rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8433

# **Abstract**

Differences in earnings between male and female workers persist in developed and developing countries despite a narrowing of gender gaps in educational attainment over the past half-century. This paper examines the gender wage gap in Vietnam and shows that a nontrivial part of the gap is associated with occupational sorting. The paper considers three explanations for why occupational sorting emerges. First, it explores whether women sort into occupations with better nonmonetary characteristics, such as paid leave and shorter hours. The data from Labor Force Surveys support this hypothesis. Second, it checks if occupational sorting among the adult labor force is driven by social norms about gender roles learned and internalized at an early age. To do so, the paper checks for evidence of sorting in the aspirations of 12-year-old children. Specifically, the analysis simulates what the gender wage gap would be if boys and girls pursued the occupations they aspired to at age 12, and the distribution of salaries remained unchanged. The paper does not find support for the hypothesis that gender norms drive occupational sorting by inducing aspirational sorting at an early age. Finally, for individuals with higher education, the paper checks if occupational sorting occurs during the school-to-work transition, when women face higher barriers in finding a job in their field of study. The analysis does not find evidence to support this last hypothesis. - Overall, the findings suggest that in Vietnam gender-spe cific preferences for nonmonetary job characteristics play a key role in the emergence of occupational sorting.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. 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/research. The authors may be contacted at eperova@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
sri lanka country risk profile 13

# 50-year precipitation flood in Rathnapura

<!-- Start of picture text --> 120 Subsistence line<br>Increase of 7,500 (0.6% of regional pop.)<br> in income subsistence<br>100 Poverty line<br>Increase of 11,300 (1.0% of regional pop.)<br> in income poverty<br>80<br>60 Pre-disaster income<br>(FIES data)<br>Post-disaster income<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Income (USD per person, per year)<br>120 Subsistence line<br>Increase of 0 (0.0% of regional pop.)<br> in consumption subsistence<br>100 Poverty line<br>Increase of 11,000 (0.9% of regional pop.)<br> in consumption poverty<br>80<br>60 Pre-disaster consumption<br>(FIES data)<br>Post-disaster consumption<br>(modeled)<br>40<br>20<br>0<br>0 250 500 750 1000 1250 1500 1750 2000<br>Consumption (USD per person, per year)<br>Population (,000)<br>Population (,000)<br><!-- End of picture text -->

**Figure 3:** Per capita income (top) and consumption (bottom) in Rathnapura district, before and immediately after 50-year precipitation flooding, in US$. Income losses take into account the lost productivity of disaster-affected assets, while consumption losses additionally include reconstruction costs, post-disaster support, and savings.

Therefore, income losses describe better than asset losses the real impacts of disasters on Sri Lankans’ wellbeing and prospects.

To return to a historical example: damages from the 2017 floods totaled roughly US$400 million in the 5 most directly-affected districts (Galle, Hambantota, Kalutara, Matara, and Rathnapura). In Rathnapura, total damages were estimated at US$51.6 million, more than 6 times greater than long-term average annual losses in the district (US$8.2 million, cf. Table 1). These losses correspond roughly with the 50-year
# A **O** P FCOMM

|I.|Int|roduction|1|
|---|---|---|---|
|II.|The|Change in Regimes Facts and Models|3|
||A.|Some Facts|4|
||B.|Models|6|
|III.|Tim|es Seiies Evidence and Comparisons|10|
||A.|Summary Statistics|10|
||B.|Responses to Shocks|16|
||C.|Cross-Sectional<br>Variabilities|20|
||D.|Persistence of Fluctuations|21|
||E.|Relating the Findings to the Models|23|
|IV.|The|Changing of the Nature Inflation/Unemployment<br>Tradeoff|25|
||A.|The High Inflation Period|26|
||B.|The Post Stabilization<br>Period|29|
||C.|The Tradeoff in the Transition|29|
|V.|Con|clusions|34|
|VI.|Ref|erences|36|

The authors would like to thank Plhanan Uelpman, Miguel Kiguel, and. participants at a World Bank seminar for their comments and suggestions. This paper is part of World Bank RPO 674-24.
28

**Table 5. Wage Premium Regressions, Short Run**

||Cam<br>|bodia<br>|Sri L<br>|anka<br>|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.205***|-0.209***|-<br>0.568***|-<br>0.570***|
||(0.016)|(0.016)|(0.023)|(0.023)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.042***|0.044***|0.149***|0.149***|
||(0.004)|(0.004)|(0.006)|(0.006)|
|Education|0.026***|0.026***|0.046***|0.045***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|T&G dummy|0.392***|-0.136**|0.067***|0.088***|
||(0.026)|(0.063)|(0.010)|(0.013)|
|1999–2004 dummy||0.033*||0.059***|
|||(0.019)||(0.007)|
|2005 and above dummy|0.280***|0.307***|0.066***|0.092***|
||(0.013)|(0.020)|(0.007)|(0.007)|
|T&G* (1999–2004 dummy)||0.686***||-<br>0.054***|
|||(0.075)||(0.020)|
|T&G* (2005 and above<br>dummy)|-0.234***|0.383***|-0.043*|-<br>0.067***|
||(0.030)|(0.073)|(0.022)|(0.024)|
|Hazard ratio|0.002|0.010|0.681***|0.683***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.392***|10.246***|4.806***|4.783***|
||(0.124)|(0.127)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011, and the Sri Lankan 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. _Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two. and three asterisks respectively.

