rafmacalaba/datause-encoder-data huggingface dataset:
with an average employment over 50, which leaves us with roughly the top 1% of all observations for that sector or 932 firms.

We deflate output by the producer price index (PPI) for the three-digit NACE sector, obtained from the _Statistics Year Book of Romania_ . We measure labor input as the number of employees, and capital as deflated tangible fixed assets. The capital deflator is a simple average of PPI from five NACE sectors.<sup>6</sup> We define material inputs as material costs deflated by the weighted average of PPI of the supplying sectors with the weights given by 2000 input-output matrix provided by the Statistical Institute of Romania. Real wage is deflated by the consumer price index from the IMF’s _International Financial Statistics_ (IFS).

To control for region-specific demand, we calculate the average real wage per worker at the regional level. We use data on wages and employment of all companies operating in Romania during the period of 1997- 2005 listed in Amadeus data base, including all firms active in agriculture, industry, and services sectors. The data are deflated by the same consumer price index. Finally, we also use information on imports and exports obtained from the UN’s COMTRADE database and deflate it by the GDP deflator from IFS.

# **5. Global Retail Chains in Romania**

# **5.1 Expansion of Global Retail Chains**

While the focus of this study is the relationship between the presence of global retail chains and the performance of the supplying industries, we first turn to developments in the Romanian retail sector. Relative to other services, retail and wholesale sector accounts for a large portion of Romania’s economic activity. In both 1997-2000 and 2001-2004 periods, it contributed about 10% to total employment and value added of the economy (Fernandes 2007). It was the largest service sector in terms of employment.

Compared to other Central and Eastern European countries, the retail sector in Romania is a late boomer in terms of FDI inflows. The first entry of foreign retail chains into the Czech Republic, Hungary, and Poland took place in the early 1990s and a broader expansion of these chains occurred around the mid-1990s. The first entry of global retail chains into Romania, however, did not take place until 1997 when the German chain Metro opened its first Metro Cash

> 6 These are: machinery and equipment; office, accounting, and computing machinery; electrical machinery and apparatus; motor vehicles, trailers, and semi-trailers; and other transport equipment.

8
0 1t t 2 3t t 4N N 5f 6 ii ii ii ii ii ii ii ii ii yy = μμ + μμ tt + μμ aaffttbb + μμ tt ∗aaffttbb + μμ + μμ ggttff yyt tt + μμ tta aatt mmttggffttgg + δδ<sup>**�**</sup> + ττ Finding μ3 > 0 would suggest that applicants exhibiting bias against women at baseline benefit from the program more.

# **Results**

In this section, we describe the baseline characteristics of the sample and present the results of our empirical specifications. The program appears to have on average increased labor market mobility and augmented the returns to skills. Effects were concentrated in one location and were higher among women who harbored biases against female professionalism at baseline.

# _Descriptive statistics_

The random assignment of the program produced balance across the treatment and control groups, with no significant differences across 26 covariates, as shown in Table 1. As one might expect, given that college graduates were the target population, the sample appears to be relatively welloff in the Nigerian context. For example, 81 percent of respondents’ mothers and 94 percent of fathers are literate, while the literacy rates overall for Nigerians aged 45-49 are 36 percent for women and 65 percent for men (Demographic Health Survey, Macro International 2014).

Appendix Table 3 shows that over 90 percent of participants were re-interviewed at end line, a relatively high response rate among other experimental studies in similar settings that sought to measure labor market outcomes more than one year after a job training program (McKenzie, 2017).<sup>8</sup> There was some differential attrition, as we could complete the end line survey with 92 percent of the treatment group, but only 88 percent of the comparison group responded to the survey. We discuss the implications of this for our results in Section 4 and estimate upper and lower bounds for our main treatment effects.

