rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6 Optimal Network Investments in General Equilibrium**

Having simulated many scenarios in partial equilibrium by modifying one link at a time or jointly adding/improving a selection of (high marginal value) links and computing MA returns, this section goes a bold step further and considers welfare-maximizing network investments in general equilibrium (GE). In particular, using a quantitative framework established by Fajgelbaum & Schaal (2020), I optimize over the space of networks after setting an infrastructure budget. The framework accommodates trade in multiple goods and requires exogenous productivities for the production of each good in each location. Cities must have different productive endowments to create incentives for trade. For my application to the African network, this is a strong limitation, firstly because such city-level productivity data is unavailable, and secondly because global optimization with many goods traded on a large network is computationally infeasible.

To remedy the lack of detailed productivity data, I follow Armington (1969), Fajgelbaum & Schaal (2020), and Graff (2024) and assume that products are differentiated by city of origin and each city produces just one good. To overcome the dimensionality problem, I create 22 traded products differentiated by origin, of which 17 are produced by selected mega-cities<sup>26</sup> , and the remaining 5 are divided among large port-cities (population _>_ 1M and outflows _>_ 1M TEU), large cities (population _>_ 2M), ports (outflows _>_ 0), cities (population _>_ 200K), and smaller cities/towns or unpopulated nodes (which do not produce traded goods).

In my model specification and parameterization, I largely follow Graff (2024). I set the existing level of infrastructure along each link from city _j_ to city _k_ , I _jk_ , to the average link speed in km/h I _jk_ is bounded from below by the present speed (I<sup>min</sup> _jk_ = I _jk_ ), which
to be very high in the transitional economies, followed by Africa. Foreign banks pay lower taxes only in transitional and industrialized economies. Finally, only in Africa and in Latin America do foreign banks achieve higher net profitability than the domestic banks.

# 4. _Empirical results_

Barth, Nolle, and Rice (1997) using bank-level accounting data for 1993 have considered the impact of bank powers on their return to equity for a set of 19 countries. Demirgi.i<;-Kunt and Huizinga (1997) use a broader data set for banks in 80 countries for the period 1988-1993 to examine how a of variety bank variables (including ownership), and additional tax policy, legal and financial structure variables affect banks' net interest income and profitability. The results indicate that foreign ownership (as a bank characteristic) leads to higher net interest margins and profits in developing countries, while this result is reversed in developed countries. Extending this work, this section examines how foreign bank penetration measures affect the operation of domestic banks, specifically their margins, profits and overheads.

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households and governments in multiple product markets and across several countries and regions of the world. The model incorporates well-developed dynamic features such as accumulation of capital through changes in savings and investment.

It is also worth noting some of the key limitations of the model, including the lack of positive dynamic feedback loops concerning the accumulation of knowledge and the absorption of foreign technology through PTAs-facilitated FDI. It also does not allow for modeling of extensive margins in exports. Therefore, the gains captured here may underestimate the eventual impact and represent the lower bound of potential benefits. In contrast, PTA-driven productivity increases in member countries could undermine the competitiveness of non-member countries and exacerbate the detrimental effects on non-member countries.<sup>4</sup>

# **2.2. Micro-simulation model**

The impact of PTAs is differentiated across different types of households and workers. Such heterogeneity is key in determining the poverty and distributional impacts of any trade agreement. We use the GIDD modeling framework in order to model these distributional consequences of the macro-economic results of the PTAs.<sup>5</sup> The GIDD is a top-down macro-micro simulation framework, which distributes the macroeconomic results of the CGE model to households using SUSENAS 2014 as the source of data. The microeconomic model distributes the effects while keeping consistency with the aggregate behavior observed in the macro model. The two models operate mainly through changes in labor supply, skill formation, and real earnings, as a result, they are linked through key specific variables that reflect these changes (see Annexes 1 and 2 for details).

