rafmacalaba/datause-encoder-data huggingface dataset:
Finally, it is important to mention that this paper does not consider the role of essential sectors or workers (whose jobs are not affected by social distancing measures) since there is substantial heterogeneity in these policies between and within countries that cannot be fully accounted for in this paper.<sup>4</sup> Our individual-level measures of WFH amenability, however, can be used to assess the potential impacts of essential work policies.

The rest of this paper is structured as follows. Section 2 describes the data and the main features of the methodology. Section 3 describes the results and Section 4 concludes. The paper includes an Appendix with more detailed information on the methodology, and the estimated indexes by detailed socioeconomic group and country.

# **2. Data**

We use three data sets covering 53 countries at different levels of development to estimate our WFH measure (see Table 1). First, we use the Surveys of Adult Skills of PIAAC (Programme for the International Assessment of Adult Competencies) for 35 countries. This survey collects information about working-age individuals and covers both rural and urban areas. Second, we use the STEP (Skills Towards Employability and Productivity) surveys for 15 developing countries.<sup>5</sup> The surveys are representative of urban areas (except Sri Lanka and the Lao People’s Democratic Republic, which included both urban and rural areas) and collect information about working-age individuals. Finally, we use the Labor Market Panel Surveys (LMPS) for three countries in the Middle East and North Africa (MENA) region, namely the Arab Republic of Egypt, Jordan and Tunisia. These are standard labor force surveys that, in addition to the typical labor market information, also collect data about specific tasks carried out at work. Our final sample for all three data sets includes employed individuals ages 16 to 64 years.

> 4  Garrote Sanchez, Gomez Parra, Ozden, & Rijkers (2020) consider the role of essential workers in their assessment of WFH measures in the European Union.

> 5 We exclude China since the data are only representative of Yunnan province. There is no STEP survey for El Salvador, thereby we use instead a skills survey that includes a similar questionnaire.

5
the impact of war on average education. As a final step, we used propensity scores matching to minimize the selection bias and confounding in the causal effect. The average effect of war as identified by the victimization categories reports a .2 to .9 fewer average years of education for the war victims in comparison to the matched control group. The moderately satisfactory outcomes of double-robust models lower chances of misspecification in the estimated models. The outcomes are also robust when we use a number of sensitivity analyses including alternative matching methods, using different educational outcome variables and estimating the North and the South subsamples separately.

Understanding the mechanism though which war affects education is critical in order to disentangle the causal effects of war on education. The education and war nexus in Côte d‟Ivoire provides a complex picture and in this paper we attempted to explore the channels through which war could possibly affect education. Nevertheless, some caveats apply. The role of third parties, such as NGOs in promoting primary and secondary education in the North is difficult to measure in the estimated causal effect. However, in the presence of NGO activities the estimated coefficients can be considered as a lower bound of the causal effect that war has on education. It is also possible that the existence of internally displaced populations and the timing of the survey could downplay the estimated causal effect. Overall, the empirical evidence derived from our study on Côte d‟Ivoire provides robust support to the existing studies on how war has a detrimental impact on education.

# **References**

Abdi, Ali A. 1998. Education in Somalia: history, destruction, and calls for reconstruction. _Comparative Education_ 34, no. 3 (November): 327-340.

Akresh, Richard, and Damien de Walque. 2008. _Armed conflict and schooling: Evidence from the 1994 Rwandan genocide_ . Policy Research Working Paper 4606. Washington: World Bank.

Bang, H., and J. M. Robins. 2005. Doubly robust estimation in missing data and causal inference models. _Biometrics_ 61: 962–973.

Brück, Tilman. 1997. _Macroeconomic effects of the war in Mozambique_ . QEH Working Paper Series. Queen Elizabeth House: University of Oxford International Development Centre, December.

26
proportion of researchers in the workforce has been continuously declining, compared with an increasing trend in Europe and OECD countries. Nevertheless, Russia is among the world leaders, after the United States, Japan, and China, in the absolute numbers of R&D staff. In 2008, it had 761,300 people engaged in R&D, including researchers, technicians, and support staff. The ratio of 3,191 researchers per million people in Russia that year by far surpasses Eastern European country averages (1,600 in Poland and 876 in Romania), as well as BRIC countries (Brazil 628, China 1,070). The relative number of researchers in Russia is, however, much lower than in economies at the technological frontier (e.g., the United States, 4,663, Sweden, 5,214) (UNCTAD 2010).

