rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 13. Export and productivity scenarios: Total real exports (mn 2008 lei)**

<!-- Start of picture text --> 90,000<br>80,000 indupweff<br>70,000 agrpweff<br>60,000 transeff<br>50,000 base<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->

**Figure 14. Export and productivity scenarios: Real exchange rate (2008=1)**

<!-- Start of picture text -->
1.05<br>1<br>0.95 base<br>transeff<br>0.9 agrpweff<br>indupweff<br>0.85<br>0.8<br>2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->

Figure 14 shows that the real appreciation of the exchange rate is larger under _indupweff_ than under _agrpweff_ . This reflects that, ceteris paribus and for indicated reasons, for _indupweff_ , the combined price and productivity shock leads to a larger, export driven initial balance of payments surplus, the elimination of which requires a larger appreciation, mitigating the export increase and encouraging higher imports. All in all, these changes tend to discourage production of non-targeted tradables. By contrast, the exchange rate change (slight appreciation) is very moderate for _transeff_ .

In Table 9 we show that the GDP shares of different activities change by the end of simulation period. It is evident that _indupweff_ would lead to largest structural changes, whereas _agrpweff_ would cause the smallest shifts from base development, leaving _transeff_ in the middle. The share of nonmanufacturing industries (energy and water services, construction) is not changing much, as the 24
Policy Research Working Paper 7334

# **Abstract**

This paper presents direct evidence on the quality of health care in low-income settings using a unique and original set of audit studies, where standardized patients were presented to a nearly representative sample of rural public and private primary care providers in the Indian state of Madhya Pradesh. Three main findings are reported. First, private providers are mostly unqualified, but they spent more time with patients and completed more items on a checklist of essential history and examination items than public providers, while being no different in their diagnostic and treatment accuracy. Second, the private practices of qualified public sector doctors were identified and the same doctors exerted higher effort and were more likely to provide correct treatment in their private practices. Third, there is a strong positive correlation between provider effort and prices charged in the private sector, whereas there is no correlation between effort and wages in the public sector. The results suggest that market-based accountability in the unregulated private sector may be providing better incentives for provider effort than administrative accountability in the public sector in this setting. While the overall quality of care is low both sectors, the differences in provider effort may partly explain the dominant market share of fee-charging private providers even in the presence of a system of free public healthcare.

This paper is a product of the Human Development and Public Services 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 jdas1@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 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
# I. Introduction

Individual income dynamics characterize society in important ways. The degree to which individuals move across different sections of the income distribution is often summarized by one parameter, income mobility. Indeed, income mobility is probably the single most important indicator of individual income dynamics used in public policy discussions.<sup>1</sup> Income mobility is important as it informs us about the opportunities afforded by society to escape one’s origins.<sup>2</sup> At the same time, mobility may also be driven by variability in incomes that reflect the risk to which individuals are exposed in the economy.<sup>3</sup> In this paper, we develop an analytical framework for the estimation and welfare-theoretic evaluation of individual income dynamics that takes into account these different drivers of income mobility. In addition, we provide an application of our framework using individual income data from Mexico that yields striking results: Much of measured income mobility is driven by measurement error or transitory income shocks and therefore (almost) welfare-neutral. A smaller part of measured income mobility is due to either welfare-reducing income risk or welfare-enhancing catchingup of low-income individuals with high-income individuals, both of which have economically significant effects on social welfare. Decomposing mobility into its fundamental components is thus crucial from the standpoint of welfare evaluation.

The literature on income mobility has often focused on two important questions: the quantitative/empirical measurement of the extent and nature of the change in individual incomes and, separately, the social-welfare-theoretic evaluations of such changes.<sup>4</sup> Two methodological issues have arisen in this area. First, the parametric formulations used in the

> 1In a developing country context, see, for example, a flagship publication of the World Bank in 2012, “Economic Mobility and the Rise of the Middle Class,” that focuses on the income mobility in Latin America. For the US, the New York Times article “Harder for Americans to Rise From Lower Rungs,” by Jason deParle (Jan 04,2012) describes the importance of economic mobility for the upcoming presidential election.

