rafmacalaba/datause-encoder-data huggingface dataset:
- 32 -

# What Information is Needed?

Educational and economic outcomes. costs. and the implementation performance of schools In current U.S. practice, educational achievement data hold pride of place: equity concerns lead to collection and reporting of the data not only by school and district, but also by socio-economic characteristics of students. These data are supported by a range of management information on enrollments, staffing, costs, facilities, and so on. Implementation processes are assessed through surveys, observation, qualitative analysis, tacit knowledge, and, less frequently, special studies. Accreditation studies provide a basis of formative information on overall institutional quality.

Information on the economic outcomes of general education is not routinely assembled at the institutional level; perhaps because of the failure of the VEDS system, there is also very little information on the employment outcomes of vocational training.

The absence of data on educational and economic achievement is the most glaring weakness in most developing country contexts, not only for project institutions, but for the system as a whole. The Kenya example cited earlier provides one of the few exceptions. Data on the unit costs of education of various kinds is equally weak. Management information systems have a spotty history, and information on implementation at the school level is exceedingly rare.

The consequences for governments and the World Bank have been discussed at length. A great deal of information is available on inputs and aggregated project costs; very little is available on the factors that permit learning about the effectiveness of schools, projects or systems.

Much of this is attributable, of course, to the general failure to address crucial data needs in the design of M&E investments, and to the failure of most investments in M&E capacity. In particular, the failure to establish achievement testing systems in many countries has made it impossible to assess the educational effectiveness of investments. The attempt to establish tracer studies of employment outcomes has similarly failed in most countries. Declining levels of Bank supervision, larger and more complex projects and weak inspectorate systems have combined to reduce knowledge of implementation processes.

More success in developing the right kinds of information will depend on much stronger designs for M&E systems that address the need to specify decision-makers and their needs at different levels, emphasize the formative purpose of monitoring and evaluation, and give priority to data on achievements, costs and implementation processes.
- Jorgenson, Dale W. and Fraumeni, Barbara M. (1989), “The Accumulation of Human and NonHuman Capital, 1948-1984,” in R. Lipsey and H. Tice eds., _The Measurement of Saving, Investment and Wealth_ , Chicago, University of Chicago Press, NBER, 227-282.

- Jorgenson, Dale W. and Fraumeni, Barbara M. (1992a), “Investment in Education and U.S. Economic Growth,” _Scandinavian Journal of Economics,_ Vol. 94, supplement, 51-70.

- Jorgenson, Dale W. and Fraumeni, Barbara M. (1992b), “The Output of the Education Sector,” in Z. Griliches, T. Breshnahan, M. Manser, and E. Berndt eds., _The Output of the Service Sector_ , Chicago, NBER, 1992, 303-341.

- Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2009).  "Governance Matters VIII: Governance Indicators for 1996-2008". World Bank Policy Research June 2009

- Klenow, P.J., and A. Rodriguez-Clare (1997), “The Neoclassical Revival in Growth Economics: Has It Gone Too Far?”, in: B.S. Bernanke, and J.J. Rotemberg, eds., NBER macroeconomics annual 1997 (MIT Press, Cambridge) 73-103.

- Klenow, P.J., and A. Rodríguez-Clare, (2005). “Externalities and Growth.” In P. Aghion and S Durlauf, eds., _Handbook of Economic Growth_ , Amsterdam: North Holland.

- Krueger, A., Lindahl, M. (2001). Education for growth: Why and for whom _? Journal of Economic Literature_ 39, 1101-1136.

- Lane, Philip R. and Gian Maria Milesi-Ferretti, (2007). "The External Wealth of Nations Mark II" , _Journal of International Economics_ 73, 223-250, November 2007

- Mankiw, N.G., D. Romer, and D.N. Weil (1992), “A Contribution to the Empirics of Economic Growth”, _The Quarterly Journal of Economics_ 107(2):407-437.

- Mauro, Paolo.(1995).  “Corruption and Growth.” _Quarterly Journal of Economics_ , 1995, 110(3), pp. 681-712.