Despite significant R&D financial resources and human capital, Russia’s standing has been eroding in the past 15 years. Russia’s relative position is either similar or worse than in the mid-1990s in both R&D spending as a share of GDP and the number of researchers as a proportion of the population (Figure 2). Further limited evidence for 2009 indicates that budgetary expenditure on R&D was cut by around 30 percent that year along with decreases in private sector R&D.

**Figure 2: Evolution of R&D investments and number of researchers, 1996– 2008**

<!-- Start of picture text --> 3,900 1.40<br>1.20<br>3,600<br>1.00<br>0.80<br>3,300<br>0.60<br>0.40<br>3,000<br>Researchers in R&D per million R&D / gdp 0.20<br>2,700 0.00<br><!-- End of picture text --> Source: Author’s elaboration based on WDI (2011).

The output from Russia’s scientific community has been decreasing as the productivity of researchers – published articles in science and engineering journals – has declined (Figure 3).<sup>1</sup> In Russia, academic output has always been low **Figure 3: Science and engineering journal articles per researcher** relative to other countries, which is partly related to the 0.035 performance of R&D in 0.034 government research 0.033 institutions rather than in universities. Worryingly, 0.032 academic productivity has 0.031 decreased in the last few 0.030 years of available data, despite the higher R&D 0.029 investments as a proportion 0.028 of GDP during the 2000s. 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Scientific publications are mostly concentrated in Source: Author’s elaboration based on WDI (2011). physics (30 percent), chemistry (20 percent), and

> 1 Fields considered by the National Academy of Sciences: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.

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Page 41 radio. In Burkina Faso, which has the highest incidence of polygamy, the introduction of cotton has reportedly contributed to an increase in polygamy. Mechanized producers have an average 2.5 wives; those with animal traction, 2.1 wives; and manual cultivators, 1.7 wives. In Burkina Faso, women have had to make up the loss of earnings from their household plots by secondary activities. The survey shows that almost all activities that yield supplementary income - - - such as the labor requirements for processing of groundnut oil, karite butter (65 percent), and beer (32 percent) -- are undertaken by women. Revenue from individual fields accounts for 26 percent.

Source: World Bank OED "Cotton Development Programs in Burkina Faso, Cote d'Ivoire, and Togo,' 1988.

Livestock in Burkina Faso. A village livestock project in Burkina Faso failed because the information and resources to improve the production of small livestock were directed at men, although women were responsible for most of the small livestock (sheep, goats, and poultry). Resources and knowledge about small livestock did not "trickle over" from men to women as expected. Men did not give the women the resources they needed to improve livestock production. Source: Carloni 1987
**Table 3.4. Matching of household surveys to livelihood zones**

||Ethiopia|Lesotho|Malawi|Mauritania|Mozambique|Niger|Nigeria|Zambia|Zimbabwe|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|Maize mixed|2,913|377|26,172|0|2,610|0|0|5,580|12,614|50,266|
|Highland|10,651|2,444|0|0|227|0|29|0|1,695|15,046|
|Roots and<br>lowland|0|0|0|0|0|0|5,705|0|0|5,705|
|Agropastoral|0|0|0|0|0|2,315|2,278|0|0|4,593|
|Pastoral|0|0|0|11,463|0|2,178|0|0|0|13,641|

_Source: World Bank. Note:_ Livelihood zones are generated from Dixon et al. (2019). Agropastoral and pastoral regions in Ethiopia are not included in the analysis. Pastoral includes pastoral and arid pastoral oasis farming systems. Highland includes highland perennial and highland mixed farming systems. Roots and lowland combines root and tuber crop, cereal root crop, and humid lowland tree crop farming systems.

