rafmacalaba/datause-encoder-data huggingface dataset:
ies in Kenya (e.g. Kremer et al. (2002) for primary school teachers, Chen and Kremer (2002) for preschool teachers). A possible explanation for this multiple evidence that teachers are little responsive to incentives is that, beyond a certain level, attendance starts to be very costly to the teacher due to factors like sickness or social obligations. For example, the Welfare Monitoring Survey (1994) ...nds that rural poor in rural Western Kenya missed an average of 5.9 days of work per month due to sickness. Over half of the respondents reported that they had fever or malaria in the preceding two weeks. In addition, people miss work for social reasons like funerals. For example, the parent representatives in the treatment schools were reported absent due to a funeral in 3 percent of the cases.

# 6 Price e¤ects and the generalizability of the ITT estimate

External aid can have substantial implications for school ...nance, which in turn can in‡uence outcomes like school participation. Assume an NGO enters a region and picks out a school for allocating aid to that school. Ceteris paribus, if the program has positive value to the parents, it will increase their valuation of school attendance in that school. If the market for schooling was competitive before the NGO started working with the school, the intervention can change competitive relations between the providers of education. A program that provides free school meals to the children attending school does not change the cost curve of supplying education, but it does change the demand for education in that school. For each unit of education provided, parents are willing to pay more, because their children receive a perk with each unit of education. By assigning pupils to their original treatment school rather than to the treatment status of their current school, the intention-to-treat estimator avoids upward biases that would arise from transfers into the treatment school One problem with the ITT estimator is that it does not take into account the general equilibrium e¤ects of the program on prices. As mentioned in section 3, school fees provide the bulk of funding in Kenya’s rural preschools. ICS instructed the treatment preschools not to raise their fees as a response to the program. The data con...rm that o¢ cial fees in treatment schools were not higher than in the comparison schools after the start of the program (Table 15, column 1). However, since o¢ cial fees were not binding prior to the program (cf. section 3), the treatment schools had the possibility of becoming more strict about fee payments of children who showed up in the preschools without transgressing the instructions, and the data strongly suggest that they did indeed become more strict. In addition, prices dropped in comparison schools located close to a treatment school. The important point is that some of these price e¤ects are not likely to occur 38
# **Table 2.  Distribution of Firms by Formality Degree**

|<br>|**Average Tax**<br>**Compliance**|<0.50|**Share o**<br>Degree of Ta<br>0.50-0.75|**f Firms**<br>x Compliance<br>0.76-0.99|1|
|---|---|---|---|---|---|
|**Size:**||||||
|**Micro (1-9)**|0.713|22%|18%|19%|40%|
|**Small (10-49)**|0.767|17%|18%|18%|47%|
|**Medium (50-99)**|0.817|13%|14%|19%|55%|
|**Large (100-249)**|0.825|12%|12%|17%|58%|
|**Very large (250+)**|0.873|10%|7%|15%|69%|
|**Tax rate perceived as an obstacle**|0.752|18%|19%|18%|45%|
|**to firm's activity**||||||
|**Tax administration perceived as**<br>**an obstacle to firm's activity**|0.746|18%|20%|19%|43%|
|**Access to finance perceived as an**|0.743|19%|19%|17%|45%|
|**obstacle to firm's activity**||||||
|**Have a line of credit**|0.799|14%|16%|17%|53%|
|**External finance>50%**|0.811|17%|14%|15%|55%|
|**Informal Finance> 50%**|0.699|29%|17%|17%|36%|

**Table 3.  Access to Finance Variables**

|**Size**|**Formality**|**Credit**|**External**<br>**Finance**|**Informal**<br>**Finance**|**Local**<br>**bank**|**Foreign**<br>**bank**|**Banks**|
|---|---|---|---|---|---|---|---|
||(mean)|(mean)|(mean)|(mean)|(mean)|(mean)|(mean)|
|**Micro (1-9)**|0.58|0.21|0.23|0.19|0.19|0.01|0.19|
|**Small (10-49)**|0.71|0.44|0.40|0.17|0.35|0.02|0.36|
|**Medium (50-99)**|0.77|0.63|0.55|0.12|0.48|0.05|0.50|
|**Large (100-249)**|0.78|0.64|0.53|0.08|0.48|0.07|0.50|
|**Very large (>250)**|0.84|0.64|0.55|0.07|0.47|0.11|0.50|
|**Total**|0.72|0.48|0.43|0.14|0.37|0.05|0.39|

