rafmacalaba/datause-encoder-data huggingface dataset:
results. You would be well advised to get a new doctor!<sup>5</sup> Or imagine that a new car comes on the market that collapses all those dials on the dashboard into just one composite index, on which you are supposed to decide what to do (slow down or get fuel). You would surely not buy this car!

Essentially the same point applies to the task of prioritizing policies for fighting poverty in a given country (or other geographic area). We will naturally want to look at the country’s attainments in various dimensions, rather than focusing on its performance with respect to a single composite index. Should we be focus on promoting job creation (say) or better health and education services? Such an approach does not deny that poverty is “multidimensional.” Rather it says that forming a single (uni-dimensional) index may not be particularly useful for sound development policy making.

Consider now the MIP developed by Alkire and Santos (2010a) for the 2010 _Human Development Report_ (UNDP, 2010). They choose 10 variables for their MIP under the same three headings—health, education and living standards—as the UNDP’s _Human Development Index_ (HDI). There are two variables for health (malnutrition, and child mortality), two for education (years of schooling and school enrolment), and six for deprivation in “living standards” (namely cooking with wood, charcoal or dung; not having a conventional toilet; lack of easy access to safe drinking water; no electricity; dirt, sand or dung flooring and not owning at least one of a radio, TV, telephone, bike or car). Poverty is measured separately in each of these 10 dimensions. The equally-weighted aggregate poverty measures for each of these three main headings are then weighted equally (one-third each) to form the composite index, also echoing the HDI. A household is identified as being poor if it is deprived across at least 30% of the weighted indicators. While the HDI uses aggregate country-level data, the Alkire-Santos MIP uses household-level data, which are then aggregated to the country level. Alkire and Santos construct their index for more than 100 countries.

> 5 In certain emergency situations (such as on the battle field), treatment decisions often require a prioritization of patients (“triage”) and it appears that this is typically based on the probability of survival, which is a single index. But then one is not creating a “maship index” since the variables and weights are entirely determined by their ability to predict that probability. There is nothing analogous to this probability in a MIP. As Mead Over points out in a blog comment: “In the physical examination example, where the situation is not life threatening, both the doctor and the patient presume that the patient’s valuation of the information deserves priority, since presuming otherwise would be unnecessarily paternalistic.”

4
Table 4: Bemft Cost aid een, of Car Ownerdhp rca Ridatn Miuue Area ad ic Road Ptldg Schem , by Vehile Clm pw year compared to 1991 Rde e (in m im of 195 Hong Ko doa)

|C|r<br>x<br>ReswtT<br>_Mmmr_|<br>_Am._<br> <br>_8c&_|HI<br>_Shm__<br>A|iRP<br>Scham<br>B*|HRP<br>M<br>Sd<br>C|Opdim<br> <br>O|
|---|---|---|---|---|---|---|
|Avup pink-a|-|H.O|_H$0_<br>8.4|H9.8|H9.7|ms10.5|
|AnalBflts, B|||||||
|Ptivae cm|-29<br>(4.10)|124<br>(0.37)|202<br> <br>(0.28)|<br>235<br>(0.27)|216<br>(0.24)|279<br> <br>(0.22)|
|Taxi|38<br>|30<br>|_53_<br>|61<br>|68<br>|-21<br> <br>|
||(0.13)|(0.09)|(0.07)|(0.07)|(0.07)|<br>(-0.2)|
|PubL;c<br>Iunxwt|158<br>|118<br>|299<br> <br>|<br>350<br>|389<br>|607<br> <br>|
||_(0.52)_|(0.35)|<br>(0.41)|(0.40)|(0.42)|<br>(0.49)|
|Goods vehicle.|134<br>(10.45|66<br>)<br>(0.19)|180<br>(0.25)|<br>225<br>(0.26)|246<br>(0.27)|385<br> <br>(0.31)|
|_Bmft_for All vehul|301|338|734|871|919|1250|
|_As_a Sbha ofhe_BunAdt_<br>of doThXeica Opum|(0.241|<br>(0.27]|<br>_[0.59]_|[0.70]|[0.741|<br>(1.00l|
|_'mm_Revea G rniud,_R_|1200|188|39S|<br>_465_|540|976|
|Awmnmad Capt =d<br>|||||||
|Opeaug cuba ofaOw<br>Mclzazaimw<br>_C_|0|10-1S|49.8|49.0|S1.7|>51.7|
|BunAtNmSy_em_Coea,_<br>_NoI-B-C_|301|323-328|684|821|869|1200|
|_B_uaAd_<br>stlals, WC|a<br>|2.5-338|147|17.8|17.8|<241|
|RI_w.cuR lad[%,<br>lUC||DJ.-18.|73|_9._|10.4|<W|