**Table 3.5. Descriptive statistics for variables by** **<u>year and country</u>**

|**Year**|**Country**|**Average per**<br>**capita**<br>**consumption**<sup>**a**</sup><br>**(2011 US$ PPP)**|**Share of**<br>**households**<br>**with land**<br>**ownership**<sup>**b**</sup>|**Share of**<br>**households**<br>**with**<br>**educated**<br>**head**<sup>**b**</sup>|**Share of**<br>**households**<br>**with**<br>**female**<br>**head**|**Share of**<br>**households**<br>**interviewed**<br>**during the lean**<br>**season**|**Share of**<br>**households**<br>**in locations**<br>**considered**<br>**remote**|
|---|---|---|---|---|---|---|---|
|2011|Ethiopia|950|0.98|0.37|0.15|0.34|0.68|
|2016|Ethiopia|1,227|0.96|0.38|0.17|0.35|0.69|
|2017|Lesotho|1,281|0.79|0.16|0.39|0.48|0.44|
|2010|Malawi|590|0.94|0.45|0.20|0.42|0.21|
|2016|Malawi|543|0.90|0.47|0.27|0.58|0.19|
|2019|Malawi|498|0.90|0.50|0.29|0.42|0.22|
|2014|Mauritania|1,782|0.44|0.36|0.27|0.96|0.54|
|2019|Mauritania|1,755|0.42|0.36|0.33|0.00|0.55|
|2014|Mozambique|626|0.96|0.49|0.22|0.12|0.67|
|2018|Niger|869|0.88|0.06|0.13|0.42|0.16|
|2011|Nigeria|738|0.82|0.46|0.08|0.55|0.19|
|2013|Nigeria|873|0.84|0.43|0.08|0.54|0.19|
|2016|Nigeria|859|0.84|0.46|0.06|0.54|0.19|
|2015|Zambia|486|0.89|0.65|0.18|0.67|0.72|
|2017|Zimbabwe|950|0.68|0.47|0.34|0.40|0.69|

_Source: World Bank. Notes_ : a. Or per adult equivalent consumption, depending on the country. b. See appendix B, table B.2 for country-specific definitions used.

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stopping behavior (DSB) may have adverse implications for the outcomes of girls, who will tend to grow up in larger families. Moreover, as Table 7 shows, the differences in family size by child gender are largest in those areas where girls are most likely to suffer from discrimination in other ways, in particular in South Asia.

# **IV. Conclusion**

In this paper we investigate the fertility response to the gender composition of children using data from 159 Demographic and Health Surveys carried out in 65 countries. Gender composition of earlier births is a significant determinant of subsequent fertility in many developing countries. We find fertility behavior consistent with son-preference in many regions of the developing world, with the clearest patterns apparent in South Asia and ECA. Specifically, the absence of sons increases the probability of an additional birth by significantly more than the absence of daughters. We refer to this phenomenon as son-preferred differential stopping behavior (DSB).

When we explore heterogeneity, we find that widely used measures of “modernization,” including urbanization, higher education levels, and household wealth, are associated with an _increase_ in son-preference, as captured in the DSB. The presumption that this manifestation of son preference will dissipate over time is also not supported by the data. The results from regressions using a simple multivariate framework suggest that this may be a result of reductions in family size. While it is possible that greater urbanization, female education and household wealth all reduce a latent son preference, the reductions in fertility that accompany modernization also make it more likely that a latent son-preference can be detected in behavior.

18
# **<u>Nonwhite Women</u>**

|||||||||Sim|ulated y|ears of|schoolin|g||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|Total|
|0|6023|161|164|240|811|32|48|35|180|13|25|188|11|9|18|63|38|4236|12295|
|1|0|1028|1|4|47|1|5|4|14|0|2|26|2|2|1|4|12|365|1518|
|2|30|11|1795|22|126|19|16|13|57|11|16|63|5|3|8|21|17|684|2917|
|3|56|30|17|2429|97|19|20|25|60|12|16|79|5|6|11|32|27|958|3899|
|4|84|15|8|11|5020|41|24|13|52|16|24|101|17|11|10|66|51|1706|7270|
|5|33|16|2|6|29|1974|8|3|29|4|4|37|5|5|11|16|19|815|3016|
|6|23|10|2|2|4|14|1400|4|25|11|8|30|7|5|3|15|20|515|2098|
|7|23|11|5|3|8|6|3|1562|23|7|12|26|9|9|1|11|11|617|2347|
|8|19|7|1|0|3|2|0|0|3031|3|10|40|9|16|7|28|34|947|4157|
|9|11|4|1|1|9|0|1|6|17|890|8|23|5|8|5|13|10|262|1274|
|10|10|4|0|1|1|18|5|6|21|8|988|23|9|3|8|8|15|302|1430|
|11|54|22|4|8|31|17|20|15|59|6|14|3967|20|37|24|65|72|1187|5622|
|12|0|0|0|0|0|0|0|0|1|0|0|0|141|4|1|1|3|21|172|
|13|0|1|0|1|1|0|0|0|0|0|0|0|0|140|1|1|0|39|184|
|14|0|0|0|0|0|0|0|0|0|0|0|0|0|2|214|3|3|33|255|
|15|5|0|0|1|1|0|0|1|1|0|0|0|0|5|2|581|20|89|706|
|16|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|178|32|210|
|17|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|73|74|
|Tota|6371|1320|2000|2729|6188|2143|1550|1687|3570|981|1127|4603|245|265|325|929|530|12881|49444|
<!-- Start of picture text --> foe}<br>><br>&<br>ao<br>Go} OY<br>5x<8 {0 Ow.nL OO~~ O °<br>ot O OGL o<br>Go ~Sy<br>> are) OTN<br>& O > sO<br>2 ‘s<br>oN NAY<br>To SS<br>< SA<br>fe) ‘wo<br>oO<br>0 10 20 30 40<br>Duration of stay, years<br><!-- End of picture text -->
**Figure 7:** Relative risk rates obtained from multinomial logit regressions: improved (above) and worsened (below)