The micro simulation framework is performed in 5 steps. Steps 1 to 4 change the distribution of benefits across individuals, keeping the national average intact; while step 5 applies a distributional-neutral growth for all individuals. Briefly explained, step 1 changes the demographic structure of the household survey according to exogenous population and education projections. The second step allows for the migration of labor from shrinking to expanding sectors in the economy while changes in skill and sectoral wage premia
GMM<br>SE<br>(Cutoff = 1)|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|---|---|---|---|---|
|Intercept|-.71932918|.39269048|-.89149666|.46470872|
|ln Teacher qualification in 1991|.00313908|.00588544|.00077822|.00636871|
|Years of Schooling in 1991|.06201083|.01286978|.06663528|.01474433|
|Total Rainfall|.0000674|.00002795|.00007651|.00002947|
|Government Accountability|.02179158|.03504303|.03300684|.035756|
|Delta ln Employment|-.74141878|.24696933|-.89418358|.27639595|
|ln Transport Cost SP in 1995|-.01160188|.02949758|.00306388|.03553532|
|ln Transport Cost Capital in 1995|-.00840394|.02391369|.00218674|.02606355|
|Delta ln Transferences|.13809767|.12475475|.05152986|.13939323|
|Delta ln Market Potential|1.1562178|.34490082|1.3035762|.36538869|
|crit. fn. test of overid. restrictions|19.804367|19.804367|14.762519|14.762519|
improved maternal mortality, contraceptive access, and girls’ educational attainment (DHS, 2016).

However, a significant gap persists between national policy and local attitudes, particularly in rural areas. Traditional gender norms about women’s subservience to men remain influential, and many are unaware of legal changes. For instance, in our study sample, only 24% of men and 12% of women know that Rwandan law establishes husband and wife as equal heads of household.

# **2.2 Couples Training Intervention**

There is growing global interest in couples-based approaches to IPV prevention. These programs aim to reduce violence by engaging both partners in critical discussions about gendered power

> 6These rates increased from the 2014–15 DHS, which reported 34% lifetime IPV and 21% previous-year IPV. The DHS collects IPV data through face-to-face interviews.

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Policy Research Working Paper 10057

# **Abstract**

Despite concerns that the COVID-19 economic collapse would torpedo international remittances, formal remittances to several developing countries ballooned early in the pandemic. This increase might, however, have reflected a shift from informal channels to formal ones rather than a change in actual flows. This paper employs Mexican data to explore this and finds that remittance channels did change.

The rise in formal inflows was larger among municipalities that were previously more reliant on informal channels (for example, near a border crossing). Households there also experienced a disproportionate increase in bank accounts opened after lockdown measures. The paper also rules out hypotheses related to the US Coronavirus Aid, Relief, and Economic Security (CARES) Act and altruism.

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 ldinartediaz@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
explain climate migration and, as such, detailed agricultural data collected at the household level are key information for studying this nexus. The community survey complementing the household level data collection in the LSMS survey system is also an important source of information for better understanding changes and events affecting entire communities, such as climate shocks, environmental shifts, and migration inflows and outflows.</mark> <mark>The longitudinal nature of many LSMS surveys is clearly a fundamental feature for the study of climateand migration-related issues as it allows individuals, households, and communities to be tracked across time and space. In recent years, LSMS has been advancing the methodological frontier in the collection of quality</mark> intra-household, self-reported, and <mark>individually-disaggregated data in low- and middle-income countries.</mark><sup>26</sup> <mark>This is particularly relevant from a climate migration perspective as it allows for capturing</mark>

> 25 This is done through the <mark>LSMS Integrated Survey on Agriculture (LSMS-ISA) project in collaboration with national statistical offices of client countries. F</mark> or more information about the LSMS-ISA project, see: <u>https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA.</u>

> 26 This is part of the Living Standards Measurement Study – Plus (LSMS+) program, established in 2016. For more information about the LSMS+ program see https://www.worldbank.org/en/programs/lsms/initiatives/lsms-plus.

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is problematic in this context to assume that selection bias becomes ignorable after conditioning on demographic characteristics such as age, gender, and education.</mark> <mark>The second quality check compares reweighted estimates of labor market indicators from the internet survey with other nationally representative data available in the same time period. This comparative data came from PNAD in Brazil, labor force surveys from Sri Lanka, Türkiye, and Indonesia, and the Continuous Household Survey from Kenya. Looking at the employment-topopulation ratio, the reweighted online survey captures the national picture reasonably well in Türkiye, but considerably overestimates the employment-to-population ratio in the other countries. On average, the internet surveys give employment-to-population ratios that are 30 percent above the rates from the benchmark data. We also examined the relationship between internet coverage in a country and how well the online survey in that country captures the employment-to-population ratio accurately. Notably, Türkiye has the lowest deviation and the highest Internet coverage, but other than that there is no systematic relationship between Internet coverage and prediction accuracy.</mark> <mark>We also examine two other labor outcomes, namely, the formal employment-to-total employment ratio, and the self-employment-to-total employment ratio. The sample size for these indicators is much smaller than that for employment as these questions were only administered for a subsample of those employed.</mark><sup>6</sup> <mark>The performance of the online survey in tracking formality</mark>

> 5 Besides non-probability-based sampling, another issue with standard Internet surveys is the potential duplication of responses. The same respondent can potentially answer the survey several times.  However, the RDIT can identify the device of the survey respondent using a combination of the respondent’s IP address, device details such as device type, internet browser, operating system, and other device details, and thus prevent the same device owner from participating in the survey multiple times. While the same person could potentially take the survey from multiple devices, the opportunity cost of time could prevent
## <u>Variable details:</u>

- Migration: indicator equal to one for respondents that report living in a different location outside the baseline district. Movers between Grama Niladharis but within the same baseline district are not considered as migrants. We exclude respondents who are deceased, whose location is unknown, or who did not complete the survey.

