rafmacalaba/datause-encoder-data huggingface dataset:
29

**Table 6. Wage Premium Regressions, Long Run**

||Cam|bodia|Sri L|anka|
|---|---|---|---|---|
||1|2|3|4|
|Female dummy|-0.172***|-0.336***|-0.556***|-<br>0.551***|
||(0.019)|(0.030)|(0.023)|(0.024)|
|Hours|0.015***|0.015***|0.012***|0.012***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Age|0.041***|0.042***|0.148***|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|0.281***|0.275***|0.060***|0.059***|
||(0.021)|(0.021)|(0.010)|(0.010)|
|1999–2004 dummy||-0.005||0.059***|
|||(0.023)||(0.008)|
|2005 and above dummy|0.269***|0.272***|0.076***|0.101***|
||(0.014)|(0.023)|(0.008)|(0.008)|
|Female* (1999–2004 dummy)||0.225***||-0.017|
|||(0.033)||(0.013)|
|Female* (2005 and above<br>dummy)|-0.068***|0.099***|-0.044***|-<br>0.050***|
||(0.021)|(0.032)|(0.013)|(0.014)|
|Hazard ratio|-0.002|-0.001|0.678***|0.680***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.404***|10.389***|4.811***|4.787***|
||(0.125)|(0.128)|(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 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. Sri Lanka Household Income and Expenditure Survey was excluded from the analysis as hours worked were not available in this survey. 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. 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.
Table A1: LEAP effects on exogenous networks and socio-demographic characteristics (These results are included as a placebo test and balance check)

||(1)<br>Family|(2)<br>Neighbor|(3)<br>Krum|(4)<br>Sex|
|---|---|---|---|---|
|_Full sample_|||||
|LEAP|0.12|0.02|-0.00|-0.00|
||(0.09)|(0.09)|(0.12)|(0.03)|
|Constant|0.70**|0.66**|0.45**|0.16**|
||(0.08)|(0.06)|(0.09)|(0.02)|
|N|526|526|526|523|
||(5)|(6)|(7)|(8)|
||||Education in||
||Age|Household size|yrs.|Average effect|
|_Full sample_|||||
|LEAP|-0.13|-0.04|-0.21|0.10|
||(1.44)|(0.21)|(0.42)|(0.13)|
|constant|42.30**|4.97**|2.75**||
||(1.02)|(0.16)|(0.36)||
|N|521|521|521||

Note: Columns 1-7 report OLS estimates with village-clustered standard errors. Column 8 reports the standardized average effect, as described in Kling et. al. (2004) and Clingingsmith et. al. (2009), across the measures in columns 1-7

* = significant at the five percent level, **=significant at the one-percent level

43
# Table 15A. (part 2). 16-sectors labor remuneration (in percentage)