mw~~~~~~~~~~~~~~~~~~~~ HE7.8 - _Ut1_ ad B=10.1 _-_ * (195 a pgu ). CM (1OYCPM ) - 1.48 (Cm_voh w _vsfuS_ do Coemw Pdo ode hr a2 _km)_ Figu. 1. wod - mdh ao =M* dbm.. * a"P Ssm. a In asmiby lbs Am Psisin pu,oml (wib _u~4*_ vaziatln) simulated in 1Tampwt Dpuuma ad Wibur 3mnbh and Assodute (196). Give tmit 3RD Sibmu A, 3 aniC hmacrs h ooumixiy and ms*m. harg levolin the simuatd pftk-bw chwru wmbypdudoftbesimmaluiun. 3mb tkhauldeth mgsiWa hmvelde aopsathgcostof theWild and asdlnma* of dos tole of, bunm s IW _amO_ b. g se tued la the hflwhgS umr, wham .od 8<sup>g</sup><sup>**_go_**</sup><sup>_&u_bomb 6gpm.7he</sup> ta bm*W _fSgus_ use h. _so_ out dbs coe of tol cdWlem gur_ of depek-h dIg. - by ous leyof 31 Haue.o
nearly 0.3 percent of the total, and results from collinear observations being dropped and model specificities.

The third column uses the same PPML estimator including all observations (including zeros). These estimates show the same signs and significance than the second column, and almost the exact same magnitude, prevailing then the same implications as the previous paragraph.

Though not directly comparable due to differences in model specifications, these results are consistent with those in Robertson et al. (2021). Like us, Robertson et al. (2021) find that the coefficient on the interaction term “Egypt as an Importer x RTA” is not statistically significant. Also, they find that the estimated coefficient on “Egypt as an Exporter x RTA” is positive and statistically significant.

**<u>Table 1: Gravity Estimation Results</u>**

|VARIABLES|(1)<br>OLS|(2)<br>PPMLHDFE|(3)<br>PPMLHDFE<br>w/0|
|---|---|---|---|
|RTA|0.552***|0.058***|0.057***|
||(0.009)|(0.013)|(0.013)|
|Egypt as Exporter x RTA|0.573***|0.450***|0.461***|
||(0.074)|(0.133)|(0.135)|
|Egypt as Importer x RTA|0.416***|0.042|0.105|
||(0.078)|(0.059)|(0.075)|
|WTO Importer|0.107***<br>(0.019)|||
|WTO Exporter|0.099***<br>(0.019)|||
|Log Distance|-1.642***<br>(0.005)|||
|Log GDP Importer|0.682***<br>(0.012)|||
|Log GDP Exporter|0.424***<br>(0.012)|||
|Constant|-1.726***|16.317***|16.290***|
||(0.277)|(0.007)|(0.007)|
|Observations|580,132|578,223|995,075|
|R-squared|0.732|||

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. OLS stands for Ordinary Least Squares. OLS estimation includes fixed effects for year, exporter, and importer. PPMLHDFE stands for Poisson Pseudo Maximum Likelihood High Dimensional Fixed Effects and includes country-specific time trends, country-pair fixed effects, and year fixed effects without including the “zero” observations. PPMLHDFE w/0 includes all observations (including zeros).