- Operating business: for the 10-year endline, the operating business indicator equals one if the respondent is running the business or engaged in another/new business according to question F.5 of the March 2016 survey. It also includes respondents who have moved residence to another city or gone overseas (question F.5) and report non-missing business income during the previous month (question 6.6a). For the 5-year endline, we consider the variable provided in the replication file of DeMel et al. (2012) as it is complemented with observational data not included in the surveys.

- Any employment: for the 10-year endline, we check whether the respondent is (i) still operating a business, (ii) engaged in wage work (according to question F.5), or (iii) currently engaged in any economic activity (question 3.1). For the 5-year endline we consider respondents who are still running a business or employed as wage workers.

# **C.5 Sri Lanka Start-and-Improve Your Business (SIYB)**

We use data from the randomized experiment designed by DeMel et al. (2014), which tested whether business training alone has a different impact on microenterprises than a package of training and an unrestricted grant of about $130. The training component is modeled according to the Start-and-Improve Your Business (SIYB) program, and includes a three day training for potential entrepreneurs (Generate Your Business), a five day course covering the main aspects of starting a business (Start Your Business), and a five day course for existing business owners that want to develop their business (Improve Your Business)
<sup>7</sup> The Ministry of Education and Technical Education (MoETE) oversees KGs and provides public KG classes in public primary schools. The majority of KG enrollment is in the public sector, with private provision at 26%.<sup>8</sup> Private KGs are primarily attended by children from wealthy households (El-Kogali & Krafft, 2015). There is substantial inequality in pre-primary enrollment in Egypt in general, with children from wealthier, more educated households more likely to attend pre-primary (El-Kogali & Krafft, 2015; Krafft, 2015; Krafft & El-Kogali, 2021). For instance, only 16% of children from the poorest quintile of households attended pre-primary, compared to 65% of children from the richest quintile of households. Likewise, only 20% of children with mothers who had no education attended pre-primary compared to 65% of children with mothers with higher education (El-Kogali & Krafft, 2015).

Pre-primary enrollment in Egypt has historically been substantially below the world average but has recently been rising (Figure 1). Around 2000, the pre-primary gross enrollment rate hit 10%, and reached 28% as of 2010 but then plateaued.<sup>9</sup> The “echo” of the youth bulge, the result of the youth bulge entering childbearing age compounded by a rise in fertility in the early 2010s, placed demographic pressure on Egypt’s pre-primary and primary education system (Assaad, 2020; Krafft, 2020; Krafft & Assaad, 2014; Krafft, Assaad, & Keo, 2022).

> 7 Authors’ calculations based on KG sample data.

> 8 Authors’ calculations based on 2018/19 EMIS data. Excludes Azhari (religious) pre-primary.

> 9 According to official reports, the pre-primary gross enrollment rate was down to 22% in the 2021-2022 school year (Ministry of Education and Technical Education, 2022), possibly due to the ongoing COVID-19 pandemic.

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2005 and between July 2011 and June 2012, respectively (NSSO 2005, 2012). They report household consumption information on an itemized form. We use monthly consumption based on the mixed recall period and divide by household size to compute the monthly nominal household expenditure per capita.

To account for spatial price variations, we deflate the resulting nominal expenditure by a year‐specific subnational deflator that differs across state and between rural and urban areas. To allow for comparability across years, we further deflate the resulting expenditures by the nationwide consumer price index.

One caveat of using household‐level surveys is their vulnerability to low response rates among the rich and to underreporting of expenditures among those who respond (Ravallion 2003). As a result, survey‐ based consumption aggregates for nationally representative samples typically fall short of private consumption as measured by national accounts. With the underestimation being larger in better‐off locations, the convergence coefficient ( β<sup>�</sup> or β<sup>�</sup> ) could be biased in the direction of greater apparent convergence. However, the discrepancy between levels of consumption as measured by India’s NSS and national accounts has stabilized since the late 2000s, which mitigates the risk of bias for the period under consideration in this paper (2004 to 2011).