||Young|FE|MALES(|withouty|oungwor|kers)||MALES(w|ithoutyo|ungwork|ers)<br>All 4|ALL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||workers|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|All 4 occu-<br>pations|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|<br>occu-<br>pations|Labor|
|**Agriculture/Forestry/Fishing**|3.32|0.21|16.67|0.02|0.00|16.89|1.61|28.04|0.06|0.02|29.74|49.94|
|a. Cattle. beef&dairy&small animals|0.19|0.01|0.54|0.00|0.00|0.55|0.13|2.29|0.00|0.00|2.42|3.16|
|b. Crop growing|3.10|0.20|16.09|0.02|0.00|16.31|1.45|25.21|0.05|0.02|26.74|46.14|
|c. Agricultural & forest services|0.01|0.00|0.02|0.00|0.00|0.02|0.00|0.07|0.00|0.00|0.08|0.10|
|d. Fishing|0.03|0.00|0.01|0.00|0.00|0.01|0.03|0.47|0.00|0.00|0.50|0.54|
|**Mining & quarrying**|0.02|0.00|0.05|0.00|0.00|0.06|0.07|0.21|0.02|0.00|0.29|0.37|
|**Manufacture**|0.03|0.06|0.50|0.03|0.02|0.60|0.55|2.77|0.28|0.05|3.65|4.28|
|i. Grainmillproducts&Food canning|0.01|0.02|0.15|0.01|0.00|0.18|0.16|0.82|0.08|0.02|1.08|1.27|
|ii. Apparel. spinning. weavingand finishing|0.01|0.03|0.28|0.02|0.01|0.34|0.10|0.53|0.05|0.01|0.69|1.04|
|iii. Furniture making&Non-Metallic<br>Minerals|0.01|0.01|0.06|0.00|0.00|0.07|0.28|1.42|0.14|0.03|1.88|1.96|
|**Electricity. Gas and water**|0.00|0.00|0.02|0.00|0.00|0.03|0.01|0.39|0.04|0.09|0.54|0.56|
|**Construction**|0.01|0.01|0.07|0.00|0.00|0.08|0.27|2.92|0.07|0.02|3.28|3.38|
|**Trade**|0.21|2.72|3.55|0.22|0.02|6.51|2.31|9.55|1.16|0.16|13.17|19.89|
|A. Trade|0.17|2.01|2.62|0.16|0.01|4.81|2.11|8.73|1.06|0.15|12.05|17.02|
|B. Restaurants & Hotels|0.04|0.71|0.93|0.06|0.01|1.70|0.20|0.82|0.10|0.01|1.13|2.87|
|**Transport & Communication**|0.00|0.03|0.33|0.04|0.04|0.44|0.19|3.04|0.26|0.25|3.74|4.18|
|**Finance, Insurance & Business services**|0.00|0.03|0.16|0.00|0.04|0.24|0.03|0.58|0.30|0.58|1.49|1.73|
|**Personal services**|0.04|0.80|1.14|1.60|0.18|3.72|1.04|4.55|5.43|0.88|11.90|15.66|
|Public services|0.02|0.21|0.30|0.41|0.05|0.96|0.47|2.08|2.48|0.40|5.43|6.41|
|Other personal services|0.03|0.59|0.85|1.18|0.14|2.76|0.56|2.48|2.95|0.48|6.47|9.26|
|**COUNTRY TOTAL**|3.62|3.87|22.49|1.91|0.30|28.57|6.08|52.06|7.61|2.05|67.81|100.00|

Source: Author’s own elaboration based on the ILFS (NBS, 2002a). The information merges all the data from the Tables 1, 6, 9 and 13 in this Appendix 47
# **Table 1.  Policies that work by changing automatic thinking**

|Problem|Solution|
|---|---|
|Too little adherence to<br>antiretroviral treatment by<br>AIDS patients in Kenya|Send patients a weekly_reminder_stating “This is your reminder,”<br>through a low-cost message service on cell phones. Patients<br>who received a weekly reminder increased adherence to the drug<br>regime by13percentagepoints.|
|Too little enrollment in<br>post-secondary schools in<br>Colombia|Reduce the bimonthly conditional cash transfer that rewards<br>parents of students with good school attendance, and distribute<br>the funds lump sum at the time of the students’ high school<br>graduation. This policy increased enrollment in post-secondary<br>institutions by 49 percentage points. The_change in timing_<br>meant parents did not need to think about saving the money and<br>could instead use it shortly after receipt to make enrollment<br>payments.|
|Too little teacher effort in<br>the Chicago public schools|Pay teachers a bonus at the_beginning_of the school year, which<br>they will lose if their students do not meet a threshold<br>achievement level at the end of the school year. This policy<br>increased teacher effort and student performance, whereas a<br>policy of paying teachers the bonus at the_end_of the school year<br>conditional on student performance did not increase effort or<br>performance. The psychological_aversion to loss_made the<br>bonus in the former case more salient than in the latter case.|
|Too much borrowing in the<br>United States from high-<br>cost “payday lenders” by<br>those who cannot obtain a<br>credit card|<br>_Anchor_the costs of borrowing funds_._An anchor can be<br>provided on the envelope in which individuals receive borrowed<br>funds. The envelope can show the dollar costs of the payday<br>loan compared with the dollar cost of borrowing that amount on<br>a credit card. Borrowers who received envelopes with the<br>anchor were 11 percent less likely to borrow from payday<br>lenders in the next four months compared to the group that did<br>not.|