9
unemployment insurance benefits, social safety net payments). These effects are long-lasting and are present even in the fourth year after the takeover. We show that these effects are not driven by lower wages. Instead, we estimate a 1% reduction in the likelihood that workers are employed anywhere and no impact on overall hours worked, suggesting that workers who are not retained are not able to find employment quickly.

We next turn to heterogeneity analyses to better understand which workers are affected by takeovers the most. We find that a dominant mechanism driving job loss in this setting is labor restructuring, changes in the consolidated firm’s production process. We highlight two types of workers affected by restructuring activities: “over-placed” workers who are compensated at higher rates than what would be expected based on their human capital and “duplicative” workers who are likely to be made redundant by the consolidation.

We present two pieces of evidence consistent with the importance of labor restructuring. First, we show that labor restructuring leads to more job loss among “over-placed” workers. As in Schmieder, von Wachter and Heining (2022) and Lagaras (2023), we characterize over-placed workers as workers who have secured employment at a firm with a higher wage premium than is expected for workers’ skills that are valued equally across employers, estimated using a standard Abowd, Kramarz and Margolis (1999) decomposition. We find that over-placed workers are 3.3 times more likely to be separated from the consolidated firm post-takeover compared to workers who are not over-placed, consistent with firms targeting restructuring activities at more generously compensated workers.

Second, we use worker-level skill data to create a measure of human capital relatedness (HCR) between acquirer and target firms participating in a takeover (Lee, Mauer and Xu, 2018). We find that high HCR takeovers, that is, takeovers where the target and acquirer workers have a more similar skill distribution, generate larger employment reductions at the consolidated firm in terms of both retention and hours worked. We then exploit our rich individual-level data to study the outcomes of specific workers who have similar skills to the workers at acquirers and thus are most likely to serve similar roles. We find that for a worker of a specific skill level, being acquired by a firm with a larger share of workers with that same skill level increases the likelihood of separation. For a given worker, being acquired by a firm where over two-thirds of the workforce has a similar skill set leads to a 18.9% reduction in retention compared to 9.4% for workers being acquired by where fewer than a third of workers have the same skills.

2
WPS4026

# **The Basic Analytics of Access to Financial Services**

## **Thorsten Beck and Augusto de la Torre**<sup>*****</sup>

**Abstract:** Access to financial services, or rather the lack thereof, is often indiscriminately decried as problem in many developing countries.  This paper argues that the “problem of access” should rather be analyzed by identifying different demand and supply constraints.  We use the concept of an access possibilities frontier, drawn for a given set of state variables, to distinguish between cases where a financial system settles below the constrained optimum, cases where this constrained optimum is too low, and—in credit services—cases where the observed outcome is excessively high.  We distinguish between payment and savings services and fixed intermediation costs, on the one hand, and lending services and different sources of credit risk, on the other hand.  We include both supply and demand side frictions that can lead to lower access.  The analysis helps identify bankable and banked population, the binding constraint to close the gap between the two, and policies to prudently expand the bankable population.  This new conceptual framework can inform the debate on adequate policies to expand access to financial services and can serve as basis for an informed measurement of access.

**JEL classification codes:** G18 ; G21 ; G28 ; O16

**Keywords:** Access to Financial Services; Credit Market Imperfections; Competition; Regulatory Policies

World Bank Policy Research Working Paper 4026, October 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Thorsten Beck is a senior economist in the Development Research Group of the World Bank; Augusto de la Torre is the Regional Financial Sector Advisor for Latin America and the Caribbean of the World Bank.  This paper has been prepared as part of the Latin American Regional Study on Access to Finance.  It has benefited from discussions and comments by Abayomi Alawode, Ole Andreassen, Jerry Caprio, Stijn Claessens, Asli Demirguc-Kunt, Patrick Honohan, Martin Jung, Soledad Martinez Peria, Sergio Schmukler, Tova Solo, Robert Stone, as well as from comments and suggestions from participants at various seminars in the World Bank, the Asobancaria seminar on Bancarización in Cartagena 2004, the World Savings Banks Institute conference on Access to Finance in Brussels 2004 and the Dia de la Competencia in Mexico City 2005.
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# **1 Introduction**