- Mincer, J. (1974) _Schooling, Experience and Earnings_ . Columbia University Press: New York.

- Mundlak, Y. (1996) "Production Function Estimation: Reviving the Primal," _Econometrica_ 64: 431-38 .

- Paldam, Martin, and Gert Tinggaard Svendse (2006) “Social Capital Database for a CrossCountry Study.” In _Trust, Social Capital and Economic Growth: an International Comparison_ , ed. M. Paldam and G. T. Svendsen. Cheltenham, U.K.: Edward Elgar.

- Psacharopoulos, G., Patrinos, H., 2004. Returns to investment in education: A further update. _Education Economics_ 12(2), 111-134.

20
BOX 3.1 TEXT REFERENCE FOR THIS BOX

# **Little formal saving in the Commonwealth of Independent States**

Account penetration varies widely among the countries of the Commonwealth of Independent States (CIS), ranging from 72 percent in Belarus and 67 percent in the Russian Federation to less than 5 percent in Turkmenistan. Yet however much they vary on this measure, these countries do share a common pattern in savings behavior among people who have an account: in 2014 about 60 percent or fewer account holders reported having saved any money in the past year. Moreover, few of those who saved did so at a financial institution: in most CIS countries only 10–20 percent of account holders reported using their account for saving (figure B3.1.1). Moldova alone had a somewhat higher rate, with 28 percent.

## FIGURE B3.1.1

<!-- Start of picture text --> Savings behavior among account holders in CIS countries<br>Adults with an account by savings behavior in the past year (%), 2014<br>0 10 20 30 40 50 60 70<br>Armenia<br>Azerbaijan<br>Belarus<br>Kazakhstan<br>Kyrgyz<br>Republic<br>Moldova<br>Russian<br>Federation<br>Tajikistan<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>Saved Saved both Saved Saved using Did not<br>formally formally and semiformally other methods save<br>semiformally only<br><!-- End of picture text --> _Source:_ Global Findex database.

> THE GLOBAL FINDEX DATABASE **57**
# **Welfare Impacts of Rural Electrification:**

# **Evidence from Vietnam***

## **By**

## **Shahidur R. Khandker**

## **Douglas F. Barnes**

**Hussain Samad**

**and**

## **Nguyen Huu Minh**

*This panel study of the impacts of rural electrification was conceived in about the year 2000 by Anil Malhotra, the task manager for the first Vietnam Rural Energy Project (RE), and more recently, it has been supported and administered by Van Hung Tien (World Bank Office, Hanoi) who is the task manager for second Vietnam Rural Energy (REII), and Ky Hong Tran (World Bank Office, Hanoi).  The Swedish International Development Agency (SIDA) funded all data collection and preparation, New Zealand’s International Aid & Development Agency (NZAid) provided technical assistance in the field, and the surveys were undertaken by the Vietnam Institute of Sociology.  Nguyen Vinh, has been the primary person compiling data from 2002 and 2005 surveys. The original survey was conducted by ASTAE, and we gratefully acknowledge the ASTAE program for their support in completing this work. In this respect we thank Kevin Fitzgerald for valuable inputs on the survey instruments and data.  We acknowledge with thanks the support we received from the DIME and ESMAP for both data analysis and report writing phase of this research. We would like to thank Ariel Fiszbein, Pedro Olinto, Niall Keleher, Victoria Kwakwa, Will Martin and two anonymous reviewers for helpful comments and suggestions.  Views expressed in this paper are entirely authors’ and do not reflect those of the World Bank or its affiliated organizations.
21 Figure 2.2 shows households' desired improvements in their housing conditions. The most common wish is to move to a larger apartment (30% of Vladimir and 26% of Gorodets households); 11 and 7% respectively would like to exchange their apartments for two or several so as to split with relatives; 6 and 8% would like to move into more convenient housing or apartments on par with their present ones. Very few households (only 1% in each city) would consider moving into smaller or less convenient apartments (e.g., to reduce the utilities payments).

