rafmacalaba/data-use-mentions-extended huggingface dataset:
predictions are then generated by taking the population-weighted mean of the AGEB-level predictions.<sup>1</sup> This approach leverages AGEB-level data, which leads to more accurate and efficient estimates than area-level models specified at the municipal level and requires minor modifications of publicly available software to generate point estimates and confidence intervals.<sup>2</sup> Six main findings emerge:

1. Combining survey and geospatial data substantially improves the precision and accuracy of state-level estimates of both labor force participation (LFP) and unemployment rates. Estimated mean absolute deviation falls by approximately 62 and 50 percent for female and male participation rates, respectively, and by approximately 52 and 25 percent for female and male unemployment rates.

2. When considering municipal estimates of labor force participation rates, incorporating geospatial data significantly improves accuracy and greatly improves precision. In estimates from repeated simulations, estimated mean absolute deviation falls by about 43 percent for women and 53 percent for men, and rank correlation increases by 0.13 for women and 0.11 for men. After adjusting direct survey estimates to equalize coverage rates, estimated mean squared error for male and female LFP falls by a factor of more than four.

3. Because unemployment rates are very low, municipal estimates of unemployment rates based on a linear model are not reliable. Although estimated mean absolute deviation falls for both man and women, relative bias is high, rank correlation falls significantly for women and the estimates exhibit little variation.

4. Accuracy is substantially lower in out-of-sample municipalities than sampled municipalities.

5. When using a sample that simulates fully enumerating sampled AGEBs, small area estimates offer very minor improvements on direct survey estimates.

6. A model specified at the AGEB level generates more accurate estimates than one specified at the municipal level. The improvement due to using AGEB rather than municipal variation is much larger for male LFP, which is harder to predict, than for female LFP.

In short, the results support the use of augmenting survey data with geospatial data when estimating state-level
regional trade agreements (RTAs). These agreements have resulted in a significant increase in export volume, providing an excellent context for assessing the impacts of trade on labor market outcomes.

Lastly, despite recent economic reforms, Egypt's labor market faces several challenges, including slow private-sector employment growth, a rise in informal employment, and increasing wage inequality. Notably, there is a persistent regional disparity in employment growth and job quality between Upper and Lower Egypt, as highlighted by the latest Labor Force Survey (LFS). Furthermore, labor-intensive manufacturing sectors such as garments and furniture have experienced declining employment shares, while low-end non-traded services such as construction, storage, and communication have witnessed growth.

Although existing literature on Egypt has extensively documented the direct effects of rising trade integration on labor and welfare outcomes, there are remaining gaps in estimating indirect impacts. For instance, Zaki (2011) demonstrates the positive effects of exports on aggregate employment and individual-level employment outcomes, specifically male wages, and female labor force participation. Using multiple rounds of the Egyptian Labor Market Panel Survey (ELMPS), Salem and Zaki (2019) examine the impact of trade reforms on informal and irregular workers in Egypt, revealing a positive association between tariffs and both informal and irregular employment. They argue that tariff increases force less productive informal firms
# _Quality of Indices_

For detailed information on the component indices used, we refer those interested to the original works for discussions of the index strengths and weaknesses. In cases where the indices have been produced for several years (HDI and gini coefficients), the authors have had time to respond to criticisms of the initial versions. The indices used from Mauro were constructed by a commercial organization, Business International Corporation, which sells updated versions of its indices. Presumably, the version made public by Mauro is old enough that its commercial (and, perhaps, academic) value is low. Unfortunately, an updated version was out of the range of our research budget.

The political freedom index created by Haq is new both in the sense that there are no previous versions and in that it addresses a topic that had previously not been subjected to formal indexing. The corruption index is updated annually and reflects the results of several surveys on bureaucratic honesty. The outward orientation index is an updated version of an index first created in the mid-1980s. The central bank independence index is relatively new and represents the legal characteristics of a country's central bank. As the authors note, there is often a discrepancy between the statutory independence of a central bank and its independence in practice. The authors attempted to capture this effect, by developing a second index which measures the frequency of turnover of heads of central banks. This second index has not been incorporated into this paper because of the small number of countries covered.

Note that although all of these indices have been published in the past 3 years, the data from Mauro and Cukierman, Webb and Neyapti are somewhat older. This may bias results for, or against, specific countries which have experienced rapid change since the early 1990s. The Mauro indices, for example, have assessments of Liberia and Nigeria which, given more recent events, seem high. The indices have not been adjusted to reflect these changes in order to preserve the internal consistency of these indices.

