rafmacalaba/datause-encoder-data huggingface dataset:
”Human capital accumulation takes place in school, in research organizations and in the course of producing goods and engaging in trade.” Robert Lucas (1993)

# 1 Introduction

Theory identi...es the e¤ect of trade on on-the-job human capital accumulation as an important channel through which trade may enhance economic growth. But whether trade increases or decreases on-the-job human capital accumulation is ambiguous from a theoretical point of view. Trade may foster the acquisition of human capital by facilitating the transfer of ideas and technology from technologically more advanced countries to less advanced economies. Technology transfer a¤ects human capital accumulation through two channels. First, implementing and working with a new technology increases the knowledge of workers. On-the-job learning is hence a by-product of trade. Second, trade may lead to an increase in wages of skilled relative to unskilled workers inducing workers to invest more in human capital (e.g. Hall and Jones (1999), Pissarides (1997) and Goh and Olivier (2002)).

Opening up to trade may also theoretically reduce on-the-job human capital accumulation. If some productive activities carry a higher rate of skill acquisition than others, moving from autarchy to free trade may depress learning-by-doing. This happens if trade induces countries to import high-quality goods rather than to produce them (Stokey (1991) and Young (1991)).<sup>1</sup> Approaching the question whether trade increases of decreases on-thejob human capital accumulation empirically requires a measure of on-the-job accumulation of human capital, such as the return to experience. Crosscountry data on returns to experience is not well suited to estimate the e¤ect of trade on human capital accumulation since the return to experience is determined by the price of on-the-job human capital, as well as its quantity and quality. The identi...cation of cross-country di¤erences in human capital accumulation requires to hold this price constant across countries. But the price of on-the-job human capital is a function of country-speci...c variables such as technology, supply of human capital, labor market institutions and governmental quality.

If all these country-speci...c variables were constant across time, panel data would be a solution to this problem. But panel data on cross-country 1 Routine or traditional production techniques, for example, are likely to be associated with less learning than more complex, technology-intensive tasks (Lucas (1988, 1993)).

1
Table 7. Average Characteristics for Tea and Non-Tea Households

|Characteristics|Tea|Non-Tea|ALL|
|---|---|---|---|
|Age|44.99|46.30|45.68|
|Education (years)|2.89|2.43|2.65|
|Male|0.75|0.67|0.71|
|Household Size|5.19|4.84|5.01|
|Land (ares)|20.37|0.02|9.83|
|Livestock|3.49|3.01|3.25|
|Bicycle|0.07|0.04|0.05|
|Road30|0.24|0.22|0.23|
|Water30|0.28|0.24|0.26|
|Sample Size|986|1053|2039|

Source: Authors’ calculations Table 7 presents some average characteristics for 986 tea households and 1053 non-tea households. The most striking difference between these two groups relates to land ownership. Average landholding among tea households is more than a thousand times the average for non tea households. As one would expect, land ownership is a key determinant of participation in the tea sector. This fact is confirmed by the estimation results of a logit model of participation presented in table 8. In this model, land ownership has a very high level of statistical significance. Beyond land ownership, these results also indicate that gender (i.e being male) and years of education have a significant and positive impact on the likelihood that a household is engaged in the tea sector.

Table 8. A Model of Participation in the Tea Sector

|Variable|Coefficient|Std.Error|z-Statistic|Prob.|
|---|---|---|---|---|
|Constant|0.277728|0.531392|0.522642|0.6012|
|Age|-0.063520|0.025603|-2.480920|0.0131|
|Age Squared|0.000333|0.000265|1.253871|0.2099|
|Years of Education|0.121048|0.049172|2.461716|0.0138|
|Years of Education Squared|-0.016652|0.005332|-3.122850|0.0018|
|Male|0.513665|0.160782|3.194791|0.0014|
|Household Size|0.069233|0.039357|1.759115|0.0786|
|Land Area|1.413235|0.178340|7.924366|0.0000|
|Livestock|-0.029024|0.019176|-1.513571|0.1301|
|Bicycle|0.472874|0.294378|1.606347|0.1082|

Source: Authors’ calculations 18
them to identify the size of income losses for each type of household. Essentially, each household, given their probability of income loss, gets a fraction of the total sectoral growth rate in national accounts.<sup>9</sup> Using the sizes of income changes for each household estimated by Mahler et al. (2022), we can use the same extrapolation method as in Section 2 to estimate poverty in 2020. Furthermore, as we did in Section 2, we report (i) a scenario influenced by COVID-19 (“with COVID”) and (ii) a scenario not influenced by COVID-19 (“without COVID”). We also report the net impact of COVID-19, which is the difference between the with COVID and without COVID scenarios. We use this methodology for 20 countries in Africa to estimate poverty changes in 2020.

