rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8873

# **Abstract**

Open data and open government data have recently attracted much attention as a means to innovate, add value, and improve outcomes in a variety of sectors, public and private. Although some of the benefits of open data initiatives have been assessed in the past, particularly their economic and financial returns, it is often more difficult to evaluate their social and political impacts. In the public sector, a murky theory of change has emerged that links the use of open government data with greater government accountability as well as improved service delivery in key sectors, including health and education, among others. In the absence of cross-country empirical research on this topic, this paper asks the following: Based on the evidence available, to what extent and for what reasons is the use of open government data associated with higher levels of accountability and improved service delivery in developing countries? To answer this question, the paper constructs a unique data set that operationalizes open government data, government accountability, service delivery, as well as other intervening and control variables. Relying on data from 25 countries in Sub-Saharan Africa, the paper finds a number of significant associations between open government data, accountability, and service delivery. However, the findings suggest differentiated effects of open government data across the health and education sectors, as well as with respect to service provision and service delivery outcomes. Although this early research has limitations and does not attempt to establish a purely causal relationship between the variables, it provides initial empirical support for claims about the efficacy of open government data for improving accountability and service delivery.

This paper is a product of the Governance 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 author may be contacted at mjelenic@worldbank.org.

_The
Policy Research Working Paper 7932

# **Abstract**

This paper studies the question of whether exchange rate policy affects the impact of remittances on economic growth in recipient countries. The paper utilizes a comprehensive data set that comprises annual observations for 135 developing and transition countries, spanning 1970−2007. The data for exchange rate regimes is based on the Reinhart and Rogoff exchange rate regime classification, whereas the data for remittances and all other variables are from the World Bank’s World Development Indicators database. The findings indicate that more flexible exchange rate regimes are associated with a greater increase in economic growth following an increase in remittances, but also that the impact of remittances on growth is positive under a fixed exchange rate regime. The estimates suggest that a 1 percent increase in remittances increases per capita growth by about 0.79 percent under a fixed exchange rate regime, and that this effect increases by about 0.13 percent for a 1 point increase in the exchange rate flexibility - index. The results further suggest that the effect of remit tances under a fixed exchange rate regime is positive in less financially developed countries as well, but do not provide conclusive evidence that this effect varies inversely with exchange rate flexibility in such economies as theorized.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 author may be contacted at elartey@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 cited accordingly. The findings, interpretations, and conclusions expressed in this
superior productivity performance but do not expand employment much in countries such as Tanzania and Ethiopia.

• **Service sectors (Trade, Transport and Finance and others):** There is evidence of unconditional convergence across many service sectors (IMF (2018); Kinfemichael and Morshed (2019)). The transport and financial services sectors show convergence across three different balanced panel datasets. In contrast, there has been no evidence of convergence in trade services (wholesale, retail trade, accommodation, and food services) over 1995-2018 despite having shown unconditional convergence over an earlier timeperiod. Lagakos (2016) argued that in the retail trade sector, developing countries rationally choose “traditional technologies” with low measured labor productivity instead of “modern technologies” with high productivity across two dimensions. First, low car ownership rates among households in poor countries cause modern stores to locate further than traditional stores from residential centers less attractive. This situation is related with “appropriate technology” suggested by Basu and Weil (1998) and Acemoglu and Zilibotti (2001). Second, traditional retail technologies offer an opportunity for entrepreneurs to operate informally, thus earning a price advantage over modern retail technologies, which are larger in scale and cannot evade taxes as easily as smaller, traditional stores.

# **3.3 Robustness analysis**

## **3.3.1 Robustness 1: Time-varying regression**

The baseline results were based on three sample periods (1970-2018, 1970-1995, and 1995-2018). For robustness and to provide further insights, a rolling window methodology is employed in which regressions (2) and (3) are estimated with OLS over the 10-year rolling window. This results in 34 regressions. Figure 6 shows the time-varying contributions of within and between sector effects on aggregate convergence. The result is line with the baseline regressions. The between sector effects have contributed to aggregate convergence largely and continuously since 1990s. In addition, the within sector growth has played an important role in aggregate convergence since 2000. Furthermore,
refers to highly disaggregated administrative areas akin to groups of villages. We then aggregate predictions to obtain estimates at the target area for each country.

