rafmacalaba/data-use-mentions-extended huggingface dataset:
employment elasticities in the studies reviewed a range from 2.17 (Katz and Krueger, 1992) to

-4.6 (Abowd, Kramarz, and Margolis, 1999). The mean and median of all the cumulative elasticities are -0.22 and -0.11, respectively, suggesting that on average the impacts of various minimum wage hikes in the countries under review have been marginally negative.

For LMI countries specifically, DPRU (2016) notes 32 statistically significant minimum wageemployment elasticity estimates from 11 countries. The elasticities range from a negative value of -1.3 to a positive value of 1, with a median elasticity of -0.08 and a mean elasticity of -0.11. Empirical studies on the effect of increased minimum wages on employment in LMI countries thus suggest that increases in wages will have either benign or only slightly negative employment effects.

This is the first study to consider the effect of minimum wages on employment in Mauritius specifically. Using data between 2004 and 2014, we find that a 10 percent increase in the minimum wages brings about a slightly positive effect on employment in the covered sector, with an estimated employment elasticity of 0.113. This is within the range of elasticities found in previous studies of employment effects of minimum wages in LMI countries.

2
Policy Research Working Paper 9475

# **Abstract**

This paper assesses the impact of Ethiopia’s flagship social protection program, the Productive Safety Net Program on the adverse impacts of the COVID-19 pandemic on the food and nutrition security of households, mothers, and children. The analysis uses pre-pandemic, in-person household survey data and a post-pandemic phone survey. Two-thirds of the respondents reported that their incomes had fallen after the pandemic began, and almost half reported that their ability to satisfy their food needs had worsened. Employing a household fixed effects difference-in-difference approach, the study finds that household food insecurity increased by 11.7 percentage points and the size of the food gap by 0.47 months in the aftermath of the onset of the pandemic. Participation in the Productive Safety Net Program offsets virtually all of this adverse change—the likelihood of becoming food insecure increased by only 2.4 percentage points for Productive Safety Net Program households and the duration of the food gap increased by only 0.13 month. The protective role of the program is greater for poorer households and those living in remote areas. The results are robust to various definitions of program participation, different estimators, and different ways of accounting for the non-randomness of mobile phone ownership. Productive Safety Net Program participants were less likely to reduce expenditures on health and education by 7.7 percentage points and less likely to reduce expenditures on agricultural inputs by 13 percentage points. By contrast, mothers’ and children’s diets changed little, despite some changes in the composition of diets, with consumption of animal source foods declining significantly.

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 authors may be contacted at ktafere@worldbank.org.

_The Policy Research
.

Several prominent empirical studies describe the political power channel. Foster and Rosenzweig (2004) use panel data on villages in rural India to show how the share of the population made up of landless poor affects public investment when the village leader is or is not elected. They find that in democratic villages, an increase in the share of landless increases road investment (which the landless want) and decreases irrigation investment (which they do not want) more than in non-democratic villages.<sup>2</sup> Bardhan and Mookerjee (2006), using a longitudinal

> 2Several studies find similar results. Bates (1973, 1976, 1981) shows that some ethnic groups in postindependence Africa increased their access to public funds by increasing their political salience. Varshney (1995), Jaffrelot (2003), Pande (2003), Chandra (2004), and Banerjee and Somanathan (2007) find similar results for low caste groups in India. And Mansuri and Rao (2004) demonstrate, in a review of World Bank community-driven development projects, that most projects are dominated by local elites and do not target the poor.

5
**Decoupling and recoupling**

Yet another source of concern arises from the less-than-perfect correspondence between stocks holdings and the volume of flows. Levy Yeyati and Williams (2010) report that the relationship between the size (that is, the absolute value) of this year ́s flows and end-of-lastyear ́s holdings (based on holdings data from LMF and BoP flow data) is significant for EM equity and FDI but not for debt instruments. However, using EPFR ́s global fund data, they find that the link is significant and stronger for both equity and debt instruments. At any rate, to the extent that flows are only imperfectly characterized by initial holdings, it is useful to proxy globalization both by foreign liability stock (as is typically done in the literature) and flow ratios.

