rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Labor Market Segmentation Estimation Results

||Wage w|orkers|Self-e|mployed|
|---|---|---|---|---|
|**Variable**|**Coefcient**|**Std. Err.**|**Coefcient**|**Std. Err.**|
|dedu2|0.274|0.197|0.978<br>|0.640|
|dedu3|0.563<sup>_∗∗_</sup>|0.197|1.250<sup>_†_</sup>|0.640|
|dedu4|0.641<sup>_∗∗_</sup>|0.200|1.429<sup>_∗_</sup>|0.643|
|dedu5|0.807<sup>_∗∗_</sup>|0.201|1.381<sup>_∗_</sup>|0.646|
|dedu6|1.417<sup>_∗∗_</sup>|0.198|1.999<sup>_∗∗_</sup>|0.642|
|exp|0.052<sup>_∗∗_</sup>|0.004|0.054<sup>_∗∗_</sup>|0.006|
|exp2|-0.001<sup>_∗∗_</sup>|0.000|-0.001<sup>_∗∗_</sup>|0.000|
|dfem|-0.189<sup>_∗∗_</sup>|0.022|-0.714<sup>_∗∗_</sup>|0.049|
|durb|0.246<sup>_∗∗_</sup>|0.026|0.488<sup>_∗∗_</sup>|0.048|
|dsec2|0.941<sup>_∗∗_</sup>|0.216|0.233<br>|0.501|
|dsec3|0.690<sup>_∗∗_</sup>|0.043|0.229<sup>_†_</sup>|0.126|
|dsec4|0.780<sup>_∗∗_</sup>|0.103|1.676<sup>_∗∗_</sup>|0.353|
|dsec5|0.427<sup>_∗∗_</sup>|0.055|0.708<sup>_∗∗_</sup>|0.125|
|dsec6|0.540<sup>_∗∗_</sup>|0.045|0.691<sup>_∗∗_</sup>|0.121|
|dsec7|0.778<sup>_∗∗_</sup>|0.062|0.719<sup>_∗∗_</sup>|0.145|
|dsec8|0.653<sup>_∗∗_</sup>|0.052|0.905<sup>_∗∗_</sup>|0.178|
|dsec9|0.490<sup>_∗∗_</sup>|0.045|-0.095|0.144|
||N|5782|N|2696|
||R<sup>2</sup>|0.417|R<sup>2</sup>|0.349|
||F (17_,_5764)|196.477|F (17_,_2678)|87.385|

Significance levels : _†_ 10% _∗_ 5% _∗∗_ 1% _Note:_ Observations are weighted by sample weights, and robust standard errors are reported. Education dummies ( _dedu_ 2 to _dedu_ 6) are defined as follows: less than complete primary, complete primary, less than complete secondary, complete secondary, more than secondary. No formal education is the reference dummy. Sector dummies ( _dsec_ 2 to _dsec_ 9) are defined as follows: mining, manufacturing, water, electricity, gas, construction, commerce and hotels, transport and communications, financial services, public and social services. Agriculture and fisheries is the reference sector. _Source:_ Author’s calculations using data from 2004 EPHPM.

of the government. Therefore, there are important revenue considerations related to tariff liberalization vis-`a-vis the US, as the government will need to either significantly curtail its spending, find other sources of income to make up for the budgetary shortfall, or else borrow more and increase its debt. The choice of instruments to close the financing gap has important implications for the outcome of the reform (or model simulations). Higher indirect taxes tend to disproportionately affect the poorer households and are usually more difficult to collect, while raising direct taxes can lead to undesirable consequences such as income under-reporting or even reduced remittance inflows. Decreasing public spending could jeopardize important social programs, while taking on additional debt could threaten long-term solvency and macro stability. Whatever the mechanism, however, some of the gains in consumer surplus will be eroded if the implementation of tariff reform is revenue-neutral.

The average tariff rate faced by Honduran exporters on the global markets is 8.4 percent, but 9
remain unchanged since 2013<sup>28</sup> ; IDR 450,000, 750,000, and 1 million for each beneficiary at primary, junior secondary, and senior secondary level, respectively (World Bank, 2018). Poor households sometimes choose between sending their sons and daughters to school when resources are limited. Data from Susenas 2017 indicate that among households with out-of-school children, more than half of households with out-of-school boys (52 percent) report that economic condition is the major reason for not going to school, while only 44 percent of households with out-of-school girls stated the same reason (see also MoWECP and BPS 2018a).