For predictors, we use satellite-derived geospatial indicators that are available across much of the globe, meaning that the methods and data evaluated here are widely applicable in cases where geolocated survey data are available. We use shapefiles from the seven countries to pull geospatial data from multiple sources, which is then combined with samples drawn from the unit-level census data. In each country, we simulate 100 two-stage samples – first randomly selecting enumeration areas and then randomly selecting households based on survey designs of actual household surveys in each country – and compare the overall performance across simulations, ensuring that the results are derived from one hundred possible samples rather than a single sample. Under these conditions, XGBoost and Cubist regression tend to outperform EBP and BRF in terms of accuracy, as measured both by Pearson correlations and Mean Squared Deviation. This can be seen clearly in the first two rows of Table 1.

> A key contribution of the paper is the evaluation of a two-stage residual block bootstrap to estimate uncertainty

> 2See Das and Haslett (2019) for a comparative analysis of several poverty mapping methods including the ELL method, EBP, and M-quantile. Pratesi and Spagnolo (2023) offer a recent overview of small area estimation methods for measuring poverty. Another potential SAE method is ESPREE (Isidro et al., 2016). However, we know of no readily available software packages that implement either M-quantile or ESPREE.

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- the degree of liberalization involved was relatively low, because trade flows between the US, Mexico and Canada were already relatively free. In particular, the extent of US trade liberalization was very small; insofar as significant concessions were made, they were on the Mexican side;<sup>22</sup>

- as a consequence, economic estimates of the direct macroeconomic effects of NAFTA on the US were tiny (of the order of a few tenths of one percent of GDP over a decade, and a few tens of thousands ofjobs, less than the margins of error in the quarterly national accounts statistics);<sup>2 3</sup>

- yet NAFTA aroused strong political opposition in the US, while it was relatively popular in Mexico, and was regarded as by far the most important economic priority by Mexican policymakers (in Canada it was largely a non-issue, since the principal political battle over trade liberalization had been fought over the Canada-US FTA).

This suggests that "non-traditional" gains (or losses) are likely to have been a significant factor behind NAFTA.

A number of authors have made arguments relating to the time-consistency or insurance mechanism set out above to explain Mexico's desire to join NAFTA, despite the small direct gains found by most estimates (e.g. Whalley (1996)). However, it is not all clear what types of domestic policy reform were involved. NAFTA does not cover macroeconomic policy at all, so it is difficult to see how it could have operated as a commitment device. If it was intended as such, it does not appear to have been effective in this instance, since NAFTA did not ultimately work to make Mexican fiscal and monetary policy more credible, as the 1994 devaluation demonstrated.

Nor did NAFTA cover domestic microeconomic reforms such as privatization or deregulation; indeed Mexico has made relatively little progress in these areas since NAFTA, recently backtracking on its commitment to privatize the state petroleum monopoly. It does commit Mexico to some fairly modest
Appendix A), it is unknown whether such a correlation is observed for urban and rural poverty rates. Figure A8 demonstrates a linear relationship between urban and rural poverty rates observed across countries: countries with lower urban poverty rates also tend to have lower rural poverty rates. As a result, when urban areas are defined by the DOU or DB methods, urban and rural poverty rates tend to be lower in countries with higher per capita GDP levels (Figure A9 in Appendix A).

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*

Survey-to-survey imputation (S2S) predicts household per capita consumption in a household survey that collects information only on selected covariates of consumption by using information from another survey that collected both the per capita consumption and its covariates. This literature builds largely on the poverty mapping literature (Elbers, Lanjouw, and Lanjouw, 2003) and has been widely implemented (Dang 2020, Newhouse et al. 2014, Stifel and Christiaensen 2007). This literature is in contrast to the multiple imputation literature inspired by Rubin (1987), which largely focuses on filling missing data within a survey rather than using information from one survey to make predictions in another one. There are generally two types of S2S studies in the literature: one that conducts the imputation over time and another that conducts the imputation within the same period but across surveys. The first type includes studies such as those by Newhouse and Vyas (2022) and Stifel and Christiaensen (2007). Studies that impute across surveys within the same time frame include Beltram et al. (2020), Dang (2019), Lucchetti et al. (2018), Dang et al. (2018), and Cuesta and Ibarra (2017). Dang (2020) and Dang et al. (2019) provide guidance on the appropriate methodology and caveats to using S2S.

