rafmacalaba/datause-encoder-data huggingface dataset:
For implementing the procedure, since our focus is on examining the life cycle from 12 to 40 years of age, we drop all observations with a _median_ age below 12 or greater than 40 in any given survey year _t_ , and define the median age in any given survey year _t_ as:

# (1) Age = t – c

where _c_ is the median year of birth of each cohort. Since the cohort definition is homogenous across countries, the median age for a given cohort will always be the same in a given survey year _t_ . Once we define cell means (which represent the proportion employed, enrolled in school, or Osow for a given cohort in a given year), we plot these against the median age for each cohort and survey year.

Given the definition of each cohort in _t_ , the composition and size of each cell (for each country) can be defined theoretically as:

# (2)

However, since not every cohort exists in every survey year (given our median age restrictions), the real cell size is smaller. Since we are interested in cell means, we first consider our indicator of interest as a function of cohort tendencies and an error term, which allow us to decompose the variability of a given indicator for each individual in a given year-cohort. Following Attanasio (1993), for each individual _i_ with a median age _a_ in cohort _c_ in time _t_ , we consider the following: where δ represents cell means (in our case, the proportion of enrollment, employment, or Osow individuals), and ε is a random error (deviations from δ) with the assumption that E[ε] = 0 and known variance σ<sup>2</sup> ; �N(.).<sup>16</sup> As in related studies, in our estimation methodology, cell means are adjusted by cell size, such that cohorts with more individuals (within a given country) are weighted appropriately. Since our indicators (X’s) are binary (and thus not censored) and not restricted over the age profile we are considering, we can estimate the � as simple weighted proportions of enrollment, employment, and Osow status.<sup>17</sup>

> 16 We make this assumption since we use OLS rather than maximum likelihood estimation methods.

> 17 While theoretically, we should be able to capture enrollment and employment data for all individuals between 12 and 40, in a few cases (eg. Venezuela between 1998-2008) some surveys restrict the education or employment modules to smaller age ranges. Where this is the case, we interpolate from previous/subsequent surveys.

15
# **1. Introduction**

Two prominent stylized facts about economic development are that there is an advantage <u>of backwardness, such that on comparing two otherwise similar countries the one with the lower</u> initial mean income will tend to see the higher rate of economic growth, and that there is an <u>advantage of growth, whereby a higher mean income tends to come with a lower incidence of</u> absolute poverty. Past empirical support for both stylized facts has almost invariably assumed that the dynamic processes for growth and poverty reduction do not depend directly on the initial level of poverty. Under that assumption, the two stylized facts imply that we should see poverty <u>convergence: countries starting out with a high incidence of absolute poverty should enjoy a</u> higher subsequent growth rate in mean consumption and (hence) a higher proportionate rate of poverty reduction.

That poses a puzzle. The data on poverty measures over time for 90 developing countries assembled for this paper reveal little or no sign of poverty convergence. Figure 1 plots the proportionate rate of change in poverty—specifically the annualized log difference between household surveys in the percentage of each country‘s population living below $2 a day at 2005 purchasing power parity—against its initial level. (The data are described later.) There is no sign of convergence; the regression line has a slope of 0.006, with a t-ratio of 0.590, based on a robust (White) standard error. The overall poverty rate of the developing world has been falling since at least 1980 (Chen and Ravallion, 2010), but the proportionate rate of decline has been no higher in its poorest countries.

Clearly something important is missing from the story. Intuitively, one hypothesis is that either the growth rate in the mean, or the impact of growth on poverty, depends directly on the initial poverty rate, in a way that nullifies the ―advantage of backwardness.‖ To test this hypothesis, the paper estimates a model in which the proportionate rate of progress against poverty depends on the rate of growth in mean consumption and the poverty rate, while the rate of growth in the mean depends in turn on the initial poverty rate as well as the initial mean.

The results suggest that mean-convergence is counteracted by two distinct ―poverty effects.‖ First, there is an adverse direct effect of high initial poverty on growth—working against convergence in mean consumption. Second, high initial poverty dulls the impact of growth on poverty. On balance there is little or no systematic effect of starting out poor on the proportionate rate of poverty reduction.

2
the ‘true’ value of welfare measure obtained from the census. We present figures that indicate the share of municipalities containing the true value out of 853 municipalities for each survey; 2. For each municipality in the state, we compute the fraction of simulations, 41 poverty maps, for which the simulated confidence intervals encompasses the ‘true’ value of the welfare measure. We present a histogram of the share of good predictions among the 853 municipalities of the state.