The proportion of households actually planning to improve their living conditions in the near term is 21% in Moscow, 24% in Vladimir and 15% in Gorodets (Fig. 2.3), which means that the overall picture in 1996 had not changed much from 1994.

**Fig. 2.2.** Household Motivations in Changing Housing (% of households polled)

<!-- Start of picture text --> 40<br>35-<br>30 * to move to a larger apartment<br>30 _ 26.7 0 to move away from relatives<br>26<br>El to move to an equivalent<br>20 - apartment<br>* to moto to a more convenient<br>10.9 apartment<br>M to move in with relatives<br>10 - 6.8<br>5 4.3 4.2 E to move to a small apartment<br>1.4 1.2 10<br>0~~~~~~~~~~~~~~~<br>Vladimir,1996 Gorodets,1996<br>Sources: Vladimir and Gorodets Longitudinal Household Survey, December 1996<br>Fig. 2.3. Households' plans to improve their housing conditions in the near future<br>(% of households polled)<br>90 9. 84.6 85.4<br>so - 77.2 76.4 76.5<br>70 -<br>60<br>40<br>30 22.8 205 23.6 2 .<br>20 - 15.4 14.6<br>10 _<br>0<br>1993 1996 1994 1996 1994 1996<br>Moscow Vladimir Gorodets<br>I U have plans * have no plans<br><!-- End of picture text --> Sources: Vladimir and Gorodets Longitudinal Household Survey, December 1996 Sources: Household Survey in Seven Cities, Moscow, December 1993; The Moscow Longitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Surveys, Oct-1994 and Dec-1996.
# Changes in Female Employment in Mexico: Demographics, Economics, and Policies

Gladys Lopez-Acevedo<sup>1</sup> , Samuel Freije-Rodriguez<sup>2</sup> , Mexico Alberto Vergara Bahena<sup>3</sup> and Diego Cardozo Medeiros<sup>4</sup>

## **JEL classification** : J21, J22, O54

**Keywords:** Female labor force participation, Mexico, gender gap, female education, and childcare services.

**Resumen:** Las brechas de género en desempleo y participación laboral se redujeron en México después de la crisis económica de 2008, dado un aumento de la participación laboral femenil (PLF). Para identificar los principales determinantes que han permitido el crecimiento de la PLF, estimamos un modelo probit con datos de 2007 y 2017 de la Encuesta Nacional de Ocupación y Empleo (ENOE), año en que la tasa de desempleo regresa a niveles precrisis. Los resultados sugieren que: el aumento en la participación laboral de mujeres entre 36 y 65 años permitió el crecimiento general de la PLF, la educación de las mujeres contrarresta cualquier característica individual o del hogar negativa para su empleabilidad, y el acceso a estancias infantiles significativamente aumenta la probabilidad de que estén empleadas.

This paper is a product of the Poverty and Equity Global Practice of the World Bank Group. We thank Carlos Rodriguez-Castelan, Gabriela Inchauste, Deeksha Kokas, Stuti Manchanda, and Matias Morales for their comments. All remaining errors are the responsibility of the authors. To contact authors please write to <u>gacevedo@worldbank.org</u><sup>1</sup> , <u>sfreijerodriguez@worldbank.org</u><sup>2</sup> , <u>mvergarabahena@worldbank.org</u><sup>3</sup> or diego.cardozo@chicagobooth.edu<sup>4</sup> <u>. Gladys Lopez-Acevedo</u><sup>1</sup> is a Lead Economist and Global Lead at the World Bank in the Poverty and Equity Global Practice Group (PEGP). Samuel Freije-Rodriguez<sup>2</sup> is also a Lead Economist at the PEGP and Global Practices and Cross Cutting Solutions Group at the World Bank. Mexico Alberto Vergara Bahena<sup>3</sup> is a Consultant at the PEGP and Diego Cardozo Medeiros<sup>4</sup> is an MBA Candidate at the Chicago Booth School of Business. 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. Postal address: 2232 40<sup>th</sup> Pl NW, Washington DC, 20007, United States of America.
# **Why Do Manufacturing Firms Sell Services? Evidence from India**