# **b. Globalization, benchmarking and financial recoupling**

Does foreign participation increase the market betas to global returns? Does FG amplify the response of cross-border flows and asset prices in times of global turmoil? A first look at the data appears to contradict this hypothesis. Although in principle there seems to be a significant link between holdings and betas (Didier et al, 2010) a closer look reveals that it is entirely accounted for by the group of non-EM developing frontier markets.<sup>18</sup>

**Table 9** illustrates the point. The first column reproduces the main result in Didier et al. (2010), which tries to explain the financial channel behind the large post-Lehman betas, controlling for market moves with time dummies: US holdings from TIC data do not explain but significantly amplify the sensitivity of EM equity to global returns (measured from a regression of the monthly change in the local stock market index against monthly returns on the S&P 500). However, as the next two columns reveal, the result is entirely driven by frontier markets: US equity holdings do not change significantly the impact of S&P returns on equity returns in either developed or emerging
Table 1: Frequency of shocks Proportion of households reporting shock:

||2005|2011|
|---|---|---|
|Food price|0.02|0.19|
|Drought|0.10|0.05|
|Crop damage|0.09|0.03|
|Job loss|0.01|0.00|
|% Crop loss (from LEAP)|23.49|13.82|
|Average monthly increase in CPI|0.2|1.7|
|Times Woreda experienced over 50% crop loss (past 15 years)||1.59|

Notes: Shocks are self-reported, except for LEAP and CPI data.

in this paper is on vulnerability to absolute poverty, rather than to downturns in the wellbeing indicator (Kamanou and Morduch, 2002; Dutta et al., 2011) or vulnerability as welfare loss due to increased variance (Ligon and Schechter, 2003). We consider our method to be complementary to qualitative studies, or other approaches. We focus on the expected Foster Greer Thorbecke (FGT) measures that are the vulnerability analogues of the static poverty measures used in national policy, combining these with the CalvoDercon measure which is more sensitive to the threat of extreme poverty. Our focus is deprivation in the space of household consumption, suitably spatially deflated and adjusted for household composition.<sup>9</sup> The first applied vulnerability measures that calculated expected poverty and probability of future poverty were developed around 15 years ago and are still most widely used (see Hoddinott and Quisumbing (2003) for a review). Empirically, household poverty is modeled as the realization of a stochastic process that generates consumption outcomes. If realized consumption falls below a poverty line _z_ , the household is classified ex-post as a poor household. Vulnerability measurement, being ex-ante by definition, thus needs a poverty measure, a poverty line, and a prediction of future consumption based on assumptions about
by<br>2014. 23 The simulations in this study use data from 2014; hence, they do not consider the full effects<br>of the reforms, but they highlight the potential benefits these reforms could have in the future.<br>Figure 3. The Herfindahl-Hirschman index in mobile telecommunication, OECD, 2017<br>5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>US$ PPP US$ PPP<br>population)<br>Mobile Cellular Subscriptions (per 100<br>Herfindahl-Hirschman index Mexico Switzerland Australia Greece Korea, South Slovenia Hungary Japan Norway Luxembourg Ireland New Zealand Turkey Belgium Estonia Austria Czech Republic Portugal Germany Netherlands OECD Iceland Finland Slovakia Italy Chile Spain France United States of... Canada Poland Sweden Denmark<br>United Kingdom<br><!-- End of picture text --> In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent), Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market was divided between a few virtual mobile operators that had just entered the Mexican market by 2014.<sup>23</sup> The simulations in this study use data from 2014; hence, they do not consider the full effects of the reforms, but they highlight the potential benefits these reforms could have in the future.

_Source:_ GSMA Intelligence 2018.