Implementation of S2S in this study first involved identifying a set of variables that are available both in HFPS and GMD. This yielded the following variables: household size, urban/rural location, subnational administrative area, share of children, and share of elderly residents in the household. This list of variables was selected by carefully comparing the data dictionaries of HFPS and GMD. Both the HFPS and the GMD are both designed to be nationally representative, except for Mozambique, where the HFPS only covers urban areas. A major difference between the HFPS and the GMD is that while the GMD sample covers both households that own phones and those that do not, the HFPS (by design) only covers households that own
characterized by both high oversight potential (such as Belgium, Germany, the Netherlands) and by low oversight potential (for example, Japan)<sup>7</sup> .

The purpose of the present paper is to investigate whether the oversight potential of parliaments is related to other variables; however, we present two major differences from Maffio’s paper. First, instead of using the 1986 IPU data, we use the data that the IPU collected in collaboration with the World Bank Institute (WBI) in 2001. Our analyses are performed on the 83 cases for which the IPU-WBI data are available<sup>8</sup> . And second, in contrast to Maffio, we investigate whether there is any relationship between oversight potential and types of constitution (i.e. presidential, semi-presidential, or parliamentary), national income levels, and levels of democracy.

In the first section of this paper, after providing a working definition of oversight, we review some of the tools that parliaments can use to oversee the activities of the executive. We underline that oversight tools can be grouped on the basis of two criteria : the first concerns whether a specific oversight tool is employed before ( _ex ante_ ) or after ( _ex post_ ) the enactment of a government-sponsored policy, and the second concerns whether oversight is exercised internally or externally to parliament.

Though the data collected by the IPU in collaboration with the WBI do not provide evidence as to the _effectiveness_ of the oversight tools, they do provide some measure regarding the _potential_ for oversight. Our line of reasoning is straightforward, as we suggest that a parliament’s potential to oversee the executive increases as the number

> 7 Roberta Maffio, “ _Quis custodiet ipsos custodes?_ Il controllo parlamentare dell’ attivitá di governo in prospettiva comparata”, _op. cit._ , p. 361.

> 8 See Table 1 in this paper.

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account for all factors of production, women and men are found to have equally high production and allocative efficiency. Finally, the study by Goldstein and Udry (2008), mentioned above, finds differences in yields and profit per hectare but suggests that these differences are explained by differences in tenure security.<sup>5</sup> The evidence provided by these and other studies point to an overriding conclusion: women are not worse farmers than men in a technical sense (and the little existing evidence suggests that women allocate resources just as efficiently as men). Rather, women often face constraints in their access to and demand for the factors of production that would allow them to have yields equal to men. We now turn to an examination of the magnitudes and causes of these gaps.

# **_Lower Access to Factors of Production: Magnitude and Causes_**

The following discussion considers the main elements needed for agricultural production and explores whether there are gender gaps and the magnitude and causes of these gaps. Almost all of this discussion focuses on comparing male- and female-headed households because this is typically how the data are structured. The data are structured this way because it is an expedient way to collect data and because, in much of the world, the farm is a household enterprise, and it is difficult to assign ownership and responsibility to only one person.<sup>6</sup> The data used in this paper are drawn from the Rural Income Generating Activities (RIGA) database.<sup>7</sup> It is important to note that female-headed households are a heterogeneous group of households that include women whose husbands have migrated, women who are divorced, and women who are widows as well as some households that are classified as female5
# 6. Conclusions

In this paper, we evaluate different alternative methods to identify poor villages in 10 Malawian districts. This is a challenging prediction exercise because villages are highly geographically disaggregated. The results show that a two-step approach utilizing a hypothetical partial registry from 450 villages performs vastly better than the PMT, geospatial prediction based solely on the household survey, or the Meta relative wealth index. The main measure used to identify poor villages is the mean predicted per capita consumption of the bottom half of households in each village, but key results hold when using the mean predicted per capita consumption of all village households as the village welfare measure.

Implementing the partial registry method requires nationally representative survey data, publicly available geospatial indicators, and the collection of a partial registry containing a subset of household characteristics found in the survey data. Several similar household surveys that collect information on welfare proxies have been fielded with the support of the World Bank through the Survey of Well-Being with Instant and Frequent Tracking (SWIFT) program, including in Malawi. Although none have surveyed the full population of households in selected villages, it is quite standard for household surveys to list all surveys in sampled enumeration areas, and we estimate that the cost of collecting approximately 40 variables from all households in approximately 500 villages could be in the ballpark of $24,000 to $73,000. This is a worthwhile investment to greatly boost the accuracy of village welfare measures constructed using geospatial data. Some countries also field periodic community surveys, which could potentially be tweaked to collect partial registries.