**Arti Grover and Aaditya Mattoo***

**Keywords:** Services, Servitization, Trade competition, Innovation, Structural transformation **JEL Classification codes:** F12, F15, F23, L23, L25, L6

*World Bank, 1818 H Street, Washington DC, USA. Arti Grover, Email:  agrover@worldbank.org; Aaditya Mattoo, Email: amattoo@worldbank.org.  Research for this paper has been supported in part by the World Bank’s Strategic Research Program and the Services-led Development flagship report, EFI. We are grateful for comments from participants in the authors’ workshop of the flagship report, in particular, Valerie Mercer Blackman, Mary HallwardDriemeier, William Maloney, Denis Medvedev and Gaurav Nayyar. 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.
Policy Research Working Paper 9914

# **Abstract**

The paper examines the patterns of economic integration of refugees in Switzerland, a country with a long tradition of hosting refugees, a top-receiving host in Europe, and a prominent example of a multicultural society. It relies on a unique longitudinal dataset consisting of administrative records and social security data for the universe of refugees in Switzerland over 1998–2018. This data is used to reconstruct the individual-level trajectories of refugees and to follow them since arrival over the life-cycle. The study documents the patterns of labor-market integration, and highlights the heterogeneity by gender and age at arrival. Refugees’ labor-market performance is compared to natives’ and other groups of migrants’ labor-market performance. The empirical analysis exploits the government dispersal policy in place since 1998, which consists of the random allocation of refugees across cantons, to identify the causal effects of the local initial conditions. The study finds that higher unemployment rates at arrival slow down the integration process, whereas the existence of a co-ethnic network does not consistently lead to a faster integration. It is shown that in locations where refugees face relatively more hostile attitudes by natives upon arrival, they integrate at a faster pace, probably due to a greater effort undertaken in environments that are more hostile. Together these results, highlight the importance of an early entry in the labor market of the host country, and the need to take a longer run perspective when examining the effectiveness of policies, as the effects may vary over time and different complementary interventions may be needed in the short vs. long-run.

This paper is a product of the Social Sustainability and Inclusion 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 martina.viarengo@graduateinstitute.ch.

_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
# **3.1 PROFILE OF THE UNBANKED**

7. **Being unbanked is clearly associated with lower incomes.** The majority of the unbanked are poor, but the unbanked are not just the poor. As Figure 3.3 shows, the percentage of unbanked persons increases as incomes fall. Slightly more than half (51%) of bank account holders belong to the top three deciles of income distribution, and about a third (34%) belong to the group earning less than the median income. Of the unbanked, over half (53%) earn less than half the minimum wage and 70% earn less than one minimum wage. On the other hand, of those earning over 4 minimum wages, 84.22% are banked and 15.78% unbanked. At the other end of the income scale, it is notable that no less than a quarter of the families in the lowest 40% of income distribution (those earning less than half a minimum wage) are banked. This data shows that even very poor households have the capacity to be banked.

