rafmacalaba/data-use-mentions-extended huggingface dataset:
where Y<sup>�</sup> ��� denotes estimated average baseline school readiness for the communities mapped to the students in the school. A non-zero estimate of β��� implies that imbalance exists between the school readiness treatment and control group students using this method of mapping. Equation (10) is also estimated using gender, the TEHCI home activity index, exposure to the CLRW intervention, and attrition as dependent variables, to measure imbalance in these variables as well.

Table 12. Estimates of equation (10) testing balance for the baseline sample

|dependent variable:|baseline<br>community<br>TEHCI<br>score (SD)|home<br>support|female|CLRW|
|---|---|---|---|---|
|_1 year of school readiness interv_<br>|_ention sample_<br>|_(starting 1st_<br>|_grade in 201_<br>|_6)_<br>|
|probability of being from a|‐0.06|‐0.02|0|0.08|
|treatment community|(0.08)|(0.01)|(0.01)|(0.09)|
|constant|‐0.01|0.78***|0.41***|0.58***|
||(0.04)|(0.01)|(0.01)|(0.05)|
|observations|1214|1214|1214|1214|
|r‐square|0.00|0.01|0.00|0.00|
|_2 years of school readiness interv_|_ention sample_|_(starting 1st_|_grade in 20_|_17)_|
|probability of being from a|‐0.09|‐0.02|0.01|
**Table 1: Municipality Characteristics for Treated and Control Groups**

||||Control<br>|Municipaliti<br>|es|
|---|---|---|---|---|---|
||||36-m|onth|Planned|
||Treated|All<br> Non-treated|Common<br> Support|Nearest<br> Neighbors|Implementation<br>Candidates|
|_Variables used for level matching_||||||
|Average monthly registrations in 2008|11.9|2.1***|3.3***|10.4|14.7|
|Average monthly registrations in 2009|12.8|2.3***|3.7***|11.2|15.3|
|Average monthly registrations in 2010|12.4|2.2***|3.4***|11.0|15.3|
|Standard deviation of monthly registrations 2008 to 2010|4.1|1.4***|1.8***|3.3|5.0*|
|Population in 2010|56847|11754***|17172***|50423|65632|
|_Variables not used for matching_||||||
|Average monthly registrations in first eight months of 2011|12.8|2.2***|3.5***|12.1|16.0|
|Standard deviation of monthly registrations in first eight months of 2011|4.1|1.3***|1.7***|3.2|4.4|
|Share of Industry in Municipal GDP (%)|26.3|14.9|17.4***|19.4*|35.8
and household survey data. As Ravallion (2003) says in a review of comparisons between national accounts (NAS) and survey data, “It is evident that when the levels or growth rates from these two data sources differ, there can be no presumption that the NAS is right and the surveys are wrong, or vice versa, since they are not really measuring the same thing, and both are prone to errors.” In terms of growth rates in Tanzania for the period between the household surveys (19932001), there is surprisingly good correspondence between the HBS data and the national accounts. The cumulative GDP per capita growth of 6.1% is very close to the 6.5% growth in consumption per adult equivalent in the household survey. This close match between the general picture painted by the micro and macro data provides support for the validity of the simulation exercise undertaken for this paper.

It is also possible to calculate the growth rate of per capita consumption directly from the national accounts data. Figures from these calculations are shown in the last column of Table 2 and are plotted in Figure 3 below, along with the GDP per capita growth rate figures. At 8.3%, the cumulative change in per capita household consumption is only slightly larger than the cumulative change in per capita GDP. Mathematically, this is because household consumption changed just slightly, from 90% to 91% of GDP, between 1993 and 2001.

While details on the particular methodology used to develop the Tanzania national accounts data are not available, typically consumption figures in national accounts statistics are estimated as a residual in the national income calculation. As a consequence, there is great uncertainty in the estimates, particularly in year-by-year fluctuations. For this reason, growth rates in GDP per capita are used in this paper, except in one variation of the simulation analysis.
0.23|0.40|

Notes: Each column represents a regression of test scores (harmonized test scores or learning poverty) on earnings differentials. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: Harmonized test scores from Human Capital Index (2020). Learning poverty data from the World Bank (2019a). Earnings differentials from survey data listed in Table 1.

44
weight _θ >_ 0, which captures the trade-off between poverty and mortality, corresponds to the fraction of the period utility lost when poor. When being poor has no utility cost, _θ_ takes the value zero and _PALE_ 0 corresponds to life expectancy at birth. When being poor (for one year) is as bad as losing one year of life, _θ_ = 1 and our index _PALE_ 1 then corresponds to the poverty-free life expectancy at birth (Riumallo-Herl et al., 2018), i.e. the number of years of life a newborn expects to live out of poverty. _PALEθ_ does not suffer from the mortality paradox as long as _θ ≤_ 1. Besides its theoretical properties, _PALEθ_ enjoys two practical advantages. First, its data-requirement are minimal as only the life-expectancy at birth and the poverty head-count ratio are necessary. Second, _PALEθ_ has a simple interpretation, as it measures the equivalent number of years of life spent out of poverty.

