rafmacalaba/datause-encoder-data huggingface dataset:
# VOLATIONAL AND TECHNICAL EDUCATION IN PERU

Peter R. Moock ard Rosemary T. Bellewl

# **1. Introduction**

The purpose of this study is to assess the economic returns to vocational and technical education (VTE) in Peru. Studies in other developing countries have concluded tilat TE is more costly to provide than 'general' (i.e., academic) education, yet it yields returns that are not significantly different. This has led to the policy recommendation that countries should promote general education at the expense of VTE. For the creation of occupation- and job-specific skills, countries should, according to this view, rely not on school-based instruction, but rather oa training to be given to individuals after they have left school and entered the labor market. Such training can be provided either on the job or in industry-based training centers including proprietary institutions.

The relative costs of and returns to VTE and general education are investigated here for Peru. The paper proceeds as follows. Section 2 describes the system of education in Peru and changes that have occurred in this system over time. Section 3 surveya the (relatively sparse) literature on comparative rates of return to VTE and general education. Sections 4 and 5 describe, respectively, the data used for this study and the model used to estimate the returns to different levels and types of education. Section 6 reports the results of the empirical analysis. Section 7 introduces data obtained from Peru's Ministry of Education on the relative costs of VTE and general education. The concluding section 8 considers the implications, if any, of the findings for educational policy.

# 2. VTE in Peru

The recent history of education in Peru, as it relates to the provision of VTE, can be divided into three distinct phases. Prior to 1972, and again after 1982, official policy split the system of education into two separate sub-systems, or educational "streams." The focus of the general stream was on the development of broad cognitive skills assumed to be useful in a wide variety of occupations and necessary also for students who wanted to continue their studies at higher levels of the education system. The VTE stream, in addition to teaching these basic skills, was intended to orient students generally to the world of work and provide them with training specific to a future occupation of their choice. Between 1972 and 1982, there was an attempt to merge the two scholastic streams and, for most students up through secondary education, to increase the vocational content of the curriculum. This attempt to "vocationalize" or 'diversify' primary and secondary education is one with many historical 1 Education and Employment Division, Population and Human Resources Department, The World Bank, Washington, D.C. 20433. The authors acknowledge the useful comments of Ana Maria Arriagada, Elizabeth King, and Philip Musgrove and the assistance, early in the preparation of this study, of Roxana Carvalho.
# Figure 1. Map of Nepal and Location of Surveyed Villages

<!-- Start of picture text --> CI CJ<br>,4 'V' /' Urban centers<br>,- 1-29C5O<br>A ,t ; < -~- ), * U)3 123$9L8'5<br>. , t_* .-- , > 4-, > 1533-<br>tLf S12 A : i V <~~~~12W9t2"2%<br>p. P 1 + 4 U. +-*' -4-. *<br><!-- End of picture text -->
<!-- Start of picture text --> Kaplan—Meyer Survival Plot Kaplan—Meyer Survival Plot<br>s Before and After the Reform (All women) 2 Before and After the Reform (Less than KFCE)<br>= 1.00 = 100<br>s 0.90 = 0.90<br>= 0.80 3 0.80<br>= 0.70 > 0.70<br>= 060 = 0.60<br>2 0.50 = 050<br>2 0.40 8 0.40<br>3 0.30 3 0.30<br>= 0.20 20.20<br>2 0.10 8 0.10<br>$ 0.00 2 0.00<br>13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32<br>Age (in years) Age (in years)<br><!-- End of picture text --> — Beforethe Redon — Afterthe Reform Note: highs! impact between tie 2 werlical lines.

= Belorethe Reform == Afterthe Flelorm Note: the women should nol be affectedby tne Reform.

<!-- Start of picture text --> Kaplan—Meyer Survival Plot Kaplan—Meyer Survival Plot<br>s Before and After the Reform (KFCE or some secodnary education) s Before and After the Reform (At least secondary complete)<br>= 1.00 % 1.00<br>« 0.90 « 0.90<br>B 0.80 B 0.80<br>Boe,~ 0.50 }~ 0.50ioe<br>Q5 ‘0: 2S 0. 43 0<br>2 0.20 2 020<br>2 0.10 2 0.10<br>g 0.00 3 0.00<br>13 14 15 16 17 18 19 2021 22 23 24 25 26 27 28 29 30 31 32 13 14 15 16 17 18 19 2021 22 23 24 25 26 27 28 29 30 31 32<br>Age (in years) Age (in years)<br>— Beforethe Reform — Afterthe Reform ~ Beforethe Retorm = Afterthe Reform<br>Note: highest impact between the 2 vertical lines. Note: highest impact between the 2 vertical lines.<br><!-- End of picture text -->
migrants also tends to decrease. Second, as the cost of living within a country increases, migrants partially offset this by sending more money home.

