rafmacalaba/datause-encoder-data huggingface dataset:
do not display any significant difference in earnings from the reference group, with the exception of the 30-34 age cohort, which earns less.

# Costs and benefits of vocational schooling

The regression analyses show that, given the higher earnings accruing to vocational school completers working in matched occupations (i.e. occupations related to course of study), overall, terminal vocational secondary education yields higher monetary berefits than general academX. education. It was shown in Table 5 that for the financial year 1982/83, which most closely relates to the year of the Census, vocational schooling costs per student were on average 8 per cent below those of academic schools. In overall terms these relative cost estimates, together with the regression results on the benefits side, point to a benefit-cost advantage of terminal vocational over academic schooling in Israel.

However, we have noted the widely held view in Israel that vocational education is the more costly, by up to a third.<sup>23</sup> / If this were indeed so, then it might be the case that the higher costs of vocational education would offset the benefits, so that the net present value (NPV) of vocational schooling over acedemic schooling would be negative. In order to test this, we subjected our overall results to a series of sensitivity tests, by experimenting with alternative values of the parameters in the following equation: 23 This view was expressed in interviews with relevant government officials.

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“People’s perception of illness varies with what they are used to, and also with their medical knowledge. In places where medical care is widespread and good, people often have higher perception of morbidity, even though they may be in much better general health. ...In contrast, a population that has little experience of medical care, and which has widespread health problems as a standard condition of existence, can have very low perception of being medically ill.” (Sen, 1998, p.18) A number of studies have emphasized the potential for reporting errors (Butler, et al., 1987; Kerkhofs and Lindboom, 1995; Groot, 2000). The work of Butler et al. (1987) should warn users of _SAH_ data against assuming that the measurement errors (relative to objective health status) are uncorrelated with the socioeconomic characteristics of respondents. Groot’s (2000) results are suggestive of psychological adaptation to declining health. Adaptation has also been used to explain the gap found between the _SAH_ of sick people and those who are asked to rate their health as if they were sick (Menzel et al., 2002). Recognizing the potential for reporting biases, Thomas and Frankenberg (2000) recommend that surveys should combine questions on _SAH_ with more objective indicators of health status.

What does all this mean for estimates of the economic gradient based on _SAH_ data? Even if we accept that _SAH_ contains health-relevant information, adaptive behaviors to poor health and correlated differences in economic circumstances and health knowledge can generate non<u>ignorable measurement errors, by which we mean errors that are correlated with income or other</u> socioeconomic controls such as education. For example, poorer people may tend to think they are healthier than they really are, such that standard methods of regressing _SAH_ on income will underestimate the true economic gradient in health.

There are also longstanding concerns about how well current incomes reveal the true gradient in health. Even using high quality surveys (in which an income aggregate is built up from many detailed questions), current incomes are unlikely to reflect well either past or expected future incomes, which are likely to matter to current health. Expenditure on current 3
Table 2: Land and labor market participation rates by initial land–labor ratios

||All|Landless 0|.1-0.6 ha/w 0|.6-1.6 ha/w|1.6-3.0 ha/w 3.0 ha/w|
|---|---|---|---|---|---|
|_Land transfers:_||||||
|Participates|45.2|36.1|42.4|41.1|53.8<br>56.5|
|Land sender|21.0|2.0|14.9|19.0|30.1<br>40.5|
|Land receiver|28.9|35.7|33.3|26.2|30.5<br>21.4|
|_Labor transfers:_||||||
|Participates|54.4|42.5|49.8|56.6|64.3<br>57.5|
|Labor sender|36.9|30.6|34.1|39.5|43.6<br>34.7|
|Labor receiver|32.3|23.8|27.5|30.6|41.0<br>39.8|
|Observations|1,625|294|255|516|266<br>294|

Note: ha/w corresponds to the number of hectares per working adult. Source: Author’s own calculations on the Gambia Networks Data 2009.

of secondary land rights serve to reduce differences in land-labor ratios across households ex ante, so as to obviate the need for ex post adjustment through labor transactions. As a result, the primary role of labor transactions is to deal with shocks in household manpower availability, e.g., through illness.

A cursory look at the data reveals that, as anticipated, households with a higher initial land–labor ratio are more likely to transfer-out land and less likely to transfer-in land. The same pattern is not evident for labor: land abundant households are not more likely to transfer in labor, suggesting that labor transfers do not serve to equalize land-labor ratios across households. For these reasons, the empirical analysis is largely focused on transfers of secondary land rights.

