rafmacalaba/data-use-mentions-extended huggingface dataset:
2012).

Only a small share of entrepreneurs in Bangladesh and other developing countries finance their initial investment through credit by formal lending institutions (C. M. Woodruff and Zenteno 2001; Banerjee and Duflo 2014). For Bangladesh specifically, international surveys and prior studies suggest that only around 20 percent of enterprises had access to start up credit (Khalily et al. 2011). These figures, drawn from international surveys like Doing Business, however, mostly cover formal businesses and may thus provide an optimistic picture of access to credit in settings where many of the enterprises are informal. In areas like rural Bangladesh, many businesses are not registered with authorities and operate at a relatively low scale. Yet, in line with these international data, Figure 3 shows that the majority of businesses in Bangladesh with up to 50 employees, including informal ones, use minimal external sources of funding.<sup>8</sup> Figure 3 further shows that government or private banks rarely Low access to credit is further restricted for low-

> 8Figure 3 distinguishes firms by their initial number of employees. Appendix Figure A2 displays a similar distribution when considering firms’ current number of employees.

11
-21-

54. The results of the analysis are shown in Table 2 below. At the end of 1997, Zambia's fiscal balance remained far from the level required for solvency and sustainability, and fiscal adjustment equivalent to about 2.4 percent of GDP would be needed to bring about solvency in the central government deficit (Table 2). However, achieving fiscal solvency for the public sector as a whole (the quasi-fiscal deficit) would require fiscal adjustment on the order of 16.1 % of GDP. Because of a substantial real devaluation in 1997, fiscal adjustment to achieve sustainability was about 25.3% of GDP, a huge requirement beyond the ability of any government. A comparison of Table 1 and 2 also shows that while the conventional measure of fiscal deficit (Table 1) shows a decline from 1996 to 1997, the solvency and sustainability indicators (Table 2) show that an increase in fiscal adjustment was needed.

**Table 2: Zambia: Public Sector Solvency and Sustainability (percentage of GDP)**

||**1995**|**1996**|**1997**|
|---|---|---|---|
|Solvency||||
|Primary Balance|6.6|6.3|5.3|
|Real GDP Growth|-2.3|6.5|3.5|
|Domestic Debt|7.5|5.8|4.6|
|External Debt|207.5|188.1|178.5|
|Primary Balance for Solvency|22.9|6.7|7.7|
|Central Government Solvency Adjustment|16.3|.5|2.4|
|Non-financial Public Sector Solvency|23.7|12.6|12.4|
|Adjustment||||
|Quasi-Public Sector Solvency Adjustment|32.8|18.8|16.
(formerly Samasource) and Busara Lab,<sup>5</sup> both based in Nairobi, Kenya.<sup>6</sup> Sama, a San Franciscobased company, has established its presence in Kenya, where it employs people to generate and process data for cutting-edge AI projects for Silicon Valley tech firms.<sup>7</sup> As these tech start-ups (including incumbent firms) grow, they require significant investments in order to survive and scale up. However, given the low levels of capital in many African countries, the importance of FDI in providing the needed capital to African technology firms cannot be overemphasized. In addition, big Silicon Valley firms like Google and Microsoft are investing directly in incubator hubs in Africa to train future tech entrepreneurs. Thus, aside from the direct impact of access to high-speed Internet on socioeconomic outcomes, the infrastructure is also a catalyst for investment in the technology services sector.

# **3 Data**

Our main data is project-level data on FDI from fDiMarkets,<sup>8</sup> a subsidiary of the Financial Times. fDiMarkets monitors cross-border investments around the globe and is a major source of data used by the World Bank and UNCTAD<sup>9</sup> to measure cross-border financial flows. It provides detailed information relating to date of announcement, sector, subsector, whether the investment is greenfield (new) or brownfield (expansion),<sup>10</sup> name of investors, source country and city (state), destination country and city, capital investment, and anticipated direct jobs created, among other things. Data on FDI from 2003 to 2018 are used in this study.

We conduct our analysis at the subnational level, specifically at the second administrative level, hereafter referred to as district. For each district and year, we compute the total number of FDI projects and value of investment for each sector. In this paper, we focus on three main sectors: manufacturing,
negotiation.<sup>6</sup> Appendix Table A3 provides a definition for each of the 10 skills under study as well as example items aggregated to construct each skill measure. The complete list of items combined to construct a given skill measure is available on request. In order to create an index for each skill, we first reversed all socio-emotional skills measures such that higher numbers indicate "higher" socio-emotional skills levels. Second, we standardized each item such that the mean is zero and the standard deviation is 1 for women in each study. We then took an average of the items for a given skill based on the categorization exercise. Finally, we standardized the index to the women’s sample in each study. This allows us to later interpret regression coefficients on gender differences relative to the variation between women.

