rafmacalaba/datause-encoder-data huggingface dataset:
available. Next, we add back the quartic in years-since-IDs to illustrate the deviation from trend, and finally, we show the actual average trend in the data. This illustration shows that the increase migrant opportunity slowed the rate of increase in high school enrollment that was otherwise occurring.<sup>6</sup>

# **_C. Heterogeneity in Preferences for High School Enrollment_**

Families with different characteristics will not necessarily respond to migrant opportunity in the same manner. Even within villages, families with differences in educational preferences, information about employment opportunities, and credit constraints affecting the ability to attend school are likely to make different high school enrollment decisions. By examining these dimensions of heterogeneity, we may better determine the relative extent to which the opportunity cost of attending high school drives the enrollment decision. Furthermore, by examining heterogeneity in enrollment patterns we can address a potential concern that estimates in Table 7 are an artifact of convergence in access to, or the cost of attending, high school across more and less remote communities.<sup>7</sup> To begin to examine heterogeneity in preferences for high school enrollment, we first expand equation (2) to include indicator variables for the following characteristics of the child’s father in the year prior to the high school enrollment decision: _father is a “professional”_ (party member, village leader or enterprise manager); _father ever had off-farm employment_ (in either the migrant or local wage labor market); _father ever enrolled in high school_ ; and _father completed high school_ . If a child completing middle school has a father with professional status, he or she is 13.7 percent more likely to enroll in high school (Table 9; column 1, row 2). A child with a parent 6A last potential concern with using the quartic in years-since-IDs were issued as the instrument is that the quartic could be sensitive to outlier villages. With this concern in mind, we successively re-estimated the model restricting the sample by dropping the three earliest ID adopters at the village level, the three latest adopters, the three highest and lowest initial migration villages, and the three villages with the most pronounced growth in out-migration. In none of these models did we find qualitatively significant differences in the econometric results.

7For example, parents with children of low ability, or who have low preferences for education, may keep their children out of high school regardless of whether a school is available near their community. With construction of new schools in remote regions, high ability students who were not previously able to attend high school will now do so. One might be concerned that the yearssince-IDs instrument is picking up convergence across communities, and thus timing of distribution is associated with the timing of when parents have the ability to decide whether or not to send a child to school, and not the the effects of migrant opportunity.

23
inflammations) and better nutritional outcomes as indicated by anthropometric measurements, particularly during years with poor harvests.

However, the gender of the transfer’s recipient as well as the conditionality leads to differentiated impacts. For school enrollment and most child health outcomes, conditional cash transfers outperform unconditional cash transfers. The gender of the transfer’s recipient does not lead to such strong contrasts, with cash given to mothers and fathers having similar impacts on child health and education. While giving cash to mothers seems slightly, but not significantly, better for education outcomes, giving cash to fathers leads to significantly better nutritional outcomes during years with low rainfall. Cash transfers given to fathers also lead to increased livestock ownership, higher agricultural production in cash crops, and relatively more investment in the household’s equipment (electricity and metal roof).

The remainder of the paper is organized as follows. Section 2 describes the context of our experiment and the design of the cash transfer pilot program. In Section 3, we discuss our empirical identification strategy. Section 4 presents the main results, the robustness checks, and a discussion of the cost-effectiveness of the different cash transfers. Section 5 concludes.

# **2. Context and Experimental Design**

# _2.1 Context_

Burkina Faso offers an important setting for exploring the effects of cash transfers on rural households, focusing in particular on children’s education and health outcomes. Even by African standards, child education and health in Burkina Faso are poor. The United Nations Human Development Index ranks Burkina Faso 181 out of 187 countries. In 2010, the net attendance ratio for primary school in rural Burkina Faso was 44.4 (45.5 for boys and 43.1 for girls) and the gross attendance ratio was 64.9 (66.2 for boys and 63.5 for girls) (Institut National de la Statistique et de la Démographie and ICF International, 2012).<sup>4</sup> Approximately 33 percent of children in Burkina Faso under age 5 were considered stunted and 25 percent were considered underweight.<sup>5</sup>

> 4 The primary school net attendance ratio is the percentage of children attending primary school who are of the official school age. The primary school gross attendance ratio is the number of primary school students, irrespective of age, as a percentage of the official primary school age population. If there are a significant number of underage or overage students in primary school, the gross attendance ratio will be higher than the net attendance ratio.

