rafmacalaba/datause-encoder-data huggingface dataset:
-70-

Exact-matching is difficult to realise, because strictly identical products are only rarely available in two or more countries at the same date. In consequence, lower degrees of product comparability have to be accepted for international comparisons than for inter-temporal comparisons within a single country. This is true not only for the present product-based study, but also for expenditure-based studies such as the ICP project carried under the direction of the United Nations.

The following paragraphs describe the problems faced in this study with regard to product matching. We discuss the alternative procedures, and show their different outcomes for the comparison of the motor vehicle industries in the three countries. This industry presents particular difficulties for product matching because of the large number of items produced and the wide range of quality differences within product groups.

# Maximalist Approach

The industrial censuses we used give value and quantity information for 100 automobile products for Brazil, 393 items in Mexico and 101 in the USA. In our first round of comparison we tried to match as many products as possible from the Brazilian and Mexican census reports with those listed in the US census.

At our first attempt we found 36 products from the Brazilian census that appeared to match 62 products as reported by the United States, and 45 products for Mexico matching 59 products for the United States. These product matches are given in tables 5.2 and 5.3 which are shown at the end of the chapter.

At this stage, products were considered "matched" provided that the product descriptions were the same or very similar, and provided also that price and quantity figures were available for both countries. This approach requires the matched products to have a more or less "homogeneous" character as well (to this issue reference will be made later on). This matching procedure is described as "maximalist" because the aim was to obtain the maximum number of matches without regard to the plausibility of the PPPs we derived from them.

It can be seen from tables 5.2 and 5.3 that some matches can only be achieved for rather aggregated "products", obtained by combining several specific items in one or both of the countries being compared. Therefore, out of the 36 Brazilian matched motor vehicle products, only 26 PPPs could be calculated, and out of the 49 matched Mexican products only 19 PPPs could be calculated.

The PPPs for matched products following the maximalist procedure are given in the penultimate columns of both the left and right hand side of tables 5.2 and 5.3. For the Brazil/US comparison they range from 0.78 to 19.58 cruzeiros to the US dollar, and for Mexico from 4.32 to 33.22 pesos to the US dollar. These widely divergent PPPs for different products were a signal that some of the matches were false. In spite of having similar (or even identical) descriptions, we inferred that some of these "outlier" products were, in reality, different from each other.
Policy Research Working Paper 10763

# **Abstract**

Quality early childhood education improves childhood development outcomes and has long-term implications for school readiness, workforce participation, and economic growth. Despite this, in Pakistan, the net enrollment rate of children ages 3 to 5 in early childhood education was only 31 percent in 2022. This paper estimates the cost of expanding access to early childhood education using an adapted version of the early childhood education Accelerator Costing and Simulation model. Using available administrative data, the paper presents cost estimates for three packages: (i) a business-as-usual package, (ii) a core service delivery package, and (iii) an augmented service delivery package. It considers how these costs might vary using alternate delivery mechanisms, such as community construction and vouchers. To ensure 100 percent net enrollment in early childhood education by 2035, Pakistan must increase the amount of the education budget spent on early childhood education from the existing allocation of 5.3 percent to 10.4 percent by 2035. This means increasing the early childhood education budget from PKR 71 billion (US$0.3 billion) in 2022 to PKR 418 billion (US$1.85 billion) in 2035, suggesting an average annual increase of 14 percent. Using alternate delivery mechanisms, such as community construction and vouchers, the required budget can be reduced to PKR 311 billion (US$1.37 billion) in 2035.

This paper is a product of the Education Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ahasan1@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(since a random assignment undoubtedly picks up some people who would not normally participate). In the case of anti-poverty programs, one ends up assessing impacts for types of people for whom the program is not intended and/or denying the program to poor people who need it — in both cases running counter to the aim of fighting poverty.

These ethical and political concerns have stalled experiments or undermined their continued implementation. This appears to be why randomized trials for welfare reforms went out of favor with state governments in the US after the mid-1990s (Mofitt, 2003) and why subsequent evaluations of Mexico’s _PROGRESA_ program have turned to NX methods.

Are these legitimate concerns? As noted in section 3, the evaluation itself is rarely the reason for incomplete coverage of the poor in an anti-poverty program; rather it is that too few resources are available. When there are poor people who can’t get on the program given the resources available, it has been argued that the ethical concerns favor social experiments; by this view, the fairest solution to rationing is to assign the program randomly, so that everyone has an equal opportunity of getting the limited resources available.<sup>22</sup> However, it is hard to appreciate the “fairness” of an anti-poverty program that ignores available information on differences in the extent of deprivation. A key, but poorly understood, issue is what constitutes the “available information.” As already noted, social experiments typically assign participation conditional on certain observables. But the things that are observable to the evaluator are generally a subset of those available to key stakeholders. The ethical concerns with experiments persist when it is known to at least some observers that the program is being withheld from those who clearly need it, and given to those who do not.