The grand mean effects of the industries are calculated; post-MFA is a dummy equal to 1 for years 2005 and later; additional controls include age squared, industry, and occupation dummies. Occupation dummies are according to 1-digit ISCO-08; the omitted category is managers. Industry dummies include 15 categories: Agriculture and Forestry; Mining; Food, Beverage, and Tobacco; Textile and Apparel; Wood; Other Manufacturing; Utilities; Construction; Sales; Transport; Financial, Insurance, and Real Estate; Public Administration; Social Services; and Other Services. Log wages are measured as logarithm of real monthly individual labor income in 2005 prices. The omitted time category in cols (1) and (3) is 2004 and earlier; the omitted time category in cols (2) and (4) is 1998 and earlier.
Figure 15: Lower income countries, tend to benefit the most in terms of competitive‐adjusted margins <!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>Low Lower‐middle Upper‐middle High<br>AFG<br>LSO<br>NPL<br>HTI<br>GRL<br>MUS<br>SWZ<br>BTN<br>HND FJI<br>SEN SLV<br>BEN BGD LCA KHM JOR SYR BLR GMB<br>BRB GTM GRD TGO KEN MWI NIC<br>VCT TCAFROMHLCHNBLZJPNLKAMNGURYPRYINDGINSOMNCLGUMABWBRAPYFHKGMNPTJKSRBRUSOASPLWNZLTONBHSMACIRNSMRGABNGAANDNRUBMUSAUQATNORLBYDJIVENVGBTKMKWTUKRSTPCYMIRQCOGSSDPAKCUWSXMGUYAGOECUPNGTCDFINAZEKAZKORCHESWEAUSTUVDZAGNQGBRARGTUNUSASLEMNEDEUAREESTRWAAUTZAFITABDICIVISRLUXFRASLBMLTDNKLVAGHABWACOMHRVBRNLBRTTOPANLTUMRTOMNATGMYSSURUZBBELHUNPOLDMAERISVNYEMVNMBGRJAMTURCZENLDIRLSVKNERGRCMLICMRROUCAFZMBTHACANFSMESPCRIPHLCYPCOLPRTMKDSGPKGZALBIDNISLKNAPERETHWSMVUTEGYBFANAMBHRZWEMOZCHLLAOSYCMARBOLLBNUGACPVDOMPSEMDAGEO MEX ARM BIH TZA MDG MMR<br>GIB<br>KIR<br>PRK<br>Competition‐adjusted preference margin (received by country, %) MDV<br>ASM<br>CUB<br><!-- End of picture text --> Source: Authors’ calculation using ITC/World Bank database 17
particularly hard, due to its reliance on foreign working capital and imported intermediate inputs. As the formal sector contracts, it sheds workers to the informal sector, and these individuals experience a sharp drop in wages. In fact, it appears this effect is overly strong, as the authors find that their microsimulation model overstates the poverty increase following the Asian crisis by a considerable margin when they conduct an historical simulation. This suggests the need for a better understanding of the wage gap between these two sectors, and what determines the movement in and out of formal sector employment.

**_Characterizing Consumption Behavior:_** There is considerable variation in the approaches researchers have taken to characterizing consumer behavior in their analyses of trade and poverty. The general equilibrium assessments all involve the specification of a utility or expenditure function which characterizes preferences globally (not just locally). These demand systems are usually “calibrated” so that preferences vary by household. For example, in their study of poverty in Brazil, Harrison, Rutherford, Tarr, and Gurgel (2003), calibrate Cobb Douglas preferences to each of ten rural and ten urban households.  Since the expenditure patterns differ across all these households, so too do the preferences.  Since this demand system holds consumer expenditure shares constant, it is essentially equivalent to the approach of Chen and Ravallion, from a spending point of view.

Decaluwé et al. (1999) take a slightly different approach, by identifying a subsistence level of consumption that is associated with the poverty line in a particular developing country. They utilize the more general, Linear Expenditure System (LES), which permits them to incorporate the subsistence quantities directly into the household demand structure. The other parameters must be calibrated for each different household group. This gives them an endogenous poverty line, which is a determined by the inner product of the subsistence quantities and prices.