While Figure 2 suggests an overall compliance rate of 54 percent, Table 2 shows some heterogeneity across strata when program take-up is regressed on indicators for each stratum. Individuals who applied during their mandatory internship were close to 10 percentage points less likely to take-up the program, while scoring above the baseline assessment score was associated

> 8 In fact, among the 12 evaluations of job training programs in low and middle income countries reviewed in McKenzie (2017), only one study had an attrition rate less than 18 percent.

13
# **Table 6: Descriptive OLS Regression of Labor-Related Indicators and Migration**

|||**OLS: Co**<br>|**nsider Mig**<br>|**rate= 1**<br>|||**OLS: C**<br>|**urrent Mig**<br>|**rant= 1**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(8)**|**(9)**|
|Share Adults UZB Working|-0.053***<br>(0.014)|||||-0.273***<br>(0.035)|||||
|Share UZB+Mig Working||-0.061***|||||0.332***||||
|||(0.015)|||||(0.037)||||
|Members Looking for Job|||0.031***|||||-0.006|||
||||(0.005)|||||(0.009)|||
|Ln(Household Income)||||-0.007***|||||0.048***||
|||||(0.003)|||||(0.007)||
|"Labor Market is Bad"|||||0.073***|||||0.073***|
||||||(0.015)|||||(0.020)|
|Constant|0.045***|0.049***|0.022***|0.131***|0.021***|0.238***|0.060***|0.163***|-0.553***|0.152***|
||(0.007)|(0.007)|(0.005)|(0.038)|(0.005)<br>|(0.016)|(0.012)|(0.011)|(0.093)|(0.011)|
|Number of observations|12,137|12,137|12,137|10,368|12,137|12,137|12,138|12,137|10,368|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.007|0.008|0.015|0.005|0.017|0.023|0.033|0.004|0.022|0.008|
|Adjusted R2|0.006|0.007|0.015|0.005|0.016|0.023|0.032|0.003|0.022|0.007|

note: .01 - ***; .05 - **; .1 - *; 18
20 are also lower in the NRXGDP countries, the difference with MFGSERV countries is not significant. Thus, any significant impact of resource exports on the employment composition in urban areas may take place over more than one decade. Next, the shares of non-tradables (e.g., NTR3) in the urban areas of NRXGDP and DEINDU countries are higher than those in MFGSERV countries. Informality is also higher in the urban areas of NRXGDP countries than in MFGSERV countries, but the difference is not significant.

**Robustness: I2D2.** Another data source for employment information by country is the _International Income Distribution Database_ (I2D2) of the World Bank. The full database consists of about 1,500 individual-level household and labor force surveys for more than 150 countries. Detailed information on sectoral and can employment self-employment be found in about 1,000 survey samples for almost 100 countries. The data was initially compiled by the World Bank’s _World Development Report_ (WDR) unit between 2005 and 2011. The database has expanded since then allowing us to include more countries than IPUMS. We use its December 2017 vintage to replicate the IPUMS-based results with the expanded country sample. The surveys are nationally representative and sufficiently large for our purpose (i.e., they have more than 10,000 observations).<sup>23</sup>

We obtain for each country the _mean_ labor shares of various sectors and types of employment in urban areas in 1990-2015.<sup>24</sup> We then use the same cross-sectional model as for IPUMS (eq. (10)). Since 2005 is the mean population-weighted year in I2D2, the variables of interest are defined relative to 2000 (as in Panel A of Web Appx. Table D.5) or 2010 (as in Panel B of the same table). While we observe a strong correlation (about 0.9) between I2D2 and IPUMS for MFGFIRE, the correlation is weaker (0.55) for urban non-tradables NTR, which has to do with the way I2D2 was harmonized.<sup>25</sup> NRXGDP and DEINDU countries have lower employment shares of MFG in their urban areas than MFGSERV countries (Web Appx. Table D.5). For MFG+FIRE, the differences are mostly not significant. No correlations are observed for FIRE and NTRI (the version of “NTR” in I2D2). However, DEINDU countries have significantly higher

> 23See for example Jedwab et al. (2020a, 2022) for more details.