# _An Analysis of Money Transfer Costs_

Next, we investigate the transaction costs associated with receiving remittances. We present no precise theoretical model of their determinants. However, we would expect greater competition in the financial services industry to have a powerful negative impact on such costs. Also, greater overall financial-sector development might lead to greater availability and lower costs for remittance services. Defective institutions and greater business risk, as proxied by our measure of financial risk, would instead be expected to reduce the willingness of agents to provide remittance services. Lack of exchange rate risk, as would be the case for dollar remittances being sent to a dollarized economy, should conversely reduce the cost of providing remittance services. Finally, domestic wages (as proxied by domestic per-capita income), if not matched by equivalent service-sector productivity, might be associated with greater cost of remittance services. We also consider market size effects below.

We therefore run the cross-sectional regression _(5) Costi_ = α + β 1 _Conci_ + β 2 _FinDevi_ + β 3 _Riski_ + β 4 _Dollari_ + β 5 _yi_ + ε _i_ , where _Cost_ denotes the total (percentage) transaction costs associated with receiving remittances; _Conc_ denotes bank concentration; _FinDev_ denotes financial development, as measured by the ratio of domestic deposits to GDP; _Risk_ denotes financial risk; _Dollar_ denotes a dollarization dummy; and _y_ denotes the log of domestic output per capita. All 17
between technology in sector-specific business functions and productivity.<sup>6</sup> The rest of the paper is structured as follows. Section 2 presents the FAT survey. Section 3 explains how we use the information collected with the FAT survey and the advice of the experts to construct our technology sophistication measures. Section 4 analyzes technology across firms. Section 5 analyzes technology within firms. Section 6 studies the relationship between technology and productivity. Section 7 conducts two development accounting exercises and the last section concludes.

# **2 The Survey**

The FAT survey collects detailed information for a representative sample of firms about the technologies that each firm uses to perform key business functions necessary to operate in its respective sector of economic activity. The data assembled with the FAT survey has five desirable properties. First, they provide a granular characterization of productive activities by covering a wide range of technologies both relevant across sectors as well as specific to one. Second, the technologies covered include both advanced and older technologies, so that we can characterize the technological landscape in any firm regardless of its technological sophistication. Third, since the survey is centered at the business function level, it provides information about the technologies used in each business function. Fourth, the questions included in the survey allow us to construct measures that reflect not just the technologies used but also how intensively they are used in conducting the relevant tasks for the business function. Fifth, by covering a wide range of sectors, the data produced by the survey allows for a rich cross-sector characterization of the sophistication of technologies in the economy. In what follows we describe in detail the survey and the process we have followed to design it and implement it (see also Appendix A).

## **2.1 Structure**

The FAT survey is composed of five modules. Module A collects information on general characteristics about the firm.<sup>7</sup> Modules B and C collect information on the technologies used by the firm. Module D collects information on barriers and drivers of technology adoption, while module E gathers information about the firm’s balance sheet and output.

The survey differentiates between general business functions (module B) which comprise

> 6One interesting exercise, would be to study whether the sophistication of practices followed by managers within the firm display similar patterns (e.g., technology curves, within-firm variance, and its correlation with average sophistication, firm size, etc.) as those we uncover for technology.

> 7For multi-establishment firms, the survey targets the establishment randomly selected in the sample.

5
Policy ReseaRch WoRking PaPeR 4650

# **Abstract**

During the past two decades many economies have opened their retail sector to foreign direct investment, yet little is known about possible implications of such liberalization on the economies of developing host countries. Using firm-level data from Romania, this study examines how the presence of global retail chains affects firms in the supplying industries. Applying a differencein-differences method, the econometric analyses yield the following conclusions. The expansion of global retail chains leads to a significant increase in the total factor productivity in the supplying industries. Their presence in a region increases the total factor productivity of firms in the supplying industries by 15.2 percent and doubling the number of chains leads to a 10.8 percent increase in total factor productivity. However, the expansion benefits larger firms the most and has a much smaller impact on small enterprises. This conclusion is robust to several extensions and specifications, including the instrumental variable approach. These results suggest that the opening of the retail sector to foreign direct investment may stimulate productivity growth in upstream manufacturing and extend our understanding of foreign direct investment in service sectors.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to investigate the implications of inflows of foreign direct investment for developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at beata.javorcik@economics. ox.ac.uk or yli7@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
**Table 2B. Baseline balance tests of household characteristics**