<!-- Start of picture text --> (a) By classes (b) By quintiles<br>MRP # Poor MRP # Poor<br>MRP # Vulnerable MRP # Vulnerable<br>MRP # Lower-middle class MRP # Lower-middle qgp<br>MRP # Middle class MRP # Middle qgp<br>MRP # Upper-middle class MRP # Upper-middle qgp<br>MRP # Poor MRP # Poor<br>MRP # Vulnerable MRP # Vulnerable<br>MRP # Lower-middle class MRP # Lower-middle qgp<br>MRP # Middle class MRP # Middle qgp<br>MRP # Upper-middle class MRP # Upper-middle qgp<br>0 1 2 3 4 5 0 1 2 3 4<br>Relative risk rate Relative risk rate<br>Improved Improved<br>Worsened Worsened<br><!-- End of picture text --> _Source:_ Authors’ calculations based on Morocco’s household surveys.

As opposed to results for “Worsened”, for which most of the coefficients are not statistically significant, the results for “Improved” relative to “Not changed” are particularly insightful with respect to the relation between consumption polarization and household self-reported welfare. Keeping all the other variables constant, if polarization were to increase, the relative probability of declaring that a household’s welfare improved, rather than not changed, is  1  0.431  100  56.9% lower for the poor,  1  0.574  100  42.6% lower for the vulnerable, and  1  0.872  100  12.8% lower for households in the lower-middle class—albeit the 95 percent confidence interval of the corresponding relative risk rate overlaps with 1 (0.713—1.068); hence, this decrease in relative probability is not significant. For middle-class and upper-middleclass households, the same relative probabilities are, respectively,  3.217  1  100  221.7% and  3.425  1  100  242.5% higher.

Similar results hold _vis-à-vis_ a robustness check where model (6) was re-estimated by replacing the factor variable _CLASS_ with a new indicator variable _QGP_ identifying quintiles of expenditure, i.e.: 22
# **Data collection**

Quantitative surveys were administered on tablets and paper questionnaires with all enrolled participants at two time points (wave 1, August 2018 - February 2019 and wave 3, June 2021 - January 2022). Surveys included questions on employment and income, client history, sexual behavior, HIV/STI risks, mental health, and gender-based violence. Surveys at the end of the study period included additional questions about changes in work and sexual activity related to COVID19. An additional survey was administered via phone to a subset of participants in the control arm only (wave 2, July-October 2020), and included a reduced set of survey questions focused on wealth and income, income shocks related to COVID-19, food security, and sexual history.

Qualitative interviews were conducted with a subset of 20 wave 3 participants using a semistructured interview guide to elicit views on the intervention and to understand the effects of COVID-19 on employment, income, client type, and work conditions. The interview guide was informed by preliminary findings from the wave 3 quantitative surveys which indicated that participants had been experiencing changes in income and work activity related to COVID-19, thus the interviews served to gather additional perspectives and insights into the experiences of FSW during COVID-19. Interviews were approximately 60 minutes in duration and audio recorded.

All data were collected in Kiswahili by trained research assistants at Innovations for Poverty Action Tanzania.