> 24Most countries had several surveys during the period. Since the surveys vary in sample size across time periods, we use as weights the size of each survey, thus giving more weight to better measured years.

> 25During the harmonization process, sectors had to be aggregated together due to the different classifications in different surveys. _Wholesale & retail trade_ and _Hotels & restaurants_ were merged together by the I2D2 team, while we ignored the latter in IPUMS due to the lack of correlations with our variables of interest. “NTR” in I2D2 (NTRI) might also include other sectors depending on the country.
# **IV. Regression Results**

We first replicated the major results of Montalvo and Reynal-Querol (2005), shown in columns (1), (2), and (4) of Table 2. These results point to the significance of ethnic polarization, not fractionalization, as a determinant of conflict. In their specification, which omits the foreign military intervention variable, religious polarization and fractionalization are not significant predictors of conflict. In other words, their results suggest that civil conflict is driven mostly by ethnic strife over public prizes.

# _A. External Intervention and Polarization_

When we include the non-neutral and non-humanitarian external military intervention variable ( _Int_nh_ ), along with the indices of polarization and fractionalization, we find that both the intervention variable and the ethnic polarization index are statistically significant and have the expected positive signs (see columns (3) and (5) of Table 2). This result suggests that, conditional on a given degree of polarization, this type of external military intervention is associated with an increase in the incidence of civil war.<sup>30,31</sup> Expression (6) for the equilibrium per capita conflict in the theory section links the incidence of civil war to the distributional measures modified by the presence of non-neutral and non-humanitarian external military intervention. It suggests that the intervention is associated with conflict through its effects on the distributional measures. In the large country case, given by expression (7),<sup>32</sup> this effect comes through the influence of the intervention on the polarization

> 30 The sample used to estimate regression models (3), (5), (6), and (7) includes observations for Lebanon. Similar results are obtained without Lebanon in the data sample. Results without Lebanon are available upon request from the authors.

> 31 We recognize that there may be reverse causality between intervention and conflict, so we interpret the effect on the intervention variable as a conditional association, rather than a causal relationship. 32 The large country case is also the baseline case in Esteban and Ray (2011).

22
|ted<br>% target|8.5%<br>12.0%<br>11.1%<br>16.7%<br>7.8%<br>14.6%<br>12.0%|s and|e the|Light<br>Farm,|Soul<br>art of||
|---|---|---|---|---|---|---|
|Recrui<br>% all|0.4%<br>0.6%<br>0.6%<br>0.8%<br>0.4%<br>0.7%<br>0.6%|Statistic|5 and tak|Hill. Red <br> Morris’|mmunity,<br>ncludes p||
|No.|100<br>219<br>179<br>240<br>86<br>175<br>999|titute of|e of 18-3|d Sugar <br>munity,|Bush Co<br>wn also i||
|d males<br>Target|1 171<br>1 822<br>1 617<br>1 435<br>1 105<br>1 196<br>8 346|Liberia Ins|he age rang|Clinic, an<br> Chop Com|ty, Banana <br>ew Kru To||
|Estimate<br>All|23 422<br>36 434<br>32 345<br>28 704<br>22 100<br>23 921<br>166 926|n data from|half are in t|Pipeline, Soul <br> Town, Goba|eld Communi<br>oint areas. N|d in Phase 2.|
|Short description|Peri-urban, along the main road from Monrovia to the<br>northeast of the country, residential but the site of one of<br>the major markets in the city, mixed income neighborhood.<br>Peri-urban, along the main road from Monrovia to the NE<br>of the country, residential but the site of one of the major<br>markets in the city, mixed income neighborhood.<br>The area consists of Mamba Point and West Point, one of<br>the busiest business areas in Monrovia.<br>Peri-urban, the north of Bushrod Island, the transit point<br>to counties of the northwest of Liberia, notorious for petty<br>crime.<br>Peri-urban, the middle of Bushrod Island, next to Freeport<br>of Monrovia, many garages and small shops and booths.<br>Peri-urban, the south of Bushrod Island, next to Central<br>Monrovia, lots of car-loaders and wheelbarrowers.|population estimates come from the authors’ calculations based o|(LISGIS). To get an estimated number of target males we assume|r targets. Red Light 1 includes Gorbachop, Woodcamp, Reservoir, <br>Chicken Poultry, Ma Kebbeh Gas Station area, Sugar Hill, Bassa|Road, Zayzay Community, Coca Cola Factory Community, Plank Fi<br>Wood Camp. Central Monrovia includes Mamba Point and West P|includes part of Mamba Point and West Point that are not covere|
|Neighborhood|Red Light 1<br>Red Light 2<br>Central Monrovia<br>New Kru Town<br>Logan Town<br>Clara Town|_Notes:_ Total male|eo-Information Services|ttom 10% decile as ou<br>includes Turtle Base,|rnard Farm, Pipeline <br>inic Community, and|lwell. Logan town also|
|Phase|1<br>2<br>3<br>All||G|bo<br>2|Be<br>Cl|Ca|