> 5 Stunting is defined as children being below minus two standard deviations from the median height-for-age of a reference population. Underweight is defined as children being below minus two standard deviations the median weight-for-age of a reference population.

4
evidence of single-type infrastructure provision on output and apparent labor productivity (which we measure with municipal GVA and GVA per capita). We do find, however, evidence of an impact of the joint provision of electricity and highway infrastructure, which we estimate to increase GVA by 38 to 100 percent and GVA per capita by 29 to 61 percent, depending on the econometric approach. Importantly, the impacts differ across decades, being highly significant in some and not significant in others. This arguably reconciles findings from the literature where short-term and long-term effects can significantly vary (Lee, Miguel, and Wolfram, 2020).

In addition, we find a positive effect of infrastructure provision (both highways and electricity separately) on TFP, though our data restricts the analysis of TFP impacts to a single decade.

Finally, we find evidence of infrastructure investment playing a role in facilitating structural transformation. The separate provision of highways and electricity increases the local GVA share of the industry by about 5 percent. These effects are found to persist over time.

These results have an important policy implication: the government should consider infrastructure provision as a package. If the objective is to promote local economic development, then failing to improve transport access along with electrification may not be effective.

# **References**

- Abbasi, Mansoureh et al. (2022). _Roads, Electricity, and Jobs: Evidence of Infrastructure Complementarity in Sub-Saharan Africa_ . Policy Research Working Paper 9976. The World Bank.

- Ali, Rubaba et al. (2015). _Highways to Success or Byways to Waste: Estimating the Economic Benefits of Roads in Africa_ . The World Bank, Washington D.C.

- Ayres, Joao, Marcio Garcia, Diogo A Guill ́en, and Patrick J Kehoe (2019). _The Monetary and Fiscal History of Brazil, 1960-2016_ . Working Paper 25421. National Bureau of Economic Research.

- Balestra, Pietro and Jayalakshmi Varadharajan-Krishnakumar (1987). “Full Information Estimations of a System of Simultaneous Equations with Error Component Structure”. _Econometric Theory_ 3.2, pp. 223–246.

24
38

**Table 2. Measures of Financial Deegenin! and Stocks of Financial Assets**

|**Year**|**SFA**|**Ml**<br>(rL.**Billion)**|<br>**M2**|**SFA/GDP**|M1/GDP<br>**(percent)**|**M2/GDP**|
|---|---|---|---|---|---|---|
|1980|1,224|738|924|28.3|17.1|21.4|
|1981|2,148|1,019|1,710|33.5|15.9|26.7|
|1982|3,335|1,407|2,679|38.8|16.4|31.1|
|1983|4,339|2,084|3,477|37.6|18.1|30.2|
|1984|7,304|2,448|5,493|40.1|13.4|30.2|
|1985|12,059|3,420|8,540|43.8|12.4|31.0|
|1986|18,356|5,357|8,682|46.7|13.6|31.3|
|1987|30,519|8,682|17,702|52.4|14.9|30.4|
|1988|49,086|11,311|27,195|48.7|11.2|27.0|
|1989|86,631|19,560|47,142|51.6|11.7|28.1|
|1990|133,379|31,399|71,571|47.7|11.2|25.6|

Variables: SFA: Stock of Financial Assets including currency in circulation, deposits, special finance house participation accounts, corporate bonds, treasury bills, mutual fund participation shares. MI = Currency in circulation + Sight Deposits M2 = MI + Time Deposits Source: Akkurt et.al. (1992).
the poor finance their migration or reduce incentives to migrate has important policy implications.<sup>7</sup> Detailed exposition of migration-related policies is beyond the scope of this note and is contained elsewhere (see e.g. Page and Plaza, 2005). However, it should be noted here that there is presently very little evidence to support the effectiveness of any of these policies, and therefore governments planning on using them should be advised to consider careful evaluation of pilot programs in order to study their impact. Underpinning this knowledge gap is a lack of good migration data – a prerequisite for sound research and policymaking.