The past decades have witnessed a surge in international and intranational migration. The United Nations (2010) documents that international migration increased from 145 million people to 191 million people during 1990–2005. In the past three decades in China, 500 million people have flocked to the city, and during 2000–10 alone, China’s urban population expanded by 210 million (Wong 2012). Outmigration is generally found to profoundly contribute to the welfare of both the recipient and sending destinations. In a literature survey, Clemens (2011) found that eliminating barriers to labor mobility can lead to a 67–147 percent increase in gross domestic product (GDP), while the corresponding numbers to eliminating all trade barriers and capital flow barriers are 0.3 to 4.1 percent and 0.1 to 1.7 percent, respectively. Meanwhile, a 10 percent increase in the remittanceto-GDP ratio is found to reduce the poverty ratio (measured by the share of residents living on less than $1 a day) by 1.6 percent (Adam and Page 2005).<sup>2</sup> Despite the significant economic benefits, however, there are still substantial barriers to labor mobility. According to the Gallup World Poll (2010),<sup>3</sup> 16 percent of the world’s adults, or 700 million adults, would like to migrate if given the opportunity. Why do so many potential migrants fail to act out their wishes? How can we increase the likelihood of outmigration?

The literature on the determinants of migration, starting from the classical Harris and Todaro (1970), emphasize the rural-urban earning differentials as the key reason. Moreover, scholars recognize that a fundamental problem in migration is uncertainty. Potential migrants do not have full information about job opportunities, wages, and the quality of life in destination cities, and some new evidence on migrants’ expectations in developing countries show how inaccurate these issues can be (McKenzie, Gibson and Stillman 2013). The literature has recognized and provided evidence that an important way to reduce information problems is through the channel of networks (Barr and Oduro

> 2 See Hanson (2010) for more discussion on the impact of emigration on sending countries.

> 3 See Neil Esipova and Julie Ray, “700 Million Worldwide Desire to Migrate Permanently: U.S. Tops Desired Destination Countries,” http://www.gallup.com/poll/124028/700-Million-Worldwide-Desire- <u>Migrate Permanently.aspx</u> (accessed March 16, 2013).

2
The Brazilian legislation for primary, lower, and upper secondary education determines a school year with a minimum of 200 days and 800 hours of instruction time. The state-managed schools are monitored by the State Department of Education, under the state government administration, and locally-managed schools are monitored by the Municipal Department of Education, under the municipal government administration. The respective Departments of Education are in charge of educational policies implemented at the school level, hiring teachers, providing textbooks, appointing principals, financing school infrastructure, and determining the length of the school day, and school breaks, as well as the beginning and the end of the school year in the schools under their responsibility. Usually, the academic year starts in February, and ends in November, summer break occurs in December and January, and winter breaks cover three to four weeks in July.

Data on students’ proficiency in reading and math come from the School Census, the Brazilian Institute of Geography and Statistics (IBGE), and from _Prova Brasil_ , which is the standardized national proficiency exam used to assess students’ learning in primary and lower secondary education.<sup>7</sup> Since 1995, all private and public schools offering primary and secondary education have participated in the annual School Census. The Census is implemented by the National Institute of Educational Studies and Research (INEP), a research agency under the Brazilian Ministry of Education.<sup>8</sup> The School Census collects information on (i) school facilities, such as libraries, sports courts, and science and computer labs; (ii) school infrastructure such as filtered water, electricity, and internet access; (iii) social services, for example, school transportation and provision of meals; (iv) students, such as gender, color of the skin, age, physical disabilities or mental illness, grade level, instruction time per day, class-size, subjects they are enrolled in, grade promotion, repetition and dropout rates; and (v) teachers, such as educational attainment, age, physical disabilities, subjects taught, and classes they are in charge of.