_Source_ : Pop-Eleches and others 2011; Barrera-Osoria and others 2011; Fryer and others 2012; Bertrand and Morse 2011.

10
A formal description of the model is presented in <u>Annex 4.</u> To the knowledge of the authors, this will be the first empirical estimation of tobacco price elasticities by income-group in Georgia. The following sections describe the data sources used to estimate each parameter and component of the ECBA in the case of Georgia.

# V. Price-elasticity of demand for cigarettes

Price-elasticities of demand for cigarettes in Georgia are estimated at the decile level, using data from the Household Integrated Survey (HIS) 2012-2016 and from the Household Income and Expenditure Survey (HIES) in 2017. Several empirical models were tested. The preferred results are plotted in Figure 2. <u>Annex 5</u> contains the econometric specification, methodological details and relevant robustness checks.

Additionally, lower- and upper-bound elasticity scenarios are simulated with distance of 0.2 from the estimated (base or medium-bound) elasticity in each decile.<sup>12</sup> Following the literature, those scenarios are assumed to represent a shorter- and longer-term outcome, respectively.

Figure 2. Estimation of price-elasticities of demand for filtered cigarettes <!-- Start of picture text --> Decile<br>0.00<br>1 2 3 4 5 6 7 8 9 10<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>-1.20<br>Medium-bound Upper-bound Lower-bound<br><!-- End of picture text --> Table 3. Estimation of price-elasticities of demand for filtered cigarettes

|Decile|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|
|Medium-bound|-0.97|-0.70|-0.60|-0.51|-0.43|-0.38|-0.28|-0.23|-0.21|-0.07|
|Upper-bound|-1.17|-0.90|-0.80|-0.71|-0.63|-0.58|-0.48|-0.43|-0.41|-0.27|
|Lower-bound|-0.77|-0.50|-0.40|-0.31|-0.23|-0.18|-0.08|-0.03|-0.01|0.00|

_Source_ : Authors’ estimation based on data from the IHS (2012-2016) and HIES (2017). _Notes:_ Deciles are created based on household per capita consumption, based on the ECAPOV harmonized aggregate by the World Bank. The upper- and lower-bound scenarios were simulated with 0.2 distance from the medium-bound estimation. The lower-bound simulations were capped to avoid positive price elasticities.

> 12 Simulating a lower-bound elasticity for decile 10 resulted in a positive value of 0.08. Hence, the upper-bound for this case was intentionally corrected to zero.

11
in Cambodia, Hong Kong SAR, China, Vietnam, the Czech Republic, the Kyrgyz Republic and Tajikistan, it became increasingly positive in Mongolia, Oman and Turkmenistan. This is yet another reason why the relative importance of supply and demand shocks associated with China’s trade dynamics is likely to vary across B&R economies.

Building on these insights, this section examines econometrically the heterogeneous impacts of supply and demand shocks associated with China’s trade dynamics on the exports of B&R economies. To measure supply (or competition) shocks associated with China’s rising global exports, we use trade flow data by detailed product category and exploit differences across B&R economies in the degree of exposure to such shocks within each sector. Specifically, we interact the change in log exports of China in sector _i_ in period _t_ with the export similarity index between China and B&R economy _j_ in sector _i_ in 1995. The sector is defined at the 3-digit level, while the similarity index is computed as in Finger and Kreinin (1979) using product-level data at the 6-digit level. This index takes values between zero and one, and the higher its value the closest is the product distribution of exports in the two countries.

