rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Fraction of individuals with home Internet access and using the Internet to find a job, by labor market status**

||_Fraction_<br>_Interne_<br>|_with home_<br>_t access_<br>|_Fraction_<br>_Internet to_<br>|_using the_<br>_find a job_<br>|_Fraction_<br>_Internet to_<br>_given hom_<br>_acc_<br>|_using the_<br>_find a job,_<br>_e Internet_<br>_ess_<br>|
|---|---|---|---|---|---|---|
||2012|2018|2012|2018|2012|2018|
|Employed|0.133|0.238|0.034|0.102|0.031|0.074|
|Unemployed|0.174|0.325|0.380|0.632|0.367|0.644|
|Out of Labor Force|0.125|0.235|0.011|0.036|0.008|0.027|
|Total|0.129|0.238|0.048|0.097|0.045|0.078|

_Notes._ This table reports the fraction of individuals with home Internet access, the fraction of individuals who report that they use the Internet to find a job and the fraction of individuals who report using the Internet to find a job given home Internet access by labor market status. The populations of employed individuals, unemployed individuals, and individuals out of the labor force are defined according to the market definition of the labor force (search required).

26
# **Appendix S1: ESPS/PSF comparison**

# **1. Questionnaire design**

The PSF and ESPS surveys differ in their way of measuring consumption by several aspects. PSF innovates in the fact that the consumption questionnaire is addressed to all household members that potentially have a say about resources allocation (the so-called “cell heads”), as opposed to the usual practice in which only one member (commonly the household head) is sought to answer that part of the questionnaire. In doing so, and since the head may not be informed of the consumption of all members, the PSF survey should capture more consumption than regular surveys, particularly in large extended households where several adults may have their own income sources. However, there are other variations in the questionnaires that may explain eventual discrepancies in the level of consumption captured by the two surveys: the degree of commodity detail and the length of the reference period are two obvious differences that have been investigated in the literature (see Beegle _et al_ . 2012 for a survey). It seems common sense to assume that the higher the number of items over which consumption is collected, the larger should be the total level of consumption in the household. However the gain in coverage warranted by a longer list of items may come at the cost of a loss in reporting precision, due to household or surveyor fatigue resulting from an exceedingly long questionnaire. Nevertheless, there is a large consensus in the literature on the fact that a short list of commodity items results in a less precise and lower aggregate consumption level. Conclusions concerning the recall period are less clear cut. On the one hand long recall periods may be better able to capture the consumption of commodities that are not frequently bought over the year and limit the risk of “telescoping” errors, in which respondent include 1
# **Table 4: Maximum Likelihood Estimates for High Technology Importsa**

|Model||(I)||(II)||
|---|---|---|---|---|---|
|Equation|Probit|Gravity|Probit||Gravity|
|_Intercept_|-5.494|-14.487|-4.794<br>||-14.313<br>|
||(-27.17)|(-26.21)|(-22.87)||(-26.95)|
|_GDPi_|0.568|0.911|0.717<br>||0.960<br>|
||(40.12)|(22.68)|(39.04)||(16.69)|
|_GDPj_|0.495|1.898|0.512||1.897|
||(36.36)|(52.12)|(36.45)||(52.38)|
|_Populationi_|-0.324|-0.086|-0.474<br>||-0.132<br>|
||(-18.71)|(-2.06)|(-22.59)||(-2.38)|
|_Populationj_|-0.170|-0.733|-0.175<br>||-0.731<br>|
||(-10.31)|(-20.70)|(-10.43)||(-20.70)|
|_Distance_|-0.421<br>|-1.115<br>|-0.466<br>||-1.124<br>|
||(-13.56)|(-19.1 1)|(-14.62)||(-19.00)|
|_Border_|0.011|0.157|-0.110||0.141|
||(0.08)|(0.64)|(-0.78)||(0.61)|
|_Language_|0.480<br>|1.154<br>|0.488<br>||1.146<br>|
||(8.54)|(9.53)|(8.43)||(9.49)|
|_EC_||0.224|||0.227<br>|
|||(0.74)|||(0.76)|
|_EFTA_||_-0.053_|||_-0.057_<br>|
|||(-0.10)|||(-O. I 1)|
|_LAIA/LAFTA_||0.798|||0.771<br>|
|||(3.24)|||(3.08)|
|_ASEAN_||3.407<br>|||3.374<br>|
|||(6.53)|||(6.46)|
|_CACM_||2.992<br>|||2.959<br>|
|||(5.63)|||_(5.55)_<br>|
|_IpRsb_|||-0.340<br>||-0.093<br>|
||||(-14.09)||(-1.50)|
|||2.229|||2.228|
|_obs._|7304|3548|7304||3548|
|||0.066||0.064||
|-21n2_(p= 0)C_||1.354||1.309||
|-21n A ({y7,}**=0)C**||7606.860||7812.27|4|