Every two years, the INEP applies a standardized national exam, _Prova Brasil_ , to assess students’ proficiency in reading and math. Since 2005, the test has been applied to 5<sup>th</sup> and 9<sup>th</sup> graders of all public schools.<sup>9</sup> _Prova Brasil_ is one of the proficiency tests within the scope of

> 7 IBGE stands for _Instituto Brasileiro de Geografia e Estatística_ . IBGE has information on GDP per capita and the population at the municipality level.

> 8INEP stands for _Instituto Nacional de Estudos e Pesquisas Educacionais_ .

> 9Schools with at least 20 students enrolled in fifth or ninth grade. Proficiency tests are also applied to students in the last grade of high school.

6
# **Table 18: Impact on the Regions of Russia of WTO Accession and Simultaneous “Cap and Trade” Market Based Emission Control System to Reduce Carbon Emissions by 20 Percent, with decomposition of carbon emission effects. (Results are Percentage Change from Base Year.)**

||**Overall**||**St.**||**North-**||||||**Far**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**average **|**Moscow**|**Peters.**|**Tumen**|**west**|**North**|**Central**|**South**|**Urals**|**Siberia**|**East**|
|**Aggregate welfare**||||||||||||
|Welfare(EV as%of consumption)|7.2|5.5|9.7|13.7|12.5|9.2|8.4|7.4|6.4|7.5|8.5|
|Welfare(EV as%ofGDP)|3.3|3.0|4.4|2.9|5.8|3.8|3.9|3.5|2.9|3.5|3.8|
|**Carbon Dioxide Emissions and Decom**|**position**|||||||||||
|CO2price(rubleper ton ofCO2)||112.4|112.4|112.4|112.4|112.4|112.4|112.4|112.4|112.4|112.4|
|CO2emissions, decomposedinto:|-20.0|-19.0|-17.7|-22.0|-18.8|-18.1|-19.1|-17.8|-21.3|-20.8|-18.3|
|Outputeffect(% ofCO2)|3.6|3.5|5.9|2.3|6.0|5.2|4.0|4.0|2.5|3.8|5.3|
|Compositioneffect(% ofCO2)|-3.6|-1.0|-0.7|-11.4|-2.7|-3.4|-1.3|-1.5|-3.0|-5.2|-2.9|
|Technique effect(% ofCO2)|-19.5|-20.8|-21.6|-13.1|-20.9|-19.1|-21.1|-19.7|-20.7|-19.1|-19.8|
|**Non-Carbon Dioxide Emissions**||||||||||||
|Sulphur Dioxide|5.9|1.6|11.7|-2.6|-8.6|7.4|-2.9|2.7|5.3|7.3|5.2|
|NitrogenOxide|-1.3|-2.9|0.3|-1.4|1.3|-0.1|-1.9|-1.6|-1.7|0.0|-1.0|
|Hydrocarbons|-3.6|-4.7|0.6|-0.5|1.2|-3.4|-2.0|-1.7|-2.8|-10.7|-16.7|
|ParticulateMatter|-0.6|-3.2|-0.4|-0.3|0.1|0.7|-2.1|-2.1|-1.2|1.0|-0.4|
|Volatile Organic Components|0.8|-1.6|2.2|0.9|17.6|1.4|9.2|1.9|-0.1|-1.5|3.3|
|Carbon Monoxide|2.2|9.5|6.0|0.7|-9.2|4.1|-3.4|2.3|1.8|5.2|3.1|
|**Aggregate trade**||||||||||||
|Regional terms of trade (% change)|2.8|14.0|15.5|12.6|14.6|14.5|13.7|13.4|12.4|13.0|14.6|
|Regionalexports (% change)|1.4|1.5|2.3|0.9|2.4|1.9|2.5|1.6|1.4|0.4|2.1|
|Realexchangerate (% change)|2.4|2.5|3.3|1.9|3.5|2.6|3.2|2.6|1.9|1.7|2.6|
|Internationalexports (% change)|8.2|16.0|13.6|3.9|12.7|9.0|10.0|9.6|8.9|7.2|13.1|
|**Returnto primary factors (% change)**||||||||||||
|Unskilledlabor|1.6|2.2|4.4|1.0|4.8|2.7|2.2|2.3|0.0|1.0|3.2|
|Skilledlabor|0.3|0.7|3.7|-1.2|3.2|1.3|1.6|1.5|-0.4|-1.8|1.7|
|Nationalcapital|1.6|1.7|2.5|1.1|2.7|1.8|2.4|1.8|1.1|0.9|1.8|
|Regional mobile capital|3.6|3.4|6.3|4.2|5.8|5.2|3.4|3.7|2.4|3.7|5.7|
|Crude oil resources|3.8|||3.6|5.8|5.3||5.5|3.4|2.9|5.3|
|Naturalgasresources|-33.4|||-34.1|-28.4|-30.6||-23.9|-26.3|-34.1|-33.6|
|Coal resources|-9.2|||||-6.5|-4.7|-6.2|-9.9|-10.0|-7.0|
|Specific capital indomesticfirms||-29.9|-21.5|-48.5|-21.9|-25.5|-17.4|-25.0|-18.2|-18.8|-27.1|
|Specific capital in multinational firms||59.3|48.4|217.4|77.4|139.6|113.4|125.9|139.0|159.2|113.6|
|**Factor adjustments**||||||||||||
|Unskilledlabor(% changing sectors)|2.1|1.8|2.1|2.5|2.8|2.3|1.8|1.4|2.4|2.6|3.2|
|Skilledlabor(% changing sector)|2.9|2.4|2.6|3.1|3.3|3.4|2.3|1.9|2.7|4.4|4.4|
|**Source: Authors' estimates**||||||||||||