vi
Figure 2: Worse IMR and Sanitation Associated with Lower Wages (A) effect of IMR, no controls (B) effect of sanitation, no controls <!-- Start of picture text --> 60 80 100 120 140 160<br>district infant mortality rate in first year of life<br>(C) effect of IMR, with controls<br>2.2<br>2.15<br>2.1<br>ln(hourly wage)<br>2.05<br>2<br><!-- End of picture text --> <!-- Start of picture text --> 60 80 100 120 140 160<br>district infant mortality rate in first year of life, residuals<br>2.25<br>2.2<br>2.15<br>ln(hourly wage), residuals 2.1<br>2.05<br><!-- End of picture text --> <!-- Start of picture text --> 20 40 60 80 100<br>district open defecation in first year of life<br>2.4<br>2.3<br>2.2<br>2.1<br>ln(hourly wage)<br>2<br>1.9<br><!-- End of picture text -->

- (D) effect of sanitation, with controls

<!-- Start of picture text --> 60 70 80 90 100<br>district open defecation in first year of life, residuals<br>2.1<br>2.05<br>2<br>ln(hourly wage), residuals 1.95<br>1.9<br><!-- End of picture text --> 32
||(0.100)|(0.100)|(0.100)|(0.100)|
|---|---|---|---|---|
|**Potosí**|0.1613|0.1492|0.1664*|0.1579|
||(0.101)|(0.102)|(0.100)|(0.100)|
|**Tarija**|-0.3484***|-0.3765***|-0.3326***|-0.3804***|
||(0.108)|(0.103)|(0.109)|(0.105)|
|**Santa Cruz**|-0.5676***|-0.5782***|-0.5675***|-0.5646***|
||(0.051)|(0.052)|(0.052)|(0.053)|
|**Beni**|-0.4402***|-0.4429***|-0.4258***|-0.4233***|
||(0.081)|(0.081)|(0.084)|(0.087)|
|**Pando**|0.0900|0.0722|0.1363|0.1112|
||(0.166)|(0.173)|(0.161)|(0.163)|
|<br>**Observations**|327|327|327|327|
|**R2**|0.279|0.282|0.284|0.293|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

In general, the direction and compositional structure of the association in 2007 is the same as in 2003, but the magnitude is much lower (Table 3). The link between spending in agriculture and vulnerability is found to be statistically significant and positively, but with a markedly lower magnitude than in 2003 (Column 1). The estimated association is being driven predominantly by restricted spending, as it was in 2003 (Column 2). As in 2003, investment spending appears to have a positive relationship with vulnerability to food insecurity (Column 3), suggesting that more investment goes where higher vulnerability exists; however, the magnitude of the effect is much lower in 2007. A similar pattern emerges for infrastructure spending in 2007, but now research and extension spending is positively associated with food insecurity (Column 4). As in 2003, Tarija, Santa Cruz, Beni and Pando are less likely to belong to high/very high vulnerable categories than La Paz, but in 2007 Chuquisaca and Potosí are more likely than La Paz to pertain to high vulnerability categories (Columns 1 through 4). Chuquisaca and Potosí are the two departments with the highest vulnerability to food insecurity.