**Table 3. Main reasons for dropping out from school by sex (percent)**

|Main Reason|<br>|Indonesia<br> <br>||
|---|---|---|---|
||Male|Female<br>|Total|
|Insufficient funds|33.01|30.68|31.99|
|Working|18.74|13.68|16.52|
|Marriage|0.37|12.27|5.59|
|Taking care of the household|0.37|3.65|1.81|
|Assume that current education is enough|4.26|5.24|4.69|
|Shame of Poor Economic condition|2.88|1.9|2.45|
|School is far|3.71|3.26|3.51|
|Disability|5.39|4.82|5.14|
|Other|31.27|24.51|28.3|
|Total|100|100|100|

Source: Susenas, 2017.

The same data source also shows that economic status is positively correlated with length of schooling. This applies for both males and females (Table 4). The higher gender gap in years of schooling, while insignificant, is faced by girls from the poorest (0.67 years) and the richest (0.66 years) quintiles. This gender gap includes those 15 years and older, and so captures the structure of the education system many decades ago which at that time was biased in favor of males. This gap is expected to decline and then reverse as children who have attended schools in the past two decades become adults and replace those who were educated under the former system.

**Table 4. Average years of schooling for population aged 15 and older by economic status and sex**

|Quintile Economic Status|Male|Female|Gendergap|
|---|---|---|---|
|Quintile 1|6.96|6.29|0.67|
|Quintile 2|7.77|7.21|0.56|
|Quintile 3|8.39|7.80|0.59|
|Quintile 4|9.19|8.57|0.62|
|Quintile 5|11.30|10.64|0.66|

Source: MoWECP and BPS (2018) Profil Perempuan Indonesia.

One of the main challenges in addressing the issue of out-of-school children (OOSC) is a lack of accurate data that could specifically identify who the OOSC are, where they live and why they do not go to school. Since 2016, UNICEF has been supporting a program that aims to empower local governments and communities to collect data on OOSC and build their capacity to develop plans to address their needs.

> 28 <u>https://indonesiapintar.kemdikbud.go.id/</u>

14
that require more skills, or compete without the right skills and techniques and therefore are more exposed to financial losses.<sup>9</sup> The last row of Table 5A confirms that state banks have a much higher average ratio of loan loss provisions to gross loans. The Bankscope database does not provide detailed information on the composition of loan portfolios, but this result probably reflects losses resulting from lending to state enterprises and favored sectors. In this regard, state banks might be fulfilling their development mandates, whereby they are directed to finance projects and sectors that may generate low returns or entail excessive risks, but that are regarded as strategic or capable of generating positive externalities. These losses are probably aggravated by the own internal operational deficiencies of state banks in fulfilling these mandates.

By contrast, private banks pay higher average wages in order to attract higher-skilled workers and develop more sophisticated and profitable business lines while being able to manage the associated risks relatively well. At the same time, they are also able to maintain a lower ratio of employees to assets. Thus taken together, these results show that private banks are ultimately able to generate higher profits compared to state banks despite their smaller average size, by exploring more profitable business lines, containing their operating costs, and managing their risks more effectively.

# _Domestic private versus foreign private banks_

Next we analyze the differences between foreign and domestic private banks. In 2008, there were 90 private banks in non-GCC MENA countries of which 45 were domestic and 45 were foreign. As shown in table 5B, we find that foreign banks are significantly smaller, reflecting at least partly their more recent entry into banking systems in the region (see the previous section).

Foreign banks have been more profitable than private domestic banks, as indicated by higher ROAs and ROEs. Although these differences in profitability are not statistically significant, this still suggests important efficiency differences, given that foreign banks are smaller. Foreign banks also generate higher NIMs but the differences vis-à-vis private domestic banks are not

> 9 Rocha, Farazi, Khouri, and Pearce (2010) show that state banks in MENA are as involved in SME finance as private banks, but do not seem to have the same levels of risk management and SME lending techniques. 13
remained available for fathers. Hanson (2011) finds that young men married at younger ages during periods in which marital deferments existed. While a large literature supports the notion that married individuals are generally healthier than single individuals, this is not necessarily a causal relationship (Wood, Goesling, and Avellar 2007).

To address this mechanism, our models control for education, marital status, and number of children. We are not able to control for age at first marriage or age at first birth, so our controls for these variables may be imperfect. However, to the extent that education and marriage promote health, failure to perfectly control for them should bias our estimates toward finding that exposure to induction risk is associated with better health later in life.

# **3. Data**

The primary data used in this project are: 1) the National Health Interview Survey (NHIS) and 2) induction data taken from Reports of the Director of the Selective Service. In this section, we describe these data and document how we constructed the variables used in our analysis.

