rafmacalaba/data-use-mentions-extended huggingface dataset:
concentration finding is simply due to better coverage of rural areas in the 2003 census than in the 1987 census, a fact documented by Kerr and McDougall (2019). There are also comparability issues between the 2003 and 2014 firm censuses that we use in this paper. We document these below and also describe how we address them.

The sources of firm‐level data mentioned above have also been used to answer several other questions about manufacturing in Ghana. These include the relationship between productivity and firm exit (Frazer 2005), the effects of credit constraints (Bigsten and Soderbom 2006) and the contributions of growth and selection to the evolution of the firm size distribution in Ghana (Sandefur 2010, Davies and Kerr, 2018). A recent paper by Cirera et al (2017) uses the 2003 Ghana firm census (as well as census data from three other African countries) and the method of Hsieh and Klenow (2009) to estimate the cost of misallocation of resources, finding that improved allocation of resources to more productive firms would raise total productivity by around 75% in Ghanaian manufacturing. There has been little work in Ghana, or for African economies more broadly, examining the role of agglomeration economies. We review this small literature after introducing the key ideas in the agglomeration economies literature in the next section.

# 2.2 Agglomeration and Economic Geography

Urbanization has traditionally been associated with agglomeration, the benefits of firms locating near other firms and workers. Agglomeration is an idea with a long history in economics, dating back to the work of Adam Smith and then Alfred Marshall (Duranton, 2016). Duranton and Puga (2004) classify agglomeration effects as either sharing, matching and learning effects. Combes and Gobillion (2015) emphasize that which of these three channels is driving any agglomeration effects is almost never investigated in the agglomeration literature, but the overall effect can be, both because this is what is important for policy makers and because it is very difficult to separate out which channel is actually driving agglomeration economies
girls of mothers with no high-school education by 0.2 standard deviation; if this effect were exclusively the result of attending a CPBA, then the effect of CPBA attendance would be 0.83 standard deviation because 24 percent of these children attended a CPBA. Effect sizes may also be understated by the exclusion of 5-year-olds, as 5-year-olds at the time of the TEHCI survey would have had the most exposure to CPBA in 2017 and preceding years. Nevertheless, even with the low participation rate in CPBAs, just the intent-to-treat can have non-trivial effects on children with less educated mothers.

For boys of mothers with high-school education, the intervention reduced participation in preschool and had a small negative effect on some domains of school readiness. It is not clear why these two effects emerge. One hypothesis is that parents may have viewed CPBA as an alternative to preschool, and the reduction in preschool participation reduced school readiness. Preschools generally charge fees for participation while CPBAs did not. For boys of mothers without high-school education, the intervention increased preschool participation.

# <u>4. Effect of the CLRW intervention</u>

The CLRW intervention was expected to improve 1<sup>st</sup> and 2<sup>nd</sup> grade students’ reading skills by changing teaching techniques and exposing students to a new pedagogic approach. The randomized assignment of schools allowed us to identify the effect of the intervention on teaching practices measured in classroom observations and on reading outcomes. Self-selection could occur if students switched schools in response to the intervention; however, no evidence of this was reported. We found that the CLRW intervention had a positive effect on the prevalence of teaching practices promoted by the intervention and a positive effect on students’ reading abilities. Effect sizes tended to be higher for those exposed to two years of the intervention rather than just one.

# _Data_

Two sources of data were used to evaluate the effects of the CLRW intervention. The first was based on classroom observations that were conducted approximately halfway through the
, or pay less frequently than once a month. Households with missing values (missing answers) may not have answered the question despite having paid (for example, the respondent may have had trouble recalling the amount paid), but all missing values were assigned zero for the purpose of this study.