# _Adjustments_

Political Freedom
The dependent variable F refers to the fraction of households being tenants in prefectureSShFFDDFF PPoo PPF _i_ in yearPP hPPRRPPFFhPPMMRRPP _t_ between 1918 and 1921. ii,tt F F F is a dummy variable indicating the occurrence of famine in prefecture _i_ during the land survey ii,tt period. We allow both contemporaneous and lagged measures of famines since it might take time for the effects of famines to show up on land ownership. We control for prefecture and year fixed effects and cluster standard errors at the province level.

The first three columns of Table 10 show that the occurrence of a famine is associated with a higher fraction of tenant households by 1.7 percentage points in the following year, indicating that many peasants lost their land completely and had to work on rented land.

The significant increases in the number of landless peasants after famines likely played into the hands of the CCP. To see if this is true, we now assess the association between the share of landless peasants and the subsequent communist revolution: C (13) Here C is the logarithm of the number of fallen communist soldiers in the communist PPii = ββLLF RRMMFFP ii + γγXXii + TTkk + εεii revolution, PPii _Landless_ takes the form of the fraction of tenant households in 1918. Column (4) of Table 10 shows that the share of landless peasants in a prefecture is positively related to the number of local people who died for communism in subsequent decades. Consistent with the seminal study by Moore (1966), the CCP indeed gained support from landless peasants. We conclude that rural land inequality, exacerbated by the warlord-era famines, was one of the many factors that helped bring down the Republic of China, as it did to the Republic of Rome.

# **7. Conclusion**

A century ago, after millennia of imperial rule, the founding President of a new Republic died. This unexpected death was followed by a dozen years of warlord fragmentation and an increasing incidence of famines. This period also witnessed the spread of Communist ideology into China
bia across the full analysis period, scaled by municipal population in 1990 to express displacement intensity as a share of baseline population. This generates variation in displacement intensity that is plausibly exogenous to local economic trends. The key identifying assumption is that municipalities with higher and lower 1990 foreign settlement shares would have followed similar development trajectories absent the displacement crisis. Although municipalities with a larger foreign presence in 1990 were on average somewhat richer, we show that these municipalities followed parallel pre-crisis trends in our main outcomes prior to 2013, which supports this assumption. The exclusion restriction further requires that the 1990 foreign settlement share affected outcomes only through its influence on displacement outflows. We provide several pieces of evidence to support this requirement. We show that municipalities with a larger foreign settlement share do not have systematically different economic structures that would cause them to respond differentially to aggregate shocks. In fact, all estimates are robust to the inclusion of interactions between a rich set of municipal controls and year trends, including oil-production concentration. We further show that national economic policies during the analysis period, namely discriminatory repression, electricity shortages, expropriations, and access to social programs, did not differentially target municipalities with a higher foreign settlement share.

Our analysis employs data on night-light density, household living conditions, political elections, and organized crime for the period 1992 to 2021. Night-light data serve as a proxy for economic growth and inequality.<sup>2</sup> We measure inequality using a spatial measure that estimates the dispersion of night-light density at the _parroquia_ (county) level within each municipality. We find that this measure positively correlates with traditional income-based inequality metrics, validated using census data from Venezuela and Colombia. To examine how forced displacement facilitated the perpetuation of the autocratic regime, we use unique web-scraped data on the results from the last five presidential and mayoral elections, as well

> 2Using ENCOVI, we validate these measures for correlation with household-level income measures. Moreover, we control for the presence of oil fields in our estimates to rule out concerns
of work, and output per worker (

## Table 6 ).

The results discussed so far suggest that the impact of the recall length on the three productivity measures will vary. For yield (output per unit of land), the expected association is positive, since there is a positive effect of recall length on output (maize harvest), the numerator, while the plot area, measured with GPS, is not subject to recall error. For output per day of work, the results suggest an ambiguous effect. Both output, the numerator, and labor-days, the denominator, are positively associated with the recall length. The direction of the composite effect depends on which of the two dominates, and it is possible that there is no effect at all. Finally, for output per worker, the results suggest a positive recall effect. The recall effect on output, the numerator, is positive, and there is no effect on workers per plot, the denominator (

## Table 6 ).

> 11 Plots listed in the Malawi IHS4 cross section are the one case in which there is a significant interaction effect between respondent characteristics and recall length. Female respondents list fewer plots than male respondents (-0.52*** plots), but the effect of recall length is smaller among female respondents (-0.05** plots/month as opposed to -0.076*** plots/month).

