rafmacalaba/datause-encoder-data huggingface dataset:
POLIcy RESEARlCH WORKING PAPElt 3087

# Abstract

Emran, Otsuka, and Shilpi present an empirical analysis of intergenerational links in nonfarm participation with a focus on gender effects. Using survey data from Nepal, the evidence shows that the motlher exerts a strong influence on a daughter's employment choice. Having a mother in a nonfarm sector raises a daughter's probability of nonfarm participation by 200 percent. The effects are truly dramatic for skilled nonfarm jobs. Having a mother in a skilled job raises a daughter's probability by 1,200 percent. Having a father in a nonfarm sector, on the other hand, does **not** have any significant effect on a son's probability of nonfarm participation when the endogeneity of education and assets is corrected for by the two-stage conditional maximum likelihood approach. But a moderate positive intergenerational correlation between fathers and sons exists for skilled jobs.

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to understand the process of development of the rural nonfarm sector. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washiingtoni, DC 20433. Please contact Pauline Kokila, room MC3-604, telephone 202-4733716, fax 202-522-1151, email address pkokila@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at semran@gwu.edu, motsuka@jhu.edu, or fshilpi@wvorldbank.org. June 2003. (33 pages)

> _The Policy Research Working Paper Series disseinitates the findings of work in progress to encourage the exchange of ideas about developnment 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._

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We also use several auxiliary data sources. Data on trade openness (defined as the sum of the merchandise import and export shares in GDP) and real GDP per capita growth are from Eurostat and _World Development Indicators_ <u>(World Bank 2022a). Data on the capital stock of information</u> and communication technology (ICT)—a proxy for technological change in a country—are from the KLEMS data set <u>(Jorgenson 2012). Our measure of job protection corresponds to two</u> Employment Protection Legislation (EPL) indices calculated by the OECD <u>(2021). We use the</u> index measuring the protection of regular workers from dismissal (EPR, version 1) and the index measuring the difficulty of hiring temporary workers (EPT, version 1). Both range from 0 to 5, with higher values corresponding to a higher degree of job protection (more difficult dismissal of regular workers and more difficult hiring of temporary workers). Table 1 presents descriptive statistics of the variables included in our empirical analysis.

<u>Figure 1</u> displays average tenure over the sample period by subgroup. There is no clear trend; macroeconomic fluctuations seem to be driving most of the movement in these aggregate measures. The average worker in Europe spent about 10 years in his or her current job. There is some heterogeneity across regions of Europe, with workers in Southern Europe having, on average, job tenure that is two years longer than their peers in Northern Europe. This difference increased over time, as tenure in Northern Europe decreased while remaining stable in Southern Europe. Tenure in Central and Western Europe mainly remained stable, at 10 years, during the sample period.

<u>Figure 2</u> shows the average tenure by cohort and age for all countries in the sample. As expected, it increases with age, stabilizing toward the end of an individual’s working life. The age profiles of each cohort are not perfectly aligned. Profiles of younger cohorts tend to shift down, indicating the shortening of tenure for younger cohorts <u>Table 2 presents the average tenure by subgroup. Although the gap has shrunk, women have a</u> shorter average tenure than men. In 1995, men had an average tenure of almost two years longer than women; by 2020, the difference was only 0.7 year. Workers with only a lower-secondary diploma have higher average tenure than those with an upper-secondary diploma or tertiary degree. However, workers with an upper-secondary diploma saw a slight increase in their average tenure, of about 0.7 year between 1995 and 2020. All age groups experienced a decrease in tenure, but the decrease was most significant for workers 40–49 (2.35 years). This difference is not surprising given the age-cohort profile observed in figure 2. The average tenure for workers with permanent 9
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# **I. Introduction**

Cognitive development in early childhood is an important predictor of success throughout life. In developed countries, children with low levels of cognitive development before they enter school have lower school achievement and earn lower wages (Currie and Thomas 1999; Case and Paxson 2006). In developing countries, low levels of cognitive development have been tied to poor performance in school in a number of settings (see Grantham-McGregor et al. 2007 for a review).