23 ITF (2015). Informe Estadístico 4to Trimestre de 2014. Instituto Federal de Telecomunicaciones, July 15, 2015. http://www.ift.org.mx/sites/default/files/comunicacion-y-medios/informes/4ite14-vf-02.pdf 12
The attrition between the two telephone survey rounds was mostly due to not being able to reach the respondents, possibly because of nonfunctioning phone numbers. Only 3 percent of respondents refused to answer the food insecurity telephone survey. To adjust for this attrition, weights were designed for this sample specifically, which we use in all our estimations. In addition to accounting for the sampling and nonresponse issues in the conflict telephone survey, they also adjust for the attrition noted in the food security telephone survey.

The results are presented in appendix C, table C.4, and they can be considered as a robustness check for tables 2 and 3 (columns 1–3 in table C.4 correspond to columns 1–3 in table 2, and columns 4–8 in table C.4 correspond to columns 2, 4, 6, 8, and 10 in table 3). We can see that the results are similar with this smaller sample of households that extends to 2017. Table C.4 also confirms the finding that property-related events are more detrimental to food insecurity than are violent events, and that events perpetrated by insurgents as well as bandits and criminals are statistically significant. Magnitudes of the coefficient estimates are similar across the different specifications.

# 5.4.3.   Unweighted results

In our main specifications, we have used sample weights to adjust for the oversampling of the conflict-affected LGAs as well as for the fact that not all targeted households were reached. To check for any biases the sampling strategy might have induced, we also run the same results without the weights.

The results are presented in appendix E, and they consistently show that they are robust to omitting the weights. Tables E.1 and E.2 present robustness checks for the results on food insecurity (tables 2 and 3) and consumption (tables 4 and 5), respectively. The results are similar across both magnitude and significance when weights are not used.

# 5.4.4.   Alternative lag structure of conflict

In
measures the distance to the ideal intake. The further away the value is from zero (ideal value), the less nutritious is the average menu offered in restaurants in the nutrients considered. Data are weighted.

In order to understand the potential relevance of the diet quality of nearby restaurants, figure 5 displays the share of women ages 15–49 who consume FAFH on a daily basis. It appears that nearly half of all women living in neighborhoods of low socioeconomic background consume FAFH every day, while the share is smaller in mid–socioeconomic status (43 percent) and high socioeconomic status (38 percent) neighborhoods.

**Figure 5: Percentage of women (age 15–49) who consume FAFH every day, by socioeconomic neighborhood**

<!-- Start of picture text --> Socioeconomic groups<br>low mid-low<br>mid high<br>70<br>60<br>50<br>40<br>30<br>20<br>Percentage of women having FAFH every day (mean) 10<br>0<br><!-- End of picture text --> _Source:_ Estimates based on data of ENAHO 2013. _Note_ : Data are weighted.

14
of 2.67 for use in this work. Thus, a 1% decrease in the return to farming, relative to the market wage, results in a 2.67% increase in off-farm labor supply.

**_Modeling Production, Exports and Imports:_** When it comes to modeling trade liberalization, an important characteristic of our CGE model is the explicit treatment of two separate foreign trading regimes.  One is the export processing regime, which receives duty-free imports and is therefore extremely open, with considerable foreign-investment.  The other sector is the ordinary trade regime.  Since the 1990s, processing exports have grown rapidly as a result of their preferential treatment. They now account for more than half of China’s total exports. Obviously, any analysis of external trade and the impact of changes in trade policy must have an explicit treatment of this dualistic foreign trading regime in the model.

Trade is modeled using the Armington assumption for import demand, and a constant elasticity of transformation for export supply. Thus, Chinese products are assumed to be differentiated from foreign products, and exports from China are treated as different products from those sold on the domestic market. The small country assumption is assumed for imports and so world import prices are exogenous in terms of foreign currency. Exports are demanded according to constant-elasticity demand curves, the price-elasticities of which are high but less than infinite. Therefore the terms of trade for China are endogenous in our simulation.