Figure 6 reports the share of households in rural and urban areas that reported a loss of income or consumption in 2020. For these 20 countries, an average of close to 52% of urban and 50% of rural households reported having experienced an income loss.<sup>10</sup> The fact that losses of urban households were on average 2 percentage points larger than losses for rural households provides further support to the wider cross-country literature suggesting that urban workers and those in the informal sector and small businesses were more affected than other types of workers (Bundervoet et al., 2022; Josephson et al., 2021). Yonzan et al. (2022), who study income changes for 34 countries in various regions across the globe, find that the urban poor (i.e., the bottom 40% of the urban income distribution) incurred the largest income losses due to the pandemic. This average, however, masks considerable heterogeneities across countries and within countries.

As shown in Figure 6, the gap between the share of urban and rural households experiencing negative income shocks varies across the 20 countries. For instance, 70% of urban households in Zambia experienced a loss in income, which is 24 percentage points larger than the share for rural households. On the other hand, some countries had more rural households negatively affected by the pandemic than urban households. For instance, 75% of rural household and 63% of urban households in Nigeria reported income losses in 2020. For most countries, however, the share of households with income losses in rural and urban areas were relatively close.

> 9 Sectoral growth rates are available in the World Bank’s Macro and Poverty Outlook. Agriculture sector growth is distributed to the households in rural areas, industry sector growth to households in urban areas, and service sector growth is distributed to rural and urban areas depending on the share of income generated by each area. For methodological detail, in addition to Mahler et al. (2022), see Narayan et al. (2022) and World Bank (2022).

> 10 In what follows, we will refer to countries reporting either income or consumption loss as income loss.

15
The next panel of Table 4 reports our predictions for the net foreign asset position based on the calibrated model of Section 3. In the first two scenarios we obtain net foreign asset positions of -5 percent and -9 percent of wealth, respectively. Increases in China's relative wealth lead to more positive net foreign assets, while increases in China's relative productivity lead to more negative net foreign assets. In the calibrations the increases in wealth dominate, so that the predicted net foreign asset positions of -5 and -9 percent of wealth are substantially more positive than the calibrations for the present of -17 percent of wealth that we saw in Section 3. In the third scenario the predicted net foreign asset position is much less negative at -3 percent of wealth, thanks to our assumption of no relative productivity growth. We finally generate predictions for the average current account deficit consistent with this future net foreign asset position. We make no attempt to seriously model the process of adjustment to this new net foreign asset position. Rather we simply assume that the foreign asset share changes linearly over time from its current value of nearly zero to its predicted value. Then for each period we can compute the change in net foreign assets (i.e. the current account, since we have no valuation adjustments), and express it as a share of GDP at market prices. We use this denominator at market exchange rates so that the resulting figures are a bit more familiar. We find that China would have to run substantial, but not unreasonable, current account deficits ranging from -2 percent of GDP in the third scenario, to -5 percent of GDP in the second scenario, in order to attain our predicted net foreign asset positions. Our results based on the non-structural empirical exercise are reported in the bottom panel of Table 4, and are not very different from those based on the calibration exercise. We obtain predicted net foreign asset positions of -3, -6, and -1 percent of wealth in the three scenarios, and the associated current account deficits are somewhat smaller than in the middle panel of the table.

While at first glance these current account deficits may seem large given our quite modest predictions for China's future net foreign asset position, it is important to remember that this predicted foreign asset share is applied to per capita wealth measured in international prices. This per capita wealth is very large relative to GDP at market prices, reflecting first China's low PPP exchange rate of about 0.25 in the Penn World Tables, and in addition the fact that projected wealth is between 1.5 and 2 times GDP at PPP. Thus a net foreign asset position of -5 percent of wealth in PPP terms 31
|Science score|0.541***|0.548***|0.00|
|---|---|---|---|
||(0.141)|(0.168)|[0.970]|
||561|565||
|Percent of correct answers|0.108***|0.097***|0.13|
||(0.024)|(0.032)|[0.720]|
||561|565||
|Percent of incorrect answers|-0.071***|-0.048**|1.02|
||(0.016)|(0.021)|[0.313]|
||561|565||
|Percent of omitted answers|-0.045**|-0.066***|0.81|
||(0.021)|(0.023)|[0.367]|
||561|565||