Other concerns have been raised about social experiments. Internal validity can be questionable when there is selective compliance with the theoretical randomized assignment. People are (typically) free agents. They do not have to comply with the evaluator’s assignment. And their choices will undoubtedly be influenced by latent factors determining the returns to participation.<sup>23</sup> The extent of this problem depends on the specific program and setting; selective compliance is more likely for a training program (say) than a cash transfer program.

> 22 From the description of the Newman et al. (2003) study it appears that this is how randomization was defended to the relevant authorities in their case.

> 23 The fact that people can select out of the randomized assignment goes some way toward alleviating the aforementioned ethical concerns about social experiments.  But selective compliance clouds inferences about impact.

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# Can Micro-Credit Support Public Health Subsidy Programs?

Britta Augsburg, Bet Caeyers, Bansi Malde<sup>_∗_</sup> _JEL_ codes: D14, G41, H24, I12, I38, O16 Keywords: Micro-finance, micro-credit, subsidies, sanitation, Swachh Bharat Mission, SBM

> _∗_ Affiliations: Augsburg - Institute for Fiscal Studies (IFS), UK (britt ~~a a~~ @ifs.org.uk); Caeyers - IFS and FAIR/NHH, Norway (be ~~t~~ c@ifs.org.uk); Malde - University of Kent and IFS (b.k.malde@kent.ac.uk). This research was made possible through the generous support of the World Bank Strategic Impact Evaluation Fund and the ESRC Centre for the Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies. Ethics approval was received from the IRBs of University College London, UK, Project ID: 2168/008; and The Institute of Sustainable Development, Chennai, India. We thank Orazio Attanasio, Susanna Smets, World Bank reviewers and participants at the WEDC 2018 and the UNC Water and Health conferences for comments and suggestions. Felipe Brugues provided excellent research assistance. All errors are our own.
Square (KLS) estimator to estimate the model as an added robustness check. In all the regressions, we control for geographical differences by including regional dummies. Throughout the paper, the standard errors that we report are robust against arbitrary heteroskedasticity.

# 5. RESULTS

5.1 ORDINARY LEAST SQUARE REGRESSIONS Table 4 presents results from the OLS regressions showing the correlation between basic sanitation use and _Voice_ in total, rural and urban populations. Before proceeding to discuss the results, a few features of the table needs highlighting: Odd numbered columns report the results from the bivariate specification whereas even number columns report results that include the control variables in the regression model.

We now discuss the results for column 1 and 2. While the estimated coefficients are positive, they are small and statistically insignificant (Column 1). Furthermore, once we control for the level of GDP per capita and log population density, the coefficient of _Voice_ becomes even smaller, close to zero and remains statistically insignificant. The relationship between per capita income and basic sanitation use is positive and statistically significant at conventional levels in all the regressions. On the other hand, we find no significant relationship between access to sanitation and population density. These results generally hold for access to sanitation in urban and rural areas (Columns 3 – 6).

**Table 4:** Ordinary least squares (OLS) cross-country regressions (Dependent variable: Sanitation)

|||Total<br>||Urban<br>||Rural<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Voice and accountability|0.074|0.006|0.053|0.001|0.083|0.012|
||(0.047)|(0.024)|(0.041)|(0.022)|(0.053)|(0.033)|
|log real GDP per capita||0.197***<br>(0.025)||0.152***<br>(0.023)||0.191***<br>(0.031)|
|log population density||0.012<br>(0.015)||-0.007<br>(0.015)||0.031<br>(0.022)|
|Regional dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|_R_-squared|0.677|0.858|0.716|0.857|0.578|0.751|
|Observations|73|72|72|71|72|71|

Notes:<sup>_∗∗∗_</sup> indicates significance at the 1% and 5% level. Robust standard errors in parentheses. South Asia is the region excluded in the regional dummies.

The finding of no significant relationship between access to sanitation and _Voice_ is not surprising for the following reasons: The OLS regression is likely to be plagued with endogeneity problems. Consequently, the OLS estimates can not be relied on for causal inference. We therefore employ instrumental variable estimation to address the possible endogeneity problem and draw casual inference on the relationship between _Voice_ and sanitation access and use.

5.2 INSTRUMENTAL VARIABLES ESTIMATION Table 5 presents the results from the two-stage least squares (2SLS) model. Odd (even) numbered columns present results for estimations where we exclude (include) the standard control variables. Panel A of the table reports the 2SLS estimates of the effect of _Voice_ on access to sanitation services.

11
Of the 4800 households 4305 (around 90%) of them could be interviewed in the second round and it is this panel of households that we use in our analysis<sup>11</sup> .