For both cases, we have just under 35,000 municipality-level poverty estimates deriving from our 41 poverty maps and 853 municipalities (853*41). Organizing these cells into a matrix form we have a matrix of 853 rows and 41 columns containing values one or zero depending on whether or not the municipality confidence interval contains the true welfare indicator. Let _K ij_ represent these indicators, where _i=1,...,853_ and _j=1,...,41_ . Case 1 is obtained by the following 853 average: _K_ . _j_ = ∑ _K ij_ 853 , representing the sum of _K ij_ over _i_ for survey _j_ ; Case 2 is obtained in _i_ = 1 41 two steps: first we compute the average over surveys _j K i_ . = ∑ _K ij_ 41 and then record its _j_ = 1 distribution.

Figure 15 indicates that, for case 1, our predictions are quite robust and in general our confidence interval at the municipality level encompass the true point estimate in more than 90% of the cases for the FGT(0) and FGT(1) but nearer 80% for the FGT(2) in all surveys. The relatively low success in the case of the FGT(2) measure appears in part related to the fact that at the poverty line we are using (R$100 per person per month), FGT(2) values are very low in absolute terms, and this affects also calculations of precision. When we recalculate poverty based on a poverty line three times higher, coverage rates for the FGT(2) measure average around 90% rather than 80%.

25
clientalism) have been the subject of a large body of research (e.g. Chattopadhyay and Duflo 2004; Besley et al. 2004, 2005; Bardhan and Mookherjee 2006; Beaman et al. 2008; Ban and Rao 2008).

A small and growing body of scholarship has examined the _sabha_ itself, and whether it serves as a mere “talking shop,” or constitutes a true deliberative forum in which citizens are able to raise and resolve issues of public relevance. Besley et al. (2005) examine survey data from 5,180 randomly chosen households drawn from 537 villages in the four South Indian states of Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu. They find, like Crook and Manor (1998), that with the exception of Kerala, _gram sabhas_ are often not held as scheduled; however, when they are held, governance sharply improves. Focusing on a specific policy administered at the local level – access to a Below Poverty Line (BPL) card, which provides an array of public benefits – they find that policies were more effectively targeted to landless and illiterate individuals when a _gram sabha_ was held. And these effects were large, raising the probability of receiving a BPL card by 25 percent.

The reason why _gram sabhas_ result in better poverty targeting is related to one of their primary roles in village government. BPL lists are first determined on the basis of a census conducted by the government which identifies poor households with a given set of criteria. However, in many states these lists have to be ratified by the _gram sabha_ , which allows the quantitative definitions of poverty to be deliberatively interrogated by the community. Rao and Sanyal (2009) examine transcripts of deliberation from 290 _gram sabhas_ from a subset of villages studied by Besley et al. (2005) and find that this process acts as a vehicle for questioning governmental definitions of poverty and creating a shared, intersubjective<sup>3</sup> understanding of what it means to be poor. They argue that _gram sabhas_ “become sites for the joint production of an understanding of what it means to be officially classified as poor,” and that “these exchanges... foster the future capability of the poor to engage in a critical dialogue with the state on definitional matters.” 12
31

# <u>Figure 7: Immunization inequalities in selected African countries in the 1990s</u>

<!-- Start of picture text --> 4 Chad; 1996 24<br>Cote d'Ivoire; 1994 38 80<br>Ghana; 1993 16 65<br>Kenya; 1993 65 87<br>Mali; 1995 17 57<br>0 10 20 30 40 50 60 70 80 90 100<br>Poorest quintile Richest quintile<br><!-- End of picture text --> Figure 8 shows trends in MCH inequalities between the 1990s and 2010s for a panel of 25 countries that have complete data for the 1990s, the 2000s and the 2010s. For some indicators (e.g. immunization and the treatment of ARI), the rate for the richest 20% has barely changed, reflecting in part the fact the rate was already high, while the rate for the poorest 20% has increased, thereby closing the gap between the poor and better off. For other indicators (e.g. ANC and the treatment of diarrhea), we see increases among both the poorest 20% and the richest 20%, even if the proportionate increase is typically still larger for the poorest 20%.
on the northeastern states was motivated by the strong concentration of displaced people in this part of the country during our study period, while in parts of the country other than the Northeast displacement was not dominant. Thus, this focus is unlikely to be a limiting factor for the interpretation of the results presented below.