Evidence from the medical and economic literature suggests that outcomes in early childhood are malleable (Heckman 2006; Knudsen et al. 2006). Randomized trials in the US show that children who benefited from intensive preschool interventions have higher school attainment, better test scores, lower rates of criminality, and earn higher wages in adulthood (Currie 2001; Schweinhart 2005), although the impacts appear to be concentrated among girls (Anderson 2007). A well-known study from Jamaica shows that children randomly assigned to receive home-based early stimulation have substantial improvements in cognitive development and subsequent school performance (Grantham-McGregor et al. 1991 and 1997; Walker et al. 2000; Powell et al. 2004). Non-experimental evidence suggests that attendance at nursery programs and preschool is associated with better school performance in Argentina (Berlinski et al. 2006), Uruguay (Berlinski et al. 2007), Colombia (Attanasio and Vera-Hernández 2004) and Bolivia (Behrman et al. 2004). There is also a large literature documenting the impacts of nutritional supplementation programs, including substantial evidence from randomized control trials (see Walker et al. 2007 for a review). In Guatemala, children exposed to a nutritional intervention have better reading comprehension and perform better on tests of cognitive development in adulthood, and earn higher wages (Maluccio et al. 2008; Hoddinott et al. 2008).

A reasonable amount of evidence is therefore available on how the cognitive development of young children responds to supply side interventions, including access to preschool or food supplementation programs. Much less is known about interventions that attempt to directly affect the investments parents make in the cognitive development of their children—either by relieving financial constraints, or by changing how resources are allocated within households.

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if the level of disaggregation is high, then different categories of products can become closely related to each other. In order to avoid this problem, we do not go beyond the 4 digit level of the ISIC classification<sup>8</sup> .

(2) The exogenous product _g_ shocks originating from China are approximately equal for the Latin American producers and for the rest of the world. This assumption may be violated if competition is localized due to high transportation costs. For example, the increase in Chinese exports might affect Vietnamese exporters more severely than Mexican exporters as a result of Vietnam’s proximity to China.

This said, we calculated the vulnerability and opportunity indexes for manufacturing, mining and agricultural products in Argentina, Brazil and Mexico. The difference between these indices tell us how much net exports were expected to increase (or decrease) because of China’s rapid growth for a typical country with given initial export structure, assuming everything else stays the same. Then we use the model presented in the next section to calculate corresponding price changes in each economy. This is the required price change to match the change in net exports within our neo-classical model calibrated with Latin American data. For example, we calculate how much the manufacturing prices should decline to account for the change implied by the net export index (i.e the difference between the opportunity and vulnerability indices in manufacturing).

# **3.2 A Model of Labor Adjustment in a Small Open Economy**

We use the informal and formal labor mobility framework introduced by Arias _et al_ (2013). Consider an economy with perfectly competitive _N_ +1 sectors and _N_ products. The last sector is the residual (or non-market) sector, which is not related to any product. Each sector, apart from the non-market sector, has an informal and a formal sub-sector. For example, manufacturing goods can be produced either in the formal manufacturing sector or in the informal manufacturing sector. In addition to perfectly competitive producers, there is a fixed

> 8We have experimented with different aggregation levels, such as ISIC 6, and found that the proposed index is robust.