**Table 2. Descriptive Statistics**

|||**1993**<br>|<br>||**1998**<br>||
|---|---|---|---|---|---|---|
||Rural|Urban|National|Rural|Urban|National|
|Household size|5.0|5.0|5.0|4.8|4.5|4.8|
|Proportion of male household heads|78%|57%|74%|77%|57%|73%|
|Education of HH Head (years)|5.3|6.8|5.6|6.5|8.3|6.9|
|Age (HH head)|45.2|47.3|45.6|47.7|50.6|48.4|
|Proportion of people living in north|57.3%|41.3%|54.3%|57.2%|43.3%|54.3%|
|Proportion of people who don’t own any land|8.1%|75.5%|20.8%|7.2%|73.2%|21.1%|
|Land cultivated (m2)|5353.0|3045.9|5137.1|7325.4|4674.6|7131.8|
|Quantity of Harvested rice (kg)|2232.0|1660.6|2213.2|2912.7|2360.5|2885.4|
|Proportion of net seller of rice|51.0%|5.1%|42.4%|46.9%|8.6%|38.9%|
|Received any technicalagriculturaladvice|58.1%|8.5%|48.8%|64.1%|13.5%|53.4%|

Source: Author’s own calculations using VNLSS 93 and 98.

Table 2 shows some of the basic descriptive statistics of the panel sample for the two years. The household size has decreased from 93 to 98; the decline was slightly larger in urban areas. Cultivated land has increased by approximately 40%. Rice production has on average increased by 30%. After the reforms in 1988, which moved away from collectives and considered households as the unit of economic activity, the government started titling the land to households. Although usable land is a small percentage of available land in Vietnam, most of the households have some land. Only 21% (21%) of the households do not own any land in 1993 (1998), and the majority of these households are in the urban area. In 1993 (1998), the landless rate in rural areas is only 8% (7%) whereas in urban areas it goes up to 75% (73%). For both urban and rural areas, more households utilized technical agricultural advice in 1998 compared to 1993.

# **_2.2 Income and Expenditure Structure_**

An important link through which trade liberalization affects household welfare is prices. The change in prices will affect the expenditure of all households and the income of some households (households that produce the affected commodities). Hence, the extent of the effect on welfare will depend on which products are important income

> 11 Since the original 4800 households were representative of the country, and the panel includes around 90% of these households, we’ll consider the panel as representative and will not deal with the weighting issues. Van de Walle & Cratty (2003) also contend that panel households are representative of the nation.

8
demographic analysis<sup>11</sup> , and for limits belonging to the social rather than biological nature of external migration, no robust framework of determinants comparable to those applied to mortality and fertility was never devised in the demographic study of migration. As Davis (1988) puts it “international migration [...] resembles mortality and fertility in being part of the fundamental balancing equation in demography, which says that any population change is a function of natural increase and net migration [...] but unlike mortality and fertility, it has no biological constraints and hence no built-in limits. There is no ‘normal’ or ‘natural’ rate of migration” (p. 245).

# **2.   Modeling the impact of international migration on birth rates**

To sum up, demography basically deals with international migration as numbers to be added (immigration) or subtracted (emigration) to any population defined by national boundaries. No individual country has a zero balance of external migration, but the entire world has, because it obviously receives no external migration. Despite this indisputable fact we argue, however, that flows of international migration might change the total number of inhabitants on earth, as a result of the impact international migration can have on natural population growth, notably on birth rates<sup>12</sup> . We focus here on two particular sub-populations: migrants themselves, and the community they have left behind in home countries.

> 11 Henry (1972)  “Dans l’état actuel de la démographie, on ne sait pas étudier les phénomènes ouverts en tant que tels [...] » “L’émigration d’une région A [...]  concerne le membres de la population étudiée et fait sortir de cette population : [...] L’immigration dans une région B résulte, elle, de l’arrivée de personnes étrangères à cette région [...] les événements qui figurent au numérateur ne concernent pas les membres de la population figurant au dénominateur. [...] Il n’y a pas symétrie entre l’émigration de A vers B et l’immigration en B provenant de A : [...] Dans ces conditions, l’étude des mouvements migratoires est, au moins sous son aspect théorique, une étude de sortie, d’émigration » (p. 198-9).

> 12 Mortality is also linked with migration. For example, an interesting “Mediterranean” pattern of health has been found among migrant populations (Khlat & Darmon 2003). Including mortality in the paper would have complicated our purpose for only little added value, since mortality does not play as important a role as fertility in contemporary international demographic differentials.