# **Data analysis**

Quantitative data were analyzed in STATA version 15.1. Descriptive statistics were generated to 1) assess the demographic characteristics of participants at enrollment (wave 1, 2018) and to compare the overall study population with those who were reached for phone surveys (wave 2, 2020) and at the end of the study period (wave 3, 2021); 2) examine self-reported data on employment and income before and during COVID-19. Linear first difference regression with robust standard errors and chi-square tests were used to generate p-values for differences in total income, sex work income, price per client, and clients per week, food security, and place of work pre- and during COVID-19. Balanced panels were generated to compare outcomes by the study population in each wave. Total and sex work income in waves 1 and 2 were adjusted for inflation (10.4% wave 1, 3.7% wave 2).<sup>22</sup> Food security was expressed both as a binary variable and categorized by frequency.

4
<!-- Start of picture text --> Figure 1. Distribution of estimated propensity scores<br>Comparison Commune Project Commune<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Propensity Score<br>Frequency Kernel-density Plot<br>3<br>2<br>Frequency<br>1<br>0<br><!-- End of picture text --> 30
of past evaluations of SME programs. Section 4 presents the data used in the analysis, which comes mainly from establishment surveys and includes information on many of the main programs and policies discussed in Section 2. Section 5 discusses the methodology employed in the paper, which follows the approached used in the regional study suitable for firm panel data. Section 6 presents the results and indicates which programs and agencies have improved the performance of SME programs through their interventions. Section 7 offers conclusions and future lines of work.

# **2.- SME PROGRAMS**

From 2001 to 2006, the Government of Mexico invested heavily on SME support programs, providing about US$80 billion to about 3.7 million SME firms (Table 1). As part of this study, an inventory of programs supporting SMEs was undertaken, as consolidated information is not available publicly. The inventory tallied 151 separate programs run by numerous different public agencies, some with overlapping mandates and target groups. Although the econometric study does not attempt to evaluate the impact of all these programs, this section provides an overview of the goals, coverage and budgets of the main programs, as a contribution to assist further analysis and evaluation of Mexico’s SME supports.

The development bank Nacional Financiera (Nafinsa) contributed more than half of the total resources to SME programs and seemed to have a significant coverage among firms. The Ministry of Economy (ME) comes second in terms of coverage, supporting more than one million firms, but providing less than 1 percent of the total SME resources. Banco Mexicano de Comercio Exterior (Bancomext) ranks second in terms of resources (about 43 percent of the total), however its programs covered less than 1 percent of the firms. The Ministry of Labor ( _Secretaría de Trabajo y Provisión Social_ —STPS) supports about 6 percent of SME firms, but with a very low budget. On the other hand, CONACyT programs had a significant budget but limited coverage among firms. An overview of the main characteristics of major SME programs in Mexico is provided below, and full analysis of the rules of operation of the programs is available upon request.

3
to send homework by email, students to pay for some minutes to download this, and then work remotely and send back an email with this work.

We collected detailed time-use information (described in the next section), which enables us to determine not just whether students are doing any schoolwork, but also how much. Figure 1 provides histograms to show the distribution of time spent on schooling (on weekdays) for students with computers and internet access, compared to those without internet access. For students with technology, the mean (median) time spent on schooling on the past day is 4.4 (4) hours, compared to 3.6 (3.5) hours for those without internet access. However, we note substantial dispersion, with 23 percent of those without internet access doing no schooling, compared to only 9 percent of those with computer and internet access.

Table 3 examines the breakdown of time spent on schooling by different student characteristics. Overall, 14 percent of students did no schoolwork on the past weekday, 42 percent did 0.5 to 4 hours, and 44 percent did more than 4 hours. In addition to formal schooling, students could be pursuing other educational activities, such as reading for fun. However, the last column shows that only 4 percent of students did any reading that was not part of formal schoolwork in the past day. We see Grade 12 students spend more time on schooling than younger grades, and wealthier students spend more time on schooling on average than students from the lowest wealth quartile. However, the amount of time spent on schooling does not differ that much by ethnicity, region, or mother’s schooling level – grade, wealth, and technology access appear to be the main correlates of how much schooling students are doing.

# **4. Time-Use**

Our surveys took place during a period when schools were closed, and a nationwide shutdown (with exceptions for certain types of workers and businesses) was in place. When asked, 82 percent of students said they had not left their house in the past day. To examine how they spend their time during the shutdown, we used a detailed 24-hour time use questionnaire based on the American Time Use Survey (Bureau of Labor Statistics, 2019). Our module asked the students to recall their activities from 4 a.m. the previous day to 4 a.m. on the interview day. This is recorded in half hour increments, using 24 pre-coded activities developed during pre-testing, plus a code for other activities.

7