> 7 For example Angelucci (2005), in the context of the randomized Mexican program _Progresa,_ finds that exogenous increase in income (such as anti-poverty transfers) cause higher international migration. At the same time conditioning grants on certain behavior (weakly) decreases migration propensity.

14
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- 14 -

# **IV. ESTIMATION RESULTS**

Tables 5 to 7 present our main regression results in the three sectors. The estimated parameters are not significantly different from those in Estache and Iimi (2008a), despite differences in their data coverage and specifications. Competition generally matters, though the coefficient associated with the number of bidders tends to be insignificant with data from the electricity sector. In addition, the larger the scale of the project, the higher the revealed equilibrium bids. For instance, a longer road work will cost more, as expected. If a treatment plant with larger water production capacity is constructed, the submitted bids will be higher systematically. The equilibrium bids also increase with electricity generation and transmission capacities.

The most important finding is that entrant bidders faced with incumbent bidder(s) have been found aggressive, regardless of sector. The coefficient of the dummy variables for entrant bids in the presence of incumbent bidder(s) is significant and negative in all sectors. The coefficient is estimated at -3.9 in road procurement auctions, meaning that a fringe bidder is predicted to respond with a 3.9 million U.S. dollar lower bid if he is competing against some incumbent bidders, holding everything else constant. The magnitude of the coefficient varies across sector. The coefficient tends to increase particularly in the electricity sector, partially because the contract amount is normally large in this sector. All coefficients are statistically different from zero, supporting the hypothesis that there are two types of bidders: incumbent (strong) and entrant (weak), and entrant bids are aggressive given the presence of incumbents.

A policy implication of this is twofold. First, encouraging fringe bidders to participate in the bidding process might be conducive to reducing procurement costs in infrastructure projects.<sup>7</sup> As far as our data are concerned, the above provides the insight that a considerable

> 7 There is an adverse effect predicted as the flip side of the same coin by asymmetric auction theory. Incumbent bidders are likely to respond less aggressively when they are faced with entrant bidders. This would mitigate the competition pressure in auctions, and the net impact will be ambiguous.
# **Explaining high transport costs within Malawi –bad roads or lack of trucking competition?**

Somik V. Lall, Hyoung Wang and Thomas Munthali<sup>1</sup> World Bank

> 1 Somik V. Lall (slall1@worldbank.org ) and Hyoung Gun Wang (hwang4@worldbank.org) are Senior Economist and Economist at the Spatial Team of the World Bank in Washington. Thomas Munthali is an Economist with the World Bank in Malawi. Emily Schmidt helped in the spatial analysis. This paper has been commissioned as a background paper for the World Bank’s Malawi Country Economic Memorandum in 2009. This work has been done in collaboration with the Ministry of Planning and the Roads Authority of Malawi. The team has received valuable feedback during consultations at the Millennium Challenge Agency in Washington and a CEM retreat in Lilongwe, and a seminar at the World Bank’s transport forum. John Francois Arvis, Lucia Hanmer, John Hine, Patricia Macchi, John Panzer, Gael Raballand, and Jos Verbeek provided very useful comments.
<!-- Start of picture text --> =Q 100%9<br>3S<br>< 75%<br>fo}<br>o 9<br>Ky 50%<br>aw<br>ie) 25% 13%<br>3<br>° 0% se we & Ss & o ve sf Se Se ec s ~<br>2X @ xO < <“ O° Ss e < a oF oO<br>er RR3 e S&<br>se<br>— Average |) National<br><!-- End of picture text -->
the gap declines for low levels of the relative cost of children and high levels of economies of scale.

# **References**

Batana, Y., Bussolo, M. and J. Cockburn (2013), "Global Extreme Poverty Rates for Children, Adults and the Elderly," _Economics Letters_ , 120: 405-407.

