rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9942

# **Abstract**

Despite a policy consensus that closing gender employment gaps will boost economic growth, relatively little is known about the size of these gains in many developing countries. This paper develops a new Gender Employment Gap Index (GEGI), which is equal to the size of long-run GDP per capita gains from closing gender employment gaps. The GEGI is simple and transparent and can be easily constructed using closed-form expressions for almost all countries using macroeconomic employment rate data by gender. The basic variant of the GEGI is the gap between male and female employment as a share of total employment.

The full GEGI is similar, but instead of using an aggregate employment gap, the full GEGI is the weighted average of a “better employment gap” and “other employment gap.” The basic and full GEGIs are similar (correlation of 0.97), and both average 19 percent across countries. This means that GDP per capita in the long run would be almost 20 percent higher if female employment were exogenously increased to be the same as men’s (other things being equal). The paper also provides an application for the Pacific Islands, for which a simple measure like the GEGI is particularly important given the lack of alternative estimates.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at spennings@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
to the rest of the world. However, when income rises, that initial advantage quickly fades away because of the lower speed of adoption. Results are robust to using all regional dummies (see Appendix Table A9).

**Table 3: Technology adoption in MENA and EAP**

||(1)<br>Bandwidth per<br>Internet User|(2)<br>Computer Per<br>Mil Capita|(3)<br>Internet Users<br>(%)|(4)<br>Number of ATM<br>per Mil Capita|(5)<br> <br>Payments by<br>Credit and<br>Debit Cards per<br>Mil Capita|(6)<br> <br>Tractor per Mil<br>Capita|(7)<br>Electricity<br>Production per<br>Mil Capita|
|---|---|---|---|---|---|---|---|
|Relative Income to the US|6.554***<br>(1.227)|0.718***<br>(0.0181)|0.835***<br>(0.0214)|0.621***<br>(0.0539)|0.649***<br>(0.0569)|1.307***<br>(0.0339)|0.848***<br>(0.0317)|
|EAP|114.2***<br>(30.67)|3.716***<br>(1.108)|9.477***<br>(1.336)|200.6***<br>(39.10)|38.39**<br>(15.99)|-2.956*<br>(1.676)|1.678*<br>(0.871)|
|MNA|149.8***<br>(32.20)|7.814***<br>(0.653)|11.98***<br>(1.311)|60.16*<br>(32.83)|15.19***<br>(5.540)|11.83***<br>(0.851)|14.08***<br>(1.193)|
|EAP * Relative Income|-4.136***<br>(1.358)|0.0430<br>(0.0395)|-0.361***<br>(0.0431)|-1.951***<br>(0.377)|-0.416**<br>(0.166)|-0.323***<br>(0.0859)|-0.0633<br>(0.0396)|
|MNA * Relative Income|-6.382***<br>(1.228)|-0.640***<br>(0.0185)|-0.653***<br>(0.0275)|-1.205***<br>(0.320)|-0.709***<br>(0.0775)|-1.317***<br>(0.0340)|-0.786***<br>(0.0338)|
|Constant|-76.26*<br>(42.48)|14.97*<br>(8.378)|-21.00***<br>(1.883)|4.214<br>(9.867)|-27.73**<br>(13.66)|-25.35***<br>(5.750)|-0.178<br>(10.90)|
|Observations|2985|1281|4193|368|372|4115|3151|
|r2<br>* p<0.10 ** p<0.05 *** p<0|0.161<br>.01|0.796|0.673|0.432|0.262|0.536|0.579|

Note: (1) Bandwidth per internet user (bits per second), (2) number of self-contained computers designed for use by one person, (3) internet users in percentage of population, (4) numbers of ATM per million capita (5) payments by credit and debit cards per million capita, (6) tractors used in agriculture per million capita, (7) Gross output of electric energy per million capita. Time-fixed effects are included in all regressions. See the list of countries included in these regressions presented in Appendix Table A8.