# **Social proximity**

The focus is on dimensions of social structure that have been widely discussed in the literature. Ethnicity is included because ethnic fractionalization has been shown to be associated with aggregate outcomes (e.g., Alesina et al., 2003, 2016) as well as with frictions and discrimination (e.g., Riach and Rich, 2002, and the other references listed in Section 2). We include kinship and family ties because they have long been suspected of being a source of market inefficiency and inequality (e.g., La Ferrara, 2003). Frictions caused by distance have also long been documented (e.g., Ravallion, 1986; Fafchamps and Gubert, 2007). These aspects of social 19
set.<sup>16</sup> Information about gross fixed capital formation was provided directly by the Economic Research Department of the Banco de Guatemala. The data is compiled using the somewhat dated 1953 UN System of National Accounts, which is currently under revision.<sup>17</sup> In line with other studies for Latin America, such as Loayza et al. (2002) and Morales (1998), the overall depreciation rate is assumed at 5 percent. This is still a rather high estimate when compared with more commonly used thumb values.

However, regarding the armed conflict, which has lasted for 36 years, and several periods of high violence in Guatemala, it was found useful to adopt a high depreciation rate in order to account for both capital _destruction_ and _distraction_ from productive use. For example, the latter may have resulted in unprofitable military spending, several forms of non-productive investments, or temporary spare capital because of infrastructure deficiencies. As to be shown in the following section, the results of the regression analyses are not sensitive to moderate adjustments in the depreciation rate. In terms of data availability over a long time period, and given the robustness to alternative assumptions about depreciation, the capital stock series with aggregated investment data is adopted in the later regression analyses.

# **_Estimate with Disaggregated Investment Data_**

Based on the PIM, Morán and Valle (2002) present a second approach for Guatemala. In their model the capital stock is estimated for eight broad asset groups for 19712000. However, presumably because of too high depreciation rates for public and private construction, they seem to underestimate the genuine level of the capital stock.<sup>18</sup> Following their methodology but applying different depreciation rates and taking into account the initial benchmark estimate from Nehru and Dareshwar (1993), a second capital stock series has been calculated with disaggregated investment data for the period 1970-2002.

The initial values are obtained from a pre-estimate starting in 1950. The data gaps for the sectoral composition of the eight assets groups prior to 1970 are filled in by extrapolation techniques. These values, however, do _not_ enter in the later regression or growth accounting exercise. They only provide reasonable initial values for the

> 16 The potential error of the estimate of initial capital stock diminishes over time due to depreciation. Based on international data, Nehru and Dhareshwar (1993) offer an estimate of the capital stock for Guatemala that was taken as a benchmark.

> 17 UNDP (2002) provides a brief summary of the associated empirical consequences and causes that prevented an actualization of the Guatemalan National Accounts.

> 18 In addition, the following results of the quality index for the physical capital stock differ. This may be due to the possibility of an oversight in the logarithmic transformation by Morán and Valle (2002), as was pointed out in a personal communication with Estuardo Morán, Banco de Guatemala, October 15, 2003.

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# Appendix Table A2 Tenure First Work

Dependent: Monthly earning growth Sample: Age started >=15 Tenure first work<4

|Experience|0.0029 -0.0035|-0.0151|
|---|---|---|
||(0.15) (0.16)|(0.63)|
|Experience * years of schooling<br>|0.0048 0.0055<br>|0.0059<br>|
||(2.31) (2.25)|(2.33)|
|Tenure first work|-0.0323 -0.0255||
||(3.46) (1.69)||
|Age started|-0.2080 -0.2056|-0.1947|
||(2.68) (2.50)|(2.37)|
|Age started squared|0.0049 0.0049|0.0045|
||(2.47) (2.31)|(2.12)|
|Female|-0.0203 -0.0213|-0.0256|
||(0.71) (0.71)|(0.79)|
|Asset 2000<br>|<br>|3.09E-08<br>(2.77)|
|Mother/guardian schooling<br>|<br>|0.0037<br>(1.90)|
|Mother/guardian age<br>|<br>|-0.0104<br>(2.01)|
|Province dummies|yes yes|yes|
|Number of observations|1672 1527|1527|
|Number of provinces|49 46|46|
|R squared|0.1127 0.1125|0.1189|

Numbers in parentheses are absolute t values with Huber robust standard errors. Current age is age started first job plus experience (years).

