rafmacalaba/datause-encoder-data huggingface dataset:
their returns from RNFE is not clearly documented, and previous studies seem to oer contrasting accounts. This study investigates the relative position of female-headed households in accessing employment/self-employment in RNFE and the amount of earnings.

We nd that returns from RNFE account for a fairly high share of household income, especially in Uganda where about 50 percent of gross household income originates from o-farm activities. In Ethiopia, only 28 percent of gross household income is from o-farm activities. There are also signicant gender gaps in access to salaried employment and self-employment in RNFE. For instance, only 7 percent of female-headed households in Ethiopia had salaried o-farm employment and 20 percent of them owned a non-farm enterprise, compared respectively to 9 percent and 24 percent of male-headed households. Analysis of industry of employment and selfemployment shows that, in both countries, female-headed households have less access to potentially more rewarding industries such as manufacturing, and transport and communications. In addition, enterprises owned by female-headed households typically operate near/inside the residence, and engage in processing of agricultural outputs instead of non-agricultural businesses. These enterprises were also typically established using own resources as a start-up capital.

Results from the return analysis reveal that female-headed households benet less from engagement in RNFE. Female-headed households in Ethiopia and Uganda earned less gross return than male-headed households. In addition, the major portion of the gender gap in return remains unexplained by dierences in endowments, especially in Ethiopia.

Four direct policy implications are drawn based on the ndings from this research: (1) The analysis presented in this paper shows that there are noticeable gender gaps in access to some industries. These access gaps are likely to translate into return gaps, 28
to the exclusion cost. It is worth mentioning that calculating _ECTB_<sup>undertheobserved</sup> transfer schedule is equivalent to calculating the optimal local reform (based solely on the targeting trade-off). For example, if we observe a ratio greater than 1, _EC_<sup>_<u>TB</u>>_1,this</sup> suggests it is optimal to increase the CCT and decrease the UCT from their observed values. Likewise, if this ratio were less than 1, this suggests it is optimal to decrease the CCT and increase the UCT from their observed values.

Finally, the optimal CCT (based on the targeting grounds alone) satisfies _EC_<sup>_<u>TB</u>_(</sup><sup>_t_</sup> _c_<sup>_∗, tu_(</sup><sup>_t∗_</sup> _c_<sup>)) =</sup> 1, i.e., the targeting benefit just offsets the exclusion cost. In order to determine the optimal CCT, we need to observe income and price effects at all values of the transfer schedule ( _tc, tu_ ), not just at the observed schedule.

# V Estimating Income and Price Effects in Rural Mexico at the time of Progresa

The remainder of the paper will focus on estimating the importance of the targeting benefit in the context of Progresa, a large CCT program in rural Mexico. In this section, we will focus on estimating the quantities necessary to recover curvature of utility - the income and price effect schedules - using evaluation data from Progresa. We will assume that the utility function is CRRA ( _u_ ( _c_ ) = _<u>c</u>_ 1<sup>1</sup> _−_<sup>_−_</sup> _γ_<sup>_γ_)and,therefore,willusetheaverage</sup> ratio of income to price effects to pin down the CRRA coefficient _γ_ (see Equation (8) in Section IV above). First, we will briefly discuss the Progresa program. Second, we will describe key facts about child incomes, child labor supply, and evidence that schooling is a discrete, costly investment that results in lower household consumption. Third, we will discuss our identification strategy, focusing on how we will identify income effects from a pure CCT program. Finally, we will estimate the income and price effects and recover the implied curvature of utility.

## _V.A Progresa: Background_

Progresa was one of the first CCT programs with the objective to alleviate current poverty while at the same increase the human capital in children so as to reduce the transmission of poverty (see Parker and Todd (2018) for a thorough review on the literature studying the Progresa program). The program was set up to transfer cash to poor households under the condition that they invest in the human capital of their children. There were two components of Progresa. The first component consisted of nutritional subsidies paid to mothers who register their children for growth and development checkups, vaccinate their children, and attend courses on hygiene, nutrition and contraception.

21
**Table 25: Mean (standard deviation) of real total returns on diversified portfolios**

|**Per**cent|**Domes**|**tic'**|**Domesti**<br>interna|**c**&<br>tional<sup>2</sup>|MeNo: Col 2<br>**less**Average<br>Earnings|
|---|---|---|---|---|---|
|United States|1.4|<br>(12.1)|1.9|(11.4)|1.7|
|United Kingdom|4.9|<br>(16.4)|4.5|(14.9)|1.9|
|Germany|6.3|<br>(11.2)|5.8|(10.5)|1.8|
|Japan|5.7|<br>(14.2)|4.7|(12.5)|0.7|
|Canada|2.4|(11.9)|3.0|(11.1)|1.3|
|France|3.7|(16.3)|3.8|(14.6)|n/a|
|Netherlands|4.5|(17.4)|4.1|(15.5)|1.1|
|Sweden|3.8|(13.5)|3.7|(12.1)|2.2|
|Switzerland|2.0|(16.5)|2.0|(14.5)|0.1|
|Denmark|5.2|(18.9)|4.5|(16.4)|1.7|

1 S0o domestic equity, 50% domestic bonds. 2 40* domestic equity, 40% domestic bonds, lo% foreign equity, 10% foreign bonds.