Production in each of the sectors of the economy is modeled using nested constant elasticity of substitution functions, and constant returns to scale is assumed. Sectors differentiate between rural and urban labor that substitute imperfectly between them. This is an indirect means of building into the model a geographic flavor – since some sectors will be located largely in urban

> China’s WTO accession (Hertel, Zhai and Wang, 2004), a sensitivity analysis was conducted with low labor transfer elasticity of 0.6. The most important consequence of this alternative simulation is that the poorest agricultural households no
the time to walk one-way to the source of drinking water, participation in income earning market-based activities, and children’s school attendance and health (as measured by anthropometric indices of growth status). Their empirical study uses cross-sectional surveys from Sub-Saharan Africa (Madagascar, Malawi, Rwanda, and Uganda), South Asia (India, Nepal, Pakistan), North Africa (Morocco), and the Middle East (Yemen). They find evidence that both boys’ and girls’ school enrollment is higher when access to water is better. More specifically, a one hour reduction in the time spent to walk to the water source increases girls’ school enrollment rates by about 10 percentage points in

> 2 In such situations, the quantity of water collected is often below five litres per capita per day (l/c/d) which implies that consumption requirements are likely not to be fulfilled and hygiene is not possible (unless it is practised at the source), Howard and Bartram (2003).

3
on LSMS‐ISA, LFS and DHS data. Table B in Appendix links each indicator to the SDG labor market indicators; Table D in Appendix shows the coverage of the SDG labor market indicators by type of survey. Note:<sup>1</sup> The 19<sup>th</sup> ICLS narrowed the definition of employment to work for pay or profit, thereby no longer classifying subsistence farmers as employed. This new definition requires an additional question about the intended destination of the produced goods. Since most of the surveys reviewed were designed and/or conducted before the 2013 revision of this definition, they did not yet include these questions. We therefore verified whether the surveys enabled classifying the population according to the previous definition of employment.

2 Most LSMS‐ISA and LSMS‐type surveys measure (household) income. Accurately measuring income is, however, notoriously difficult. It is even more complicated to estimate income by occupation. 3 NEET: Not in Education, Employment or Training.

Goal 1 and Goal 2 of the SDGs contain respectively one and two indicators related to employment that refer to poverty, labor productivity and income of small‐scale farmers, which are topics that are covered by LSMS and LSMS‐ISA surveys but not by LFS and DHS.

Goal 5 contains two indicators related to employment. The first indicator is the proportion of time spent on unpaid domestic and care work which is, remarkably, only collected in four LSMS(‐ISA) surveys. While most LSMS(‐ISA) surveys include questions on time spent on collecting water and wood, very few surveys include questions on (child and elderly) care or domestic work such as preparing meals. LFS do not do better: 4 of the 11 LFS include questions on domestic and care work. The second indicator related to employment in goal 5 is the proportion of women in managerial positions. One way to define a ‘managerial position’ is the female share of employment in senior and middle management as defined
**Figure 2: Age-earning Profiles by Level of Education**

<!-- Start of picture text --> 4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65<br>None Primary Secondary Tertiary<br><!-- End of picture text --> Source: AMSSW 2015

# **Mincer Estimates**

Table 2 shows the Mincer earnings function estimates of the average private rate of return to one extra year of schooling. Tables A3, A4 and A5 in the annex present this rate of return by sex, economic sector and level of education.

There are several points to note in Table 2. First, the private return to investment in education is 6.1 percent, which is positive but below the global average of 8-10 percent. In fact, these returns are low compared to worldwide averages. Second, the returns to schooling for women are higher by two percentage points, at 8.3 percent, compared to 6.2 percent for men. In their global survey, 11
87|95|
|Madagascar|2.98|7.49||92|55|—|
|Malawi|2.39|4.54|2.50|92|43|91|
|Niger|7.25|16.07|12.50|55|7|19|
|Nigeria|1.25|4.84|6.50|97|84|74|
|Senegal|9.31|5.77|21.50|100|100|—|
|South Africa|—|2.98|3.00|100|100|100|
|Tanzania|1.60|7.04|5.00|99|59|84|
|Uganda|9.74|5.19|6.00|20|66|55|
|Zambia|2.09|3.26|4.00|100|97|96|
|Sub-Saharan African average|4.81|6.58|7.28|86.51|72.39|73.73|

_Source:_ Adapted from Vennemo and Rosnes 2008 and Africa Infrastructure Country Diagnostic Power Tariff Database.