This paper also demonstrates the efficacy of applying gradient boosting models in settings with household-level predictors, when sufficient data are available. In particular, using training data from the full sample of households, rather than only the 10 districts of interest, substantially increases predictive performance. Using post-lasso models instead of gradient boosting in the geospatial model, the second step of the partial registry method, only slightly affects the accuracy of the predictions. In contrast, using post-lasso models instead of extreme gradient
a more meaningful increment in human capital; one additional year of schooling is unlikely to be rewarded in the labor market unless it yields the completion of a full category.

The fact that there is a maximum level of education, with very few individuals obtaining more than 21 years of schooling, introduces a ceiling effect. As countries develop and average years of schooling increase, it becomes increasingly more difficult for individuals to outperform their parents. When parents have tertiary education as well as their children, one may not want to classify these children as failing to be mobile as this may introduce a mechanical decline in absolute mobility at higher levels of development. For this reason, we will consider the subset of respondents whose parents are not in the top category.

For robustness, we also construct a measure of absolute mobility that uses education data in its most disaggregated form. The results obtained with this measure are presented in Section 6. In Appendix A, we consider two alternative measures of absolute mobility, one which considers everyone with tertiary education as mobile (denoted by _MIX_ ) and one which accounts for the magnitude of which children surpass or fall short of their parents’ outcomes (denoted by _DIF_ ).

# **2.2. Relative mobility**

_Relative mobility_ is the extent to which an individual’s position in the distribution of educational attainment is independent of the position of his/her parents. We consider the correlation coefficient between respondents’ years of schooling and the years of schooling of their most educated parent, denoted by _COR_ , as our primary measure of intergenerational persistence, and 1- _COR_ as our primary measure of relative mobility. The regression coefficient from the intergenerational regression of respondents’ years of schooling on parents’ years of schooling (denoted by _BETA_ ) denotes an alternative measure of intergenerational persistence and will serve as our robustness check (see Section 6).

An advantage of the correlation coefficient is that it is invariant to changes in the marginal distributions of child and parents’ education. The values of
required enumeration areas to be segmented into smaller enumeration blocks using satellite imagery. Enumeration blocks are small enough for enumerators to list and select households immediately before conducting the interview (section 2.2). Himelein et al. (2016) compare this methodology with other second‐stage sampling strategies designed for use in fragile and time‐sensitive settings.

Complete food and nonfood consumption modules result in an overall questionnaire length that is prohibitive in areas with high insecurity. The length of consumption modules can be reduced by removing rarely consumed items from the module or to combine categories of items (e.g. vegetables) and ask aggregates rather than individual items. Beegle et al. (2012) and Olson Lanjouw and Lanjouw (2001) provide evidence that both approaches lead to an underestimation of consumption and hence an overestimation of poverty. Fujii and Van der Weide (2013) propose an alternative approach which could be adapted for use in fragile settings, by assigning a full consumption module to households in areas without a binding security and time constraint, with only the covariates of consumption administered to households in insecure areas. Consumption and poverty could then be imputed based on those covariates. This approach, however, potentially leads to biases as the assignment of the two different modules depends on security and is not necessarily random. Instead, the Rapid Consumption Methodology (Pape and Mistiaen, 2018) was used to significantly reduce the length of the survey’s consumption modules. The Rapid Consumption Methodology used in the SHFS relies on a set of core consumption items administered to all households. The remaining items are algorithmically partitioned into optional modules distributed systematically across households, with multiple imputation techniques used to impute total consumption and poverty. Pape and Mistiaen (2018) show that this design yields reliable poverty estimates (section 4).

Third, the SHFS relies on correlates derived from satellite imagery and other geo‐spatial data to estimate poverty in areas that remained completely inaccessible as a result mainly of insecurity. A growing field of research is dedicated to predicting
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|Finland|Nov 80|4785J|UNPVSR<br>4/87(official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR 4/87|
|France|Mar 82|54335 F|Eurostat<br>1987|Eurostat<br>1987|Eurostat<br>1987|
|French<br>Guiana|Mar 82|73 J|UNPVSR 1/88 (official<br>est.)|Bank est.|Bank est.|
|French Polynesi|a Oct 83|167 F|USBOC 1985|Based on USSOC<br>1985|Based on UNPVSR<br>4/87|
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|Gubia, The<br>|Apr 83|696 F|Bank projection from census|Bank est.|Bank est.|
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