Additionally, we created three aggregated skills measures: _Intra_ that averages all intrapersonal skill indices available in a given study (positive self-concept, emotional regulation, self-control, perseverance, personal initiative and problem-solving and decision-making (PSDM)), _Inter_ that averages all interpersonal skill indices available in a given study (empathy, expressiveness, interpersonal relatedness and teamwork), and _All_ that averages all socio-emotional skill indices available in a given study. Our analysis thus considers 13 measures of socio-emotional skills (10 skill-specific and 3 aggregated measures).

# **3.2 Sample description**

Our pooled sample comprises 41,873 individual-level observations from surveys (see Appendix Table A2 for sampling criteria) conducted in 8 Sub-Saharan African countries (Benin, the Republic of Congo, Côte d’Ivoire, Ghana, Kenya, Mozambique, Nigeria, and Togo) or under the Future of Business Survey (15 SubSaharan African countries). While all studies target both men and women, and more specifically couples in Côte d’Ivoire and Mozambique, only 42.7% of the respondents are women. Most studies’ respondents are adults and young adults, in both urban and rural areas, with an average age of
of higher hierarchy entails the access by individuals to better life opportunities.

From their position of origin, half of the studied youths experienced absolute upward mobility, while one-quarter inherited their provider’s occupation and the rest moved downward to a stratum of lesser hierarchy. Although upward mobility rates have a more significant magnitude, we also saw that the probability of having a destination at the bottom of the stratification is very high for youths of all origin strata, while the probability of having a destination at the top was low, except for those whose origin are already located there. The relative mobility analysis provided additional evidence in this regard, since it showed that the association between the youths’ occupational origin and destination, once the structural changes are controlled for, is mainly determined by intergenerational inheritance, which is stronger at both extremes of the occupational stratification. That is, those who stem from the highest and lowest strata have a high probability of remaining where they begin. The barrier in long-distance occupational upward mobility for those who have their origin in the agricultural stratum is added to this, which increases the difficulty for upward social mobility of youths with more disadvantaged origins.

When observing the behavior of the absolute mobility among the analyzed subgroups, the differences by sex and, even more, by migratory status are emphasized. Women and migrants experience higher upward mobility rates, in contrast to a greater immobility by men and nonmigrants, respectively. This is also reflected on the fact that women and migrants are less likely 52
# C. **<u>Estimation</u>**

We ran ordinary-least squares (OLS) regressions estimating the impact of the interventions on four main outcome variables: 1) taking both hypertension and diabetes screening, 2) taking diabetes screening, 3) taking hypertension screening, and 4) taking at least one of the two screenings. As robustness checks, we further specified four regressions including an increasing number of control variables: only controls for facility and provincial fixed effects, further controls for the level of health facility, additional controls for age and gender of study participants and a final set of controls for a broader set of socio-economic variables collected during the baseline survey.

# **_Implementation Fidelity_**

This study protocol was approved by the Institutional Review Board of the Center of Medical Genetics and Primary Health Care in Armenia (#02570094). A process evaluation was conducted, as part of the qualitative research, to ascertain that the intervention did not deviate from its predefined design. The study was implemented consistent with the approved protocol. All the invitations were delivered to the 1,600 study participants in the intervention groups. In addition, 100 percent of the study participants who were in intervention group 3 were offered the pharmacy vouchers with the invitation letters, and 100 percent of the study participants who received Intervention 4 that reported attending screenings received pharmacy vouchers. There were no reported challenges in redeeming the vouchers in the preidentified pharmacies.

# **4. Results**

# **_A._** **_<u>Baseline characteristics and balance</u>_**

Table 4 presents the means and standard deviations for a range of baseline characteristics for the sample mean, 4 intervention groups and the control group. Appendix Table 1 reports the p-values of t-tests of whether the differences observed in the five groups are statistically significant at conventional levels indicated by the stars next to the p-value.

The mean age of our sample is 51 years old. Only 1 percent of the
Policy Research Working Paper 9118

# **Abstract**

This study examines the association of unemployment variation with intimate partner violence using representative data from thirty-one developing countries, through 2005 to 2016. It finds that a 1 percent increase in the male unemployment rate is associated with an increase in the incidence of physical violence against women by 0.50 percentage points, or 2.75 percent. This is consistent with financial and psychological stress generated by unemployment.