# **3.1.National Health Interview Survey (NHIS)**

The NHIS is a cross-sectional, nationally representative data set of the US noninstitutional population. Each year, the NHIS interviews approximately 100,000 individuals, asking questions about basic demographic and socioeconomic characteristics, military service, height and weight (from which we calculate body mass index or BMI),<sup>6</sup> and self-reported health. We use data from the 1982-1996 NHIS for our main analysis sample. Based on our sample of interest - men born from 1937 to 1956 - our main analysis sample is aged 25 to 59 during 1982-

> 6 Body mass index (BMI) is equal to weight in kilograms divided by height in meters squared.

7
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In terms of instrument relevance, we note that the first-stage Kleibergen Paap F-statistic is well in excess of 10 (17) so that according to the tabulations provided in Stock and Yogo (2005) we can reject the hypothesis that the IV size distortion is larger than 15 (10) percent at the 5 percent significance level. In Figure 1 we plot the relationship between the Gini coefficient (net of country and time fixed effects) and the instrument -- i.e. the Gini coefficient adjusted for the impact of GDP per capita (also net of country and time fixed effects). As can be seen the relationship is positive and highly significant.

In Table 2 we present estimates from a dynamic panel model that includes the one-period lag of GDP per capita on the right-hand side. Panel A shows two-stage least squares estimates where inequality is instrumented with the residual variation in inequality that is not due to GDP per capita. Panel B shows system-GMM estimates where the lagged dependent variable is instrumented with its second and third lag; inequality continues to be instrumented with the residual variation in inequality that is not due to GDP per capita.

Several findings emerge that are worthwile pointing out. First, the message from the dynamic panel estimates is qualitatively the same as from the static panel estimates: income inequality has a significant positive effect on GDP per capita for low levels of initial GDP per capita; the effect is significantly negative for intermediate and high levels of initial GDP per capita. Second, there is evidence of significant within-country convergence in GDP per capita: the AR(1) coefficient on GDP per capita is in the range of 0.7-0.8; this suggests that in the sample the per annum convergence rate of GDP per capita is in the range of 4-6 percent. Third, 2SLS regressions provide estimates that are quantitatively similar to system-GMM regressions. Fourth, the size of the contemporaneous (five year) and long-run effect of income inequality on GDP per capita is substantial.

Consider, for example, the estimates in column (1) of Panel A in Table 2. When evaluated at 9
# 1. Introduction

Most social programs in developing countries are based on the explicit objective of reducing poverty (Elbers et al. 2007). Targeting is centered on successfully identifying poor households. However, this prioritization ignores the intertemporal dimension of poverty. Nonpoor households are also at risk of becoming poor. The exposure to natural shocks, pandemics, economic crises, and climate change means there is a need to rethink social assistance to cover both the poor (improving incomes and therefore reducing poverty) and providing insurance against risks (reducing the likelihood of falling into poverty). As an integral part of designing a new generation of social assistance programs that aim to sustain previous poverty gains, it is necessary to develop new targeting mechanisms that cover currently poor households and those that are vulnerable to poverty.

We develop a novel method for producing vulnerability maps for small administrative areas combining information from a household survey and a national census. The approach integrates insights and techniques from the literature on small area estimation and the vulnerability to poverty. While conventional poverty mapping focuses on estimating the share of households that are currently poor (poverty-induced vulnerability), the proposed method also enables the estimation of the share of nonpoor households that are highly likely to fall into poverty (risk-induced vulnerability). Following the vulnerability literature, the analysis relies on a definition whereby households are considered at risk of poverty if the predicted probability that a household will become poor in the next two periods is above 0.5. A complete description of poverty and risk-induced vulnerability is provided in Günther and Harttgen (2009); implementation details can be found in the methodological section.

The application of the method described here involved the creation of a comprehensive poverty and vulnerability map of Senegal. The map offers a detailed spatial analysis of poverty-induced and riskinduced vulnerability across the country. The overarching findings indicate elevated poverty rates in rural areas and the southeastern regions. However, the results also reveal a more nuanced picture of poverty and vulnerability. Specifically, though they exhibit lower poverty rates, urban communes account for most of the households that face a considerable risk of falling into poverty.

The ability of the method to identify not only pockets of poverty, but also concentrations of highly vulnerable populations means that the method can be used as the basis for determining commune-level eligibility quotas for the expansion of the national social registry, the _Registre National Unique_ (RNU). Taking advantage not only of poverty rates, but also vulnerability rates allowed the RNU to support a 2
information on household income, geographic location, household demographics, dwelling characteristics, and economic activities, among others. The size of the dataset is especially important, as it allows us to draw repeated samples that are sufficiently large and diverse. In addition, the fact that the survey gathered detailed income information on all households allows us to reliably calculate the required poverty measures that serve as the basis for our simulation experiments.<sup>5</sup> Prior to sampling from the Intercensal Survey, we make three modifications to the data. First, as a large number of households reported earning no income, we randomly remove 90 percent of these households.<sup>6</sup> Second, to ensure that all municipalities are sufficiently large, all municipalities with less than 500 households are removed. Finally, to ensure that all primary sampling units (PSUs) are also sufficiently large for sampling purposes, we combine several neighboring PSUs such that all include at least 300 households. Our final census dataset consists of 3.9 million households in 1,865 municipalities and 16,297 PSUs. The resulting census is used to draw 500 survey samples that serve as the basis for our simulation experiments (Tzavidis et al., 2018). In what follows, we describe our approach to constructing these samples.