26
<mark>Lanka, tends to be regarded as a ‘men’s crop’ (FAO 2018), high-value and export-oriented crops such as tea feature disproportionately in the crop mix of Sri Lanka’s female farmers. We develop a simple Export Orientation Index to capture the export intensity of a farmer’s crop mix. A smaller plot size and the associated higher land productivity confer a productivity advantage on female farmers, which is reinforced by a higher-value crop mix. Third, we investigate not only the determinants of the productivity gender gap but also those of the agricultural earnings gap. This analysis reveals that the unconditional productivity advantage does not translate into higher incomes for female farmers.</mark> <mark>The remainder of the paper is organized as follows. Section 2 presents the Sri Lankan context for the agriculture sector, including details on production patterns and productivity by the gender of farmers. Section 3 describes the data and reports summary statistics. Section 4 presents the empirical methodology and main results. Section 5 discusses policy implications and concludes.</mark>

# 2. SRI LANKA: AGRICULTURAL PRODUCTIVITY AND GENDER

Sri Lanka has experienced substantial progress in poverty reduction. Between 2012/13 and 2016, the $3.20 poverty rate fell from 16.2% to 11% (World Bank 2021a). However, as agriculture remains an important source of livelihoods for the poor (World Bank 2021c), identifying opportunities to increase productivity and incomes in the sector is a priority for reducing poverty and lifting the living standards in rural areas.

Women’s labor force participation in Sri Lanka is low, at 37.8%, compared to 79.1% for men in 2019 (World Bank 2021d). Agriculture accounted for 23.6% and 27.6% of male and female employment, respectively (World Bank 2021d). Improving the returns to income-generating activities for women in the farm sector is important given that many of them work as unpaid family workers and because women have fewer opportunities to participate in nonfarm activities due to their household and care responsibilities (World Bank 2021c).

For the purpose of our analysis, ‘farmers’ in this study are defined as those who reported a positive gross crop output value in the 2016 HIES. By this definition, there were 1.7 million farmers in 2016, of whom 1.3 million were male farmers and 0.4 million were female farmers.<sup>3</sup>

> 3 These numbers should not be interpreted as representing the gender breakdown of the workforce in the agriculture sector. Because the definition considers only those farmers with a positive crop output, most unpaid family workers who account for a large share of female workers in Sri Lanka are not included. Also, these figures do not include agricultural wage workers who have different profiles from self-employed farmers (World Bank

4
DCFTA that are about 2.5 times larger than the gains estimated in the present study. We explain in appendix I that the larger estimated gains of the CASE study are due to due to a combination of two effects: (i) larger assumed distortions in the CASE study; and (ii) different modeling assumptions. The larger the distortions are, the more gains there are from their removal. As we explain in appendix I, the larger distortions in the CASE study partly reflect the fact that our study was based on estimates of distortions in 2010, while the CASE study uses estimates of distortions from 2006 or 2007. Since Armenia has implemented substantial reforms in the interim, the initial distortions in the CASE study are significantly higher. This is the case with border costs. But it is also due, in some cases, to the fact that we had greater data available to us that allowed a more accurate estimate, for example in the services survey and estimates that we conducted. Further, we assume that the benefits of services commitments in a DCFTA are limited to EU investors, while the CASE study assumes those commitments will be extended multilaterally.

The paper is organized as follows. In section II, we provide an overview of the estimation of the ad valorem equivalents of barriers in Armenian services sectors. We provide an overview of the model in section III and a discussion of the data in section IV. The central results are presented in section V and sensitivity results are presented in section VI. Conclusions are presented in section VII. In appendix A, we discuss the trade and tariff data in some detail. We document the calculation of ownership shares by sector and region in appendix B. How we obtained estimates of the Dixit-Stiglitz elasticities in goods is described in appendix C. Our estimation of the reduction in trade or border costs as a result of a DCFTA is presented in appendix D and our estimate of the reduction in standards costs is presented the section E. The estimates and methodology of the ad valorem equivalents of barriers in services is explained in 7
acquaintance. This discrepancy in identity of the perpetrator by sex of the victim is reflected in the location of the aggressive act. Women are four times more likely than men to admit to an enumerator having been victimized in the home; 45% of acts of violence reported by women took place in their own residence.

Additionally, to improve the quality of our proxy, we limit the sample to women aged 15 to 49. According to Waiselfisz (2012), the fraction of aggressions perpetrated by intimate partners is the highest for this age group. For women beyond the age of 50, while primary aggressor is still a partner or an ex-partner, children are responsible for over 17 percent of aggressions. Though this is still considered domestic violence, our analysis is more appropriate to partner relationships. The same age group was used in the WHO study in 2005 (García-Moreno et al. 2005).