## a t-statistics in parentheses

b Park and Ginarte index of the destination country of the trade flow,<sup>that is countryj in the case of exports and</sup> country i in the case of imnports.

**c** For a definition of the likelihood ratio test statistics, see Appendix A.

16
13 Table 3 examines the evolution over time of the correlation between AFT commitments and the trade-related capacity index. Clearly, the small size sample requires caution, but the results appear interesting: First, in 2009, the indicator loses statistical significant suggesting that in the beginning year of the global recession; commitments were probably influenced by other factors. Second, the size of the coefficient associated with the trade-related indicator is similar to the results obtained with the pooled regressions.

**Table 3: Aid for trade allocation and trade related capacity index**

**Cross-section regression: 2007-2010 Dependent variable: Log(aid for trade/GDP)**

|Indicator based on a simple average|2007|2008|2009|2010|
|---|---|---|---|---|
|Ln (GDP per capita)|-1.512***|-1.612***|-1.582***|-1.549***|
|Rule of law|0.836**|1.307***|1.034**|0.884**|
|TCio.|-0.973***|-1.026***|-0.909**|-0.959***|
|N|58|58|58|58|

Indicator based on weighted average (weights derived from the coefficients of the cross-section gravity regressions)

|2007|2008|2009|2010|
|---|---|---|---|
|Ln (GDP per capita)<br>-1.407***|-1.574***|-1.562***|-1.509***|
|Rule of law<br>0.925**|1.382***|1.077***|0.963**|
|TCio.<br>-0.885***|-0.649*|-0.586|-0.731**|
|N<br>58|58|58|58|
|Indicator based on weighted average<br>(weights derived from the coefficients of the pool<br> <br>|ed gravity re<br>|gression)<br>||
|Ln (GDP per capita)<br>-1.408***|-1.566***|-1.564***|-1.510***|
|Rule of law<br>0.926**|1.393***|1.075***|0.963**|
|TCio.<br>-0.867***|-0.747**|-0.563|-0.686**|
|N<br>58|58|58|58|

Figure 2 shows the relationship between the trade-capacity indicator and aid for trade as a share of GDP, controlling for GDP per capita and rule of law based on the pooled aid for trade equation and the trade capacity index constructed using the cross-section gravity estimates (Table 2, column 2). For simplicity of exposure, we show the scatter plots for the
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