> 2See, for instance, Shorrocks (1978), Lillard and Willis (1978a), Atkinson, Bourguignon and Morrison (1992), Danziger and Gottschalk (1998) and Benabou and Ok (2001a, 2001b).

> 3See again Atkinson, Bourguignon and Morrison (1992).

> 4For the former, see Lillard and Willis (1978), Shorrocks (1978b), Geweke, Marshal and Zarkin (1986), Conlisk (1990) and Fields and Ok (1996). For the latter, see, Atkinson (1983), Markandya (1982, 1984), Atkinson, Bourguignon and Morrison (1992), Dardononi (1993), and Gottschalk and Spolaore (2002). Additionally, the discussion over suitable social (income) mobility measures (indices), which may be used to evaluate mobility given the pattern of individual income changes in society, constitutes a very well researched area that has generated a number of important contributions in recent years. See Fields and Ok (1999) for a survey discussion.

1
binding credit constraints. In this section, we provide additional evidence that strengthens this conclusion. If the wholesalers faced difficulties in buying oil from the refiners because of a lack of credit, then the refiners would not be able to sell the expected volume of oil consistent with a declining world price. Based on case studies of two refiners, Nurjahan Group and Bangladesh Edible Oils Limited, Choudhury and Clara Costa (2012) report that, immediately after the reform, the demand for oil by the dealers was exceptionally low, and the refiners were unhappy with the reform. Consequently the refiners had to cut back imports of crude Palm oil as they accumulated excessive inventory.

Choudhury and Clara Costa (2012) provide evidence that the two refiners noted above reduced imports of crude Palm oil significantly following the implementation of the reform in June 2011.<sup>30</sup> More important, the evidence indicates that the reform reduced aggregate imports of crude Palm oil. Since imports are lumpy and there may not be any imports in some weeks of a month, we aggregate the data at the monthly frequency. The estimate from before-after comparison using simple OLS regression shows that the implementation of the reform had a significant negative effect on the crude oil imports (a coefficient of _−_ 20 _._ 73, significant at the 5 percent level).<sup>31</sup> This is striking evidence because one would have expected higher imports in the post reform period when the refiners move down along the demand curve with falling costs of crude oil.

Although the quantity of imports went down, it does not necessarily follow that the refiners were adversely affected by the reform. If the reform increased the market power of the refiners (for example, if they were better able to coordinate and collude), then the reduction in quantity may reflect a move towards the monopoly quantity after the reform. In fact, a simple alternative explanation of the increase in prices and weakening of pass-through after the reform is that the DOT layer of the market was competitive before the reform, and the reform facilitated monopoly practices among the refiners. If this alternative model is correct, then the refiners would try to protect their monopoly profit by cooperating with the government to make the ban on DOTs permanent. In contrast, the model based on credit rationing faced by the wholesalers when

> 30They report that crude oil import by these two refiners in 2010 was 417,000 MT, but in 2011 the import drastically dropped to 254,000 MT, implying a 39 percent reduction in volume.

> 31The monthly import data is not adequate for a formal DID analysis.

32
Policy Research Working Paper 7249

# **Abstract**

The aim of this paper is to provide feedback on the question of socioeconomic benefits from rural road development and the impact of transport infrastructure on the poor, particularly the poorest and the bottom 20 percent of the population. This paper relies on impact evaluation methodologies, which are traditionally used in social sectors but less so in the transport sector. The study, including first surveys, was launched in 2003 under the Tocantins Sustainable Regional Development Project. The paper highlights the context that led to the project’s design, which included an impact evaluation of the works envisaged under the project. The paper also highlights some of the main challenges faced by this impact evaluation and how these challenges were addressed for the present study. It then provides details about the data collected during the surveys and the key relevant characteristics of the population targeted by the surveys. It discusses the possible estimation methods envisioned to undertake the study and provides the main results of the assessment based on these methods. The analysis shows that improved rural roads changed people’s transport modal choice. People used more public buses and individual motorized vehicles after the rural road improvements. The paper also finds that the project increased school attendance, particularly for girls. Although the evidence is relatively weak in statistical terms, it indicates that the project contributed to increasing agricultural jobs and household income in certain regions.