Bloom, D. E. and D. Canning (2004), "Global Demographic Change: Dimensions and Economic Significance," Working Paper 10817, Cambridge: NBER.

Chen, S. and M. Ravallion (2010), "The Developing World Is Poorer Than We Thought, but No Less Successful in the Fight Against Poverty," _The Quarterly Journal of Economics_ , 125: 1577-1625.

Coulter, F., Cowell, F. and S. Jenkins (1992), "Differences in needs and assessment of income distributions," _Bulletin of Economic Research_ , 44: 77-124.

Cutler, D. M. and L. F. Katz (1992), "Rising Inequality? Changes in the Distribution of Income and Consumption in the 1980s," NBER Working Papers Series, Working Paper No 3964 Deaton, A. (2003), "Household Surveys, Consumption, and the Measurement of Poverty," _Economic Systems Research_ , 15: 135-159.

Deaton, A. and S. Zaidi (2002), "Guidelines for construction consumption aggregates for welfare analysis," Living Standards Measurement Study Working Paper 1135, Washington DC: World Bank.

De Vos, K. and M. A. Zaidi (1997), "Equivalence Scale Sensitivity of Poverty Statistics for the Member States of the European Community," _Review of Income and Wealth_ , 43:319-333.

Duclos, J. and M. Mercader-Prats (1999), "Household needs and poverty: With application to Spain and the U.K.," _Review of Income and Wealth_ , 45: 77-98.

Evans, B. and R. Palacios (2016), "Who is poorer? Poverty by age in the developing world," Social Protection and Labor Policy Note No 18, World Bank, Washington DC.

Klasen, S. (2000), "Measuring Poverty and Deprivation in South Africa," _Review of Income and Wealth_ , 46: 33-58.

Lanjouw, P. and M. Ravallion (1995), "Poverty and Household Size," _The Economic Journal_ , 105: 1415-1434.

Lancaster, G., Ray, R. and M. R. Valenzuela (1999), "A Cross-Country Study of Equivalence Scales and Expenditure Inequality on Unit Record Household Budget Data," _Review of Income and Wealth_ , 45: 455-482.

20
as in the European Union, migration might not lead to issue of new permits. Students who are issued long-term visas may or may not be included in the official statistics.

There are other potentially useable administrative data sources, such as border crossings, police records and social security data. There have been several innovative papers that use social security data from European countries to analyze return and circular migration (e.g., Borjas and Bratsberg, 1996), since all employed individuals can be tracked for extended time periods. Ultimately, the quality of the administrative data depends on the responsible national agency in charge of collection and dissemination, and comparability across sources varies by country. But the use of this kind of data is often limited by the fact that they are seldom made public by government officials.

# **_Household surveys_**

The final and possibly most useful and comprehensive sources of information on migration are surveys that include, among others, Labor Force Surveys (LFS), Demographic and Health Surveys (DHS), Living Standards Measurement Study Surveys (LSMS), as well a variety of purpose-specific surveys. Specialized surveys are also fielded in many countries, often to investigate specific aspects of migration. These microlevel surveys provide a rich source of data and are essential for identifying microeconomic linkages between migration and other facets of households’ livelihoods and outcomes that other data sources fail to capture. They are also useful as they better capture undercounted migrants if proper sampling frames and techniques are employed. Finally, such surveys can be conducted in both destination and origin countries, and 8
<!-- Start of picture text --> Pl I . a<br>y<br>~ Legend<br>{| Water<br>HM Existing 9 Coverage (Sep 06)<br>.<br>MN e P<br>ME SustainabilityMarket EfficiencyGap Gap<br>HB Universal Coverage Gap<br>[__] Unpopulated Area<br><!-- End of picture text -->

<!-- Start of picture text --> .<br><!-- End of picture text --> <!-- Start of picture text --> Legend<br>enters9 ' tteriat Gap<br>Zz Coverage Gap<br>[] Unpopuiated Aveswater<br><!-- End of picture text -->
**Figure 3. Employment informality**