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_Table 5_

# _Impacts of Foreign Ownership on Wages by Gender_

||||Full S<br>Subs|ample<sup>*1</sup><br>et of|Sub|set of|||Subset of Worke<br>Sub|rs in the Formal<br>set of|Sector<sup>*2</sup><br>Sub|set of|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Full Sa|mple|Skilled|Workers|Unskille|d Workers|Full S|ample|Skilled|Workers|Unskille|d Workers|
||Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|
|Foreign dummy|.36***|.46***|.49***|.44***|.27***|.45***|.30***|.26***|.37***|.37***|.25***|.24***|
||(.057)|(.047)|(.094)|(.094)|(.070)|(.053)|(.058)|(.045)|(.096)|(.083)|(.095)|(.067)|
|State dummy|.25***|.37***|.20***|.27***|.29***|.41***|.16***|.16***|.088|.20***|.25***|.16***|
||(.030)|(.037)|(.058)|(.080)|(.040)|(.044)|(.038)|(.041)|(.056)|(.058)|(.053)|(.056)|
|Private dummy|.029|.16***|.098|.069|.0095|.20***|||||||
||(.027)|(.035)|(.061)|(.086)|(.029)|(.039)|||||||
|Married|.055**|.056**|.074|.18***|.061**|.017|.049|.10***|.077|.20***|.049|..011|
||(.025)|(.026)|(.054)|(.047)|(.027)|(.030)|(.041)|(.035)|(.058)|(.046)|(.061)|(.051)|
|Minority|-.10***|.032|-.11|.090|-.11***|.0008|.00022|077|-.034|.072|.0044|-.0003|
||(.039)|(.041)|(.11)|(.087)|(.040)|(.047)|(.064)|(.067)|(.086)|(.086)|(.074)|(.0002)|
|Education|.052***|.052***|.10***|.10***|.021***|.017***|.082***|.073***|.11***|.098***|.042***|.029***|
||(.0032)|(.0039)|(.055)|(.011)|(.0039)|(.0049)|(.0064)|(.0051)|(.010)|(.011)|(.009)|(.010)|
|Potential|.028***|.022***|.027***|.023***|.025***|.015***|.033***|.027***|.032***|.025***|.029***|.021***|
|experience|(.0034)|(.0037)|(.0079)|(.0075)|(.0037)|(.00047)|(.0058)|(.0054)|(.0090)|(.0077)|(.0086)|(.0080)|
||-.0005***|-.0004***|-.0004**|-.0002|.026**|-.0002**|-.0006***|-.0003***|-.0005**|-.0003|-.0005***|-.0003|
|Experience<sup>2</sup>|(.00007)|(.00008)|(.00019)|(.0002)|(.0037)|(.0001)|(.0001)|(.00014)|(.0002)|(.0002)|(.0002)|(.0002)|
|Urban|.12***|.025|.13***|.033|.098***|.054*|.18***|.044|.17***|.030|.19***|.13***|
||(.021)|(.025)|(.037)|(.038)|(.024)|(.031)|(.030)|(.029)|(.041)|(.039)|(.047)|(.047)|
|Industry dummies|X|X|X|X|X|X|X|X|X|X|X|X|
|Regional dummies|X|X|X|X|X|X|X|X|X|X|X|X|
|Number of obs.|4096|2527|1321|1023|2775|1504|1973|1541|1100|943|873|598|
|R<sup>2</sup>|.31|.45|.34|.41|.25|.32|.34|.39|.34|.40|.32|.30|
|Wald Test|||||||||||||
|Chi-square|1.|53|.1|5|4|.42|.|34|.|00|.|05|
|_P_-value|.2|2|.7|0|.|035|.|56|.|99|.|83|

Notes:

- *1The informal wage sector is the omitted ownership category.

- *2The private domestic sector is the omitted ownership category.

The dependent variable is the log of hourly wage.

*, **, *** indicate that the coefficients are significant at the 10, 5, and 1 percent level, respectively. The robust standard errors are between parentheses.

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**Table 3: Probit Estimations**

|Pr(BRIBE=1)|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Sales in last month (log)|-0.066|0.319***|-0.000|0.236**|
||(0.115)|(0.117)|(0.115)|(0.106)|
|Started due to lack of opportunity (NECESSITY)|1.107***<br>(0.288)|0.858**<br>(0.334)|||
|No HH member w/contract (NOWAGE)|||1.045***<br>(0.271)|1.264***<br>(0.262)|
|Workers in a normal month (log)||0.139||0.246|
|||(0.267)||(0.256)|
|Age (log, years) of owner||-0.133||0.019|
|||(0.482)||(0.466)|
|Permanent, fixed premises (Y=1)||1.581***||1.471***|
|||(0.471)||(0.449)|
|Household (Y=1)||-0.270||-0.519|
|||(0.414)||(0.411)|
|Temporary, fixed premises (Y=1)||0.795**||0.915***|
|||(0.321)||(0.315)|
|Female (Y=1)||-0.416*||-0.288|
|||(0.239)||(0.227)|
|Beyond secondary education (Y=1)||-0.402||-0.046|
|||(0.303)||(0.290)|
|No or only primary education (Y=1)||-0.234||-0.136|
|||(0.439)||(0.415)|
|Harassment 'Donut' Rate(d)||4.313***||4.672***|
|||(1.297)||(0.865)|
|Constant|-1.674*|-0.729|-1.835*|-1.147|
||(1.011)|(6.183)|(0.948)|(5.008)|
|Observations|489|489|490|490|