This paper is a product of the Transport and ICT Global Practice 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 may be contacted at elancelot@worldbank.org, sogita@worldbank.org, and aiimi@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 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
farmer revenue of US$ 49 million; exports valued at FOB prices give a total export revenue of USD 110 million; and exports valued at current tax rates give USD 13 million of taxes (ex FUNPI) and USD 15 million from the FUNPI contribution. The difference between exports, taxes, farmer revenues and traders and exporters identified costs leave a difference of USD 17 million, which has to be distributed between unidentified costs and margins of traders and exporters.

**Table 4. Summary Cashews value chain for year 2013**

|**Value chain of 2011**|**tons**|**USD million**|**USD/ton**|
|---|---|---|---|
|Production|140,000|||
|- for exports<br>|138,000|||
|- localprocessing<sup>*</sup>|2,000|||
|Exports(FOBprice)||110.40|800.00|
|FOB referenceprice|||850.00|
|Tax revenues on exports(excludingFUNPI)||12.90|93.50|
|FUNPI||14.68|106.38|
|Producers revenues||49.15|351.06|
|Traders and exporters(identified)costs||16.91|122.56|
|Traders and exporters unidentified costs and margins||17.46|126.49|

Source: own elaboration based on ANCA (2014) and World Bank. *See comments on this allocation in footnote 2.

Next section introduces the model we use to calibrate the data summarized here and simulate the policy questions.

# **3. The model**

Assume a one-period production economy with three players along the supply chain: farmers, traders and exporters. Farmers produce crops ( _XF_ ) and sell them to traders taking prices as given. Traders re-sell crops to exporters ( _XT_ ), while exporters sell them ( _XE_ ) to the international market a given price _p_<sup>_*_</sup> . There are _nT_ ( _≥2_ ) traders and _nE_ ( _≥2_ ) exporters. Traders have oligopoly power facing exporters and oligopsony power facing farmers. Exporters have oligopsony power facing traders. This way, the supply chain is characterized by an oligopsony in the farmer-trader link and a bilateral oligopoly in the trader-exporter link. We rule out collusion in our model, and note that this can bias up the impacts of prices and on household welfare estimated in Sections 4 and 5.

The literature on bilateral oligopoly departs from the standard oligolpoly models (see Tirole, 1988) in the way goods and strategies are depicted. Assume that there is a physical (or consumption) commodity _x_ and a money commodity _b_ (to be considered as the numeraire). Sellers are endowed with the physical good (it can be an endowment or the output from production) and buyers are endowed with money. Sellers offer quantities of the physical good _x_ , which results in aggregate offer _X=Σx_ . Buyers bid amounts of money _b_ , which end up in aggregate bid _B=Σb_ . A (strategic) price is calculated at the trading post as the ratio between the aggregate bid to aggregate offer, _p=B/X_ , and allocations are determined by executing trades at this rate of exchange. This way of 9
# **3 Data**

## **3.1 Data sources**

Table A3 in the Appendix presents summary statistics of all variables used in the analysis.

_A. Formal remittances_ : Data from formal remittances come from quarterly reports produced by Mexican authorities starting in 2013 that contain information about remittances received by each municipality during that time. They also provide information on the origin of remittances by US state. The reports are produced through declarations from credit institutions and legal entities that provide international transfer services and primarily reflect remittances received electronically. Since quarterly remittances at the municipal level tend to be noisy, we aggregate the data at the biannual level in our main analysis.

_B. Registry of Mexican immigrants:_ To provide a comprehensive measure of the bilateral connection between Mexico and the United States, we use a confidential version of the Mexican government’s _Matricula Consular de Alta Seguridad_ (MCAS) database. The _matricula consular_ is a document (card) that Mexican consulates issue to Mexican citizens who live in the United States. These data provide robust measurement of trends in Mexican immigration (both authorized and unauthorized) in the United States. (Allen et al., 2018). We restrict our analysis to employed Mexicans living in the United States whose ages range from 18 to 69 years. Thus, we observe around 3.1 million individuals from 2009 to 2013. For each, we observe their birth municipality in Mexico, the US county of residence, and demographic characteristics such as age, gender, education, and occupation. This subnational information lets us exploit variation in labor market patterns over time and control for state-level shocks and policies that may impact remittance flows.