_Sample restricted to “Credit” regression._ 24
_Firm profits:_ these are monthly, and were asked directly of the owner as “the total income of the business during each of the last month after paying all expenses including wages of employees, but not including any income you paid yourself”. This follows the wording and recommendation of De Mel et al. (2009).<sup>20</sup> We consider several transforms of profits to deal with outliers and firm closure. This includes unconditional profits (which put zeroes in for closed firms) truncated at the 99<sup>th</sup> percentile, the inverse hyperbolic sine of profits, truncated profits conditional on the business operating, and log profits conditional on operation. Nominal values were deflated to real values using the Consumer Price Index for Colombo, gathered by the Sri Lankan Department of Census and Statistics.

_Firm sales:_ these are firm sales in the past month, deflated into real terms using the CPI. As with profits, we consider several transforms of the raw data to account for outliers and firm closure, with the variables defined analogously to profits.

# _Business Practices score:_

The _total score_ – the composite business practice score -- ranges from a minimum of -1 to a maximum of 29. The total is the sum of the following component scores: the _marketing score_ , the _stock score_ , the _records score_ , and the _financial planning score_ .

The _marketing score_ ranges from 0 to 7, and it is calculated by adding one point for each of the following that the business has done in the last 3 months:

- Visited at least one of its competitor’s businesses to see what prices its competitors are charging

- Visited at least one of its competitor’s businesses to see what products its competitors have available for sale

- Asked existing customers whether there are any other products the customers would like the business to sell or produce

- Talked with at least one former customer to find out why former customers have stopped buying from this business

- Asked a supplier about which products are selling well in this business’ industry

- Attracted customers with a special offer

- Advertised in any form (last 6 months)

The _stock score_ ranges from -1 to 2, and it is calculated by subtracting one point

- If the business runs out of stock once a month or more

> 20 De Mel, Suresh, David McKenzie and Christopher Woodruff (2009) “Measuring Microenterprise Profits: Must We Ask How the Sausage Is Made?”, _Journal of Development Economics_ , 88(1): 19-31.

50
developed economies and a region of all developing countries except the poorest SubSaharan African countries. The sectoral aggregation is shown in Table 2.

The import tariffs in GTAP are averages of MFN applied tariff rates obtained at the tariff line level and aggregated up to the GTAP concordance using trade-weights. In the case of' agricultural products, where non-tariff barriers have played a very important role, the distortions, particularly in the grains, livestock, and dairy product areas for the OECD countries, were characterized using work by Tsigas (1998). For non-OECD countries, indirect estimates of protection (including through non-tariff barriers) were derived using OECD data and protection information from the GTAP version 3 data base. The version 4 GTAP data base is designed to capture some of these large distortions in agricultural trade.

Table 2 provides "MFN" import weighted tariffs of version 4 of GTAP for each of the QUAD countries, as well as the LDC tariffs offered by QUAD countries. LDC tariffs (including Lome preferences in the case of the EU) have been calculated as follows. First, we filtered the MFN and LDC tariff data communicated by each of the QUAI) members to the Integrated Data Base of the WTO and the Trade Policy Review (in the case of the EU) in 1999 to transform it from the 6-digit Harmonized System to the 15 categories of our GTAP aggregation (we exclude services as they are not included in the preferential access initiatives). Then a preference margin was calculated for these 15 categories. The preference margin calculated from WTO tariff data was then applied to the GTAP import-weighted "MEN" tariffs in order to obtain the LDC tariff rates reported in Table 2.13 Even though not all the SSA countries in our sample are LDC countries, they were all treated as beneficiaries of LDCs preferential regimes in our simulations.1<sup>4</sup> The non-LDC countries are Cote d'Ivoire, Cameroon, Gabon, Ghana, Kenya, Nigeria, Senegal and Seychelles - of which only Gabon, Nigeria and Seychelles are not HIPC countries. In the

> 13 Note that the preferential margin obtained from the WTO was calculated using simnple averages whereas MFN tariffs in GTAP are import weighted tariffs.