Note: ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses clustered at school level. Controls: (i) students characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his home), (ii) teacher characteristics (gender, age, years of experience, if she/he has post-graduate certificates), and (iii) school characteristics (school size, 7<sup>th</sup> size, 7<sup>th</sup> repetition rate, FEPBA score in Language, school district –or location). The number of observations for regression for the sub-sample of students with low skills (high skills) in column (1) is 544 (561), in column 2 is 545 (565), and in column (3) is 543 (584). All the regressions exclude the classrooms where the Human Body Unit was not taught. Author’s analysis based on experimental data generated for this study **.** 26
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or _m_ = 3: We can then obtain an estimate of the inverse elasticity of marginal surplus, which can be written as: We show the estimation results in Appendix Table N16. Plugging in the mean of _lnPit_ and assuming a market conduct parameter of 0.2 and 0.5 for eligible and ineligible goods, respectively, we calculate a unit-subsidy pass-through rate of 0.49 and 0.15, respectively.

# **M Multi-Product Pricing Models**

## **M.1 A Simple Example**

We first illustrate using the simple example in Chen and Rey (2012) how we can generate the following result: as the valuation of one product increases, the price of the other product increases. We then extend this to the baseline model in the next section. We follow their setup and assume that there are two types of retailers. The first retailer is a large firm _L_ with a broader product range that monopolizes product _A_ and also sells product _B_ . The second type of retailers are a fringe of small firms _S_ that only sell product _B_ in the competitive market. Consumers have unit inelastic demand for both products _A_ and _B_ to rule out cross-subsidy motives based on demand elasticity differences. Consumers incur a shopping cost _s_ for visiting a store. Applied to our setting, product _A_ is a SNAP-ineligible good (non-food) whereas product _B_ is a SNAP-eligible good (food). Almost all retail stores in our data sell both food and non-food and belong to retail chains so they are considered as “large” firms. Small firms may be small discount grocers that specialize in selling food only.

A numerical example can illustrate the intuition. Suppose that _L_ can supply _A_ at no cost and _B_ at unit cost _c_ = 4, while consumers value _A_ at _uA_ = 10 and _B_ at _uB_ = 6. Suppose half of the consumers face shopping cost _s_ = 4 and half shop at no cost. If _L_ were alone, it would supply all consumers at a total price (slightly below) _p_<sup>_m_</sup> = _uA_ + _uB − s_ = 12, yielding a monopoly profit Π<sup>_m_</sup> = 12 _−_ 4 = 8: _L_ would thus extract all surplus from high-cost consumers but leave the others with a surplus of 4.

Suppose now that _B_ is also offered by a competitive fringe _S_ at a price _p_ ˆ = 2. _S_ cannot ˆ attract high-cost consumers, who can only obtain _uB − p − s_ = 0; _L_ can therefore still charge them at a total price of _p_<sup>_m_</sup> , e.g. _pB_ = 4 and _pA_ = 8. _L_ would then sell _A_ only to low-cost consumers who become “multi-stop shoppers” and yet obtain the monopoly margin on both types of consumers: _p_<sup>_m_</sup> _− c_ = _pA_ = 8. However, the presence of small rivals opens a door for screening consumers according to their shopping costs, which is best achieved by selling _B_ below cost; keeping the total price equal to _p_<sup>_m_</sup> , lowering the price for _B_ down to _p_ ˆ = 2, and increasing the price for _A_ to _p_ ˆ _A_ = 10. This does not affect the shopping behavior of high-cost consumers who still face a total price of _p_<sup>_m_</sup> but increases the margin earned on multi-stop 66
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Second, the question of whether the objective of increasing the proportion of students enrolled at SMK can be achieved will largely depend on the demand-side/household responses to supply-side policies. Expanding the SMK infrastructure will not automatically lead to increased enrollment. Household choice on the matter of whether to participate in senior secondary education and which track to participate in will also reflect differentiated preferences and expected outcomes resulting from these choices. As the economy grows; as opportunities to participate in tertiary education expand; and as the demand for a higher level of knowledge and skills in the labor market increases, the issue of whether to participate in vocational or general academic secondary level education will also evolve.

Thus, in order to determine whether striving to increase the proportion of students enrolled at SMK is an appropriate goal for the sector, this paper attempts to answer the question of how SMK graduates fare after high school compared with SMA graduates, taking into account the complexities mentioned above. Particular attention will be paid to the two key weaknesses of the existing empirical work in this area: the uncorrected data censoring due to the unconsidered impact of participation in tertiary education, and the endogeneity of SMKSMA choice. The differentiated outcomes of SMA and SMK graduates will be measured by: probability of employment; wage rate; probability of entering college; and value added in cognitive skills or academic achievement.