<!-- Start of picture text --> A. Evolution of employment informality B. Levels of employment informality<br>Percent of total employment Indonesia (ILO) Percent of total employment 2019<br>70 Indonesia (NSO) 80 EMDEs (2019)<br>EMDEs<br>65 EAP 70 EAP (2019)<br>2021<br>60 60 2020<br>50<br>55<br>40<br>50<br>30<br>45<br>20<br>40 Self- Informal Employment<br>employment employment outside the<br>formal sector<br>Source: Elgin et al. (2021, updated), ILO (2023), and SAKERNAS from Indonesia NSO.<br>A. Shaded areas are capturing recession years. The blue lines show values for Indonesia (solid from ILO, dashed from NSO).<br>Other lines show the simple averages for East Asia and the Pacific excl. Indonesia (EAP, in orange) and EMDEs excl. Indonesia<br>(in red). Here self-employment (% total employment; ILO 2022) is used as a proxy for informal employment.<br>B. The dark blue bars show values for Indonesia in 2019 (the light blue horizontal bar shows the value in 2020, and the diamond<br>shows the value in 2021). The other bars show the simple averages for East Asia Pacific excl. Indonesia (EAP; in orange) and<br>EMDEs excl. Indonesia (in red) in 2019.<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021<br><!-- End of picture text -->

# _Firm informality_

23. **Firms in Indonesia are dominated by Micro Small and Medium Enterprises (MSMEs).** The 2016 Economic Census, the last one available, classifies 99 percent of firms in the country as MSMEs. A large share of these MSMEs are micro firms, almost 60 percent, with fewer than 5 workers. This is in contrast to peer countries like Malaysia and Vietnam, which also have a large share of firms categorized as MSMEs. In those two countries, the informal sector is significantly smaller at around 9 percent (2016) and 27 percent (2019), respectively.

<!-- Start of picture text --> Figure 4. Percent of firms registered when they Figure 5. Percent of firms competing against<br>started operations informal firms<br>Percent of firms Avg. over 2009-19 Percent of firms Avg. over 2009-19<br>EMDEs 80<br>EMDEs<br>EAP<br>100 60 EAP<br>80<br>60 40<br>40<br>20<br>20<br>0<br>0<br>Source: World Bank Enterprise Survey, World Bank Staff Calculation.<br>India Brazil Turkey India Brazil<br>Turkey<br>Thailand<br>Indonesia Thailand<br>Philippines Indonesia<br>Philippines<br><!-- End of picture text --> 24. **Indonesia’s firms are predominantly informal and seem to compete with the formal sector firms.** Only 45 percent of Indonesia’s firms were legally registered when they started operations, a significantly lower share than EMDEs, EAP region, and peer-country averages 10
this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The geographic information including travel time from surveyed DSDs to major urban centers with population of 100,000 or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing different travel speed on different types of roads.

The most important variable for our instrumental variables analysis is the historical district level malaria prevalence (endemicity) rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district. The HIES 2002 has complete employment, education, age, gender, ethnicity and religion. Summary statistics of all variables used in the analysis are provided in appendix Table A.1.

In both farming and non-farm activities, a considerable share of those employed are self employed. In the empirical analysis, we thus make a distinction among five different types of employment. The main categories of employment considered are self-employment in agriculture (farming), wage employment in agriculture, self-employment in non-agriculture, wage employment in the manufacturing sector and wage employment in services. The analysis of HIES shows that among 8,948 adult males in the labor force, 14 percent are farmers, 18 percent are agricultural wage laborers, 17 percent employed in manufacturing, 37 percent in services and 14 percent as self-employed in non-agriculture (Table A.1).

# **5 Empirical Results**

The empirical analysis of structural change focuses on two different dependent variables: one representing the broader choice of agriculture versus non-agriculture, and the other making a distinction among five different types of employment choices (self-employment in farming, and in non-agriculture, and wage employment in agriculture, manufacturing and services). The dependent variable in the more disaggregated employment choice regression is an unordered categorical variable indicating sector of employment of individual labor force participants (with self-employment in agriculture as the omitted category). The 17