||**Pan**|**el 1: Household Cha**|**racteristics**|||
|---|---|---|---|---|---|
||Control|Treatment|Difference|P Value|Observations|
|Household head is male|0.597|0.582|-0.015|0.544|1601|
|Household size|4.736|4.738|0.002|0.988|1601|
|Father’s education||||||
|_None or informal_|0.247|0.218|-0.029|0.176|1601|
|_Primary_|0.051|0.034|-0.017|0.086*|1601|
|_Junior School_|0.061|0.062|0.001|0.938|1601|
|_High School_|0.235|0.243|0.008|0.713|1601|
|_Beyond High School_|0.391|0.389|-0.002|0.930|1601|
|Mother’s education||||||
|_None or informal_|0.583|0.563|-0.020|0.444|1601|
|_Primary_|0.085|0.097|0.012|0.436|1601|
|_Junior School_|0.082|0.078|-0.004|0.795|1601|
|_High School_|0.117|0.121|0.004|0.809|1601|
|_Beyond High School_|0.117|0.117|0.000|0.994|1601|
|Number of hh members<br>aged 5-11|0.587|0.631|0.044|0.344|1587|
|Proportion of children per<br>household aged 5-11 who<br>are attending school,<br>conditional on the<br>||||||
|household having any<br>children aged 5-11<br>|85.693|82.739|-2.954|0.296|646|
|Number of hh members||||||
|aged 5-14|0.842|0.894|0.052|0.376|1587|
|Proportion of children per<br>household aged 5-14 who<br>are attending school,<br>conditional on the||||||
|household having any<br>children aged 5-14<br>|87.850|81.862|-5.988|0.016**|779|
|Number hh members aged||||||
|<br>13-30|2.313|2.279|-0.034|0.652|1601|
|Proportion of individuals per<br>household aged 13-30 with<br>IGA conditional on the||||||
|household having individuals<br>aged 13-30|48.351|44.819|-3.532|0.128|1559|

32
in Ethiopia means that, on average, male IDPs in Ethiopia live in MPI poor households with those characteristics, compared to female IDPs in Ethiopia, who live in MPI poor households that experience, on average, an incidence of 45.8%. The individual-level analysis below interrogates the deprivations faced by the individual regardless of their household-level poverty status.