59
A first line of response to this critique relies on industry-level studies. For example, Fernald (1999) similarly estimated huge rates of return on investment in roads for US industries that use roads more intensively: in terms of a Cobb-Douglas specification like the ones used in state-level studies, he found an output elasticity of road investment around 0.35. After noting that this is consistent with the initial results from Aschauer, he argues that the massive interstate highway network built in the 1950s and 1960s generated a one-time boost in productivity (of approximately 1%) rather than a permanent one, also explaining the post-1973 slowdown in productivity.<sup>17</sup> In short, initial large investments in infrastructure may produce very high rate of returns, but this is no guarantee that additional investments would also be characterized by the same returns. In this view, Aschauer’s results adequately captured the pre-1973 period. Additionally, this line of argument coincides well with the idea that once basic infrastructure is in place, adequate investment in maintenance might actually have a higher rate of return than new investment, as argued in Hulten (1996), who uses a cross-country sample similar to that of Easterly and Rebelo (1993) and finds that the impact of an effectiveness index of infrastructure is more than seven time larger than that of public capital itself (see also Rioja, 2003).

# _Addressing endogeneity sources of infrastructure_

Holtz-Eakin (1994) argues that results are substantially modified when econometrically taking into account state- or region-level unobserved effects.<sup>18</sup> Indeed, when introducing fixed state-level effects in his US state panel data, Holtz-Eakin (1994) and Garcia-Milà, McGuire and Porter (1996) find the effect of public capital to be insignificant. Accordingly, in the second generation of studies incorporating these concerns, the positive estimates found were significantly smaller than those of Aschauer, with elasticities around 0.1 to 0.2 (Romp and de Haan, 2005). Note, however, that these

> 17 The total post-1973 US productivity slowdown was about 1.3%. Yeaple and Golub (2004) using an industry level panel across countries similarly find significant positive effects of infrastructure on TFP growth and on industrial specialization.

> 18 While growth accounting using single-country time-series data implicitly controls for these unobserved region-specific effects, production function-based estimates do not so they need to explicitly incorporate fixed effects.

18