It is important to note that national household expenditure survey data cannot be used to estimate bill collection losses (percent of the total billed amount not collected by the utility) from residential consumers because no survey asks what should have been paid. It is not possible to tell from non‐zero expenditure
significant differences across groups. Montenegro and Pages (2004) find that higher protection in Chile is associated with lower employment rates among young workers, women, and unskilled workers. Marquez (1998) finds that more stringent regulation coincides with a larger percentage of self employed workers, a finding confirmed by Maloney (2001), although the estimated impact was small compared to other education, productivity, or demographic variables.

# **_Theoretically, restrictions should reduce productivity growth although at present the evidence is_**

**_sketchy:_** The literature on the impact on productivity growth is thin at the international level and thinner still in Latin America. Hopenhayn and Rogerson (1993), using a general equilibrium model for job creation and destruction, find that policies that interfere with the natural process of job creation/destruction at the firm level are quite costly in terms of average productivity and, more generally, in terms of aggregate welfare.<sup>14</sup> Parente and Prescott (1991, 2000) argue that TFP differences dominant differences in per capita GDP and that firing costs are one of the policies that increase the power of inside labor in a number of industries to resist changes in work practices and adoption of new technologies. Capelli (2000) and Hobjin and Jovanovic (2001-HJ) also show that stringent hiring and firing restrictions raise the cost of labor adjustments, which is often required after innovation occurs. A more recent analysis provided by Scarpetta and Tressel (2004), using firm OECD data, finds an impact of firing regulations on productivity and technological _via_ its impact on the incentives to innovate and to adopt new technologies.<sup>15</sup> For Latin America, the results are very preliminary: Forthcoming research by Gracia and Santamaría (2004) find that at the aggregate level (national TFP), the substantial reduction of firing costs that occurred in 1990 in Colombia had a positive effect on the evolution of productivity growth. There is also a yet unexplored potential that poorly
This paper uses two samples. The first sample includes all women aged 19 to 30 who have ever experienced a pregnancy. This sample is used to demonstrate the differences in background characteristics and outcomes of interest between women who had a child during their adolescence and those who delayed childbearing until a later age. The second sample is limited to women aged 19 to 30, who experienced a pregnancy before the age of 19. This sample is used to establish the causal effect of adolescent childbearing by comparing women whose teenage pregnancy resulted in a live birth to women who miscarried. Although ENADID survey was administered to women aged 15 to 54, women under 19 are not included into the analysis. Even if they have not experienced a pregnancy by the time of the interview, they still may do so before turning 19. Thus, keeping women aged 15 to 18 in the sample may result in false inclusion of potential teenage mothers into the control group. The sample also exclude women who experienced more than one miscarriage, as repeat miscarriages may not be random and are likely to be associate with poor health status and other characteristics which may affect the outcomes (Wang et al., 2003).

The large sample includes 7,805 women aged 19 to 30. The teen pregnancy sample includes only 2,803 of these women. Among women who experienced pregnancy as adolescents, 2,543 had living children, the children of 26 women who had a live birth died; 142 women were pregnant at the time of the interview; for 85 women the pregnancy resulted in a miscarriage or a stillbirth. Only 7 women reported having induced abortion (we discuss the implications of this likely underreporting bias later in the paper).

Census data collected in 2000 is used to control for municipal-level characteristics. The respondents in the first sample live in 787 municipalities (with 10 observations per municipality on average), while respondents in the second sample come from 617 municipalities (with 5 observations per municipality on average). The number of municipalities provides sufficient variation
0.27 0.43<br><!-- End of picture text -->

|||Extraversion|Agreeableness|onsciennous:|Neuroticism|Openness|Average|
|---|---|---|---|---|---|---|---|
||All|0.42|0.56|0.43|0.27|0.34|0.40|
|Kenya<br>Sample|C08 above<br>median|0.49|0.44|0.47|0.30|0.28|0.40|
||<br>oeow|0.60|0.50|0.44|0.35|0.27|0.43|
|monies|Spain<br>Dutch|0.95<br>0.94|0.93<br>0.91|0.95<br>0.94|0.96<br>0.94|0.86<br>0.64|0.93<br>0.87|
||German|0.93|0.60|0.96|0.94|0.39|0.76|