Female unemployment rates have the opposite effect, a 1 percent decrease being associated with an increase in the probability of victimization of 0.52 percentage points, or 2.87 percent. That an improvement in women’s employment opportunities is associated with increased violence is consistent with male backlash. The study finds that this pattern of behaviours emerges entirely from countries in which women have more limited access to divorce than men.

This paper is a product of the Knowledge and Strategy Team, 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 s.b.rawlings@reading.ac.uk.

_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
Br 68.8 (birr of 2015), while new entrants in the bottom consumption decile will gain an average of Br 2.9. This translates into a relative gain of 0.25 percent among the top decile and 0.09 percent among the bottom decile. New entrants in the top decile will also benefit more than new entrants in the bottom decile, but less than proportionately in the case of the relative welfare gains among current consumers.

**Table 6. Welfare Effects, Loss of Ethio Telecom Monopoly, New Users**

|_Consumption value_|_All_|_Poorest_<br>_1_|_2_|_3_|_Co_<br>_4_|_nsumption d_<br>_5_|_ecile_<br>_6_|_7_|_8_|_9_|_Richest_<br>_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Annual mean per capita<br>consumption, birr|10,111|3,062|4,775|5,928|6,908|7,867|8,806|9,851|11,621|14,779|27,525|
|Absolute incidence, birr|28.2|2.9|10.7|15.1|17.3|21.8|17.8|24.8|42.1|60.4|68.8|
|Relative incidence|0.28|0.09|0.22|0.25|0.25|0.28|0.20|0.25|0.36|0.41|0.25|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Consumption values
br>_Yit_ = _β_0+_βEExtensioni_ +_βI IndRecommi_ +_βGGranti_ +_βFFlexiblei_ +_αc_ +_εit_, We include randomization strata fxed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard<br>deviation of the outcome for the omitted group of farmers, which in Column 1 corresponds to those who did not the grant (T4). In Columns 2-7, the omitted category is the control group of farmers. We also report p-values of linear<br>combinations of the estimates coeffcients that map into the original study design. In Column 1, the dependent variable is a dummy that takes value 1 if the farmer reported remembering the sowing recommendations were given to<br>him or her as part of our intervention. This question was asked in August 2015 during our Follow-up survey. In Column 2, the dependent variable is a dummy that takes value 1 if the farmer kept record of the soil analysis given to<br>him or her as part of our intervention. 33 farmers did not answer this question. In Column 3, the dependent variable is a dummy that takes value 1 if the farmer reported that he or she would probably follow the recommendations<br>in the following season. Data for Columns 2 and 3 were collected as part of the Commercialization survey, conducted in June 2016. In Column 4, the dependent variable is a standardized index of two individual dummies that take<br>value 1 if the farmers reported trusting the recommendations given by their input suppliers and IPAMPA, a local AES company. To compute the index, we standardize each dummy individually, add them all and standardize the<br>sum. We use the mean and standard deviation of the control group as reference for the standardized index. The questions in the index in Column 4 were asked as part of our Follow-up survey conducted in August 2015. 40 farmers<br>did not answer these questions. The dependent variable in Column 5 is the number of
# **1 Introduction**

The number of workers moving across international borders for work is increasing. By 2013, international migrants accounted for over 12 percent of the total population in the global North, over six times the share in 1990 (UNDESA, 2015). The 2011 Gallup poll estimates that more than 1 billion people want to migrate abroad for temporary work (Esipova, Ray, and Publiese, 2011). Anecdotes and media reports abound on the risks these migrants are undertaking in search of a better life for themselves or their families. For example, more than 3,770 migrants died in the Mediterranean Sea in 2015 on their way to Europe (International Organization for Migration, 2015). In 2014, about 445 people died while trying to cross the US-Mexico border (Carroll, 2015). A high death toll is not the plight only of those who try to migrate illegally or who are forced to move. _The Guardian_ reports that almost 1,000 workers, all of whom were legal migrants from Nepal, India and Bangladesh, died in Qatar in 2012 and 2013 (Gibson, 2014).