In a second step, we show how to generalize _PALEθ_ to account for the distribution of lifespans. More precisely, _PALEθ_ lends itself to the definition of a new indicator, which is consistent with the minimalist view. This requires the introduction of a normative age threshold ˆ _a_ , which corresponds to our definition of premature mortality. This new index, which we call the _expected_ deprivation index ( _EDθa_ ˆ), is the weighted sum of the number of years that a newborn expects to lose prematurely or to spend in poverty, using the same weight as in _PALEθ_ .<sup>6</sup> We call the index _expected_ deprivation, given its proximity to the concept of life expectancy in its construction, interpretation and assumptions.

To measure the real world relevance of our indexes, we combine data sets provided by the World Bank data on income poverty (Poverty and Inequality Platform (World Bank, 2023)) and an internationally comparable data set on mortality data (Global Burden of Disease Collaborative Network, 2020) from 1990 to 2019. We show
in 2017 international $ adjusted for purchasing power parity (PPP), from the World Bank’s World Development Indicators database (World Bank 2021a).

Third, we use IMF fossil fuel price and consumption data for 220 countries from 1980 to 2021 for coal, LPG, diesel, petrol, natural gas, electricity, kerosene, biomass, and other oil products, all commonly used energy types (Parry et al 2021). This data is made available by the IMF, and combines source data from IMF and World Bank country desk datasets, as well as a range of secondary sources as detailed by Parry et al. (2021, Annex B). As not all countries have data for gasoline, diesel, and coal during the study period, we reduce the number of countries included to 133. Also drawing on Parry et al (2021), we use fuel consumption in tons of oil equivalent (toe) data from the International Energy Agency, adjusted to a per capita basis using population numbers from the World Bank database (World Bank 7
- Dehejia, Rajeev and Arvind Panagariya, “Services Growth in India: A Look Inside the Black Box,” Columbia Program on Indian Economic Policies Working Paper 2010-4 (2010).

- Desmet, Klaus, Ejaz Ghani, Stephen O’Connell and Esteban Rossi-Hansberg, “The Spatial Development of India,” Working Paper (2011).

- Dinkelman, Taryn, “The Effects of Rural Electrification on Employment: New Evidence from South Africa,” _American Economic Review_ , 101(7), (2011): 3078-3108.

- Duranton, Gilles, and Diego Puga, “Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products,” _American Economic Review_ , 91(5), (2001): 1454-1477.

- Eaton, Jonathan, and Zvi Eckstein, “Cities and Growth: Theory and Evidence from France and Japan,” _Regional Science and Urban Economics_ , 27(4-5), (1997): 443-474.

- Fernandes, Ana, and Ariel Pakes, “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data,” NBER Working Paper 14178 (2008).

- Foster, Andrew and Mark Rosenzweig, “Agricultural Productivity Growth, Rural Economic Diversity, and Economic Reforms: India, 1970-2000,” D. Gale Johnson Memorial Conference Paper (2003).

21
of 0.2 percent of GDP), and of the liabilities of the central government: ‐ the assets of the FEES are invested in securities denominated in currencies with low or negative correlation with the price of the copper (such as JPY); ‐ the strategy of the central government is to issue in local currency or in currencies with a strong positive correlation to the price of copper (such as USD).

Based on financial studies, currencies were identified, and theoretical ranges were determined for the currency composition of assets and liabilities. These results are balanced with other considerations (financial returns, market supply/demand, etc.) to fix the operational objectives (asset allocation of the FEES and medium‐term debt strategy).

> 21 Available at http://www.hacienda.cl/english/public‐debt‐office/statistics/public‐debt.html. Also available <u>are</u> statistical series in Excel format.

> 22 The net position of the central government essentially includes the sovereign debt and the financial assets of the government funds (FEES, _Fondo de Reserva de Pensiones_ , etc.).

19
the second period for the welfare dynamics analysis. This table illustrates our earlier discussion that the bottom 40 percent represent the poor and the vulnerable in a country but to different extents across countries. For example, the bottom 40 percent in Egypt includes all the poor and some of the vulnerable to poverty as the cutoff line for

> 13 We do not make comparisons of monetary and subjective well-being indicators at the regional level due to the different time intervals and country compositions in the two databases.

> 14 The Gallup Poll also collects data on self-reported income. But since this variable is much less meticulously constructed compared to household consumption data in the household surveys, we do not construct synthetic panel data for income. We come back to this issue in the next section.