Hertel et al. (2004b) utilize a generalization of the LES demand system, AIDADS, which has been shown to perform well in predicting expenditure patterns across the income spectrum (Cranfield et al., 2002). They estimate this demand system using a combination of international cross-section data and household survey data from individual countries. The estimated demand system is then adjusted (calibrated) to exactly replicate the distribution of total expenditure and per capita spending by commodity in each individual country before proceeding with the policy analysis. The authors then proceed to use this demand system to predict the consumption impacts across households at different 27
in the census for 1,440 communities in Chiapas. Finally, we match information about school closures from the school census in the same year (2019).

This set of educational outcomes is particularly conducive for our analysis. Secondary school is a critical period for the educational outcomes of the disadvantaged population under study, as more then a quarter of the 12 to 14 year olds in Chiapas are out of school. Likewise, 13 percent of school-aged children are still illiterate. The year of the data collection in the census (2019, two full school years after the rollout of the _Plus_ modality at scale) is consistent with the length of exposure to the API program in the second experiment. These census-based outcomes cover the quasi-universe of the localities in Mexico and, unlike other survey-based or administrative test score measures, they are not subject to any censoring during the data collection due to school closures. This allows us to avoid the concerns about selection bias due to differential school closures induced by the program at scale (see Section 4.3).

We analyze the impact of the policy implementation of the program using the following linear regression model: where _Yj_ is a school-level outcome for school _j_ , while _Plusj_ takes a value of one if school _j_ receives a mentor during the government implementation of the _Plus_ modality, and zero otherwise. The vector **_X_** _j_<sup>_Criteria_</sup> consists of all the criteria used for the assignment of the program. The parameter of interest, _α_ 1, represents the effect of the program during the government implementation on the outcome of interest. As was the case for the exposure analysis discussed in the previous section and formalized by equations (4)-(5), to causally interpret the estimates we need the assignment of the program across communities to be _conditionally_ as good as random. In other words, conditional on the assignment criteria, schools that receive and do not receive the program at scale are similar in terms of unobserved characteristics. As before, we run some placebo tests to bolster the credibility of this identification assumption in our setting. Table B-13 shows the results. The 2017 government assignment is not unconditionally random (odd columns of the table), as priority is given to more disadvantaged communities. Instead, when we control for the vector **_X_** _j_<sup>_Criteria_</sup> , the estimated coefficients displayed in the even columns of Table B-13 are very small and statistically insignificant.

Panel A of Figure 4 shows the results for secondary school enrollment after two years from 36
land tends to be of lower quality than owner-cultivated land; i.e., E [ηci | sci = 1] < 0.<sup>6</sup> Thus, just as in the previous paragraph, ignoring this form of selection bias when it is present would lead us to understate the productivity of share-tenancy vis à vis ownercultivation. Importantly, this means that a failure to fi nd a negative γ using a household fi xed effects estimator cannot be due to adverse selection, since adverse selection can only make γ appear more negative. In other words, our estimate of the disincentive effects of share-tenancy is (at worst) an upper bound.

# 2.5 Productivity results

We focus on production of the fi ve major crops: wheat, rice, cotton, sugarcane, and maize. Wheat is the principal crop cultivated in the rabi season (November-May), whereas rice, cotton, and maize are grown in the kharif season (May-November). Sugarcane is grown year-round, but is usually planted in kharif. Land devoted to fodder and a number of minor crops is excluded from consideration, as these outputs are difficult to measure accurately. In the IFPRI-93 sample, the fi ve major crops account for 66% of cultivated area (71% of sharecropped area); the corresponding fi gure for the nationally representative PRHS-01 sample is 80% (83%). Yield is de fi ned as the value of output from these fi ve crops evaluated at median prices for that year divided by area planted to these crops.<sup>7</sup> For ease of presentation of the yield results in Table 2, the dependent variable is scaled (see Appendix) so that γ is interpretable as a percentage deviation relative to ownercultivators/ fi xed renters.

Recall that the IFPRI-93 and PRHS-01 samples partly overlap to form a panel. For our purposes, this overlap is not large given the criterion that households cultivate at least one of the fi ve major crops in both survey rounds. In our fi nal sample, 16% of the households appear both in 1993 and 2001, contributing 29% of the plots. With such panel data, there are two ways to implement the household fi xed effects procedure described in section 2.4: either group plot-level observations by household irrespective of survey year (household basis), or group plots of a given household separately by survey year (household-year basis). The latter speci fi cation is less restrictive in that it allows

> 6 While it is true that plots taken on fi xed rent are also subject to adverse selection, only 5% of our estimation sample consists of renter-cum-sharecroppers. So, for the most part, we are comparing sharecropped to owner-cultivated plots.