14
than three or four months to ensure they make enough. The RSE policy recognizes the higher costs for these two countries, allowing workers from these countries to work a maximum of nine months in an eleven month period (compared to a maximum of seven for all other countries), but few workers have been hired for this long. Table 2 shows some small growth in RSE participation from Kiribati in seasons 4 through 6 due to the ongoing facilitation measures, but that Tuvalu continues to average only 50 to 60 workers each season.

While the largest benefits of participating in such a program accrue to the migrant workers and their families, there is often a hope among policy makers that there can also be broader benefits to other households in the sending countries. There are several channels through which this can occur. The most direct impact is through migrants contributing directly to community projects. As an example, NZDoL (2010) notes that the local government in the Lolihor area of North Ambrym, Vanuatu, requires all workers to contribute approximately NZ$200 a season towards a community fund that then supports projects run by local women such as small business initiatives and crop production, as well as going towards a scholarship fund for local children. In our surveys of communities in Tonga, we found communities saying they had funded the local village water supply, street lighting, a school scholarship fund, and community halls out of contributions from seasonal workers. Based on our impact estimates, the average migrant contributes approximately 5 to 10 percent of the net gain in income per migrant to such endeavors.

The more difficult channel to measure is impacts on local development through multiplier and general equilibrium effects. For example, if migrants and their families spend the increase in income on local production, this raises incomes for the sellers of these products, but can also increase prices if supply is limited. Migrants leaving may open up jobs for the unemployed, or push up wages for other workers who have fewer individuals to compete with. However, our sense in the case of the Pacific Islands is that to date these impacts have been small. Many workers come from rural backgrounds, where their main occupations would have been subsistence farming, and there is not a strong wage labor market. Moreover, much of the spending appears to have gone on imported durable goods and renovations, potentially having limited multiplier effects.

# **6. Conclusions**

24
- **Bazzi, Samuel.** 2017. “Wealth heterogeneity and the income elasticity of migration.” _American Economic Journal: Applied Economics_ , 9(2): 219–255.

- **Bazzi, Samuel, Lisa Cameron, Simone G Schaner, and Firman Witoelar.** 2021. “Information, intermediaries, and international migration.” National Bureau of Economic Research.

- **Beam, Emily A, David McKenzie, and Dean Yang.** 2016. “Unilateral facilitation does not raise international labor migration from the Philippines.” _Economic Development and Cultural Change_ , 64(2): 323–368.

- **Beine, Michel, Simone Bertoli, and Jes ́us Fern ́andez-Huertas Moraga.** 2016. “A practitioners’ guide to gravity models of international migration.” _The World Economy_ , 39(4): 496–512.

- **Bellemare, Marc F, and Casey J Wichman.** 2020. “Elasticities and the inverse hyperbolic sine transformation.” _Oxford Bulletin of Economics and Statistics_ , 82(1): 50–61.

- **Bertoli, Simone, and Jes ́us Fern ́andez-Huertas Moraga.** 2013. “Multilateral resistance to migration.” _Journal of Development Economics_ , 102: 79–100.

- **Bian, Bo, Jean-Marie Meier, and Ting Xu.** 2023. “Cross-border institutions and the globalization of innovation.” _LawFin Working Paper_ .

- **Biehler, Nadine, David Kipp, and Anne Koch.** 2024. “The potential of bilateral migration agreements: From symbolic politics to practical implementation.” SWP Comment.

- **Bither, Jessica, and Astrid Ziebarth.** 2018. “Creating legal pathways to reduce irregular migration? What we can learn from Germany’s “Western Balkan regulation”.”