<!-- Start of picture text --> Dutch 0.94 0.91 0.94 0.94 0.64 0.87<br>monies Spain 0.95 0.93 0.95 0.96 0.86 0.93<br>German 0.93 0.60 0.96 0.94 0.39 0.76<br>Note: To have comparable results for different countries, the same substet of questions was kept, using Big5 data. data.<br>from other countries, and the factorial analysis uses varimax rotation. The absolute value of
Figure D.7. Smooth Condition Test Under Different Bandwidths (cont’d) <!-- Start of picture text --> (a) In-migration Share (b) Out-migration Share (c) Inside Highly Populated Area (1980)<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: .14 Mean
and Hoogeveen, 2002) or identified errors of omission in a gridded population model using the locations from household surveys (e.g. Tiecke et al., 2017).

Since we can not perform an evaluation of prediction accuracy for all countries, we compare the global cross-entropy model with another allocation model, which is similar to the global assessment of maize and rice production in You, Wood, et al. (2014). For the comparison, we construct a proportional allocation model using rural population density following the method in Thomas et al. (2019) for the case of Brazil.<sup>15</sup> Then, we can test the similarity of the two maps. Following Levine et al. (2009), we assume a normal distribution over the 2 million land pixels and perform a pairwise student t test to test the null hypothesis that both maps were identical. This test allows us to examine whether the mean difference in 14Specifically, the MAD and RMSE for each model are respectively: the rural population density model (28,744 and 25,397), Cross-entropy spatial allocation (8,249 and 18,347) and Spatial disaggregation from a regression on agricultural production (7,214 and 16,673).

> 15We use the 2010 Gridded Population of the World version 4 from Center for International Earth Science Information Network - CIESIN - Columbia University (2017) adjusted to the United Nation’s World Population Prospects followed by including the rural area defined by the Global Human Settlement grid for 2015: namely, “Rural cluster”, “Low Density Rural grid cell”, or “Very low density rural grid cell” (Pesaresi and Freire, 2019).

21
(GFS) - IMF), United States (Economic Research Division, Federal Reserve Bank of St. Louis), and European countries (Eurostat [Last update: 19.07.19. Extracted on: 17.09.19]).<sup>39</sup> Total government spending and revenues: Argentina (Ministerio de Economía en base a datos de la Secretaría de Hacienda), Brazil (STN, IBGE, IPEA e BCB), Chile (Dirección de Presupuestos (DIPRES) / Banco Central de Chile), Colombia (Dirección General de Política Macroeconómica, Ministerio de Hacienda y Crédito Público), Mexico (Dirección General de Estadística de la Hacienda Pública. Unidad de Planeación Económica de la Hacienda Pública.), Peru (Ministerio de Economía y Finanzas, Banco de la Nación), United States (Economic Research Division, Federal Reserve Bank of St. Louis), and European countries (Eurostat [Last update: 19.07.19. Extracted on: 17.09.19]).

Central bank interest rates: Global Financial Data and central banks’websites.

Sample period to the estimation of social transfer multipliers: Argentina (2004Q1-2019Q4), Austria (2001Q1-2019Q1), Belgium (1995Q1-2019Q1), Brazil (2010Q1-2019Q4), Chile (2005Q12019Q4), Colombia (2000Q1-2018Q4), Denmark (1999Q1-2019Q1), Finland (1999Q1-2019Q1), France (1980Q1-2019Q1), Germany (2002Q1-2019Q1), Greece (1999Q1-2019Q1), Ireland (2002Q1-2019Q1), Italy (1999Q1-2019Q1), Luxembourg (2015Q1-2019Q1), Mexico (2007Q1-2019Q1), Netherlands (1999Q1-2019Q1), Norway (2002Q1-2019Q1), Peru (1995Q1-2018Q4), Portugal (1999Q1-2019Q1), Spain (1995Q1-2019Q1), Sweden (1995Q1-2019Q1), United Kingdom (1987Q1-2019Q1), and United States (1960Q1-2019Q4).