The intense desire to migrate despite the risks has led policymakers to be concerned that potential migrants may have unrealistic expectations about migration. In countries like Nepal, where more than 7 percent of adult, working-age males leave the country for work abroad in a given year, there is great concern that they make the decision recklessly.<sup>1</sup> Policymakers and academics often have contradictory views on whether the level of observed migration is higher or lower than optimal. Policymakers believe that most potential migrants are misinformed – in particular, that they expect to earn more than they actually do upon migration and underestimate the risks of working abroad. Many policymakers also believe that potential migrants, knowingly or unknowingly, are trading off risks at unreasonably low prices to the extent that their experience is often termed exploitative.<sup>2</sup> Put together, these notions suggest that the observed
35|191|3.2|
|UAI|6|1,085|63|2|1|2|68|1.1|
|RAI||6,969|0|12|5|24|40|0.7|
|Total|6|10,554|63|227|10|81|380|6.3|
|||||**Pragmatic**|**scenario**||||
|National|0|1,193|0|42|0|19|61|1.0|
|Regional|0|1,307|0|62|0|22|85|1.4|
|UAI|6|462|20|2|1|2|25|0.4|
|RAI||6,969|0|3|1|50|54|0.9|
|Total|6|9,931|20|109|2|93|224|3.7|

_Source:_ Adapted from Carruthers, Krishnamani, and Murray (2009). _Note:_ UAI = Urban Accessibility Index; RAI = Rural Accessibility Index.

19
values from some third party such as a tax assessor, or the self-appraised property values of homeowners. Regarding the latter, several studies have pointed towards a large variance of self-appraised values which in large enough samples like ours, positive and negative errors tend to cancel each other out (e.g., Follain & Malpezzi 1981; Goodman & Itter 1992). The difference between the transaction and the listed property price is dependent on multiple factors including the overall state of the housing market, the demand for housing, the time the property remains on the market before willing and able buyers come forward, as well as cultural aspects pertaining to e.g., negotiation. Despite these issues, in the absence of transaction price data sets, listing prices offer a good proxy to estimate the state of the housing market as researchers have consistently found rather low deviations between listed price and transaction price (Arnott 2009; McGreal & Taltavull de La Paz 2013; Haurin et al. 2010).

# 3.4 Descriptive Statistics

Across the _final data set_ of the five EMDEs in our sample, we observe different patterns in terms of availability of apartments versus single family houses (Figure 1). While in Costa Rica, Pakistan, and South Africa, the number of apartments and houses are well distributed, Albania and Morocco have many more apartments than houses available within the data. In South Africa and Costa Rica, the right-hand side of the distribution is dominated by rather expensive single-family houses and only few, expensive apartments. Similar patterns are observable in the price-size relationship (Figure 2). While in Pakistan the price and size differences between houses and apartments are marginal, South Africa – and to a lower extent Costa Rica – have noticeable price differences between houses and apartments, which could potentially be attributed to composition effects as houses and apartments are not equally located in all places. The strength of observed correlation between house price and size also varies across countries (Figure 3). In South Africa, this correlation is weakest –
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(Uneven) Progress Against Poverty.” _Journal of Development Economics_ , 82 (1): 1–42.

- Ravallion, Martin, and Monika Huppi. 1991. Measuring Changes in Poverty: A Methodological Case Study of Indonesia during an Adjustment Period. _The World Bank Economic Review_ 5 (5): 57–82.

Rural Income Generating Activities (RIGA) data. <u>http://www.fao.org/economic/riga/en/</u>

- Shapley, L. 1953. “A value for n-person games.” In, _Contributions to the Theory of Games_ , ed. H. W. Kuhn and A. W. Tucker 2. Princeton, N.J.: Princeton University Press.

- Shorrocks A.F. 1999. Decomposition Procedures for Distributional Analysis: A Unified Framework Based on Shapley Value. University of Essex and Institute for Fiscal Studies. Mimeo.

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# **Figure 7. Employment vs Export Sales a. 2020**

<!-- Start of picture text --> 10 15 20 25<br>ln Export Sales<br>c. 2013<br>5 10 15 20 25<br>ln Export Sales<br>8<br>6<br>4<br>ln Employment<br>2<br>0<br>10<br>8<br>6<br>4<br>ln Employment<br>2<br>0<br><!-- End of picture text -->

# **b. 2016**

<!-- Start of picture text --> 10 15 20 25<br>ln Export Sales<br>10<br>8<br>6<br>4<br>ln Employment<br>2<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using data from the Enterprise Survey for Egypt, 2013, 2016, and 2020.

39
- Conover, E., M. Khamis, and S. Pearlman (2022): “Job quality and labour market transitions: Evidence from Mexican informal and formal workers,” _The Journal of Development Studies_ , 1–17.

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