Note: International diversification should cause a convergence in ret -ns by increasing below average returns and lowering those above average. This would occur if purchasing power parity held all the time. In fact, large deviations between exchange rates and purchasing power parities may prevent this convergence from materializing, at least in the short to medium run until corrections in exchange rates take place.

32c
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# **Figure 3: Labor force participation, with interaction with citizenship. Males.**

<!-- Start of picture text --> 0<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0.1<br>-0.12<br>Figure 4: Labor force participation, with<br>interaction with citizenship. Females.<br>0.1<br>0.05<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>Whites out citizenWhites out not citizenBlacks inBlacks out citizenBlacks out not citizenAsians inAsians out citizenAsians out not citizenHispanics inHispanics out citizenHispanics not citizen<br>Whites out citizenWhites out not citizenBlacks inBlacks out citizenBlacks out not citizenAsians inAsians out citizenAsians out not citizenHispanics inHispanics out citizenHispanics not citizen<br><!-- End of picture text --> Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The figures display the marginal coefficients and their 95% confidence intervals on interaction terms for racial groups, foreign born status (“in” for natives, “out” for foreign born) and citizenship status in probit regressions with labor force participation as the dependent variable. “White natives” is the omitted dummy. The regression results are further detailed in table A2, columns 1 (males) and 2 (females).

22
8

## **1.2. Steady State**

Regardless of _m_ 0, it is straightforward to show that the distribution of schooling and experience in this environment will converge to a stationary distribution _m_<sup>_∗_</sup> _t_<sup>after</sup><sup>_a_ ̄</sup> periods, where _m_<sup>_∗_</sup> _t_ +1<sup>=</sup><sup>_gbmt_andthedistributionofschooling</sup><sup>_s_andhumancapital</sup><sup>_h_</sup> are constant over time (i.e. the share of the population with a given level of education or experience is the same). The age structure of the model would then be a stationary distribution where the population of each age group rises over time by the factor _gb_ . Alternatively, if we define _μt ≡_ <u>�</u> _<u>mdmt</u> t_<sup>, there exists a unique</sup><sup>_μ∗_such that lim</sup><sup>_t→∞μt_=</sup><sup>_μ∗_,</sup> � _dμt_ = 1 and _pcGDPt_ = � _e_<sup>_hit_</sup> _dμ_<sup>_∗_</sup> after at most ̄ _a_ periods regardless of _μ_ 0. This is because each generation born after date zero has a deterministic composition of _s_ and _p_ , given by the probabilities _πs_ ( _·_ ) and _πe_ ( _·_ ) and the composition of the previous generation.<sup>15</sup> For our experiments, we will assume that the model begins in stationary state _μ_<sup>_∗_</sup> . Then, we subject the model to the pandemic shock. The economy eventually returns to _μ_<sup>_∗_</sup> , once all agents affected by the shock have left the market.

# **2. Calibration**

We calibrate the framework to match certain statistics in a pre-pandemic steady state. We do so for three groups of countries: high-income, middle-income and low-income economies (HIC, MIC and LIC, respectively). The reason for doing so are as follows. It is of interest to identify the impact of the shock on countries at different income levels. However, it is best to do this for groups of countries (rather than for individual countries) as averaging avoids the undue influence of outliers and measurement error.

## **2.1. Calibrating the Steady State**

Calibration requires selecting values for the return functions for schooling and experience _rs_ ( _·_ ) and _re_ ( _·_ ). We also need values for _gb_ and _δ_ ( _·_ ), as well as the age at which 15We do not introduce a growth trend as it is easier to identify and discuss deviations from steady states than deviations from trends. Note that we later assume that, for purposes of measuring welfare effects, agent utility over consumption stream _{ct}_<sup>_∞_</sup> _t_ =0<sup>is</sup> Suppose _ct_ = _c_ ̃ _te_<sup>_gt_</sup> where _g_ is the growth rate and ̃ _ct_ is stationary. Then All that matters are the deviations from trend ̃ _ct_ .
In terms of labor productivity, Argentina’s performance is similar to Chile’s and better than Brazil’s and Mexico’s (figure 3), but falls within the low labor productivity growth characteristic of the last two decades in the region. Argentina’s labor productivity increased by 7 percent between 2007 and 2016 but lagged behind Brazil (9 percent) and Chile (12 percent). These figures suggest that innovation investments, especially in the private sector, are not sufficient and/or effective enough to have an impact on the much-needed productivity growth. In addition, the fact that the ratio of labor productivity to R&D expenditures fell by 40 percent in Argentina but only by 30 percent in Brazil between 2007 and 2016 suggests that there might be room for improving the profitability of expenditures on R&D.