Information on the roster of host locations to be surveyed in every round is derived from information collected on a daily basis by key informants on the ground (the Emergency Tracking Tool). Our data comprise 3,444 unique locations.<sup>10</sup> Section 6 provides additional descriptive statistics and information about these data.

_IDP profiling data._ We also use data from the Nigeria IDP Survey project _Profile of Internally Displaced Persons in North-East Nigeria 2018_ to calculate descriptive statistics for the displaced population and the population of host communities. These survey data were collected between June and August 2018, with the objective of obtaining a socioeconomic profile of IDPs and host communities in northeastern Nigeria. The survey contains information on poverty level, education, access to essential services, employment, livelihoods, and perceptions. The total sample contains 1,510 host households and 1,437 IDP households. The sample excluding Borno includes 270 host and 250 IDP households. The survey was conducted in 111 enumeration areas randomly selected from the six northeastern states in a number proportional to the size of the displaced population in each state, and it covers displaced households living in both camps (48 enumeration areas) and host communities (77 enumeration areas). The sampling frame was derived from data on IDP counts collected by the IOM as part of the DTM 2017 project.

# **3.2 Descriptive Statistics**

Table 1 shows descriptive statistics by survey year for women and men. It shows the main outcome variables used for this analysis, which we classify as financial and nonfinancial outcomes (see Table A2 in the appendix for an overview of the outcome variables). We restrict the sample to married men and women to analyze how conflict could impact gender

> 10We use the site and location data from round 13 (June 2017), the round in which the data collection was expanded beyond camps to host communities, to round 30 (March 2020).

17
maximum rate is 10 percent in municipalities, 12 percent in cities with a population below 30,000, and 15 percent in cities with a population over 30,000, except for Zagreb, where the current rate is 18 percent and could increase to a maximum of 30 percent.

**Table 1. Croatia: General Government Revenue, 2014**

||Fiscal data<br>(in HRK)|<br>% of GDP|Included|Portion<br>in analysis (|fiscal data)|
|---|---|---|---|---|---|
||||Yes/No|<br>included|% of GDP|
|**Total revenue**|139,959|42.6||67,673|20.6|
|Total tax receipts|81,528|24.8||67,673|20.6|
|_Direct taxes_|17,612|5.4||11,890|3.6|
|Personal income tax + Surtax|11,954|3.6||11,890|3.6|
|Employment earnings (general schedule)|10,410|3.2|Yes|10,410|<br>32|
|Income from self-employment (general schedule)|784|0.2|Yes|784|<br>.|
|Capital tax (dividends, interest) - single 12% rate schedule|438|0.1|Yes|438|0.1|
|Rental income - single 12% rate|258|0.1|Yes|258|0.1|
|Income tax per annual report, net|64|0.0|No|||
|Corporate income tax (20%)|5,658|1.7|No|||
|_Indirect taxes_|55,783|17.0||55,783|17.0|
|VAT|40,923|12.5|Yes|40,923|12.5|
|Excise taxes|11,780|3.6|Yes|11,780|3.6|
|Beverages tax|1,067|0.3|Yes|3079|09|
|Other indirect taxes|2,013|0.6|Yes|,|.|
|_Other taxes_|8,133|2.5|No|||
|Social contributions|41,702|12.7||41,702|12.7|
|General health contributions|16,732|5.1|Yes|16,732|5.1|
|Occupational health contributions|576|0.2|Yes|576|0.2|
|Employment contributions|1,933|0.6|Yes|1,933|0.6|
|Pension contributions A<br>Pension contributions B1|22,460|6.8|Yes|22,460|6.8|
|Other revenue<br>Sources: World Bank staff based on Ministry of Finance|16,729|5.1|No|||

In terms of social contributions, the 2002 reform of the Croatian pension system introduced three pillars: (i) intergenerational solidarity, (ii) mandatory individual savings, and (iii) voluntary individual savings. Savings in the second and third pillars are collected by private pension funds. Two parallel contributory schemes were created: scheme A for people who participate in pillar 1 only, and scheme B for people who participate both in pillar 1 and pillar 2.<sup>4</sup> Persons who were aged above 50 (below 40) in January 2002 were automatically enrolled into scheme A (B), while people aged between 40 and 50 were able to choose to become members of scheme A or B. People in scheme A pay a 20 percent contribution to pillar 1 only; these contributions are called Pension

> 4 The terminology for scheme “A” and scheme “B” is taken from Urban and Bezeredi (2016).