15
**Table 2: Baseline Characteristics and Balance of Experimental Sample**

|Non‐Experimental<br>Existi|Treatment<br>ng Firms|Control|Non‐Experimental<br>Ne|Treatment<br>w Firms|Control|
|---|---|---|---|---|---|
|Winners|Group|Group|Winners|Group|Group|
|_Applicant Characteristics_||||||
|Female<br>0.17|0.18|0.17|0.19|0.17|0.18|
|Age<br>32.5|32.0|31.8|30.1|29.3|29.6|
|Married<br>0.60|0.50|0.56|0.42|0.34|0.36|
|High School or Lower<br>0.10|0.13|0.12|0.06|0.11|0.10|
|University education<br>0.71|0.63|0.67|0.79|0.69|0.71|
|Postgraduate education<br>0.12|0.08|0.12|0.13|0.05|0.06|
|Lived Abroad<br>0.14|0.10|0.11|0.18|0.06|0.09|
|Choose Risky Option<br>0.59|0.56|0.52|0.63|0.57|0.55|
|Have Internet access at home<br>0.68|0.57|0.61|0.60|0.47|0.48|
|Own a Computer<br>0.94|0.87|0.88|0.92|0.84|0.86|
|Satelite Dish at home<br>0.74|0.67|0.71|0.64|0.68|0.64|
|Freezer at home<br>0.64|0.57|0.61|0.63|0.51|0.55|
|_Business Characteristics_||||||
|Crop and Animal Sector<br>0.14|0.16|0.16|0.22|0.22|0.22|
|Manufacturing Sector<br>0.28|0.28|0.26|0.23|0.28|0.24|
|Trade Sector<br>0.05|0.06|0.05|0.06|0.04|0.04|
|IT Sector<br>0.14|0.15|0.14|0.04|0.07|0.06|
|First Round Application Score<br>59.0|57.2|56.6|59.9|59.9|59.9|
|Business Plan Score<br>61.7|45.8|45.4|74.4|53.7|55.5|
|Number of Workers<br>9.11|7.35|7.73||||
|Ever had Formal Loan<br>0.11|0.06|0.09||||
|Joint orthogonality test: treatment versus control|0.9|20||0.8|84|
|Joint orthogonality test: non‐experimental vs treatment<br>0.000|||0.000|||
|Joint orthogonalitytest: non‐experimental vs treatment(no score)<br>0.012|||0.000|||
|Note: p‐values shown for joint orthogonality tests. Test for comparison of treatment an|d control co|nditions o|n regional strata.|||

39
- [33] Vidal, J.-P. (1998): The e¤ect of emigration on human capital formation, Journal of Population Economics, 11, 4: 589-600.

- [34] Wong, K.-Y. and C.K. Yip (1999): Education, economic growth, and brain drain, Journal of Economic Dynamics and Control, 23, 5-6: 699-726.

- [35] World Bank (1994): Kingdom of Morocco - Poverty, adjustment and growth, Report 11918-MOR.

- [36] World Bank (2000): World Development report, Washington: The World Bank.

- [37] World Bank (2003): World Development Indicators, Washington: The World Bank.

37
**G. Beyond the firm: "The micro-foundations of macroeconomics"1 0**

The scope of firm-level surveys goes well beyond testing theories of the firm. As many researchers have suggested, firm-level data has become key to understand the macro economy. Macroeconomic analysis concentrates mainly on understanding the determinants of output, unemployment and inflation. Researchers have thus built macro-economic variables from industry-level data under the assumption of a "representative firm" within each industry. Recent studies in developed economies, however, demonstrate that seemingly similar firms in the same industry exhibit different behavior in terms of output, investment, and employment, suggesting that aggregation may obscure important phenomena. Though these findings do not justify rejecting the representative firm assumption, they highlight how we can more completely understand changes in macro variables by analyzing these variables at the firm level.

Firm-level data is also useful for macro-analysis.since it helps test robustness by complementing aggregate, official, sources of data. Robustness tests are especially relevant for transition economies, where pre-transition data is often not reliable and the process of collection of post-transition data is still under revision. Firm-level surveys can help reconcile seemingly contradictory aggregate results by disentangling contrasting effects and offering a different perspective on the issue in question.

Next, we provide a brief overview of the most important theoretical macro-economic issues, which can benefit from this micro approach to macro-analysis, and of the (limited) empirical testing done using World Bank surveys.

# **Growth and Investment**

An important aspect of macro-economic analysis is the link between growth and investment. In particular, recent studies suggest that the rate of investment is positively linked to the rate of growth. To better understand this link, researchers have focused on the determinants of the investment decision and found that this decision depends on financial constraints, the risks firms face, and the productivity of investment itself (Jenskin, 1998; Gyimah-Brempong and Traynor, 1999). This line of research however has used mainly aggregate (country-level) data.