When using living standards as performance indicator to study drivers of growth, the finding can be biased by self‐selection and sorting. Households are heterogeneous in their mixture of skills, energy and entrepreneurship, and the most productive often drawn to more productive places (Combes, Duranton and Gobillon 2008; Combes et al. 2010). To account for this possibility, we consider the growth in 13
participation of the training in the control group does not lead to an underestimation of program impacts.

# **5. Main Results: Impacts on Self-Employment in the short and medium-term**

Table 2 provides the main findings on impacts on self-employment in the short and medium run. The main outcomes include self-employment during the last 7 days (Panel A) and self-employment history between graduation and endline (Panel B). Based on an employment history module included in the medium-term follow-up survey, a binary variable is constructed to capture whether respondents were self-employed at any time during 6-months spells between 2011 and 2013 (Table 2, panel B).

Estimates in Panel A show that entrepreneurship education led to a small increase in selfemployment among program participants approximately one year after graduation (in 2011). The ITT estimate for self-employment during the last 7 days shows a 3 percentage point increase in the probability of being self-employed. For those students who completed the entrepreneurship track (education and coaching), the LATE estimate reveals a 4 percentage point increase in the likelihood of being self-employed in any activity in the last week. Since the rate of selfemployment is low in the control group, these small absolute effects lead to relatively large effect sizes. In fact, a 3 percentage point increase in self-employment is equivalent to a 68 percent increase over the self-employment rate in the control group. These effects on self-employment are slightly weaker when excluding self-employment activities that do not operate year-round (column (2)).

The small positive effects observed in the short-term are not sustained in the longer term. When considering similar outcomes from the endline survey collected for years after graduation (in 2014), ITT and LATE estimates show that there are no lasting impacts from the entrepreneurship track on self-employment among graduates.<sup>15</sup>

> 15 When testing equality of the 2011 and 2014 treatment effects with respect to self-employment, we cannot reject that the impacts are equal for both years. Still, the only significant results are for 2011. In addition
multilevel model that decomposes the error term into idiosyncratic and community shocks. This method relies on interpreting the residuals and may, therefore, be particularly sensitive to measurement errors (Gunther and Harttgen, 2009). On the other, Hill and Porter (2017) include both covariate and idiosyncratic shocks in the estimates of vulnerability to poverty. In this study, we improve on Hill and Porter’s paper by excluding transfers from the welfare measures. Similar to Hill and Porter (2017), using nationally representative data allows us to calculate estimates of vulnerability to poverty along different dimensions (such as rurality and regions).

Malawi suffers from recurrent weather shocks that are detrimental to poverty reduction effort. The latest shock is tropical cyclone _Idai_ that hit Malawi and neighboring countries in March 2019 and led to devastating floods. The post-disaster needs assessment (PDNA) estimated that 500,000 farmers or small businesses had their income affected by this event (Malawi Government, 2019). Three years earlier, in 2016, a severe drought resulted in poor harvests and prompted humanitarian emergency. This historical drought followed the 1 in 500 years flood of January 2015. The country was also affected by floods in January 2016. On 4
increase in access to childcare improves maternal labor force outcomes.<sup>3</sup> In this paper, we specifically examine whether enactment of laws governing the provision of childcare in a country influences women’s labor market outcomes in that country using a difference-in-difference estimation strategy. We utilize the World Bank’s Women, Business, and the Law (WBL) database to obtain information on the dates of enactment and commencement of childcare laws and other indicators related to accessibility, affordability, and quality of childcare services across countries. We merge this data with panel data on women’s labor market outcomes from ILOSTAT and other socio-demographic data from the Our World In Data database and the Barro-Lee Educational Attainment dataset. Our estimation strategy relies upon temporal and country-level variation in the presence of childcare laws to causally estimate their effect on FLFP.

The effect of childcare laws on women’s participation in market work is, _a priori_ , unclear. On the one hand, availability of childcare services can enable women to reallocate their time from unpaid care activities at home to paid market work, increase working hours, productivity, wages and income, and influence the type of employment. Women may also take advantage of work opportunities in the childcare industry to increase their LFP. On the other hand, women’s willingness to do so will depend on the cost of childcare services relative to their potential labor market income, which in turn depends on the structure of the economy and local labor market conditions, among other things. In a 2016 household survey in the European Union (EU), over 40 percent of families cited

> 3See Ajayi et al. (2022), Sanfelice (2018), Calderón (2014), Dang et al. (2022), and Clark et al. (2019) for evidence from Burkina Faso, Brazil, Mexico, Vietnam, and Kenya, respectively.

3