**Table 13. Sectoral results for electricity investments, VAT funded**

|||||Electrici<br>|ty (VAT funded<br>|)<br>||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Benin|Mali|Senegal|Tanzania|Uganda|Cameroon|
||Crop agriculture|–0.06|0.01|0.00|–0.07|–0.04|–0.27|
||Export agriculture|0.24|0.27|–0.15|0.15|–0.06|0.43|
|Va (value|Mining and gas||0.41||0.38|0.07|0.34|
|<br>added or<br>|Industries|1.18|1.17|–0.31|0.93|–0.17|0.68|
|output)|Construction||1.65|3.56|2.26|1.29|7.03|
||Private services|–0.05|0.88|0.91|0.94|0.14|0.49|
||Public services|6.96|1.03|3.51|1.85|1.88|3.61|
||Crop agriculture|1.22|0.69|0.76|0.75|0.25|1.22|
||Export agriculture|2.60|0.45|0.54|0.54|0.15|1.41|
||Mining and gas||0.93||0.80|0.20|0.46|
|pq<br>Market prices|Industries|1.77|0.26|1.54|0.19|0.76|1.44|
||Construction||0.34|4.89|0.63|0.73|1.37|
||Private services|2.37|0.01|–0.72|–0.15|0.35|0.78|
||Public services|3.56|0.50|1.29|0.53|0.35|1.34|
||Crop agriculture|1.12|0.74|1.20|0.62|0.19|1.03|
||Export agriculture|2.92|0.72|0.92|0.56|–0.01|1.72|
|r<br>|Mining and gas||1.27||0.99|0.15|0.45|
|Borrowing rate<br>of capital|Industries|0.21|–0.86|–0.76|–0.99|–1.13|–1.47|
||Construction||1.21|12.48|3.14|1.84|8.21|
||Private services|–2.17|0.26|–0.64|0.56|0.06|0.35|

With the exception of Benin, we note a dwindling capital payment rate for industry when excluding the construction sector. In Benin and Senegal, capital payment rates decline for private services. But all others rise or remain steady. The largest increases are seen in the two agricultural sectors and in mining and gas for all countries—again, excluding the construction sector.

# **Electricity investment funded by income tax**

In this section, we compare the second funding option for electricity investments. This is the first scenario in which we fund investment with a household income tax increase. We would expect this option to be least favorable to households (as opposed to firms) at least in aggregate terms. This is because households fund these investments through higher taxes. At least this is the partial equilibrium we would achieve. But since we are working in a general equilibrium context the outcome might be different because price and income effects are taken into account, and in the VAT option, consumers face higher prices, which also impairs their welfare. The macro results are presented in table 14.

Here we compare the income tax funding scenario with the VAT option. Starting with the equivalent variation, we compare the results with what we estimated would happen. The household impact is almost identical to the VAT option in all countries. We observe minuscule differences in four countries where the income tax option is more favorable—by 0.02 percent in Benin, Mali, and Tanzania, and by 0.07 percent in Cameroon. (These figures represent the gap between the income tax funding option and the VAT funding option; thus they do not appear directly in the table above.) The impact on GDP is identical 19
- **b. Middle-income countries experience a hump-shaped emigration response in which emigration steadily rises and then falls off**

_Sources_ : Original estimations based migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the emigration trajectory for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migration stock relative to the baseline year. Each lightly colored line represents the trajectory for a particular country, with a few examples highlighted. The solid lines show the locally linear fit between increases in incomes and changes in emigration rates compared to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms, (2017 PPP$) was at its lowest level since 1960. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middle-income countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

Again, there is substantial variation in the experiences of individual countries. For instance, Guatemala and the Philippines experienced steady increases in migration as their incomes more than tripled between 1960 and 2020. Azerbaijan experienced the highest levels of migration in 2000 following the economic collapse in the early 1990s. As the economy recovered, migration rates subsided. For large, populous countries such as the Islamic Republic of Iran and Nigeria, migration rates barely changed even though their economies faced large ups and downs.

The emigration response observed here is a gradual process spanning decades. For instance, countries in the bottom third of the income distribution in 1960 had, on average, barely increased their emigration when their income levels tripled. It took these countries nearly 40 years to double their incomes from their average level in 1960. Likewise, if low- and lower-middle-income countries today were to grow at the same rate that they did between 2000 and 2020, it would take them another 32 years to reach the average income of PPP$12,000 around which the migration rate peaks.