> 38 IMF (2017). Quarterly National Accounts Manual. Statistics Department, International Monetary Fund, 2017 Edition, Washington, D.C.

> 39 Since the series on unemployment insurance spending are not available at quarterly frequency
</mark>

> 31 <mark>Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. & Ward, P. J. A global reanalysis of storm surges and extreme sea levels.</mark> _<mark>Nat. Commun.</mark>_ **<mark>7</mark>** <mark>, 1–12 (2016).</mark>

> 32 <mark>Alfieri, L. et al. Global projections of river flood risk in a warmer world.</mark> _<mark>Earth's Future</mark>_ **<mark>5</mark>** <mark>, 171–182 (2017).</mark>

> 33 <mark>Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming.</mark> _<mark>Nat. Clim. Chang.</mark>_ **<mark>8,</mark>** <mark>781–786 (2018).</mark>

> 34 <mark>Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment.</mark> _<mark>PloS one</mark>_ <mark>,</mark> **<mark>10</mark>** <mark>, e0118571 (2015).</mark>

> 35 <mark>Smith, A. et al. New estimates of flood exposure in developing countries using high-resolution population data.</mark> _<mark>Nat. Commun.</mark>_ **<mark>10</mark>** <mark>1–7 (2019
poverty**

Most development interventions aim to help people escape poverty. But escaping poverty is no guarantee of not falling back in. Every year millions of people fall into poverty due to unexpected shocks such as natural disasters, high medical bills, or sudden unemployment. In poor countries, safety nets are often absent, so people rely on their social networks for emergency funds. Mobile money accounts let people send money through simple text messages, which makes it easier to collect funds from faraway friends and relatives. As a result, they are less likely to become poor during an economic emergency. These findings are relevant to the World Bank's work on resilience and poverty reduction.

> 138 Muralidharan, K., P. Niehaus, and S. Sukhtankar. 2016. "Building State Capacity: Evidence from Biometric Smartcards in India." _American Economic Review_ 2016, 106 (10): 2895‐2929.

> 139 Aker, J. C., R. Boumnijel, A. McClelland, and N. Tierny. 2016. "Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger." _Economic Development and Cultural Change_ 65 (1): 1‐37.

39
members’ participation in the labor force and also on the hours of work of household members who participate in the labor force.<sup>.</sup> Similar effects have been found in other countries with high remittance receipts. Grigorian and Melkonyan (2011) find that households which receive remittances work fewer hours than households which do not in Armenia. Kalaj (2013) applies a propensity score matching procedure to Albanian household survey data to study whether the labor market decisions of households which receive remittances differ from those which do not. She finds that there are no statistically significant differences in the labor market decisions of males but that females in remittance recipient households work fewer hours than their counterparts in non-remittance recipient households.

Görlich et al (2007) look more closely at the reasons why households which receive remittances reduce participation in the labor market. Using labor force survey data from Moldova, they find that the receipt of remittances by households has a small but significant negative impact on labor force participation, but that this is associated with increased participation in higher education among younger workers and increased household production activities (e.g. childcare or subsistence farming) among middle age workers. Ivlevs (2016), using data from surveys in six transition economies in 2009, finds that remittances increase the likelihood that households will engage in informal labor employment: this effect pertains to both households which receive remittances and those which do not but which reside in areas where there is a high prevalence of remittance receiving households.