**Table 3. Effects of Per Capita Agricultural Spending on Vulnerability to Food Insecurity 2007**

||(1)|(2)||(3)||(4)|
|---|---|---|---|---|---|---|
|**VARIABLES**|Total exp|Restricted<br>extended<br>definitions|and|Current<br>investment<br>categories|and|By function|
|**Total expenditure**|0.5843***||||||
||(0.220)||||||
|**Restricted expenditure**||1.2889***|||||
|||(0.489)|||||
|**Extended expenditure**||0.3106|||||

14
**Table 3. Business Cycle Synchronization – orthogonal to US business cycle**

||Costa Rica|El Salvador|Guatemala|Honduras|Nicaragua|
|---|---|---|---|---|---|
|Costa Rica|1.000|||||
|El Salvador|**0.409**|1.000||||
|Guatemala|**0.488**|0.006|1.000|||
|Honduras|0.104|0.157|**0.421**|1.000||
|Nicaragua|-0.141|0.115|-0.076|-0.063|1.000|
|Panama|0.134|0.014|-0.021|0.118|0.065|

# **2.1.2 Monthly Data: 1995 - 2003**

The business cycle is usually defined in the range of 6 to 32 quarters, as such, the low frequency of annual data might be insufficient to fully assess the degree of business cycle synchronization. In this section we therefore complement our analysis in the previous section with an analysis of monthly data, where output is proxied by seasonally adjusted monthly indices of industrial production and economic activity.

We use spectral analysis to estimate the correlation at different frequencies and use the average coherence at business cycle frequency (6 to 32 quarters) of year-over-year changes in economic activity as a summary measure of business cycle synchronization (Garnier, 2003). The advantage of using cross-spectral densities over simple correlations in the analysis of business cycle synchronization is twofold. First, spectral analysis avoids possible business cycle distortions due to filtering, it is well known that the cycles change with the de-trending method (Canova, 1998). Second, contemporaneous correlation is unable to take lagged co-movement into account. As coherence measures the correlation between two series in the frequency domain and further provides information on the phase lead/lag it captures provides a richer analysis of the business cycle dynamics. While the coherence measures to what extend two business cycles are dominated by the same frequency, the phase lag shows to what extend elements with the same frequency lag each other. In sum, a high degree of business cycle synchronization implies a high coherence and a low phase lag.

Table 4 shows the average coherence at business cycle frequency between year-over-year growth rates of economic activity during 1995 and 2003. Encouragingly, the results broadly confirm the findings of the previous section.

7
# **Introduction**

Progress against absolute poverty is now a widely accepted yardstick for assessing the overall performance of developing economies. The best data for assessing progress against poverty come from surveys of the living standards of nationally-representative samples of households. In the last 25 years there has been enormous progress in designing, implementing and processing such surveys for developing countries — thanks in large part to the efforts of national statistics agencies throughout the world, and the support of the donor community and international development agencies.

Drawing on these data, this paper provides internally-consistent estimates of a time series of measures of absolute poverty for the developing world, and by region, at roughly three-yearly intervals from 1981 to 2004. We use data from over 500 household surveys spanning 100 countries. We follow exactly the methods outlined in Chen and Ravallion (2004), which was the last up-date of the World Bank’s “global” poverty measures, providing estimates up to 2002. In addition to including new data available since 2004, we have recalculated all prior estimates back to 1981, to incorporate any updates or revisions from past data sources.

The paper also discusses the implications of incorporating an urban-rural poverty line differential. This is of interest given the widely heard popular concerns about the urbanization of the developing world’s population and (one expects) of poverty.

The paper begins by reviewing the assumptions and methods and then presents and discusses the results.

