Bank’s World Development Indicators (WDI) database. It captures national competition, but also other aspects of the national and institutional environment.

# **4 Regression Results**

## **4.1 FDI Spillover Potential**

## **_Overall Results_**

In the following, we apply OLS and IV 2SLS regressions. All regression results follow equation (3) and include sector, subnational region, and year fixed effects. Standard errors are robust to heteroscedasticity and clustered at the country-sector level.<sup>16</sup> Foreign output share is defined as in equation (2). The summary statistics are reported in Appendix 4. Our data sample only includes domestic manufacturing firms, i.e. all firms with a foreign ownership share of less than 10 percent (see Appendix 2). Table 1 shows how the FDI spillover potential influences domestic firm productivity. Columns 1 to 4 report the OLS regression results, while columns 5 to 10 report the IV 2SLS regression results<sup>17</sup> . Foreign firm characteristics are averages across all foreign firms within a specific sector of a country. To rule out the possibility that the impact of a foreign firm characteristic on FDI spillovers which we observe is not the result of another foreign firm characteristic for which we do not simultaneously control, we assess the correlation of foreign firm characteristics with each other. The correlation matrix (available upon request) reassures us that foreign firm characteristics show a very low correlation of at most 0.26.

As could be expected, capital intensity shows a positive and significant effect on labor productivity. More importantly for this paper, intra-industry foreign output share mostly shows a negative effect on labor productivity of domestic firms, although in Table 1 the impact is only significant in column 2. What may explain the negative FDI spillovers? In the short term, local firms might
There are several insights from this paper that open up interesting future research avenues. The consumption of imports process we propose is easily implementable since it requires only high-frequency trade data and estimates of the inventory-sales ratios. However, its necessity arises only due to the lack of data on inventories (consumption of imports) at the same level of aggregation at which elasticities are estimated. Although Alessandria et al. (2011a) have shown similar dynamics with inventory data for the automotive industry, our paper illustrates that incorporating inventory data is important to fully understand the response of trade to liberalizations and that progress can be made once more disaggregate inventory data becomes available.

Moreover, in the episode studied in this paper, the implementation of the tariff reductions was certain to take place. Nonetheless, the anticipatory effects we document here are potentially sizable even when the policy change is uncertain. In fact, during the annual renewal of China’s MFN status in the 1990s, US imports from China rose systematically before the US Congress approved the renewal in anticipation of higher future tariffs (Alessandria et al. (2019)). In that sense, besides the uncertainty surrounding the current rise of protectionism, our paper provides a mechanism to understand the stockpiling in advance of events such as Brexit and a way to study the consumption response of imported goods in this context.

31
quality. A large number of studies seek to assess the magnitude of such effects (e.g., Pritchett and Viarengo, 2015; Sass et al., 2012; Hanushek and Rivkin, 2006), in some cases adjusting for family background. However, studies of this sort are rare for low-income contexts,<sup>9</sup> and even fewer attempt provide a multi-country analysis, as we do here. Furthermore, just a handful explicitly estimate the variance contributions of both schools and households (e.g., Carneiro, 2008), and since most such studies are school-based they generally rely on a relatively limited set of observed proxies for family background.<sup>10</sup> For instance, Freeman and Viarengo (2014) use PISA data to investigate the (sources of) variance in school effects. They report that a regression of test scores on school dummies (only) explains around two-thirds of the variation in the data, while a limited set of observed family background variables accounts for just one-third, after controlling for school effects (also via dummies).