**Figure 3. Labor Productivity, Argentina and Comparators, 2007–16**

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To fully benefit from increases in R&D expenditure and encourage private participation, it is important to identify possible inefficiencies in the role of knowledge in the production function, which might be deterring the contribution of R&D to labor productivity. In fact, when we compare R&D intensity and increases in labor productivity by sector, we do not always find the positive relation we would expect. Some sectors have high levels of R&D intensity but low productivity growth and vice versa (figure 4). In addition, an important share of firms manage to become innovators without committing any investment resources or by doing so very sporadically (Suárez, Lugones, and Moldovan 2008). This suggests that these firms are either taking advantage of their contextual environment—for example, through imitation, or linking to science and technology (S&T) organizations, or by employing skilled personnel who had worked previously in other innovative firms—and/or through the type of innovation that is very incremental and does not require any formal R&D efforts, but only imitation and technological adaptation or adoption.

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POLICY RESEARCH WORKING PAPER 2740

# Summary findings

The economies of Latin America have undergone extensive reforms, raising concerns about how these changes have affected the labor market. But there is also increasing concern that the reforms may have deeper social ramifications as the new economies strain the ability of certain groups of men to work and to earn good wages, fulfilling their traditional role as providers.

Using household surveys broadly covering the period 1988-97 in urban areas of Argentina, Brazil, and Costa Rica, Arias examines the patterns of unemployment and real wage growth for distinct groups of male workers to see whether there is evidence of a deterioration in men's ability to be economically self-sufficient. He finds no general trend of male economic marginalization.

The incidence and duration of unemployment have increased the most for the typically vulnerable groupyoung, less educated, informal sector workers-but the increased duration of unemployment has also affected older and more educated men. With respect to wages, density and quantile regression analysis indicates that the usual stories of wage marginalization of vulnerable workers can hardly explain the observed variety of wage growth patterns in the three countries. The positive wage performance has been concentrated mainly in the higher quantiles of the _conditional_ wage distribution. This suggests that differences in unobservable worker characteristics, such as industriousness, labor market connections, and quality of schooling, have been key determinants of the ability of male workers in the region to adapt to economic restructuring.

These results suggest that assistance should be targeted to some groups so that frustrations in asserting an economic identity do not lead to aggressive behavior. But they also show that we must look elsewhere for the roots of the increase in socially dysfunctional behavior.

This paper-a product of the Gender Sector Unit, Latin America and the Caribbean Region-is part of a larger effort in the region to understand the role of gender in developing country labor markets. Copies of the paper are available free from the World Bank, 1818 H StreetNW, Washington, DC 20433. Please contact Selpha Nyairo, room 18-110, telephone 202473-4635, fax 202-522-0054, email address snyairo@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at omara@iadb.org. December 2001. (42 pages) _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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
The second column indicates that countries outside the _ECA region_ are much less likely to participate in PISA as compared with those in the ECA region, for all regions other than North America, where the odds of participation are slightly higher than in the ECA region; North America, however, includes only three countries (Canada, Jamaica and the United States; Canada and the US participate in PISA). Non-OECD countries (column 5) in regions other than ECA are also less likely to participate than countries in ECA.