12
goods. During the period 1989-1997, only 38 percent of firms in the sample import capital goods, while most of the firms (73 percent) import intermediate goods. Moreover, firms import intermediate inputs on yearly basis, while firms import capital goods more sporadically. Looking at the firms that source both foreign goods reveals that almost all firms that import capital goods (99 percent) also purchase imported intermediate goods. The fact that only half of the firms that import intermediate goods are able to source imported capital equipment goods suggests that the decision to source capital goods from abroad is related to a technological choice that involves a fixed investment cost.<sup>4</sup> _Empirical fact 1: A large proportion of firms imports intermediate goods, while only a subset of those firms also imports capital equipment goods._ This small subset of firms that produces with imported capital goods technology performs better than non-importers of capital equipment goods. Table 1 shows estimations of importer premia of capital goods. We regress firms’ sales, capital stock, wage-bill, profits and the share of imported inputs (imports of intermediates over total inputs) on a dummy variable equal to one for firms with positive values of imports of capital goods (importer of capital goods) and zero for those firms that do not import (nonimporter of capital goods), including industry and year fixed effects. The results show that within an industry-year, firms that import capital goods have larger sales, capital stock, wage-bill, profits and imported input share 4Using detailed product-level data on imports by Indian manufacturing plants, Fernandes et al. (2012) show the existence of fixed costs of importing.

7
# 5. Sample and Experiment Design

<mark>The 2012 World Bank Group‐supported Economic Revitalization of KP and FATA (ERKF) project provided a unique setting to compare individuals who received a cash grant to rebuild or establish businesses in the conflict‐ affected area with individuals who received both a grant and the 5‐ week PM+E training intervention. This allowed for the testing of the hypothesis that financial assistance coupled with the psychosocial training would be more effective than just receiving cash grants in reducing depression and anxiety, the most common manifestations of psychosocial distress.</mark>

# 5.1 Sample Size Calculations

<mark>The unit of randomization in the study was individual entrepreneurs, with an equal number of entrepreneurs randomized into the intervention and control groups. The sample size was estimated using a recent study that measured changes in the occurrence of depressive symptoms as measured by the PHQ‐9 (as a proxy for the PHQ‐ADS, which has not been applied in similar contexts). The study utilized a sample of primary care (clinical) participants in the same area of Pakistan that showed a similar PM+ intervention had an effect size of 0.725 with a PHQ‐9 total score as the outcome. An effect size which is one‐third of the precedent effect size was assumed because the current sample was not derived from a clinically‐referred population. Factoring this in, a 20 percent reduction (roughly, one‐third of 0.725) in the occurrence of psychological distress</mark> was considered meaningful.

<mark>Power calculations suggested a minimum sample size of 84 participants per arm (power = 0.90; α = .05, 2‐sided). To account for a 25 percent attrition at the 3‐month follow‐up, a total of 235 participants were included and randomized into intervention (n=118) and control (n=117) arms (Saraf, Rahman, Hamdani, Jamison, and others, 2018).
affect GDP growth forecast errors, and we account for it using self-employment as a proxy, which is defined as the lag of total self-employed as a percentage of the total employment based on modeled ILO estimates.

Our findings are robust to the inclusion of natural disasters. Unpredictable and costly occurrences like natural disasters can have a detrimental impact on many economies. Celasun et al. (2021) found that the IMF’s WEO GDP growth forecasts have a tendency to be upwardly biased in part due to the rarity of unforeseen growth booms compared to significant growth collapses brought on by natural disasters, armed conflicts, and systemic financial crises. Loayza et al. (2012) investigated the effects of the different natural disasters (i.e., droughts, floods, earthquakes, and storms) separately by economic sector (agriculture, industry, and services) and found that natural disasters have a greater impact on growth in developing countries than in developed ones, with more industries being impacted and more significant economic repercussions. Depending on the type of disaster and the affected economic sectors, disasters can have positive or negative effects on economic growth. Severe disasters have no beneficial growth impact, even though moderate calamities (such as mild floods) can spur growth in some industries (Loayza et al., 2012). In a recent study, Kong et al. (2021) examined the changes in analyst optimism degree in earnings forecasts following earthquake events in China. They revealed that analysts utilize heuristics to forecast earnings and become irrationally pessimistic when an earthquake occurrence is salient. Analysts do, however, rectify the bias after the initial irrational predictions. Hence, we include in our analysis a natural disasters shocks dummy that is equal to 1 when severe natural disasters had occurred in a country. Severe natural disasters are those that generate damages equivalent to at least 1 percent of the GDP of the country affected that year (based on the total estimated damages reported by EM-DAT). Disasters include floods, earthquakes, droughts, storms, landslides, volcanic
0.465|0.890|0.504|
|Change in Subjective Wellbeing<br>from Five Years Ago to Today|0.143|1.000|0.354|1.000|0.650|1.000|0.263|0.237|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, an indicator for never had sex, and whether the respondent participated in the pilot phase of the development of the testing instruments. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