> 12 Plot managers are presumably best informed about harvest quantities and inputs used, suggesting that self-reporting from female respondents may be more reliable than that from male respondents.

16
11 where Tot. Ven. Inflow _t_ is the aggregate number of (legal) Venezuelans entering Colombia every year (as recorded by migration authorities), Population<sup>1993</sup> _md_<sup>isthetotalpop-</sup> ulation of municipality _m_ in 1993, which is kept fixed prior to the migration shock to avoid further endogeneity concerns. Venezuelan Share<sup>1993</sup> _md_ is the share of Venezuelans living in municipality _m_ according to the 1993 population census to the total number of Venezuelans living in Colombia.<sup>10</sup> For robustness, we aggregate our outcome variables at the municipality level and reestimate equation 4.1 changing the school fixed effects for municipality fixed effects.

4.1. **Challenges to identification.** Our identification relies on the plausibly exogeneity of our predicted migration shock. We now discuss potential threats to this assumption, and how we have dealt with them.

First, it is worth noting that we are not using the _predicted_ cumulative inflows as an instrument of the _actual_ inflows of Venezuelans to each municipality/year. This is because there are no administrative records of where do the arriving Venezuelan nationals settle on a yearly basis. We therefore use the predicted shock to estimate a reduced-form equation. The magnitude of our results should therefore be interpreted as only capturing the numerator of a standard 2SLS estimator (and thus likely as lower bounds of the latent IV estimate).

The first assumption that we rely on for identification is, therefore, that the _predicted_ migration is strongly correlated with the (unobserved) actual Venezuelan migration.

> 10 We use the 1993 census because by the time of the next census (2005) the Venezuelan political crises -and thus migration to Colombia- had started under the rule of Hugo Chavez. Our results are however largely unchanged if we use the 2005 census to compute the predicted migration flow of each municipality. Indeed, the correlation of the 1993 and the 2005-based measures if 0.93
highly" labor intensive if their ratios are 40 points or more below the U.S. average, while items with a "high" labor intensity are those with ratios between 0.60 and 0.74. "Moderate" labor intensive products are those with factor proportions ratios between 0.75 and 0.90. Appendix Table 6 lists the four-digit SIC labor intensive products selected using these criteria and also provides related shipment and employment statistics.
find NREGA to increase human capital investment. On the other hand, Islam and Sivasankaran 2015; Li and Sekhri 2020 and Shah and Steinberg 2021 conclude, using nationally representative data sets, that school enrollment numbers declined while child labor increased due to the spillover effects of NREGA. Using DISE and ASER data, Adukia (2020) also finds small negative effects of NREGA on educational outcomes but concludes the magnitude of these spillovers to be inconsequential, especially relative to the positive spillovers on adult employment outcomes.<sup>19</sup> I replicate results from Shah and Steinberg (2021) to estimate NREGA’s impact on school enrollments and child labor using the following equation where _Sidty_ is an outcome of interest (such as enrollment status or child labor) for individual _i_ in district _d_ in year _t_ who is age _y_ . _ψy_ is a vector of year-child-age fixed effects. All other controls have the same interpretation as before. _β_ is the coefficient of interest, and it measures the effect of NREGA on the outcome variable S. Following Shah and Steinberg (2021), panels A, B, and C of table 4 report results for schooling and child labor for the full sample (children aged 5-17), adolescents (children aged 13-17), and young children (aged 5-12), respectively. Since SGRY data is not available for some states like Jammu and Kashmir, the (NSS and SGRY) matched data set is approximately 90% of the data used by Shah and Steinberg (2021). The comparison of results with and without SGRY is made on the matched sample so that any changes in coefficients can be attributed to the presence or absence of the SGRY control and not to a change in observations.<sup>20</sup> Consistent with Shah and Steinberg (2021), NREGA decreases school enrollments in the full sample by 1.1 percentage points and increases child labor by 1.8 percentage points. The results are mostly driven by the adolescents aged 13-17 while there are no
Table C.19: National Estimates of Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference - Combined Instrument