Outside ECA, Kim (2007) and Ndiaye et al (2016) find that remittances reduce labor market participation in Jamaica and Senegal respectively. Airola (2008) finds that hours worked by household heads in Mexico are negatively correlated with the receipt of remittance income and Amuedo-Dorantes and Pozo (2006) find that, in Mexico, remittances did not reduce the overall hours worked by men but that they were associated with a shift from formal to informal 4
sectors. Template 10 collects all the data related to spending on public infrastructure by any level of government (central or local). _This template with all its occurrences is the core of the SPI exercise._

# **Macroeconomic parameters for budgetary context of infrastructure spending (template 9)**

The data for template 9 should be gathered from the government. It must be kept in mind that even if the definitions used by a government are different from the standardized definitions suggested in this manual, a proxy for infrastructure spending based on institutions that provide services (rather than infrastructure services provided by infrastructure and non-infrastructure specialized institutions) can always be found. This back-of-the-envelope estimation will guide the user in positioning infrastructure within the overall fiscal framework.

# **Template 9. Macroeconomic parameters for budgetary context of infrastructure spending**

|**Policy**<br>**code**|**Series**<br>**code**|**Variable**|**Definition**|Unit|
|---|---|---|---|---|
|re||Total budget||LCU per year|
|ructu<br>)||Infrastructure||LCU per year|
|frast<br>(NIN||Education||LCU per year|
|onin<br>||Health||LCU per year|
|N||Other sectors||LCU per year|

_Source:Authors’elaboration_ . _Note:_ LCU = local currency units.

# **Functional and economic classification of central and local government expenditures (template 10)**

Template 10 is probably the single most relevant output of the SPI exercise. Its purpose is to provide, for all relevant sectors (4-digit categories or classes—see below for definitions), expenditures according to the GFSM 2001 functional and economic classification. It should be filled in for the central government to cover the most recent
Living in a remote and marginalized area might affect citizens’ access to public services, as well as the quality and frequency of their interaction with local authorities, and, consequently, their beliefs toward these authorities. Thus, we also control for the remoteness of respondents’ residences. We derive this measure from the community-level NPS questionnaire. Community leaders were asked about the distance in miles between their locality and Freetown, as well between their locality and the district headquarters. Finally, we control for the population density of enumeration areas, as large population might pose additional pressures on Local Councils.<sup>8</sup>

## **Local Government Outreach Activities and Access to Information**

Unfortunately, the NPS did not ask respondents about the quality of their interaction with councillors. The NPS asked respondents whether they have talked with a local councillor or participated in a meeting organized by the council within the past year. We control for

> 8We also calculated the distance between the center of enumeration areas and Freetown. This variable was not significant and did not affect model estimates. We are also in the process of calculating the distance between the center of enumeration areas and district headquarters. We will include this variable in the model, which we will estimate for the next version of this paper.

12
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Increased reliance on surface water and open defecation, as population growth outpaces growth in rural access to water and sanitation**

Population gaining access per year between 2002 and 2006 <!-- Start of picture text --> a. Water b. Sanitation<br>6 5<br>5 4<br>3.7<br>4 3 3.0<br>3.7<br>2.5<br>3 3.0 2<br>2.5<br>2 1<br>1 0<br>National Urban Rural<br>0 -1<br>National Urban Rural<br>-1 -2<br>Piped Water Standposts Flush Toilets Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> _Source_ : WHO–Joint Monitoring Program 2010; Census 2002; and Demographic and Health Survey 2006.

The sanitation sector lags far behind the water sector, even though some decline in the reliance on open defecation has been achieved at the national level. In 2006 62 percent of the population practiced open defecation versus 74 percent in 1996. Even though the improvement has been significant, the percentage of population practicing open defecation is still extremely high, in particular when compared to low income countries and middle income countries (table 9).

The lack of sewerage systems in Benin is a major challenge given rapid urbanization and increasing industrial growth. Only in Cotonou is there a septic sludge disposal plant, but its capacity is greatly exceeded. Most domestic wastewater is discharged into streets and gutters. The greater Cotonou metropolitan area, including Porto Novo, is home to more than 80 percent of the industries of Benin. In the absence of adequate