# **III. Data**

The longitudinal “Indonesia Family Life Survey” (IFLS) provides a unique opportunity to explore these issues. This paper follows a cohort of 958 high school students from the 1997 survey (IFLS2) and looks into their labor market and schooling outcomes in 2000 (IFLS3)<sup>3</sup> . Table 1 depicts the distribution of activities of the 1997 cohort of high-school students in 2000. In 1997, around 40% of the high school students were enrolled in SMK, while the remainder was enrolled in SMA. In 2000, of those who did not go on to tertiary education and who were unemployed, 57% were SMA graduates and 43% were SMK graduates.

> 3 Once the IFLS4 (2008) is released to the public in early 2009, a third observation point can be added to the analysis.

8
**Table 3: Balance across Treatments**

||Sequence vs.<br>Control|Coaches vs.<br>Control|Coaches vs.<br>Sequence|
|---|---|---|---|
|_Student-level variables_||||
|Percent female|-0.03|-0.02|0.01|
|Age|0.06**|0.01|-0.04|
|<br>Percent of Argentines|0.01|0.00|-0.01|
|<br>Mother or father education|-0.01|-0.04|-0.03|
|(secondary)||||
|Have internet in their home|-0.03|-0.02|0.00|
|Have air conditioning in their home|0.00|-0.02|-0.02|
|Have at least one car in their home|0.02|-0.02|-0.04|
|At most, missed one class per|-0.02|-0.02|-0.01|
|month||||
|_Teacher-level variables_||||
|Percent female|0.04|0.04|0.00|
|Age|3.06|3.16|0.11|
|Percent with Post-Graduate|0.15|0.10|-0.05|
|Certificate||||
|Percent with University degree|0.13|0.11|0.10|
|Seniority in teaching (in years)|2.26|1.84|-0.42|
|Seniority in teaching Science (in|1.23|0.94|-0.29|
|years)||||
|<br>Percent of teachers that participated|0.05|0.08|-0.14*|
|in trainings||||
|Percent of teachers that used a|0.15|0.07|0.09|
|teaching sequence||||
|<br>_School-level variables_||||
|Students per school|-26.98|-19.72|7.26|
|<br>Students of 7th grade|-5.38|-3.42|1.96|
|<br>School promotion rate (%)|-0.01|0.00|0.01|
|<br>School drop-out rate (%)|0.00|0.00|0.00|
|<br>School over-aged rate (%)|0.01|0.00|-0.01|
|<br>7th student’s repetition rate (%)|0.02|0.00|-0.02|
|FEPBA score in Language|0.83|2.63|1.80|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ Each entry indicates the mean difference between the two experimental groups in the column for the corresponding variable in each line. * indicates that the difference of means test is significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

1
headcount index could be reduced by the same amount with a budget which is 159 million to 361 million soles smaller; the poverty gap could be reduced by the same amount with a budget which is between 288 million and 353 million soles smaller; while the P2 measure, finally, could be reduced by the same amount with a budget which is 309 to 397 million soles smaller. Clearly, substantial savings can be achieved with geographic targeting in Peru.

## _Non-parametric density estimates_

Figure 3. 1, finally, graphs the non-parametric (kernel) density estimates of the log of PCE _after_ hypothetical transfers totaling 2.2 billion soles have been made to households in the 1997 LSMS. I do not show the comparable picture for 1994 because it is very similar indeed. To avoid cluttering the picture, and because outcomes are so similar across indicators, I present density estimates for the FONCODES index and the no targeting scenario only. A vertical line which corresponds to the log of PCE at the poverty line is also included. Figure 3.1 clearly shows that the left tail of the distribution for the no targeting scenario has more mass than the corresponding distribution for the FONCODES index: that is, the fraction of people with very low levels of income would be higher had transfers to districts been based simply on population rather than on the FONCODES index. By contrast, more mass is concentrated around the middle of the distribution when transfers are made using the FONCODES index. The cumulative density functions of log PCE (not presented, but available from the author upon request) show that the distribution corresponding to the FONCODES index first-order stochastically dominates the no targeting distribution up to the poverty line (the two distributions intersect at about the poverty line).

# **5. Conclusion**

Geographic targeting of social programs has become increasingly important in Peru. Expenditures in basic health, primary education, nutrition programs, and basic justice conducted by the regular line ministries through the Basic Social Expenditure Improvement Plan have generally been directed towards the poorest departments. The Municipal Compensation Fund, the main block grant from the central governmen **t** o local governments in Peru, is allocated with a formula which takes into account the population, infant mortality rate, and proportion rural in each district. FONCODES has used its own poverty maps since 1992, and other programs in the Ministry of the Presidency have been encouraged to prioritize investments in a sub-sample of districts.

In this context, it is important to see how well different targeting indicators perform in practice. This paper evaluates alternative indicators for geographic targeting available at the 13