92
# **1 Introduction**

Aid allocations of many multi- and bilateral donors became more policy-selective starting in the 1990s – that is, donors provided more aid to recipient countries with better policies. This fact, which we discuss in more detail below, raises two important questions that this paper seeks to answer: First, does increased policy-selectivity of donors provide recipients with an incentive to improve policies? Second, has the increased policy-selectivity of donors translated into improved policy levels in aid recipient countries? Clearly, the second question is relevant only when we answer the first one affirmatively.

We address these two questions theoretically and empirically. We develop a game-theoretic model that shows that policy-selective aid by a donor leads to a contest for aid among recipients in terms of policies, which increases policy levels in recipient countries in equilibrium. Comparative static results of the model show, however, that incentives to participate in that contest depend on the level of aid and other resource windfalls a recipient receives unconditionally of its policy choice. This result holds if the donor’s aid allocation is both policy- and poverty-selective, as a poverty-selective donor, conditional on policies, balances out resources across recipients. A recipient receiving a sufficiently large amount of unconditional aid has no incentive to participate in the contest for aid. Because not all aid is given policy-selectively, including large financial flows by China and other emerging donors more recently, it is then an empirical question whether for a recipient government improving policy levels tends to lead to more aid as our model predicts.

We provide evidence for an economically and statistically strong and robust relationship between lagged policy and current aid conditional on lagged aid using recipient-year panel data. Thus, our estimates suggest that a positive change in policy is associated with an increase in aid money. We estimate that an increase in the World Bank’s CPIA policy index from 1.5 to 2 for a recipient is associated with about a 13% increase in aid.

The second question – of whether policy-selective aid produces better policies – poses
# **1 Introduction**

Modern economic growth is accompanied by shifts in the distribution of economic activity across the three broad sectors of the economy. As a sustained period of growth takes off, the share of agriculture in employment and output declines and this loss is picked up by industry and services. While services continue to expand, industry reaches a threshold after some time after which it starts to lose its share in the economy. These stylized facts of structural transformation have been documented since the late 1950s (Clark (1957), Chenery (1960), Kuznets (1957)). More recently, using data from multiple sources Herrendorf _et al._ (2014) documents these patterns of transformation for a broad set of countries.<sup>1</sup> Nonetheless, there exist deviations in the transformation experience of countries. In particular, Rodrik (2016) documents an intriguing aberration in the industrialization pattern of the developing countries in which the transformation started much later compared to the early industrializers. Industry in many developing countries fails to reach the thresholds achieved by the developed countries in the past. Moreover, the onset of deindustrialization from this lower peak happens at a lower level of GDP per capita. The Latin American economies are more adversely affected by this _premature deindustrialization_ compared to the other regions of the world.

In this paper, I investigate the relative strength of different channels in accounting for the low share of industry in Latin American economies. I employ a version of the open economy multi-sector accounting framework developed in Uy _et al._ (2013) in which the sectoral good is an aggregate of a continuum of tradeable varieties which are in turn produced using labor and composite sectoral goods as intermediates (Eaton & Kortum (2002)). The specification links sectoral production to the production activity in other sectors as well as in other countries. The framework yields a system of equations that establishes a relationship between the sectoral share of the economic activity and various channels that are readily measured from the data and
Policy Research Working Paper 7986

# **Abstract**

Simultaneity of borrowing, withdrawal of savings, and loan defaults due to the pronounced seasonality of ag-riculture often leads to investment failure of rural financial institutions. Lack of borrowing leads to lack of in-come- and consumption-smoothing, and in turn, causes inefficient resource allocation by rural households. Financial institutions that are active in rural areas take different measures to address the covariate risks in in-termediation. For example, microfinance institutions have sought various measures such as supporting non-farm activities to diversify income, introducing seasonal loans, and bringing flexibility in loan repayments to reduce non-payments in lean seasons. This paper examines whether the financial inclusion policies of micro-finance institutions have successfully helped reduce the adverse effects of covariate risks. Analysis of household and program level data from Bangladesh suggests that despite the innovative measures taken by the MFIs to cope with the covariate risks, seasonality of income still affects seasonality of borrowing and invest-ment decisions of both the households and MFIs beyond and above what is caused normally by agricultural seasonality. Innovation is needed to promote, among other things, sectoral diversification of financial inter-mediation and to avert the extreme seasonality of rural income. Rural labor markets should be diversified enough to address the seasonality of income and consumption. Public policies guiding rural financial interme-diation must reflect such realities of rural economies.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at hsamad@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