> **10** This section draws heavily from Haltiwanger (1997) and Campbell (1997).

19
who do the same job. Many Congolese employees appreciate being given paid employment without formal registration procedures and status, which many of them do not possess. However, most of them were dissatisfied with their lower salary and poor working conditions. Maurice is a 34-year-old Congolese male refugees who came to Nairobi in 2015. He is employed by a Kenyan security company as a security guard. He explains about the wage discrimination as follows:

[At our company] There are about 40 Congolese refugee employees. . . Refugees accept cheaper payment, so it is easier for an employer. Now I am paid 6000 KES per month [about 55 USD]. I work from 6pm to 6am. 12 hours per day and 7 days a week. I have no holidays. . . [For the same job] Kenyans get 12000-15000 KES [110-240 USD] per month.

The same pattern applies to Congolese refugees and Ugandans in Kampala. Nevertheless, these refugee workers rarely express their frustration with respect to exploitative economic relationships or other negative perceptions of hosts. One Ugandan coordinator of a CBO who has been working for refugees in Kampala since 2012 explained as follows: Refugees are careful to have good reputation and not to cause any problem with hosts because they understand they will be kicked out of here if something occurs. This is why they are disciplined. (Ugandan coordinator of CBO, Kampala) The comment above suggests that Congolese refugees sacrifice considerable human labour and make significant efforts to maintain a good image among hosts in order to make a living.

# **5.3 Consistency with Existing Literature**

This is among the very first papers to explore the ‘contact hypothesis’ for refugee-host community interaction in a developing country context. Until now, nearly all of the research examining the impact of refugee-host interaction on host community attitudes towards refugees had focused on Europe (one exception comes from Rozo and Vargas (2021), who assessed the impact of the recent refugee crisis in Colombia). Consequently, it is particularly interesting to explore how our findings diverge from the existing, predominantly rich-country literature on contact and ‘native’ attitudes. What is similar and what is different?

While the European literature focuses on both contact (as sustained interaction) and exposure (as short-term interaction during transit), our focus is very much on the former (Ajzenman et al. 2020; Steinmayr 2021). Our OLS regression analysis findings are consistent with the contact hypothesis (Allport 1954), revealing a positive and statistically significant correlation between interaction and positive perceptions. We have sought to address issues – – of endogeneity issues reverse causality in particular by using an IV approach. The introduction of the IV leads to the disappearance of the positive correlation between contact and attitudes when considering the full Uganda sample or data from the Nakivale settlement.

31
of each detailed occupation mapped to that occupational group to total hours of work in the previous week. In order to be able to compare the skill content across occupational groups, we standardize the skill measures using the mean and standard deviation taken across the six occupational groups. These normalized skill measures are the input we use to generate skill measures for each province-industry-year in the Sakernas survey using the following calculation: � <mark>where ω</mark> ��� <mark>is the share of occupational group</mark> _<mark>i</mark>_ <mark>in province</mark> _<mark>p</mark>_ <mark>, industry</mark> _<mark>j</mark>_ <mark>, and year</mark> _<mark>t</mark>_ <mark>total</mark> �� <mark>employment obtained from Sakernas, and s is the value of the normalized skill measure</mark> _<mark>k</mark>_ <mark>for occupational group</mark> _<mark>i</mark>_ <mark>from the Philippines STEP survey. When constructing skills measures by demographical groups –women, men, high-skilled workers and low-skilled workers, we transform previous expression using the shares of each occupation in each province, industry and year total employment for each specific demographical group:</mark> �� where <mark>ω</mark> ��� is the share of occupational group _i_ in province _p_ , industry _j_ , and year _t_ total <mark>employment of demographic group</mark> _<mark>g</mark>_ <mark>. Demographic groups include female workers, male workers, high-skilled and low-skilled workers.</mark>

# **B) Skills measures using the U.S. O*Net database**

To check the sensibility of our results using the Philippines STEP survey, we construct skills measures using information from the U.S. 2004 O*Net database, i.e., a year previous to our period of analysis, to avoid the skills measures to be affected by the technological advancement taking place after that year.