Formally, we define the China supply shock faced by B&R economy _j_ in sector _i_ in period _t_ as: where ���� denotes multilateral exports of China in sector _i_ in year _t_ , and ������� the export similarity index between China and B&R economy _j_ in sector _i_ in 1995.

Exposure to the global demand shocks associated with China’s rising imports is measured in an analogous way, exploiting the fact that some countries initially exported relatively more of what China buys. Formally, the demand shock faced by B&R economies is defined as: where ���� denotes multilateral imports of China in sector _i_ in year _t_ , and ������� the similarity index between China’s imports and the exports of B&R economy _j_ in sector _i_ in 1995. In the 16
# **Appendix Table 3. Effect of preschool availability on female’s work participation with continuous number of eligible children**

||||**Work par**<br>|**ticipation**<br>|||
|---|---|---|---|---|---|---|
|Econometric strategy:|D|D|DD|D|DD|-FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Public**|||||||
|Preschool * Eligible|||0.045***|0.047***|0.065***|0.064***|
||||(0.016)|(0.016)|(0.017)|(0.017)|
|Preschool density|-0.028|-0.014|-0.012|0.004|-0.015|-0.006|
||(0.023)|(0.027)|(0.014)|(0.017)|(0.014)|(0.017)|
|Eligible child|||-0.037***|-0.038***|-0.024***|-0.024***|
||||(0.006)|(0.006)|(0.005)|(0.005)|
|**Panel B: Private**|||||||
|Preschool * Eligible|||-0.001|-0.001|-0.000|-0.000|
||||(0.002)|(0.002)|(0.001)|(0.001)|
|Preschool density|-0.011***|-0.009***|-0.002|-0.003|-0.001|-0.003**|
||(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|
|Eligible child|||-0.026***|-0.027***|-0.014*|-0.013*|
||||(0.009)|(0.009)|(0.008)|(0.008)|
|Observations|22,737|22,737|76,951|76,951|76,951|76,951|
|Mean|0.538|0.538|0.534|0.534|0.534|0.534|
|District Trend||X||X||X|

Note: Sample in column 1-2 is restricted to mothers with preschool-aged children (age 3-6). Sample in column 3-6 includes mothers with and without preschool-aged children (age 3-6). Sample is further restricted to PODES years. Column 1-2 regress work participation on preschool density in a difference-in-differences (DD) strategy (Section 4.1). Column 3-4 regress work participation on preschool density, a dummy for having a preschool-aged child, and the interaction between the two in a difference-in-difference-in-differences (DDD) strategy (Section 4.2). Column 5-6 builds on the DDD strategy and adds individual fixed effect (DDD-FE) to allow comparison within-mothers (Section 4.3). Column 2, 4, 6 add district-specific trends. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. Panel A and B look at the effect of public and private preschool densities separately. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Observations, work participation means, and inclusion of district trends are indicated in the last three rows.

71
# **Introduction**

Vietnam has known significant changes over the past 25 years. The country has become gradually more integrated in the global economy, culminating in joining the WTO in 2006, shortly before a time of financial and economic turmoil in the world. The impact of the crisis translated in a slowdown in economic growth: the average annual growth rate was reduced to 6.5% in 2008 and 5.3% in 2009 from rates of 8.5% in 2004-2007. This impact was lower than in other Asian countries that went into recession. At the same time, the recent period 2007-2009 saw a renewed increase in the labor force participation rate; the slowing economy has hence faced the challenge of creating jobs. Some evidence has suggested that most of these newly created jobs occurred in the informal economy and that the quality of employment has decreased (Nguyen et al. 2010; Cling et al, 2010).

In addition to achieving economic growth and promoting job creation, a challenge for any government is to make economic growth inclusive. This means in particular ensuring that the poorest sections of the population are living in conditions that are acceptable in terms of nutrition, access to services, and access to housing. Moreover, this also means making sure groups that may be at a particular disadvantage, such as in terms of access to gainful employment (for example, the youth, women, the unskilled, and ethnic minorities), also benefit from improved economic conditions and are not disproportionately affected in times of downturns.