5
8 prevalence in the control group (based on the 5-month follow-up results), intracluster correlation of 0.09 (based on 5-month follow-up), and 1.3 children under 5 years per household (based on 5- month follow-up), we required 10 households per village.

We estimated intervention effects according to random assignment (intention to treat), irrespective of adherence to the intervention.

For both primary and secondary outcomes, whether binary or continuous, we fitted linear models with a binary variable indicating whether the participant was in a treatment or control cluster [29]. Because randomization was stratified by province and number-of-villages-per-cluster, we included binary variables (fixed effects) for each stratum (n=12) in the model. We also included gender and age (month) indicator variables for all child health outcomes. We clustered standard errors at the cluster (i.e. group of villages) level.

For the primary outcome of WASH institutions, and secondary outcomes consisting of multiple measures, we calculated a summary index to avoid over-rejection of the null hypothesis due to multiple inferences. We rescaled each outcome so that higher values implied better outcomes, and averaged standardized values relative to the control group. Treatment effects were estimated as the difference in the summary index between treatment and control groups, such that treatment effects are expressed in standard deviation units relative to the control group.

We pre-registered two analyses restricted by subgroup: by province (for all three primary outcomes), and by gender (for diarrhea and length-for-age). We test for interaction on the additive scale, using interaction terms in linear models.

Statistical analyses were conducted in Stata version 16.0.

## Role of the funding source

This study was funded by the UK Foreign, Commonwealth, and Development Office (FCDO) (https://www.gov.uk/government/organisations/foreign-commonwealth-development-office), via Amendment No. 3 to the Supplemental Arrangement with the World Bank regarding Multi-Donor Trust Fund for Impact Evaluation to Development Impact (TF072617, parallel to TF072161). AC, KC, and EM were staff at Development Impact at that time. The Healthy Villages & Schools program was a DRC government national program funded by UK’s FCDO and implemented with UNICEF’s support. The funder and implementing partners provided inputs at the design stage to ensure the study addressed policy and program priorities of importance to them. <mark>The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.</mark>

# Results

Of the 1,312 respondents in 328 villages interviewed for the 5-month follow-up, 1,133 (86%) in 328 villages were re-interviewed at the 3.6-year follow-up, between November 24, 2022 and February 10, 2023 (Figure 1). We also reached two villages that were not accessible during 5- month follow-up and lost one village that was surveyed at 3.6-year follow-up due to conflicts. In 39 households with a 5-month follow-up interview, a new respondent was interviewed at 3.6
_permisionarios_ do) are more likely to join cooperatives. Revenue may be paid out in annual bonuses, which may be based on fishermen’s total annual catch, equal for all members, or determined by some other rule (McGuire 1983). Finally, in some cooperatives, members also enjoy income security through sources such as retirement benefits or credit (McCay et al. 2014; G. Hinojosa-Arango, pers. com., Feb. 2012).

In the theoretical model below, we model these benefits received by cooperative members in two ways: (i) a lump sum payment reflecting discounts on equipment (including boats and motors), credit, or annual bonuses from the cooperative leadership; (ii) a factor reducing the costs of fishing reflecting access to fishing permits and state subsidies for fuel, as well as the absence of costs associated with searching for buyers. To the extent that the size of an annual bonus is dependent on annual catch, the bonus is not appropriately classified as a lump sum payment, as it affects incentives for effort. We do not have information on how often such catch-dependent bonuses occur, but it is important to note from the above discussion that other forms of compensation also make up the lump sum payment.

While the features above are generally shared by many cooperatives in the Gulf of California area, there are key differences among the three cooperatives for which we have daily logbook data and conduct empirical analysis. Figure 1 shows the location of these cooperatives. Pichilingue is located on the outskirts of La Paz, the largest city in the state and a major market in the region, and Sargento is a short drive away from La Paz. Punta Abreojos, a member of the federation of cooperatives in Northwest Baja California known as FEDECOOP, is on the Pacific side of the peninsula, adjacent to other cooperatives from FEDECOOP. For the purposes of the empirical work below, we utilize the fact that Abreojos effectively has more secure property rights than Pichilingue and Sargento and can therefore manage its own members and restrict access for nonmembers more easily.