_Table 5: MPI, H, and A by displacement status and gender_

|Country|Displacemen<br>by Gender|t Status|Populati|on Share|MPI||H (inc|idence)|A (int|ensity)|
|---|---|---|---|---|---|---|---|---|---|---|
||||(%)|(_n_)|(decimal)|(_c.i._)|(%)|(_c.i._)|(%)|(_c.i._)|
||IDP|_Male_|43.12|11,435|0.238|_(0.199,_<br>_0.278)_|40.1|_(33.2,_<br>_47.1)_|59.3|_(58.2,_<br>_60.4)_|
|Ethii|Population|_Female_|44.86|11,897|0.277|_(0.232,_<br>_0.322)_|45.8|_(38.4,_<br>_53.3)_|60.4|_(59.6,_<br>_61.2)_|
|opa|Non-IDP|_Male_|5.80|1,538|0.060|_(0.046,_<br>_0.073)_|10.2|_(7.8,_<br>_12.7)_|58.1|_(56.7,_<br>_59.5)_|
||Population|_Female_|6.21|1,648|0.070|_(0.048,_<br>_0.091)_|11.9|_(8.5,_<br>_15.4)_|58.6|_(56.5,_<br>_60.7)_|
||IDP|_Male_|15.23|2,673|0.127|_(0.090,_<br>_0.163)_|21.1|_(15.4,_<br>_26.8)_|60.1|_(58.3,_<br>_61.9)_|
|N.E.|Population|_Female_|13.28|2,330|0.154|_(0.118,_<br>_0.190)_|25.9|_(20.4,_<br>_31.3)_|59.5|_(56.9,_<br>_62.0)_|
|Nigeria||||||_(0.045,_||_(7.1,_||_(60.3,_|
||Non-IDP|_Male_|33.69|5,910|0.111|_0.177)_|17.8|_28.5)_|62.3|_64.3)_|
||Population|_Female_|37.79|6,630|0.103|_(0.042,_<br>_0.163)_|16.3|_(6.5,_<br>_26.1)_|63.0|_(60.9,_<br>_65.2)_|
||IDP|_Male_|17.77|4,850|0.404|_(0.352,_<br>_0.455)_|64.5|_(56.3,_<br>_72.7)_|62.5|_(61.1,_<br>_63.9)_|
|Sli|Population|_Female_|17.88|4,880|0.388|_(0.319,_<br>_0.456)_|62.0|_(50.9,_<br>_73.0)_|62.6|_(60.8,_<br>_64.3)_|
|omaa|Non-IDP|_Male_|32.57|8,888|0.295|_(0.251,_<br>_0.339)_|46.1|_(39.5,_<br>_52.8)_|63.9|_(62.8,_<br>_65.0)_|
||Population|_Female_|31.77|8,670|0.277|_(0.236,_<br>_0.317)_|43.4|_(37.3,_<br>_49.5)_|63.7|_(62.6,_<br>_64.9)_|
||IDP|_Male_|6.29|286|0.183|_(0.079,_<br>_0.287)_|30.9|_(12.9,_<br>_48.9)_|59.2|_(55.5,_<br>_63.0)_|
|South|Population|_Female_|6.99|318|0.221|_(0.113,_<br>_0.329)_|37.0|_(18.3,_<br>_55.6)_|59.8|_(57.0,_<br>_62.6)_|
|Sudan|Non-IDP|_Male_|40.42|1,840|0.112|_(0.077,_<br>_0.147)_|18.4|_(12.9,_<br>_23.9)_|60.8|_(58.9,_<br>_62.6)_|
||Population|_Female_|46.30|2,107|0.125|_(0.092,_<br>_0.157)_|20.4|_(15.3,_<br>_25.6)_|60.9|_(59.5,_<br>_62.4)_|

15
5 them Type A) raise issues of economic inefficiency and social injustice: large segments of the population, often with useful skills, are kept out of the productive economy, which obviously makes the process of national wealth creation suboptimal. This is true even if the unemployed are less productive than those who hold employment. Furthermore, the people left outside the economic system are often a burden to those who are employed and feel disenfranchised, which makes them a potential source of social instability and a constant threat to social peace.

Government agencies and international organizations usually approach and count unemployment differently. Broadly speaking, they only focus on people of “working age” (whatever they choose it to be), who are out of work (whatever is considered “work”) and capable of submitting evidence (whatever is deemed acceptable) of having looked for work in the recent past. Clearly, it is possible for a person to qualify for these Type B situations and not qualify for Type A; for example it is possible for a skilled worker who has been laid off and is looking for a job not to qualify for inclusion in unemployment statistics because he/she is temporarily helping in a family business (and therefore technically not out of work) or has not been able to produce “acceptable evidence” of having looked for a job. And vice versa: it is possible for a young college graduate who is officially included in unemployment statistics to actually be seeking work at real wages in excess of his/her potential contribution to society.

Such plausible scenarios raise several questions that economic theories of unemployment have attempted to answer over the decades, generally with little success: Why are market economies often unable to provide all the jobs that people are looking for? Why are certain economic systems unable to attract workers who would be willing to demand real wages that fall short of their potential contribution to society? Why are firms unwilling or unable to capture the economic opportunities that are associated with the existence of a large pool of unemployed workers? Why are involuntary unemployed workers unwilling or unable to employ themselves (and others) by starting new firms or to underbid their employed counterparts? Etc.

Unemployment theorists have so far not been able to design rigorous analytical tools to provide convincing answers to these questions. Defining unemployment requires that one implicitly assigns value to various types of work, occupation patterns, and sources of income. This
# **How Does Trade Respond to Anticipated Tariff Changes? Evidence from NAFTA**<sup>_∗_</sup>