<mark>We work with a set of 41 work activities describing 914 detailed occupations. We classify the 41 work activities into five skills categories: n</mark> on-routine analytical, non-routine interpersonal, routine cognitive, routine manual, and non-routine manual. Table A1 provides details on the classification we use.

We map the occupations available in the 2004 U.S. O*Net database that follows the SOC 2000 classification (Standard Occupational Classification 2000) to the ISCO 2008 used in the STEP survey, and then to <mark>the Indonesian Classification of Occupation 1982 that is used in the Sakernas.</mark> In this process we mapped 899 occupations from the 914 occupation that were originally in the O*Net database. Using the ISCO 2008 we identify the same six occupational groups we identify in the STEP survey and that can be match to the Sakernas survey: <mark>administrative and managerial workers; professionals and technicians; clerical workers; sales and services workers; agriculture, animal husbandry, forestry workers, fishermen and hunters; and production workers, transport and equipment operators, and laborers.</mark> 55
While table 2 shows the coefficients and standard errors of the interaction terms only, note that the respective aid, aid squared and UNSC variables are included in each regression (as are the remaining control variables). As can be seen, the only significant coefficient is the one following our previously proposed and theoretically most likely timeline (Aidt‐1*UNSCt‐2) that we show here for comparison. The regressions thus support our proposed timeline, and our considerations behind it. The table shows that this also holds when we focus on future UNSC membership, which we included here as a placebo test.

We further test the robustness of our results in a number of ways. First, we replace the share of years a country has served on the UNSC with a binary indicator variable for a country’s presence on the UNSC. Second, we first‐difference the UNSC variable rather than including it in levels. Third, we lag all control variables by one period rather than including them contemporaneously. Fourth, we employ early‐impact aid as defined in Clemens et al. (2012) instead of all aid. Fifth, in light of the identifying assumptions discussed above, we include a number of additional variables (as changes between (t−2) and (t−1)) which could potentially induce omitted variables bias. Most importantly, we control for changes in the institutional environment by including the International Country Risk Guide’s (ICRG) variables measuring Bureaucracy Quality, Corruption, Democratic Accountability, Ethnic Tensions, External Conflict, Government Stability, Internal Conflict, Investment Profile, Law & Order, Military in Politics, Political Risk Rating, and Religious Tensions. One at the time, we also include imports of goods and services (as a share of GDP), trade (as a share of GDP), Foreign Direct Investment inflows (as a share of GDP), the recipient country’s voting alignment with the United States in the UN 29
# _3.2.1 The Poverty-growth Elasticity method_

The PE method computes the GDP per capita growth elasticity of poverty between –2 and –1 and uses that information to nowcast poverty in moment 0. The World Bank currently uses this method to produce poverty forecasts based on GDP per capita forecasts.<sup>4</sup> Let yit be the total household per capita income for household _i_ in moment _t_ , P(yt) is the corresponding poverty rate in moment _t_ defined as the proportion of the population with household t,t−1 income lower than a poverty line _z_ ;<sup>5</sup> g<sup>t</sup> the GDP per capita in moment _t_ ; rg the real GDP per capita growth rate between t and t−1; and ε<sup>t,t−1</sup> the GDP per capita growth elasticity of poverty between t and t−1. Poverty in moment 0 is defined as: However, notice that the value of ε<sup>0,−1</sup> is unknown, since it depends on the actual value of poverty in period 0. However, if ε<sup>0,−1</sup> is assumed to be a unique nonstochastic value over time for any poverty measure, the elasticity between –1 and –2 is an approximation for the unknown elasticity between 0 and –1.<sup>6</sup> Then, poverty nowcast in moment 0 is obtained as follows: where P(y<sup>0</sup> )<sup>E</sup> refers to nowcasted poverty in period 0 obtained from applying the PE method.

The PE method does not use household-level microdata; it only uses aggregated GDP per capita and poverty indicators -i.e., there is no subscript _i_ in equation (5). The level of accuracy of the PE method depends on how similar ε<sup>−1,−2</sup> and ε<sup>0,−1</sup> are -i.e., how accurate is the past information used to estimate the unknown elasticity.

# _3.2.2 The Neutral Distribution Growth method_

The NDG method assumes that all households’ incomes are affected by the same factor—generally GDP per capita growth—from period –1 to period 0. However, given that GDP per capita growth encompasses more economic elements than household income, its growth rate is usually different from the household income growth rate. Let θ be the adjustment factor that accounts for the

> 4 See World Bank (2015).