||||Sector of W|ork||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service<br>/ Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.017|0.044|0.016|-0.039|-0.902|
||(0.043)|(0.015)|(0.028)|(0.033)|(0.493)|
||[0.692]|[0.004]|[0.573]|[0.241]|[0.068]|
|First Stage F-Statistic|19.68|20.64|20.64|20.64|19.37|
|N|24,882|24,607|24,607|24,607|24,649|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical responses � are assigned
“Non-Economic”. Additionally, a large set of legislation regulates the public sector – in particular public administrations and municipalities, as well as courts and judges. We combined these laws and regulations under “Public_Administration_and_Judiciary” and remove them for the sectoral analysis of legislative predictability. Second, legislation can tackle multiple sectors at the same time without any NACE category dominating. To account for such laws and regulations, we added the category “multisector”. Third, taxation and budgetary legislation plays a significant role for businesses and households in a horizontal, cross-cutting manner, e.g. changing VAT rates impacts all sectors similarly from mining to IT. This was considered by creating a dedicated taxation and budget category. The resulting modified NACE classification will be called GCO sectoral categorization which allows us to select those laws which have economic relevance and apportion them into specific sectors where legislative activities are likely to differ. (Table 2). While this task is automatized for the broader GCO dataset, we additionally manually checked and corrected all Jordanian laws since 2000 and a subset of Jordanian regulations since 2000 for assuring the highest quality analysis in this paper.

**Table 2. Sectoral Classification labels. NACE original labels and modified/additional labels (GCO categorization)**

|**GCO**|**NACE**|
|---|---|
|Agriculture, forestry and fishing|Agriculture, forestry and fishing|
|Mining and quarrying|Mining and quarrying|
|Manufacturing|Manufacturing|
|Energy|Electricity, gas steam and air conditioning supply|
|Water and waste|Water supply; sewerage; waste management and remediation<br>activities|
|Construction|Construction|
|Wholesale and retail|Wholesale and retail trade repair of motor vehicles and<br>motorcycles|
|Transportation|Transporting and storage|
|Tourism and hospitality|Accommodation and food service activities|
|Information and communication|Information and communication|
|Finance|Financial and insurance activities|
|Real estate|Real estate activities|
Coffman, E., Timin, B., Kelly, J. T. (2018). The estimated change in the level and distribution of PM2.5attributable health impacts in the United States: 2005–2014. Environmental Research. Vol. 167: 506–514.

- Freire, S., Schiavina, M., Florczyk, A., MacManusc, K., Pesaresi, M., Corbane, C., Borkovskac, O., Millsc, J., Pistolesic, L., Squiresc, J., Sliuzasb, R. (2020). Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice, _International Journal for Digital Earth_ . Vol. 13(1): 61-77.

- Freire, S., MacManus, K., Pesaresi, M., Doxsey-Whitfield, E., Mills, J. (2016): Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; _Association of Geographic Information Laboratories in Europe (AGILE)_ .

- GBD 2019 Risk Factors Collaborators (2020). Global burden of 87 risk factors in 204 countries and territories, 19902019: A systematic analysis for the Global Burden of Disease Study 2019. _Lancet_ . Vol. 396(10258):1223-1249

- Hajat, A., Hsia, C., O’Neill, M. (2015). Socioeconomic Disparities and Air Pollution Exposure: A Global Review. _Current Environmental Health Reports._ Vol. 2(4): 440–50.

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- Jbaily, A., Zhou,
share of local investment may reflect as much the failure to attract FDI as it does success in attracting local investors. A second explanation, particularly in programs like Senegal, is the existence of “single unit” licenses and the propensity of locally‐based firms to switch into the EPZ program. On a more positive note, it may reflect the relative perceived advantage of the EPZ operating environment even to local firms, and so perhaps indicates a pent‐up demand for investment opportunities by the domestic private sector. From the data we have available, we are unable to test which, if any, of these explanations holds true.

Focusing on the FDI only, Figure 4 illustrates the main sources of foreign investment into each of the zone programs, by region of investment origination. Several points stand out. First is the general dominance of East Asian investors, particularly in those programs focused on the garments and textiles sectors (with the exception of Dominican Republic and Honduras). Second, while the two Asian EPZ programs are mainly used as export platforms by regionally‐based (Asian) investors and the two Latin American zones (Dominican Republic and Honduras) play a similar role for US investors, the African zones have no such dominant investor source. European investors do play a relatively greater role in EPZ investment in the African zones, but they are by no means the dominant source. Indeed, the African

> 13 The data shown are percentages of firms not of investment value. The difference can be significant – for example, according to data from the Ministry of Planning and Investment, local investors accounted for more than half of all firms in the industrial zones in 2009, but only 26% of investment by value.

> 14 The share of local versus foreign ownership in Vietnam is based on data (2009) from the Ministry of Planning and Investment. This takes into account both firms in Export Processing Zones and Industrial Zones. By contrast the surveys were carried out only with firms in the Export Processing Zones, which