> 7 A small number of observations (27 in all) were dropped because of probable coding errors that generated excessive yields. Including these observations has a negligible impact on the results, but does raise the standard errors on the coefficient of interest by about 6%.

6
# **Figure A.6 Educational Enrollment Rates of Teens and Young Adults by Residential Registration (** **_Hukou_ ) Evidence from the Population Census**

<!-- Start of picture text --> Rural Men Urban Men<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>14 15 16 17 18 19 20 21 22 23 24 25 14 15 16 17 18 19 20 21 22 23 24 25<br>Age Age<br>1990 2000 2005 1990 2000 2005<br>Rural Women Urban Women<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>14 15 16 17 18 19 20 21 22 23 24 25 14 15 16 17 18 19 20 21 22 23 24 25<br>Age Age<br>1990 2000 2005 1990 2000 2005<br><!-- End of picture text --> Note: Enrollment may be in urban or rural areas, these rates are calculated as share of registered rural and urban population enrolled regardless of current residence location. Source: 1990 and 2000 Population Census and 2005 Population Sample, National Bureau of Statistics (Beijing).

62
11

**Figure 2-3.  Not all absenteeism is illegal; teachers may show up but not teach when expected**

_Source: author_ <!-- Start of picture text --> • Source: Barbara Bruns<br><!-- End of picture text --> _Why are teachers absent?_ Prominent stated reasons include teacher participation in educational workshops, in-service training sometimes lasting for several weeks,<sup>50</sup> personal problems, traveling to administrative centers for salary compensation, and casual leave.<sup>51</sup> Pregnancy and housework may affect female teachers.<sup>52</sup> Illness in general and HIV/AIDS in particular have become major causes of absenteeism in certain countries.<sup>53</sup> Some research links poor physical school conditions to absenteeism.<sup>54</sup> Another aspect of absenteeism is refusal to take up postings in desirable locations. For example, in Ghana, a significant number of newly trained teachers every year refuse posting to rural schools, and many of those who accept eventually will ask for a transfer to urban schools or quit teaching.<sup>55</sup> In some countries teacher appointments are given as political favors, and appointees may not be expected to teach.

However, the stated causes for teacher absenteeism often make the fact that the institutional environment does not really require teachers’ constant presence.<sup>56</sup> At the root are limited authority by schools, lack of interest in enforcing sanctions, and the lack of parental involvement.<sup>57</sup> Absenteeism is often lower among female teachers, among teachers born in the district where the school is located, and among teachers of children whose parents are more literate.<sup>58</sup> However, few studies have explicitly explored the linkages between accountability and absenteeism. Longitudinal research is needed to establish cause-effect relationships and ferret out the conditions and mechanisms that inhibit or promote teacher absenteeism.

50. Fairhurst et al. 1999; Dia 2003 51. EARC 2003 52. El-Sanabiy 1989 53. World Bank 2002 54. OED 2004.

55. EARC 2003; the study reported teacher attrition, 12.5% of the overall school staff. 56. Rogers et al. 2004 57. King and Ozler 2001; Lockheed and Verspoor 1992 58. Chaudhury et al. 2005, OED 2004; Lewis and Lockheed 2006.
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World Bank. 2022a. “Angola Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2022b. “Cameroon Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2022c. “Türkiye Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2022d. “G5 Sahel Region Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2022e. “Vietnam Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2022f. “Nepal Country Climate and Development Report.” World Bank, Washington, DC.

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World Bank. 2022i. “China Country Climate and Development Report.” World Bank, Washington, DC.

22
|Decision number|||(12.99)|||(12.99)|(18.81)|
|---|---|---|---|---|---|---|---|
|Treatment X||||-5.19||19.9|19.9|
|Risk preference||||(119.9)||(119.5)|(119.5)|
|Treatment X|||||-1.967*|-1.988*|-0.570|
|Religious preference|||||(1.10)|(1.11)|(1.43)|
|Religious preference X|||||||0.244**|
|Decision number|||||||(0.11)|
|Treatment X|||||||-0.218|
|Religious preference X Dec|ision number||||||(0.14)|
|Constant|3145.4***|2626.2***|2529.9***|2619.7***|2606.0***|2534.5***|2663.5***|
||(82.44)|(948.50)|(949.40)|(964.70)|(939.20)|(956.00)|(957.70)|
|Within R2|0.371|0.371|0.373|0.371|0.371|0.373|0.376|
|Between R2|0.014|0.108|0.108|0.108|0.133|0.133|0.133|
|Overall R2|0.292|0.313|0.315|0.313|0.318|0.320|0.322|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. The baseline is where the fund with fixed returns and risk is the traditional fund, the treatment is where the fund with fixed returns and risk is the socially responsible fund. * 10%, ** 5%, *** 1% significance level.