*** p<0.01, ** p<0.05, * p<0.1 (d) Rate of harassment within 500m but excluding nearest 200m. Survey-weighted (using Stata’s svy prefix). Standard errors clustered by stratum. All columns include stratum, sector, and ward (neighborhood) fixed effects as well as measured distances to eight municipal offices. Sectors are manufacturing, food provision (including bakeries), retail, and other services. Reference category for premises: no fixed premises (hawkers).

**Table 4: Marginal Effects, Pr(BRIBE=1)**

||NECE<br>|SSITY<br>|NOW<br>|AGE<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Opportunity Cost (D=1)|18.7%***|10.1%***|19.9%***|16.5%***|
|S.E.|(5.4%)|(4.1%)|(6.2%)|(4.7%)|
|1 SD change (log) sales|-2.0%|5.5%**|0.0%|3.8%**|
|S.E.|(3.4%)|(2.2%)|(3.4%)|(1.8%)|
|1 SD change in harassment||10.1%***||10.1%***|
|S.E.||(3.2%)||(2.2%)|

*** p<0.01, ** p<0.05, * p<0.1

Note: the difference in predicted values from (mean -.5*SD) to (mean + .5*SD) of (log) monthly sales, an increase of about $483 in sales per month. The difference in harassment exposure is the nearby (donut) harassment rate is (mean -.5*SD) to (mean + .5*SD), or 1% to 20%. Marginal effects produced using margins and mlincom commands in Stata, using vce(unconditional)option as it is survey data.

In line with expectations, there are positive, significant signs for coefficients for NECESSITY and NOWAGE; the marginal effect of the former is a 10% increased likelihood of being exposed to (22)
Restructuring is probably best understood as the transition process from a highly distorted economy with many loss-making firms to a "normal" market economy in which the overwhelming majority of firms are profitable. Evidence from the transition economies shows that the speed of the restructuring process varies greatly across countries (Figure 1). Firms in the countries with rapid adjustment, the Czech Republic, Hungary, Poland, and Slovakia have reached total factor productivity (TFP) growth rates equal to those in the fastest growing economies. I Bulgarian and Romanian firms, on the other hand, experienced a relative decline in productivity.<sup>2</sup> What explains these differences in performance?

**Figure 1: Average Restructuring Indicators 1992-95**

<!-- Start of picture text --> 9.2%<br>Bulgaria I 04%<br>Czech Rep. 6.1%<br>z~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~S , , I .- I rL I A 6.0%<br>Hungary<br>7.0%<br>Poland ;3%<br>4.7%<br>m19.2%<br>Romanla -120%<br>Slovalda 4.6% F<br>4.5%<br>Slovenia 1.4%<br>-2.0% 0.0% 2.0% 4.0% 60% 86.0% 10.0%<br>|Aver ge Annual Labor Shedding SAvenrag Annual TFP Growth 0 Average Annual Labor Producivity Growth<br><!-- End of picture text -->

# Fource: Claessens et al., 1997

Germanv and Japan experienced 4-5% TFP growth in the 1950s. and Japan sustained this level through the 1960s (Wolff, 1996); South Korea experienced a 4.8% TFP growth in 1971-1981, Taiwan and Hong Kong - 4.3% in 1966-1976 (Young, 1995).

- 2 The methodology used in calculating the restructuring indicators in Claessens et al., (1997) is identical to the measures described in Section V and the Appendix to this paper. This allows a direct comparison of the prefornance of the fmns in our sample and the whole manufacturing sector in Slovakia. Figure 1 is based on the manufacturing censuses and covers 48%, 64%, 44%, 42%, 92%, 93%, 91% of 1992 manufacturing employment in each (alphabetically listed) country.