8
**_Table 1: Emigration Rates by Level of Education in 2000_**

||Tertiary<br>Education|Secondary<br>Education|Primary<br>Education|
|---|---|---|---|
|Swaziland|0.5%|0.0%|0.0%|
|Namibia|3.5%|0.2%|0.1%|
|Botswana|3.6%|1.0%|0.1%|
|Lesotho|4.3%|0.2%|0.0%|
|South Africa|7.5%|0.8%|0.3%|
|Tanzania|12.4%|1.4%|0.1%|
|Zimbabwe|12.7%|1.8%|0.2%|
|Zambia|16.8%|1.1%|0.2%|
|Malawi|18.7%|2.5%|0.1%|
|Angola|33.0%|7.7%|1.9%|
|**Mozambique**|**45.1%**|**6.3%**|**0.6%**|
|Mauritius|56.2%|9.1%|7.5%|

Source: Docquier and Marfouk (2004).

Note: The table shows for each country’s migrants with a given education level as a share of all natives and migrants in that country with that same education level.

# **2.2 A Cross-Country Perspective on the Availability of Professionals**

The Investment Climate Surveys conducted by the World Bank provide evidence of the limited use of professionals and of tertiary educated workers in manufacturing firms in Mozambique relative to the use by firms in other African countries (Table 2). In fact, Mozambique has the lowest share of professionals and of those with more than 12 years of education in the manufacturing workforce.

**_Table 2: Professionals and Tertiary Educated Workers in Manufacturing Firms_**

||Average Share of<br>Professionals in Total|Average Percentage of<br>Workers with Tertiary|
|---|---|---|
||Workers|Education|
|**Mozambique**|**2.7%**|**1.6%**|
|Mauritius|2.8%|3.9%|
|Ethiopia|3.5%||
|Eritrea|4.1%||
|Nigeria|4.3%||
|South Africa|4.6%|11.5%|
|Malawi|4.6%||
|Kenya|4.6%|15.0%|
|Zambia|5.2%|14.7%|
|Madagascar|6.1%|12.0%|
|Uganda|6.9%|35.4%|
|Senegal|6.9%||
|Mali|7.3%|11.5%|
|Benin|8.1%|15.6%|
|Tanzania|11.9%||

Source: Investment Climate Surveys. Note: The surveys were conducted for different countries in different years.

A more disaggregated comparison, at the level of manufacturing sub-sectors, is possible with South Africa. Even though the definitions of the skill categories are not the same across the two countries, the differences in skill intensity in each of the sub-sectors shown in Table 3 are stark.

15
Table 1 summarizes the _NRA_ and _RRA_ for democracies and autocracies (see below for definitions). The table indicates that autocracies are associated with negative levels for both _NRA_ and _RRA_ , whereas democracies have positive levels. Moreover, the differences are significant. The average _NRA_ ( _RRA_ ) is −0.15 (−0.26) for autocracies and +0.45 (+0.31) for democracies, a difference of 0.60 (0.57) or 60 (57) percentage points.

**TABLE 1** . NRA and RRA over Time and across Political Regimes

||Full s<br>|ample<br>|Auto<br>|cracy<br>|Demo<br>|cracy<br>|
|---|---|---|---|---|---|---|
||_NRA_|_RRA_|_NRA_|_RRA_|_NRA_|_RRA_|
|1956–1959|0.41|0.18|−0.13|−0.29|0.66|0.41|
|1960–1964|0.28|0.08|−0.16|−0.30|0.54|0.30|
|1965–1969|0.27|0.07|−0.13|−0.27|0.51|0.27|
|1970–1974|0.10|−0.01|−0.24|−0.33|0.46|0.26|
|1975–1979|0.10|0.02|−0.23|−0.31|0.44|0.31|
|1980–1984|0.09|0.03|−0.22|−0.29|0.38|0.28|
|1985–1989|0.29|0.20|−0.06|−0.22|0.59|0.47|
|1990–1994|0.23|0.18|−0.14|−0.23|0.41|0.37|
|1995–1999|0.19|0.15|−0.13|−0.19|0.28|0.23|
|2000–2005|0.20|0.16|−0.08|−0.20|0.26|0.21|
|_All years_|_0.21_|_0.11_|_−0.15_|_−0.26_|_0.45_|_0.31_|
|No. of<br>countries|74|69|38|34|67|64|

_Source:_ Own calculations based on the data described in the text.

_Notes:_ The figures report _NRA_ and _RRA_ averages for the full sample, autocracies, and democracies in different subperiods. The number of countries refers to the total number in each category in the 1955–2005 period and changes over time due to entry and exit.

Although these statistics demonstrate that the average _NRA_ and _RRA_ values are much higher in democracies than in autocracies, they say nothing about the potential causal effect of democratization on agricultural policies. To obtain further insight on this issue, we examine the _NRA_ and _RRA_ values of countries in the data set that have experienced a political transition from autocracy to democracy. Specifically, figures 1 and 2 present the average _NRA_ and _RRA_ values in the predemocratization and postdemocratization periods for 23 countries that have experienced permanent democratization, as defined by Papaiannou and Siourounis (2008).<sup>4</sup> 5