_Notes:_ See Annex 12 for further details

- (*) it is assumed that a bill is affordable if it is below 5 percent of the household budget.

— Not available.

A frequent argument used for not raising tariffs to full cost recovery levels is the potential impact on poverty. However, empirical evidence suggests that the immediate poverty-related effect of raising tariffs to cost-recovery levels is generally quite small, although it may have second-order effects. Detailed analysis of the effect of significant tariff increases of the order of 40 percent for power and water services in Senegal and power services in Mali confirms that the immediate poverty-related effect on consumers is small, essentially because very few poor consumers are connected to the service (Boccanfuso, Estache, and Savard 2008a; 2008b; 2008c). As
sample by HS-6 phaseout category are very similar to the original at HS-8 level and reported in Table 2.

Table 2: Sample Characteristics at HS6 Level

|Categories|HS6 Goods|HS6-Year|M<br>%|exico’s Import Share<br>1990-1993|Median Duty<br>1993|
|---|---|---|---|---|---|
|Phased-out|451|5,283|16.5%|12.3%|7.7 %|
|Class A|129|1,541|4.8%|14.0%|6.5%|
|Non NAFTA|2,274|15,182|78.7%|73.6%|0.2 %|
|TOTAL|2,854|32,006|100%|0.8%||

> 19We drop observations at HS10, month, country of origin, district of entry level - the most disaggregate level available in census data - when applied duties are larger than 100%. These are outliers that were not subject MFN nor NAFTA rate provisions. Results are not sensitive to this choice.

10
# **1. Introduction**

During most of the 2000s, Guatemala grew at a relatively high rate, fertility rates declined and educational attainment of women rose without precedent.<sup>1</sup> However, female labor force participation (FLFP, henceforth) remained the lowest in the Latin America and the Caribbean region (LCR, henceforth) with a rate of only 32% in 2018 according to the population census. Despite its high-middle-income status, Guatemala is today one of the countries in the world with the lowest rates of FLFP (World Bank, 2021).<sup>2</sup> This under-utilization of female labor poses a large cost to the country’s growth rate. Most women are not engaged in paid jobs outside the house, which generally remain low paid, and tend to be the main caregivers for children or the elderly in the household. There is also wide dispersion in FLFP rates within the country with municipalities where not even one out of ten women aged 15 to 64 years old participate in the labor market and some others where more than half of women participate.

This paper investigates the role of different factors in influencing women’s decision to participate in paid employment in Guatemala to understand the forces behind its trend between 2002 and 2018 and the wide within-country dispersion. We assess the role of: (i) individual and household socioeconomic characteristics (such as the level of education or the number of children), (ii) local social norms and attitudes towards women (at home and in the community), (iii) social public policies (such as investments in pre-schools), and (iv) local labor market characteristics (such as the ratio of male to female employment or the composition of employment across sectors of activity). We conjecture that regional differences in these factors influence women’s willingness and ability to participate in paid employment as they all shape incentives and ultimately individual behaviors. Take the case, for instance, of the local labor markets
change following output performances.

Flows into AE are all declining over the period while flows into F are all increasing indicating a major reallocation of workers into individual farming. These flows are larger from AE but they also become very large from NAE showing a very significant flow of people from non agricultural activities into individual farming. Flows into NAE are higher on average but remain stable from AE and decline from F and from SE. These flows are also confirmed by data on agricultural 29