Another advantage of using homicide data is the absence of reporting bias: it is impossible to selfreport (or not) a homicide. Nevertheless, it is indisputable that the chosen metric has multiple limitations. First, it only captures the most violent forms of domestic violence – those resulting in death of the victim. Our paper will not be able to provide any insights on the dynamics of lighter forms of violence. Second, it is a noisy estimate: although it is well established that a large share of female homicides are due to domestic violence, there are no precise time-specific estimates of the magnitude of this share. In spite of these concerns, we find this measure to be superior to the alternatives due to its reliability.

Our data come from Brazil’s national mortality data base SIM (Sistema de Informacoes sobre Mortalidade), a registry of every death in the nation. We consider a death to be a homicide if it is classified as one, or if, in a separate question with more specific delineations of the cause of death as defined by the WHO international classification of diseases, the cause of death is coded to be aggression (the discrepancy is only 5%.) Data on homicides are also collected by individual states’ department of public security, but these are more difficult to access. We were able to perform a small data validation exercise, comparing SIM data with homicides<sup>4</sup> records from Sao Paulo department of public security. Concordance has improved over time, from a 30 percent difference in 1998 to a 4 percent difference in 2005, the most recent year for which the state level data are available. This remaining discrepancy between these numbers and the SIM data base has been recognized as resulting from slight differences in definitions: deaths resulting from legal

> 4 Transit homicides, or traffic accidents, were excluded from both data bases.

9
married, educational dummies, a dummy variable for living in rural areas, as well as geographical regions dummies.

E E E E i 1 i 2 i i (3) ii ii = γγ IIUUIIEEIIUUEEII jjEEjj ssEEssIIssh + γγ XX + γγ + γγ + εε

# **3.2 Impact of Internet job search and digitalization on wage growth**

Apart from examining the effect of Internet job search on employment, I also examine the impact of Internet job search among employed job seekers on their wage growth, as well as the impact of using a computer connected to the Internet at work on earnings growth. First, I estimate equation (4), in which I examine the impact of using Internet job search on the logarithm of the difference in inflation adjusted hourly wage between the years 2018 and 2012, conditional on being employed in 2012. The vector of control variables is equivalent to the vector of control variables described in equation (1). I also estimate the same model examining the impact of using a computer ii,2012 XX connected to the Internet at the workplace on earnings growth controlling for the general use of computers at the workplace and for the same set of covariates included in the regressions. )| E E E E<sup>= δδ</sup> 1<sup>IIUUIIEEIIUUEEII jjEEjj ssEEssIIssh+ δδ</sup> 2<sup>XX+ ωω(4)</sup> ii,2018 ii,2012 ii,2012 ii,2012 ii,2012 ii LLEELL (ww −ww

I also employ a fixed effects model to estimate the effect of using a computer at the workplace and of using a computer connected to the Internet at work on hourly wages, conditional on being employed. I estimate equation (5), in which the dependent variable is the inflation adjusted hourly wage and the main independent variable of interest is a dummy variable indicator for using a computer that is connected to the Internet, while controlling for the general use of computer at the workplace. The vector i includes the same set of time-varying control variables as those reported in equation (3). The regressions also include individual and time fixed effects. Individual fixed XX effects capture all time-invariant unobservable characteristics, while the time fixed effects capture all aggregate factors that likely affect digitalization and labor market outcomes. i 1 i 2 i i (5) ii ii HHEEHHIIE wwssLLEE = θθ CCE EEHHIIEEII ssII wwEEIIww + θθ XX + θθ + θθ + εε

# **4. Results and robustness checks**

# **4.1 Impact of Internet job search on employment and heterogenous effects**

First, I examine the impact of Internet job search on the probability of employment within 5 years following an unemployment spell in 2012. In the ELMPS 2012, individuals were asked about the purposes of their Internet use, which included searching for a job. Internet job search is therefore 10
well-structured tax incentives, and conducting substantial advertising campaigns. By considering these factors, other countries can potentially adapt and apply successful strategies to their unique circumstances, ultimately bolstering their domestic revenue mobilization efforts.

# **References**

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- **Berniell, Inés, Dolores de la Mata, and Matilde Pinto Machado** , “The Impact of a Permanent Income Shock on the Situation of Women in the Household: The Case of a Pension Reform in Argentina,” _Economic Development and Cultural Change_ , 2020, _68_ (4), 1295– 1343.

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21