**Figure 3. 3  Use of banking services in relation to income**

<!-- Start of picture text --> 100%<br>90% 81%<br>80% 74% 75% 73% 76%<br>70%<br>70% 64%<br>59%<br>60% 55% 56%<br>50% 41% 45% 44%<br>36%<br>40%<br>30%<br>30% 26% 25% 27% 24%<br>19%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Population distribution by income level<br>Banked Unbanked<br>Source: DANE household study of Bogotá, 2003<br>%<br><!-- End of picture text --> 8. **The unbanked have three times greater unemployment than the banked** (10% are unbanked and unemployed versus 3.6% banked and unemployed). Figure 3.4 shows that the banked population prevails among the government employees<sup>43</sup> , formal sector employees, and employers, most of whom also benefit from social security and health insurance programs. On the other hand, the unbanked tend to work in areas of lower job security and beyond the social safety networks. The unbanked are more likely to be among the self-employed (72% of the total, which includes informal sector workers), domestic workers (81.5%) and unpaid workers in family businesses (78%). The financial 43 Perhaps due to recent policy decisions encouraging payment by direct deposit to bank accounts, 83% of government employees are banked 47
of nuts and when they are included, it has a significant agricultural trade surplus (almost 44 percent of its imports). Senegal is a special case where its major agricultural exports are peanut oil, which is included in the category of processed foods and thus not included in both raw food and all agriculture definitions. Thus, if real vulnerability is the main reason for the concern about net food importers, even with a very narrow definition of food, very few countries would be seriously affected by a significant food price increase.<sup>13</sup> When food trade balances of LDCs are analyzed, the results are similar to the results of low-income countries. Only 8 of the 50 LDCs have a raw food surplus, but 23 of them have an agricultural trade surplus. In terms of vulnerability, using the 10 percent cut-off point, only 8 of the 50 LDCs are vulnerable. Of these 8, 1 is an oil exporter, two are in conflict, 2 are small island states; leaving only three low-income countries listed above that can be termed as “vulnerable”.

# **IV. Net Food Status: All Agriculture**

Using a limited number of products in the measurement of food trade balance, and using this balance to infer about the potential impact of price changes assumes that the countries can not switch production from one group of agricultural commodities to another if relative prices change significantly. While the substitution might not be perfect and not every product can be efficiently and economically substituted, in most cases farmers can substitute across a wide range of agricultural products. It might, therefore, be important to measure the agricultural capacity of these countries by measuring their overall agricultural exports and imports.<sup>14</sup>

**Table 3: Country Classifications by All Agricultural Trade**

||No. of C<br>|ountries in All Agricul<br>|tural Trade<br>|
|---|---|---|---|
|CountryGroup|Net Exporter|Net Importer|Total|
|Industrial Countries|16|17|33|
|Middle-income, all|41|64|105|
|Oil Exporters|5|15|20|
|Civil Conflict States|0|4|4|
|Small Islanders|8|22|30|
|Other Middle-income|28|23|51|
|Low-income, all|34|24|58|
|Oil Exporters|4|3|7|
|Civil Conflict States|2|6|8|
|Other Low-income|28|15|43|
|World,Total|91|105|196|

Notes: All agriculture is defined as all raw food, cash crops and agric raw materials in SITC Revision 2, excluding processed food and seafood products

> 13 If we add the small island economies to the list, only two of them have raw food imports that are more than

10 percent of their imports. These are Comoros and Kiribati, whose food imports are only US$24 million. 14 To the raw food definition, tropical crops, feeds, and agricultural raw materials are added.

9
how transfers are allocated. Such factors could result in an overestimation of the impact of the targeting scheme on poverty reduction.

Thus the results in this paper should be viewed as illustrative only. At all times, the gains from targeting should be juxtaposed against the potential costs and political-economy considerations, as well as scrutinized against other possible policy objectives. In practice, a combination of geographic targeting between villages and means-tested targeting within villages may be the best way forward. Policymakers need to assess such programs on a caseby-case basis to determine just how far to rely on fine-geographic targeting as the central element in their poverty alleviation strategy.

To conclude, how useful are poverty maps for the purpose of designing geographically targeted poverty alleviation schemes? Our analysis indicates that, in addition to the factors already discussed above, the utility of poverty maps will hinge on two key issues. First is the question of whether the poverty map is able to convincingly distinguish between localities in terms of poverty. This will hinge on the statistical precision of the poverty estimates which in turn will be largely driven by the accuracy and explanatory power of the consumption models estimated using the household survey data. In the extreme case where a first stage model has no predictive power (an extremely low R<sup>2</sup> ) the resulting community level estimates of poverty from the census will all be approximately the same, and confidence intervals around those estimates would be so large as to make it impossible to reject equality of poverty rates across all communities. There would be no gains from geographic targeting over a simple uniform transfer of the available budget.

The second issue concerns how real welfare is distributed in a country. Even if estimates of poverty at the community level are fairly precise, as they have been found to be in 22