# **Methods for measuring poverty in the developing world**<sup>1</sup>

We rely heavily on nationally-representative household surveys for measuring poverty. This is one of the purposes for which these surveys exist. There is no alternative to using survey data for measuring the distribution of relative consumptions or incomes — “inequality” for short. But there is an alternative source of data on average consumption, namely the national accounts

> 1 Here we only summarize the assumptions and methods; for details see Chen and Ravallion (2001, 2004) and Ravallion et al. (2007).

2
# **Figure 12. Relative wages between sectors**

<!-- Start of picture text -->
1.5 10<br>8<br>1.3<br>6<br>4<br>1.1<br>2<br>0.9 0<br>-2<br>0.7<br>-4<br>-6<br>0.5<br>-8<br>0.3 -10<br>Wage SE/Wage F Wage I/Wage F G.D.P. (H&P) Right Scale<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text -->

Note: Monthly Labor Survey (PME) from Jan 1983 to Dec 2002. The series have been averaged per quarter. I=Informal Salaried, SE=Informal Self-employed, F=Formal Sector.

56
# **2. Data Sources**

Five data sources containing information on gender outcomes and attitudes have been examined:

- Demographic and Health Survey (DHS)

- World Values Survey (WVS)

- Multiple Indicator Cluster Surveys (MICS).

- Gallup Poll

- Gender and Adolescence: Global Evidence (GAGE)

Table A1 below summarizes the availability of data from these surveys across countries in the region and through different time periods.

Table A1: Data Availability by Survey

||Years available (by su|rvey)|||||
|---|---|---|---|---|---|---|
|Country|DHS<br>(Cross-sectional)|MICS<br>(Cross-sectional)|WVS<br>(Cross-sectional)|GAGE<br>(Cross-sectional)|Gallup<br>(Panel)||
|Afghanistan|2010<sup>+</sup>,2015(2)|2011(1)|(0)|None|||
|Bangladesh|1993-94, 1996-97,<br>1999-00,<br>2001<sup>*</sup>,<br>2004, 2007, 2011,<br>2014,2017(9)|2006, 2012-2013,<br>2019 (3)|1994-1998, 1999-<br>2004,<br>2017-2019<br>(3)|Baseline<br>survey<br>(2017/2018)<br>Only conducted in<br>Dhaka(1)|Every<br>between<br>2020 (15)|year<br>2005-|
|India|1992-93, 1998-99,<br>2005-06, 2015-16<br>(4)|(0)|1989-1993, 1994-<br>1998, 1999-2004,<br>2005-2009, 2010-<br>2014(5)|(0)|Every<br>between<br>2020 (15)|year<br>2005-|
|Maldives|2009,2016-17(2)|(0)|(0)|(0)|2017 (1)||
|Nepal|1987<sup>**</sup>, 1996, 2001,<br>2006, 2011, 2016<br>(6)|2010, 2014, 2019<br>(3)|(0)|Baseline survey (1)|Every<br>between<br>2018(13)|year<br>2005-|
|Pakistan|1990-91, 2006-07,<br>2012-13, 2017-18<br>(4)|2010, 2011, 2014,<br>2016-2017, 2017-<br>2018(5)|1994-1998, 1999-<br>2004, 2010-2014,<br>2017-2019|(0)|Every<br>between<br>2018(13)|year<br>2005-|
|Sri Lanka|1987<sup>**</sup>, 2006, 2016<br>(3)|(0)|(0)|(0)|Every<br>between<br>2015 and<br>2019(13)|year<br>2005-<br>2017-|
|Bhutan|(0)|2010 (1)|(0)|(0)|2013, 201<br>(3)|4, 2015|

Among these data sources, DHS provided the most comprehensive combination of gender-relevant outcome variables across all coutnries and time periods. WVS, though not available for all countries in the region, was selected due to availability of gender attitide variables that could serve as proxies for relevant norms. A brief description of each is included below.

> + Special DHS data type and not used.

> * Special DHS data type.

> ** Not used in the analysis.

50