Our sampling procedure is intended to reflect standard design elements used in many face-to-face household surveys conducted in the developing world (Grosh and Muñoz, 1996). Mexico’s 32 states comprise the intended domains of the sample and the indicator of interest is household income per capita. For each sample, we seek to achieve a relative standard error (RSE) of 7.5 percent for average household income in each state.<sup>7</sup> We use a two-stage clustered design wherein PSUs or clusters are selected within each domain and then a sample of 10 households is selected within each cluster. Our decision to select 10 households within each cluster is consistent with the design of other large-scale household surveys conducted in developing countries. Under simple random sampling, the minimum sample size required for each state is as follows: where _w_ ̄ _s_ and _σs_ represent average household income per capita and the standard deviation of per capita income for state _s_ , respectively.

The minimum sample size under simple random sampling must be adjusted to account for the clustered design. The design effect due to clustering is accounted for by estimating the intracluster correlation of per capita income within each state. The correlation estimates can then be used to adjust the above sample sizes for the clustered design as follows:

> 5Income is defined as money received from work performed during the course of the reference period by individuals aged 12 or older.

> 6This is done to ensure some missing values are present in the data used but not as many as in the original data.

> 7The desired precision of 7.5 percent is somewhat arbitrary, but corresponds to precision targets in similar surveys and yields samples of reasonable sizes.

5
- 34 -

Table 11 The Effects of Participation in PROBECAT -- Men and Women

|Model<br>:<br>Speoificatton .<br>.<br>i<br>-Si<br>without<br>coef.|1<br>mple OL<br>correc<br> <br>t|S<br>tions:<br>:t-:st|-(2).<br>CorXO0t0^<br> <br>.traini<br>atie<br>c<br>.<br>t|-for<br>6Selecton.<br>atat:|
|---|---|---|---|---|
|||MALE|SAMPLES||
|A. Logarithm of Monthly Salary<br>Unweighted<br>-.108|-3.26||.177|2.19|
|Weighted<br>-.081|-1.49||.269|1.54|
|B. Hours of Work Per Week<br>Unweighted<br>.978|1.32||7.796|4.32|
|Weighted<br>1.260|1.12||8.408|2.30|
|C. Logarithm of Hourly Wage<br>Unweighted<br>-.095|-2.61||-.007|-0.07|
|Weighted<br>-.076|-1.34||.049|0.26|
|A. Logarithm of Monthly Salary<br>Unweighted<br>-.122|-2.42|FEMALE<br>|SAMPLES<br>.033|0.25|
|Weighted<br>-.124|-1.40||.021|0.08|
|B. Hours of Work Per Week<br>Unweighted<br>4.484|4.19||6.234|2.06|
|Weighted<br>4.268|2.43||1.185|0.23|
|C. Logarithm of Hourly Wage<br>Unweighted<br>-.261|-4.48||-.105|-0.68|
|Weighied<br>-.250|-2.50||.180|0.61|

Source: see tables 3.1 to 3.14 in Annex 1.
# Figure 5: Female Secondary Enrollment, Contraceptive Prevalence and TFR, 1988

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Table 3: Excluding top-3 most robotized sectors: automotives, rubber and plastics, metal

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)<br>(1)|_log_(1 +_exp_)<br>(2)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(3)|_log_(1 +_imp_)<br>(4)|_log_(1 +_exp_)<br>(5)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup><br>(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.2009*|0.4443***|-0.2434***|0.8618**|1.6272***|-0.7654***|
||(0.0987)|(0.1571)|(0.0811)|(0.3259)|(0.4248)|(0.1765)|
|First stage:|||||||
|_Replaceability * initial GDPpc * global robot stock_||||0.0023***<br>(0.0002)|0.0023***<br>(0.0002)|0.0023***<br>(0.0002)|
|Observations|722,592|722,592|722,592|722,592|722,592|722,592|
|_R_<sup>2</sup>|0.6163|0.7595|0.3723|0.6106|0.7446|0.3670|
|Kleibergen-Paap rk Wald-F-stat||||115.011|115.011|115.011|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, excluding the top-3 most robotized sectors from the baseline estimation sample (automotives, rubber and plastics, metal). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.