This arises for five reasons. First, Abreojos fishes in a relatively isolated area, while Pichilingue and Sargento fish in areas that have many cooperatives and fishers (McCay et al. 2014). There is a larger pool of potential fishers for Pichilingue and Sargento to compete with. Second, Punta Abreojos and the other cooperatives in FEDECOOP successfully retained exclusive fishing rights to lobster, abalone, snails, and a few other species even after 1992 (McCay et al. 2014; Cota-Nieto 2010; J. J. Cota-Nieto, pers. com., May 2012). The ten cooperatives in FEDECOOP each have separate, clearly defined polygons in which no other cooperative of FEDECOOP and no non-FEDECOOP fisher can fish these species, unless it is for subsistence 8
56

# Meade, J.E. and F.H. Hahn. 1965. ‘The rate of profit in a growing economy," Economic Journal, 75:445-448,

# Mill, John Stuart. 1848. Principles of Political Economy.

Mino, Kazuo. 1990. "Mor 2y and endogenous growth in a cash-in-advance economy.” mimeo., Hiroshima University.

- Moran, T.H. and contributors. 1986. Investing in Development: New Roles for Private Capital? (U.S.-Third World Policy Perspectives, No. 6, Overseas Development Council) New Brunswick, NJ: Transaction Books.

- Morris, C.T. and |, Adelman. 1988, Comparative pattems of economic development, 1850-1914. Baltimore, MD. Johns Hopkins University Press.

- Moschos, Dematrics. 1989. "Export Expansion, Growth and the Level of Economic Development: An Empirical Anatysis.* Journal of Development Economics 30: pp.93-102.

- Murphy, Kevin, Andrei Shieifer, and Robert Vishny. 1989. "Industrialization and the Big Push.” Journalof Political Economy 97:5, pp. 1003-2026.

- . 1990. ‘The Allocation of Talent: Implications for Growth." University of Chicago.

- Nelson, Richard R. 1956. *A theory of the tow-levei equilibrium trap in underdeveloped economies." American Economic Review 41: pp. 894-908.

- . 1981. “Research on productivity growth and productivity differences: dead ends and new departures." Journal of Economic Literature 19:1029-1064.

- Nishimizu, Mieko and John Page Jr. 1986. "Productivity Change and Dynamic Comparative Advantage." Review of Economics and Statistics 68(2): 241-247.

- , and Sherman Robinson. 1986. ‘Productivity growth in manufacturing." In H. Chenery, S. Robinson, and M. Syrquin.industrialization and Growth: A Comparative Study. New York: Oxford University Press.

- Norsworthy, J.R. 1984. "Growth Accounting and Productivity Measurement." Review of income and Wealth Series 30, No. 3: pp. 309-29.

- North, Dougias C. 1989. “Institutions and economic growth: A historical introduction," World Development 17, No. 9.

- Ofer, Gur. 1987. "Soviet economic growth: 1928-1985." Journal of Economic Literature 25 (Dec.): pp. 1767-1833.

- . 1990. "Macroeconomic issues of Soviet reform." In O. J. Blanchard and S. Fischer, eds. NBER Macroeconomics Annual 1990. Cambridge, MA: MIT Press.

- Olson, Mancur, 1982. The Rise and Fall of Nations: Economic Growth, Stagflation, and Social Rigidities. New Haven, CT: Yale University Press.

- Orphanides,MonetaryA. andEconomics.R. Solow. 1990. “Money, Inflation and Growth." In B. Friedman and F, Hahn, eds., Handbookof Pack, H. 1988. “Industrialization and trade." in Chenery, H. and T.N. Srinivasan, eds. Handbook of Development Economics: Volume |. New York: North-Holland.

- Panagariya, A. 1990. ‘The paralle! market in centrally planned economies: A dynamic analysis." Journal of Comparative Economics 14 (Sep.):pp. 353-71.

- Papageorgiou, Demetris, Michael Michaely, and Armeane Choksi, eds. 1990 forthcoming. Liberalizing Foreign Trade. Volumes 1-7. Oxford: Basil Blackwell.

- Pasinettl. 1962. ‘Rate of profit and income distribution in relation to the rate of economic growth," Reviewof Economic Studies 29: 297-279.

Pearce, David. 1990. "Environment and Development: An Overview." Background paper, World Development Report 1991.