8
children before and after the implementation of _Shombhob_ to derive impact estimates. Ideally the net impact of the pilot would be measured by comparing the change in the presence of the intervention and in the absence of the intervention. However since it is not possible to observe the same households and children in both of these states, the DiD estimator _assumes that the average change in the comparison group represents the counterfactual change in the treatment group if there were no treatment_ . This allows a reliable inference of program impact by comparing the pre- to post-intervention change in the outcome of interest for the treated group relative to a comparison group. The net impact of _Shombhob_ is defined, as shown in equation 1 below, as the change in the outcome of interest of beneficiary households and their children (treatment) minus that of non- _Shombhob_ households and their children (control), before and after the project implementation: _Impacty = (yshom, after – yshom, before) – (ynon-shom, after – ynon-shom, before) (1)_ where ( _yhh status, time_ ) indicate the enrollment status of a given household before and after project implementation. The change in the outcome of _Shombhob_ households is given as _(yshom, after – yshom, before)_ . Similarly, the change in the outcome of non- _Shombhob_ households over the same period is given as _(ynon-shom, after – ynon-shom, before)_ . Therefore, the net project impact on the change in the outcome is calculated by equation 1, which yields the difference of growth in the outcome for the _Shombhob_ households (treatment) and the non-Shombhob households (control) before and after the project implementation. The estimated impact is illustrated in Figure 1 below.

14
volatility (risk-induced vulnerability). Lastly, in comparison to the urban areas, in the rural areas of both Afar and Somali **,** covariate shocks have a bigger impact on household vulnerability relative to idiosyncratic shocks.

**Table 5. Poverty and vulnerability rates in urban and rural drought-prone lowlands**

|||Drought<br>lowla<br>|-prone<br>nds<br>|Af<br>|ar<br>|So<br>|mali<br>|
|---|---|---|---|---|---|---|---|
|||Urban|Rural|Urban|Rural|Urban|Rural|
|Poverty and<br>vulnerability|Vulnerability rate<br>(%)|28|63|15|51|48|63|
||Poverty rate (%)|15|29|11|26|24|22|
||Ratio (V/P)|1.84|2.13|1.33|1.94|2.04|2.82|
|Poverty- versus<br>|Poverty induced|14|38|6|27|26|38|
|risk-induced<br>vulnerability|Risk induced|14|25|9|24|22|24|
||Ratio (P/R)|1.07|1.54|0.74|1.12|1.16|1.57|
|Idiosyncratic|Covariate|18|46|9|33|32|47|
|versus covariate<br>vulnerability|Idiosyncratic|26|62|15|49|46|62|
||Ratio (C/I)|0.7|0.75|0.59|0.68|0.7|0.76|

Source: Author calculations based on HCES/WMS, 2016.

# 6. Conclusion

Using the 2016 HCES/WMS data, we estimated vulnerability to poverty in the drought-prone lowlands of Ethiopia relative to other ecological zones using a two-level hierarchical regression model that includes both idiosyncratic (household level) and covariate (community level) variables. We found that vulnerability to poverty is remarkably higher in the drought-prone lowlands than in the other ecological zones (56 percent vulnerability rate for the drought-prone lowlands versus 38 percent for the whole country). The ratio of vulnerability rate to poverty rate is also significantly higher in the drought-prone lowlands – 2.1 in the drought-prone lowlands versus 1.6 in the whole nation. Furthermore, the analysis reveals important differences in the nature of vulnerability between the drought-prone lowlands and the other ecological zones including that in the lowlands: (i) though the vulnerability rate due to covariate shocks is lower than vulnerability due to idiosyncratic shocks in absolute terms, the relative importance of covariate shocks is higher in the drought-prone lowlands than in the other ecological zones; and (ii) while both poverty induced (vulnerability due to low 20
a maximum of 10 workers we also find significantly positive effects on the retention of white incumbents. This suggests that in small peer groups, where relationships among coworkers may be deeper, losing a coworker of a different race may make also white workers more likely to stay at the firm.

# **4.3 Heterogeneity**

We will now exploit our diverse sample of workers and peer groups to investigate potential heterogeneities in how incumbent workers’ retention is affected by losing same-race coworkers.