**Table 6. Logistic regressions, all countries and non‐OECD countries**

||All cou|ntries Odds|Ratios|Non‐OECD|countries O|dds Ratios|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Dependent variable: In PISA|Income|Region|Capacity|Income|Region|Capacity|
|Low Income (omitted)|‐‐‐|||‐‐|||
|Lower Middle Income (lagged)|9.38***<br>(4.50)|||9.17***<br>(4.40)|||
|Upper Middle Income (lagged)|35.99***|||25.75***|||
||(16.89)|||(12.21)|||
|High Income (lagged)|77.94***|||18.73***|||
||(36.06)|||(8.97)|||
|Europe and Central Asia||‐‐|||‐‐||
|East Asia and Pacific||.255***|||.496**||
|||(.048)|||(.118)||
|Latin America and Caribbean||.147***|||.383***||
|||(.030)|||(.091)||
|Middle East and North Africa||.161***|||.470**||
|||(.04)|||(.130)||
|North America||1.30***|||‐‐||
|||(.666)|||||
|South Asia||.013***|||.044**||
|||(.014)|||(.045)||
|Sub‐Saharan Africa||.002***|||.007***||
|||(.002)|||(.007)||
|Previously in TIMSS|||8.14***|||8.20***|
||||(1.26)|||(1.58)|
|In regional assessment|||0.66|||1.23|
||||(.19)|||(.385)|
|National assessment|||4.08***|||2.64***|
||||(.61)|||(.504)|
|Constant|‐0.02***|1.54***|0.013***|.016***|.485***|0.059***|
||(0.01)|(0.17)|(.0008)|(.007)|(0.074)|(0.009)|
|Number of observations|1269|1269|1269|1081|1075|1081|
|LR chi2|341.06|380.64|341.9|109.05|133.3|157.79|
|Degrees of freedom|3|6|3|3|5|3|
|Pseudo R2|.229|.255|.229|.119|.146|.173|

Note: Standard error in parentheses, *** p<.001, ** p<.01, * p<.05 12
# **Table A6. Alternative Measures of Bank Exposure to the Energy Sector**

In this panel, we display the main estimates of a regression of total bank lending on bank exposure to firms in the energy sector, under four alternative bank exposure variables: (i) energy sector loans divided by total loans, (ii) energy sector loans divided by total assets, (iii) energy sector loans divided by total bank equity, and (iv) energy sector loans divided by the tier 1 ratio in December 2012. The top panel displays the results for the energy sector, and the bottom panel displays the results for the non-energy sector. The dependent variable in all regressions is the total lending volume (in logs) to firm _f_ by bank _b_ in month _m_ . _Postm_ is an indicator that the observation is after August 2014. Standard errors are clustered at the bank*month level. Detailed variable definitions are provided in Table A1. Observations are at the firm-bank-month level and the sample period is from January 2013 to June 2016. *** p<0.01, ** p<0.05, * <u>p<0.1.</u>

|Energy sector||Total Le|ndingf,b,m||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ExposureEnergyb,Aug14*Postm|0.028*|0.136|0.098***|0.045*|
||(0.015)|(0.104)|(0.029)|(0.026)|
|Observations|6,994|6,994|6,994|6,994|
|R-squared|0.947|0.947|0.948|0.947|
|Bank-firm FE|Yes|Yes|Yes|Yes|
|Firm-month FE|Yes|Yes|Yes|Yes|
|SD(ExposureEnergyb,Aug14)|6.2|6.2|6.2|6.2|
|Non-energy sector||Total Le|ndingf,b,m||
||(1)|(2)|(3)|(4)|
|ExposureEnergyb,Aug14*Postm|-0.012***|-0.131***|-0.033***|-0.006*|
||(0.002)|(0.023)|(0.006)|(0.003)|
|Observations|1,262,640|1,262,640|1,262,640|1,262,640|
|R-squared|0.795|0.795|0.795|0.794|
|Bank-firm FE|Yes|Yes|Yes|Yes|
|Firm-month FE|Yes|Yes|Yes|Yes|
|SD(ExposureEnergyb,Aug14)|6.1|6.1|6.1|6.1|

42
23 over the 1986-1993 period. Also, we compute a measure of banking "efficiency." SPREAD equals the difference between bank lending and borrowing rates.'" A3. **Nonbank development:** We also measure the size of nonbank financial corporations, such as finance companies, mutual funds, brokerage houses, etc. PNB/GDP equals private nonbank financial intermediary assets divided by GDP. The four countries with the largest ratio of nonbank financial intermediary assets to GDP were Sweden, Singapore, Korea, and the United States. Indonesia, Pakistan, Turkey, and the Netherlands had very low values of PNB/GDP over the 1986-1993 period." A£4. **Insurance and Pension Companies:** Finally, we measure the size of private insurance and pension companies. INPE/GDP equals private insurance company and pension fund assets divided by GDP. The three countries with the largest ratio of private insurance company and pension fund assets to GDP were the Netherlands, Great Britain, and the United States. the Philippines, Thailand, and Pakistan had very low INPE/GDP ratios.

' This measure may not accurately capture banking efficiency because the interest rate data may not accurately reflect borrowing and lending costs. SPREAD also will not provide accurate information on how well banks monitor firm managers, nor will SPREAD capture government intervention in the banking system in an very informative way. But, SPREAD is widely used and availabe across countries. We include it for completeness. For better measures of financial repression for a few select countries see Giovannini and De Melo (1993).

lWe collected data on private nonbank financial corporations, insurance companies, and pension funds from individual country reports, including documents published by Ministries of Finance, Central Banks, and regulatory agencies.