Shafaat Yar Khan<sup>_†_</sup> and Armen Khederlarian<sup>_‡_</sup>

## **JEL Classifications:** F12, F13, F14

**Keywords:** Anticipation, Trade Elasticity, Inventories, NAFTA, Phaseouts

> _∗_ We thank Costas Arkolakis and two anonymous referees for helpful comments and suggestions. We are greatly indebted to George Alessandria for his invaluable discussions, guidance, and support throughout this project. We are also extremely grateful to Mark Bils, Yan Bai and Dan Lu for numerous comments and suggestions. Moreover, we would like to thank Mario Crucini, Alexandre Skiba, B. Ravikumar, George Schaur, Mostafa Beshkar, Ahmad Lashkaripour, Russel Hillberry, John Romalis, Lorenzo Caliendo, Kei Mu Yi, Carter Mix, Oscar Avila and seminar participants at the University of Rochester and conference participants at the ETSG 2018 (Warsaw), the Spring 2019 Midwest Macroeconomics Meetings (UGA), the Spring 2019 Midwest International Trade Meetings (IUB), the 2019 Economics Graduate Student’s Conference (WashU) and Lisbon Meetings in Game Theory & Applications (LSEM).

> _†_ sykhan@worldbank.org, World Bank

> _‡_ armen.khederlarian@gmail.com, University of Rochester
9 fraction of family unpaid workers in North Africa. Although this decline<sup>in the</sup> fraction of women is family employment is large in the population weighted it may be par.ly a result of spurious changes in the definition of the labor force between in the Indian censuses of 1961 and 1971. However, similar declines are evident in Pakistan from 1961 to 1971, and in Bangladesh from 1961 to 1974, except in wage employment. It has also been argued that the 1961 Indian and Pakistan Censuses modified traditional labor force definitions with a resulting upward shift in the reported level of economic activity for both women and men, compared with either 1951 or 1971.8 This patterns of decreasing labor force activity rates by women in South and West Asia, that is apparent at least through the early 1970s, has not been extensively analyzed. It may be due to the sluggish overall growth of the region except for oil-exporters, or to the lower levels of school enrollment among women than men that placed women in the region at a disadvantage relative to men in market employment.

Disaggregating by sector the female fraction of the labor force and the female fraction of wage earners in Table 4 provides further evidence of the contrary trends in women's participation bv region. Even among wage earners, the fraction of women in manufacturing is low in North Africa and both portions of Asia, for in these regions women in manufacturing are mostly self-employed or family unpaid workers, presumably in home-based craft production. In commerce (i.e. wholesale and retail trade) women are a smaller fraction of wage earners in Asia than they are of the total labor force, because (particularly in East Asia) women are frequently traders and shopkeepers. Women constitute two-thirds of the family workers and half of the self-employed working in commerce in Asia. The roles of women in agriculture are heterogeneous across regions of the world and agriculture is the dominant sector in the largest and poorest nations .<sup>9</sup>
**<u>Table 5. Failure rates among students who gave no literacy reply</u>**

||_1998 F_<br>_Con_|_ourth G_<br>_Non_|_rade_<br>_Diff_|_200_<br>_Con_|_0 Sixth_<br>_Non_|_Grade_<br>_Diff_|
|---|---|---|---|---|---|---|
|_Percent_|||||||
|Gave no failure reply|12.02|4.97|7.05|4.08|2.51|1.57|
|Overall failure rate|0.33|0.18|0.15|0.32|0.17|0.15|
|Failure rate if gave no literacy reply|0.39|0.29|0.09|0.43|0.29|0.15|
|Difference|0.06|0.12|0.06|0.12|0.12|0.00|

Source: SEP Data We performed similar comparison for the literacy rates of students who gave no failure reply, and for the global scores of students who gave no failure reply. In every case, the rate of non-response among CONAFE students was significantly higher than the rate of non-response among non-CONAFE students. It also appeared that the bias created by non-response for a given student among CONAFE and non-CONAFE students was comparable. In other words, the difference between responding and non-responding students was comparable among both CONAFE and non-CONAFE students. Since a higher portion of CONAFE students failed to provide background information, and since non-responding students were more disadvantaged, we interpreted that non-response bias made the CONAFE students more disadvantaged than the non-CONAFE students. For CONAFE results in the present paper to equal non-CONAFE results, then, CONAFE students must overcome the additional disadvantage of their backgrounds that response bias prevents us from measuring. This finding further highlights the caveat that results of this report are an absolute lower bound for the positive impact of CONAFE on student achievement.

# **Descriptive Statistics**

In a given grade-year, CONAFE supported 35-45 percent of primary school students in the sample; and 30-40 percent of secondary schools a given grade-year were _telesecundarias_ . CONAFE support was also relatively static over the sample period. Nearly 70 percent of schools 20