**Occupation characteristics.** We start by examining whether effects differ across occupation characteristics. First, we split our sample into white-collar and blue-collar occupations.<sup>18</sup> The nature of coworker interactions may differ substantially between these occupations. Moreover, if the demand for workplace amenities – specifically, the preference for same-race coworkers – increases with income (Hamermesh, 1999; Pierce, 2001), we expect the effects to be more pronounced in white-collar occupations. Second, we differentiate workers by the importance of teamwork in their occupation, using O*NET data on occupational task descriptions.<sup>19</sup> Two contradictory mechanisms could be present here: On the one hand, one could expect that in jobs requiring more teamwork, coworkers interact more strongly and thus may be more affected by the racial composition of their colleagues. On the other hand, the contact hypothesis would predict that racial biases are mitigated when workers interact more (Allport, 1954), such that workers in occupations with more teamwork might be less affected by a change in the racial composition of their peers. Results are presented in Panel A of Table 3. We find that the negative effects of a non-white vs. white death on the retention of non-white incumbents are only driven by workers in white-collar occupations, while we do not find significant effects on bluecollar workers. Moreover, the effects appear to be weaker in jobs that involve more teamwork, suggesting that in these jobs, coworker interactions may have reduced preferences for same-race coworkers. Overall, the results highlight that changes in the racial coworker composition may have different effects across different segments of the labor market.

**Tenure of incumbent and deceased worker.** We further examine heterogeneity in the job experience that the incumbent and the deceased worker have in common. In particular, we consider the minimum value of the incumbent’s and the deceased worker’s tenure at the firm.

> 18For this, we map the profession-based CBO classification in RAIS to the skill-oriented _International Standard Classification of Occupations_ (ISCO-88) using the crosswalk provided by Muendler _et al._ (2004). This allows us to distinguish between white- and blue-collar occupations based on the ten major ISCO-88 occupations.

> 19We match the CBO classification to O*NET jobs using crosswalk tables from the U.S. Bureau of Labor Statistics (2023). High teamwork occupations are then defined as those with above-median ratings in the ‘Work with work group or team’ category.

17
42 devalued the currency significantly in 1983, imposed an austerity budget, reduced price supports and controls on many consumer products, privatized some state-owned enterprises, and improved public sector efficiency (see World Bank 1988:116; Younger 1989). The introduction of these politically difficult measures was supported by external financing, increased investment in key economic sectors, and an increase in public sector salaries. From the perspective of neoclassical economics, all of these changes could be viewed as improvements in existing economic conditions. Nevertheless, despite "sweeteners" to some groups, the policy changes had negative short term effects on important groups and imposed heavy social costs on broad sectors of the population, changes that politicians motivated primarily by the desire to maximize their power might wish to avoid at all costs. Given the initial context, such initiatives would not have been predicted by neoclassical political economists, nor, once agreed upon, could they be explained using the political logic of the approach. The rent-seeking behavior that explains economically irrational policy choices cannot readily explain the adoption of choices that conform to neoclassical notions of enlightened policy.

Significant structural reforms would certainly not have been predicted in Mexico, an almost classic case of rent-seeking lobbies associated with a strategy of import substitution holding policy captive to its economic interests. A currency float that meant massive ongoing devaluations, drastic cut-backs in imports,
|11-100<br>100+|||-0.00942<br>(0.133)<br>-0.109|
|---|---|---|---|
||||(0.138)|
|40-49 hrs||-0.482***|-0.359***|
|||(0.108)|(0.122)|
|50+ hrs||-0.482***|-0.231|
|||(0.127)|(0.143)|
|Constant|-0.0858|0.223|0.303|
||(0.160)|(0.325)|(0.362)|
|Observations|5326|5280|4695|

Source: Authors’ estimation using RLMS Note: Columns (1) and (2) include all observations, Columns (3) excludes self-employed informals. This table shows the estimated coefficients from logit regressions that estimate how informal and formal workers differ in their likelihood of being in the bottom quintile of the per capital household income distribution, controlling for other factors. Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. All specifications include region dummies. Omitted groups are age group (25-34), education (below secondary), sector (agriculture), occupation (managers), firm size (1-10) and hours worked (<40 hours). Armed forces are dropped from the sample. All observations are weighted using the individual weight. *, ** and *** indicate significance levels at 10 percent, 5 percent and 1 percent, respectively 65 Informal Employment and Worker’s Wellbeing in Russia / June 2019