- **Borusyak, Kirill, Xavier Jaravel, and Jann Spiess.** 2024. “Revisiting event-study designs: robust and efficient estimation.” _Review of Economic Studies_ , rdae007.

- **Bryan, Gharad, and Melanie Morten.** 2019. “The aggregate productivity effects of internal migration: Evidence from Indonesia.” _Journal of Political Economy_ , 127(5): 2229–2268.

- **Bryan, Gharad, Shyamal Chowdhury, and Ahmed Mushfiq Mobarak.** 2014. “Underinvestment in a profitable technology: The case of seasonal migration in Bangladesh.” _Econometrica_ , 82(5): 1671–1748.

25
2 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **1 Introduction**

Cities are central to the productivity and growth prospects of the Latin American and Caribbean region (LCR). The vast majority of the region’s output is generated in cities – Mexican cities generate 87 percent of gross value added (Kim and Zangerling, 2016)), while Argentine cities contributed almost 80 percent of national GDP in 2007.<sup>1</sup> Yet it is unclear whether LCR’s cities are realizing their full potential – for the region’s major economies, GDP per capita levels are well below what one would predict based on their urbanization levels,<sup>2</sup> and recent policy reports by, for example, the McKinsey Global Institute have argued that cities in the region are being weighed-down by excessive diseconomies of agglomeration (Dobbs et al., 2011; Cadena et al., 2011).

According to the United Nations, Latin America and the Caribbean is the most urbanized region on the planet with 80% of its population living in cities (UN, 2012). The rapid urbanization of cities, together with the loss of density, has caused the expansion of cities beyond their administrative borders, absorbing surrounding urban areas and creating large urban extents (metropolitan areas) covering several administrative units. Since no local government has the tools to address all challenges and opportunities within a metropolitan area on its own, this form of urbanization poses new challenges to local authorities in terms of governance and integration. The way in which these large, multicity, urban extents manage aspects such as transportation, urban planning, infrastructure provision and other social and economic affairs, can make the difference between enjoying the benefits of prosperous economies of agglomeration or suffering the consequences of the diseconomies of agglomeration (Ahrend et al., 2014).

Unfortunately, there are two aspects that make it difficult for academics to make public policy recommendations for Latin American cities (LAC). First, the existing empirical evidence is mostly concentrated on metropolitan areas in developed countries; and second, there is supporting evidence in favor of the three models of metropolitan governance: The polycentrist model, which advocates

> 1 McKinsey Global Institute Cityscape database, version 1.1

> (http://www.mckinsey.com/insights/urbanization/urbanworld).

> 2 This conclusion is based on GDP per capita data from WDI and urban population share data from the UN’s World Urbanization Prospects (2014 Revision) database <u>(http://esa.un.org/unpd/wup/).  One issue that arises with the urban</u> population data from the WUP database, however, is that it is based on official national definitions of urban areas, which differ from country to country.  An important question to address, therefore, is the extent to which the apparent underperformance of LCR countries is driven by this inconsistent measurement of urban population shares.  This will be addressed in another of the report’s background papers.
25 The policies hiave however different time dimensions of implementation. Improving the tax administration is a more prolonged task than changing the structure and rates of taxes and penalties.3 ' In the short term, because of limited administrative resources, it may be possible to detect and discipline only a small number of marny evaders. The administration will tend to focus on Aarge tax payers, hence leaving smaller potential taxpayers to confront lower risks of detection. There are also limitations on penalties for evasion, in particular in the new post-communist democratic environment.

Given the administrative constraints and the propensities for tax evasion, what form should the tax structure take?<sup>39</sup> Beyond adherence to general principles of tax design,40 is replication of the tax structure of developed market economies appropriate for the transition? The conditions of the transition suggest that in choosing a tax rate structure, regard be taken of a transition-Laffer curve that encompasses both supply incentives and tax evasion. That is, it is possible that lower marginal tax rates combined with publicized penalties may increase revenue.