The general point emerging from the above (albeit brief) review is that existing studies have generally focused on estimation of either the contribution of households or of schools to variation in educational outcomes. These are of substantive interest, but such estimates correspond to special cases where the contribution of any covariance (sorting) between these effects is effectively absorbed into the main factor under consideration. Furthermore, even in cases in which such upper-bound estimates are tightened by introducing additional controls, such as proxies for one of the sets of effects, past studies have not reported the full variance decomposition incorporating the implied covariance between households and schools. However, it is precisely this covariance that can be of critical interest: it tells us about the extent to which schools – or educational policies more generally – exacerbate or compensate for differences deriving from given family background. In light of this gap, the next section outlines how a more complete
0.026<br>(0.098)|-0.036<br>(0.106)|
|DS=0.30|||||||
|±0.02|34|0.720|0.667|-0.053<br>(0.181)|||
|±0.03|43|0.667|0.615|-0.051|||
|||||(0.160)|||
|±0.04|48|0.706|0.643|<br>-0.063<br>(0.150)|||
|_Controls_|||||||
|Socio-ec.||||No|Yes|Yes|
|background|||||||
|Educational||||No|No|Yes|
|background|||||||

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition
33

## RAdams

L:\ADAMS\Guatemala. pov paper combine. doc.doc September 24, 2004 1:41 PM

# <u>Notes</u>

1 While no estimates are available regarding the size of internal remittances, Ratha (2004) has recently estimated that official international remittances to the developing world now total $93 billion per year, making them the second most important source of external funding in developing countries.

2 For estimates of the economic costs of international migration, see Stahl (1982) and Adams (1991, 1998).

3 While Stark, Taylor and Yitzhaki (1986) and Adams (1992, 1995) examine the impact of internal and international remittances on income inequality in Mexico and Pakistan, respectively, they do not analyze the effect of these financial transfers on poverty.

4 This 2000 Guatemala household survey was implemented as part of the “Program for the Improvement of Surveys and Measurement of Living Conditions in Latin America and the Caribbean” (ENCOVI), which was sponsored by the Inter-American Development Bank (IDB), the World Bank and the Economic Committee for Latin America and the Caribbean (CEPAL).

5 For more details on this 2000 Guatemala ENCOVI household survey, see World Bank (2004).

6 The five possible responses to this “where do these people sending (your household) remittances live” question were: (1) Guatemala; (2) United States; (3) Mexico; (4) Central America; and (5) other countries.

7 As explained on page 8, the focus here is on predicting per capita household expenditure (excluding remittances), rather than income. See page 8 of text.

8 This method of predicting incomes (expenditures) assumes that the only way in which households receiving no remittances differ from households receiving internal and international remittances is that the latter group of households receive remittances. Non-remittance receiving and remittance receiving households are not assumed to differ in any entrepreneurial or other way which might affect their expenditure
_For labor or for divorce?_ backing this conclusion, figure 2 in Appendix A illustrates how Morocco has had the highest increase in the WBLaw index over the full studied period, which corroborates the results we observe. The widely cited literature on collective labor supply models generally explains this type of decline in women's employment by a shift in bargaining power within the household in favor of women (Chiappori et al., 2002; Lise and Seitz 2011; Voena, 2015; Corradini and Buccione, 2023). In these models, women do not switch completely to employment if the utility cost of domestic work is as high as that of the labor market. Thus, we argue that while legislative reforms increased women’s bargaining power within the marriage, the lack of ‘women friendly’ jobs in the Moroccan labor market deterred many from labor force participation and made marriage more or equally attractive.