> 5 Given that we are using a constant, real value for the poverty line, we ignore _z_ in this analysis. 6 Section 5 tests the sensitivity of the method to the selection of other periods.

9
|**Table 5: Co**|**rrelates of Wi**<br>|**thin household**<br>|**Inequality**<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|Theil T within|Theil T within|Theil T within|Theil T within -<br>food|Theil T within –<br>nonfood|
|Dakar|-0.0230*|-0.0177|-0.0208*|0.00223|-0.107***|
||(0.0128)|(0.0127)|(0.0125)|(0.00741)|(0.0219)|
|Other urban areas|-0.0171|-0.00960|-0.0110|0.00395|-0.0668***|
||(0.0106)|(0.0107)|(0.0105)|(0.00528)|(0.0198)|
|Log per cap. hh cons.|0.0379***|0.0380***|0.0369***|-0.00425|0.0533***|
||(0.00860)|(0.00862)|(0.00844)|(0.00283)|(0.0116)|
|Household size|0.00168|0.000615|0.000128|0.00141|-0.00237|
||(0.00186)|(0.00181)|(0.00171)|(0.00150)|(0.00315)|
|Nb of cells in the hh||-0.00218|-0.000857|0.00353|0.00104|
|||(0.00545)|(0.00560)|(0.00245)|(0.0124)|
|Nb of children 0-4 y.o|0.00512|0.00512|0.00439|-0.00127|0.0138**|
||(0.00320)|(0.00329)|(0.00326)|(0.00271)|(0.00659)|
|Nb of children 5-14 y.o|0.00454*|0.00515*|0.00558**|-0.00197|0.0165***|
||(0.00270)|(0.00268)|(0.00266)|(0.00190)|(0.00583)|
|Nb of women 15-65 y.o.|-0.00391|-0.00337|-0.00422|-0.00492**|-0.00322|
||(0.00301)|(0.00310)|(0.00316)|(0.00202)|(0.00608)|
|Nb of elderly 66+ y.o|0.00626|0.00449|0.00412|-0.00126|9.53e-05|
||(0.00597)|(0.00617)|(0.00620)|(0.00315)|(0.0117)|
|2-cell hh, head + other||-0.0219***|-0.0229***|-0.000136|-0.0791***|
|||(0.00748)|(0.00756)|(0.00575)|(0.0152)|
|3+-cell hh: head and wives.||0.0232**|0.0225**|0.00328|0.0644**|
|||(0.0109)|(0.0107)|(0.00811)|(0.0276)|
|3+-cell hh: head, wife (wives), + other||0.0208*|0.0208*|-0.00210|0.0308|
|||(0.0106)|(0.0108)|(0.00417)|(0.0219)|
|3+-cell hh: head + others (no spouse)||-0.0159*|-0.0217**|-0.0186***|-0.0425**|
|||(0.00853)|(0.00913)|(0.00560)|(0.0198)|
|HH without horizontal extensions|0.00737<br>(0.00612)|||||
|All meals taken together|||-0.0538***|-0.0786***|-0.0395*|
||||(0.0129)|(0.0145)|(0.0202)|
|Constant|-0.432***|-0.423***|-0.360***|0.125***|-0.440***|
||(0.102)|(0.101)|(0.0997)|(0.0421)|(0.141)|
|Observations|1,426|1,426|1,426|1,426|1,426|
|R-squared|0.067|0.080|0.109|0.155|0.105|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations.

Note : Correlates of intra-household inequality. Sample of households with at least two cells, N=1426. OLS regressions. Additional controls: religion and ethnicity of the household head. Reference category for household structure is a household with two cells: head and spouse. “2-cell hh: head + other” refers to households composed of 2 cells, where the second cell is not that of the head’s spouse. “3+-cell hh: head and wives” refers to polygamous households, where all the cells are headed by members of the conjugal unit. “3+-cell hh: head, wife (wives) + other” refers to households with at least 3 cells and where at least one of the cells is not headed by a spouse of the head. “3+-cell hh: head+others (no wife)” refers to households with more than 3 cells and where none of them is headed by a spouse of the household head. Robust standard errors in parentheses, clustered at the PSU level, *** p<0.01, ** p<0.05, * p<0.1 41