_C. Unemployment of Mexicans in the United States:_ To test the relationship between remittances and unemployment in the United States, we use unemployment data from the CPS, which comprise a monthly sample survey of 60,000 eligible households conducted by the US Census Bureau for the Bureau of Labor Statistics. We use quarterly (seasonally adjusted) unemployment rates of Hispanics of Mexican origin living in the United States at the county level for the 2013–2020 period. As we explain in the next section, we can estimate the exposure measure by merging this dataset with MCAS.

_D. Municipality’s proximity to the nearest border or airport:_ To measure closeness between each Mexican municipality and the United States, we implement the following approach. First, we obtained addresses for the 50 US–Mexico border crossings and Mexico’s 60 international airports from the Ministry of Foreign Affairs. Second, we used the HERE geolocation API to calculate driving distances (in kilometers) and driving times (in minutes) from the center of every Mexican municipality to each access point.<sup>12</sup> Given that informal remittances are also conveyed through air travel, we used an alternative measure of distance from

> 12See https://developer.here.com/ and Weber and P ́eclat (2017) for details on the methods used.

6
WPS4249 countries where recipients are concentrated at the bottom of the distribution of non-remittances income – e.g. Mexico, Paraguay, Ecuador, El Salvador and Guatemala – exhibit much higher reductions in poverty headcounts attributable to remittances. The fact that in those countries recipients are often found among relatively poorer households is arguably due to lower migration costs relative to household income, which in turn may be driven by the presence of well established migrant networks – associated with the countries’ migratory histories – and/or by the relative proximity to the countries’ main migrant destinations – e.g. as in the cases of Mexico and Paraguay.

It is important to note, however, that when imputations are made for the income of remittances senders prior to migration, the magnitude of the average poverty reducing effect of remittances becomes much smaller. Indeed, those estimates suggest that in Latin America and the Caribbean remittances have reduced poverty headcounts by about 0.4 percent for each percentage point increase in the remittances to GDP ratio, a result that is remarkably similar to the one obtained in our cross-country analysis. While these effects are far from negligible, and migration and remittances do appear to have helped improving the living conditions of a large number of poor Latin American households, the region’s policy makers still face enormous challenges in terms of finding alternative means for addressing Latin America’s high levels of moderate and extreme poverty.

22
# 2.2. Bootstrap point estimates

We propose a Bayesian-inspired innovation to the synthetic panel method that allows us to present point estimates of transition quantities.<sup>6</sup> Our point of departure is as follows: we do not know the actual degree of household income correlation in Malaysia, but several panel studies have documented such correlations over time for a range of countries. We assume that the true values for Malaysia will be somewhere in this range of empirical correlations. Our proposition, then, is that a bootstrap sample of these empirical income correlations, coupled with the assumption that residuals follow a bivariate normal distribution, yields a meaningful point estimate for transition quantities, and plausible standard deviations. Obviously, we do not know what the actual Malaysian correlations are in each interval, so that we will focus on the bandwidth around the point estimate, rather than the actual estimate itself.

Our point estimates are based on the method put forward by Dang et al. (2014), the same paper that introduced bound estimates. Precision comes at the cost of introducing additional assumptions, however. Notably, we assume that the errors of our income model, and , follow a bivariate normal distribution characterized by the correlation coefficient . We derive hypothesized ii1 ii2 εε εε values for the correlation of the error terms ( ) by drawing on income correlation coefficients ( ) ρρ from actual panel data in other countries. Dang and Lanjouw (2023) provide the following proposition ρρ ρρyy1yy2 for doing so: The equation combines measures of the variation in our data with a hypothetical income correlation to arrive at an estimate of . Our sample of the income correlations has been obtained from Colgan (2023, 2024) and Dang and Lanjouw (2023), who provide actual panel correlations for fourteen ρρ countries in the EU-SILC database and the United States.

We standardize these empirical correlations to a two-year reference interval, since the correlations reported covered intervals of different duration. Subsequently, we draw a bootstrap

> 6 Dang & Lanjouw (2023) also present a method to obtain point estimates, which depends on cohort-level income correlations to infer household-level correlation. However, the robustness of this method is not generally accepted. Instead, we make use of documented empirical measurements of household-level correlation in other countries.

4