39/ See Bird and Casanegra de Jantscher (1992).

391 An answer to this question could be sought in models that specify revenue-maximizing tax and penalty structures in the presence of opportunities for evasion and probabilities of detection; see for example Polinsky and Shavell (1991) and Bebchuk and Kaplow (1993). In practice, the theoretical results are not readily translatable into concretely defensible, detailed specifications of tax and penalty rates, because of information required on individuals' factor supply elasticities and levels of risk aversion.

CY See Shalizi et al. (1991), the World Bank (1993).
Policy ReseaRch WoRking PaPeR 4833

# **Abstract**

Completing additional years of education necessarily entails spending more time in school. There is naturally a rather mechanical effect of schooling on fertility if women tend not to have children while continuing to attend high school or college, thus delaying the beginning of and shortening their reproductive life. This paper uses data from the Kenyan Demographic and Health Surveys of 1989, 1993, 1998, and 2003 to uncover the impact of staying one more year in school on teenage fertility. To get around the endogeneity issue between schooling and fertility preferences, the analysis uses the 1985 Kenyan education reform as an instrument for years of education. The authors find that adding one more year of education decreases by at least 10 percentage points the probability of giving birth when still a teenager. The probability of having one’s first child before age 20, when having at least completed primary education, is about 65 percent; therefore, for this means a reduction of about 15 percent in teenage fertility rates for this group. One additional year of school curbs the probability of becoming a mother each year by 7.3 percent for women who have completed at least primary education, and 5.6 percent for women with at least a secondary degree. These results (robust to a wide array of specifications) are of crucial interest to policy and decision makers who set up health and educational policies. This paper shows that investing in education can have positive spillovers on health.

This paper is a product of the Human Development Division, South Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cferre@worldbank.org@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
<!-- Start of picture text --> 1400 500<br>All<br>1200<br>South Sudan 1500<br>400<br>1000<br>800<br>South Sudanese 1000 300<br>Civil War<br>600<br>200<br>400 500<br>200<br>100<br>0 0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2014 2016 2018 2020<br>Year Year Year<br>Total refugee aid (millions)<br>Number of refugees (thousands)<br>Newspaper articles about refugees<br><!-- End of picture text --> Figure 1: : Impact of the South Sudanese civil war on the number of refugees in Uganda (left), the salience of the refugee issue in Uganda (center), and refugee aid levels from international organizations (right). The red line indicates the onset of the war in December 2013. Salience of the refugee issue is measured as the number of local newspaper articles that mention refugees or migration. Data sources: UNHCR, Lexis-Nexis, Factiva.

from other East African countries, mainly the DRC (27 percent) and to a lesser extent Burundi, Somalia, Rwanda, Eritrea, and Ethiopia (World Bank, 2019).

Uganda’s liberal refugee and asylum policies—as codified in its 2006 National Refugees Act— partly explain why it attracts so many refugees (Blair, Grossman and Weinstein, 2021 _b_ ). Based on data collected on refugee and asylum policies in a large sample of 128 developing countries, Blair, Grossman and Weinstein (2021 _a_ ) ranked Uganda as having the second-most liberal refugee and asylum policies in their sample. It is also a party to the 1951 Convention Relating to the Status of Refugees and its 1977 protocol, and a signatory of the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa.

# **Policy framework for refugee hosting in Uganda**

Unlike in many other countries—including neighboring Tanzania and Kenya, where refugees are forced to live in camps and have limited rights—those in Uganda can self-settle. Over 95 percent of refugees, however, choose to reside in one of 42 designated refugee settlements across 13 districts where they are provided with basic assistance such as small land plots, food, and non-food items.<sup>6</sup> UNHCR manages the allocation of refugees to various settlements in close collaboration with the Office of the Prime Minister. Allocation decisions are based mainly on the settlement’s capacity, the refugees’ county of origin and ethnicity, and whether they are joining family members already

> 6Asylum seekers in Kampala and other urban centers are not provided with such assistance.

8