**Table 6 - Labor market outcomes of Khul' reform in Egypt, Arab Rep.**

||Khul' effe<br>|ct window 1 : 19<br>|92 to 2005<br>K<br>|L<br>Khul' effect<br>|window 2 : 19<br>|92 to 2014<br>|
|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>Currentlywork<br> <br>|(3)<br>ing<br> <br>|(4)<br>|(5)<br>Currentlywor<br> <br>|(6)<br>king<br> <br>|
|Post x treated|0.0604**|<br>0.0603***|<br>0.0177|0.0479***|<br
The next step is to analyze inequality between individuals by assessing how the prevalence of jobs that can be performed from home varies with income. Figure 5 shows how the DN2020 and our internet-adjusted measure vary with income across different groups of countries, based on the ILO database of average wages across occupations. The home-based work-individual income gradient is steepest in high-income countries which have the highest prevalence of telecommutable jobs. At the top income decile in high-income countries, almost 80 percent of the jobs can be performed from home. Accounting for internet access hardly impacts the gradient in rich countries, where internet access is much less likely to be a binding constraint. By contrast, the gradient is the least steep in low income countries, which have few telecommutable jobs to start with. Accounting for internet access flattens the gradient the most in poor countries with limited internet access. The impact is strongest for the higher income people in the least developed countries since they are more likely to have jobs that can be done from home but face internet access constraints. Poorer households, in contrast, do not have jobs amenable to home-based work and are therefore not affected by internet restrictions. In short, COVID-19’s impact is quite different in rich and poor countries. Absent interventions, inequality is likely to rise the most in rich countries, but poor countries have higher shares of jobs at risk.

Figure 6 illustrates these patterns using representative data at the individual level from India, Brazil, Mexico, and Turkey. The chance of having a job that can be performed from home increases with individual income in all countries but less so in India, where labor market vulnerability is most widespread and internet access most limited. A worker in the top earnings decile in India has a 19% chance of having a job that can be done from home, whereas a worker in the bottom percentile has less than 1% chance of having such a job. By contrast, a worker in the top earnings decile in Turkey (which has the highest PPP adjusted GDP per
|DroughtlEl-Nifto shock|56.6|59.8|
|Price shock only|28.2|26.0|
|Domestic employment shock only|0.16|0.16|
|Overseas/migrant employment shock only|0.02|0.02|
|Wage shock only|0.23|0.19|
|Drought/El Niflo shock only|2.36|2.28|
|Hit by at least one of the five shocks|93.0|94.4|
|Hit by at least one of the four shocks other than<br>the price shock|64.8|68.4|
|Labor market shock (regardless of the price<br>shock)|8.1|8.6|
|El-Niflo shock (regardless of the price shock)|39.0|40.4|
|Both El-Niflo and labor market shocks (regardless<br>of price shock)|17.6|19.4|

_Source:_ Calculated from 1998 APIS data.

27
Table 3: Summary Statistics of pooled household-level data

||N|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Consumption (per day USD in 2017 PPP terms):**|||||||
|Real per capita consumption expenditures|166,957|3.99|2.91|8.13|0.06|2289.78|
|Nominal per capita consumption expenditures|167,260|3.42|2.15|8.90|0.04|2521.83|
|**Spatial defator**|||||||
|Spatial defator|166,984|1.00|0.99|0.14|0.76|1.93|
|**Demographic:**|||||||
|Household size|167,357|6.55|6.00|3.80|1.00|62.00|
|Age of household head|167,253|40.31|40.00|18.46|0.00|119.00|
|Household head is male|167,303|0.78|1.00|0.41|0.00|1.00|
|**Marital status (of household head):**|||||||
|Married|167,293|0.75|1.00|0.43|0.00|1.00|
|Never married|167,293|0.04|0.00|0.18|0.00|1.00|
|Living together|167,293|0.07|0.00|0.25|0.00|1.00|
440|0.445|0.434||
|built|2000|0.445|0.511|0.521|0.501||
||2010|0.476|0.539|0.550|0.527||
|Share of registered|1990|0.052|0.049|0.051|0.047||
|area built|2000|0.085|0.080|0.082|0.078||
|Population density|1990|954|1,055|971|1,138||
|(indiv./km<sup>2</sup>)|2000|1,356|1,499|1,459|1,539||
||2010|1,570|1,731|1,691|1,770||
|Numberofblocks||1932|1514|1.073|441||
|<br>_Sources:_LAA land administration<br>mortgages registered. WSFE for b|database from 2011 and d<br>uilt area; and gridded popu|, <br>igitization of paper<br>lation of the world|, <br>records, 1981–20<br>v4 (CIESIN) for p|11 for parcels, transfe<br>opulation density.|<br>rs, and||

21