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against duties on imports. The size of the subsidies of up to 40 percent of export value and the lack of controls has given rise to wide-spread fraud, such as the over-invoicing of exports, while the supply response from actual or potential exporters is unclear. In this context, the government should put regular mechanisms in place to evaluate the use of the EEG funds and to assess if and to what extent the program achieves its stated objective. More generally, a broader review of export incentive schemes would seem highly desirable in order to rationalize the government’s support to exporters, improve its efficiency, and minimize the risk of abuse.

# **6. Conclusions**

Discussions concerning the economic impact of a potential EPA with the EU have centered on the preferential trade liberalization that ACP countries will have to implement. Economic theory shows that the overall welfare impact of such discriminatory trade reform is ambiguous so that careful empirical analysis is required to identify potential outcomes and highlight policy options for ameliorating negative effects. In many countries these discussions have taken place in the absence of such an empirical assessment that is crafted to the economic reality of the countries concerned.

In this paper we address this issue using detailed data from Nigeria’s customs in a partial equilibrium trade simulation model. The simulation results show that overall impacts on imports are likely to be slight, firstly, since in reality an EPA will allow Nigeria to exclude the most protected products from liberalization and, second, because there is likely to be substantial trade diversion from other import suppliers towards the EU. It is this trade diversion that leads to negative economic impacts from the EPA. This can be minimized by prior reduction of high (peak) external MFN tariffs, a reform that would reduce remaining anti-export bias in the trade regime and assist more effective integration into regional and global markets.

The paper also highlights the importance of other aspects of trade policy in Nigeria and, in particular, the import bans that affect a wide range of products. Reform of these bans, in terms of tariffication and subsequent tariff reduction, is likely to be strongly welfare improving, with significant benefits for poor consumers, since imports of a number of food products, footwear and clothing are currently banned. In addition, such a reform would improve the basis for 27
13 that is those living in sampled towns as of 1981. Unfortunately, the 1991 census does not provide information about past place of residence. We thus proxy the town of residence as of 1981 with town of birth<sup>13</sup> . The evidence discussed in the previous section suggests, however, that geographic mobility is not a relevant issue in our specific sample.

The proportion of youths exempt before eligibility who completed high school by 1991 is higher by 2.3 percentage points than among similar non exempt ones (row D). The p-value of the unconditional difference is 0.088 and compares with the much lower levels of significance and point estimates close to zero detected for the other long run outcomes and for all observable circumstances of the older cohort. In particular, there is no statistically relevant difference in the proportion of males enrolled in formal education or participating in the labor market as of 1986 (rows E-F) and as of 1991 (rows G-H). Also, as of 1991 there is no difference in the proportion of those who had completed college between exempt and non exempt youths in both cohorts (row I), consistently with the idea that individuals whose behavior was changed by the exemption are those at risk of high school dropout, thus unlikely to pursue a college education. To put the difference in high school completion rates in context, it implies that in the entire quake region over 3,000 youths who would have otherwise dropped out managed to complete high school and around 200 in the sample of towns under investigation.

Finally, the last three rows of table (1) compare the probability of dying between 1991 and 2001. We recover this measure by combining the information on the initial population at risk in cohort c born in town j (Nc,j) from the 1991 population census with that obtained from the individual death data. For each death, we observe the date of the event and a variety of individual characteristics including town and year of birth. We recover the total number of deaths occurred over the relevant period in cell

> 13Proxying the place of residence at a young age with the place of birth is fairly customary, for example in the large literature started by Angrist and Krueger (1991) exploring the effects of changes in US compulsory schooling state laws. For the sake of comparability we should have performed also the analysis on 1981 data on the basis of town of birth. However, the 1981 census provides information on the place of birth only at the larger province level. Thus by using the place of birth instead of residence we would have included in the sample also people likely to live in towns more strongly exposed to the quake and to other compensatory interventions, invalidating the spirit of the research design.
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