Having a sound set of macroeconomic policies that foster economic growth and job creation is a necessary component for achieving these aims. At the same time, having a comprehensive social protection system that helps individuals and families cope during times of hardship is a timely and worthwhile endeavor for countries that have reached a sufficient level of development. In 2007, Vietnam introduced important reforms in this area, introducing unemployment insurance, and changing the laws that regulate pensions, sickness and maternity benefits, and work injury.

The motivation for this paper is threefold. First, given the availability of recent labor market data from newly improved datasets, the paper gives a picture of the evolution of the supply side of the labor market in Vietnam in the recent period (2007-2009), putting it 2
# **5 Concluding Remarks**

In this paper, we study the impact of labor provisions in trade agreements aimed at eliminating the use of child labor. Such provisions have become more common over time as trade agreements have become more expansive in scope and increasingly cover non-trade areas. Using a large set of survey data from across 101 developing countries, we employ a variety of DiD and triple DiD regressions to identify the effect of RTAs without a child-labor provisions on child labor outcomes, as well as the differential effect of an agreement that does have such a provision.

We find evidence that a trade agreement with no child-labor provision reduces the fraction of 14-17-year-olds in the labor force by a statistically and economically significant amount with corresponding increases in secondary school enrollment. We find similar effects for children under the age of 14 but these estimates are imprecise and not statistically significant. The pattern is consistent with an interpretation that a developing economy joining a trade agreement opens up possibilities for low-skill export employment and is consistent with the empirical findings of Edmonds and Pavcnik (2005) and Edmonds and Pavcnik (2006) that opening trade can reduce child labor in developing economies.

On the other hand, we find that the inclusion of child labor prohibitions in RTAs perversely _increases_ child labor—the opposite of their intended effect. We find statistically and economically significant increases in the employment of 14-17 year old children and statistically imprecise but economically significant increases in under-14 labor as well. We find corresponding drops in both primary and secondary school enrollment rates following entry into force of RTAs with child labor prohibitions.

Our results are strikingly similar to those predicted in Basu (2005) and found empirically in Bharadwaj _et al._ (2020)—a case study of the 1986 ban in India. As in these works and in our theoretical model, if the ban is enforced through a fine imposed on employers, it will tend to depress child wages, inducing the household to increase total labor supply in order to make up the lost income—including the household child labor supply. We find a strong, significant increase in employment for 14-17-year-olds but none for under-14-year-olds, which is consistent with the model 26
The global food crisis of 2007–08 involved approximately a doubling of international wheat and maize prices in the space of two years and a tripling of international rice prices in the space of just a few months. Understandably, such rapid increases in the international prices of staple foods have raised concerns about the impact on the world’s poor. Household surveys suggest that most poor people earn significant shares of their incomes from agriculture but are nevertheless often net food consumers (World Bank 2008b). Consistent with this stylized fact, several multicountry World Bank simulation studies find that poverty typically increases when food prices increase (holding all else equal), with much of the increase in poverty taking place in poorer rural areas (Ivanic and Martin 2008; de Hoyos and Medvedev 2009; Ivanic, Martin and Zaman 2011). Likewise, the U.S. Department of Agriculture’s (USDA 2009) simulation found that approximately 75–80 million people went hungry during the 2008 food crisis, a number that the Food and Agriculture Organization (FOA) of the UN (FAO 2009) applied to its precrisis baseline numbers in the absence of an FAO model that could simulate a food price shock.<sup>2</sup> Subsequent USDA simulations were used by the FAO to estimate that over one billion people went hungry in 2009, up from 873 million in 2005–06.<sup>3</sup> 2 Some basic problems with the FAO model are reviewed in Headey (2011a) and FAO (2002). In the 2008 crisis, the FAO had an underlying model that only incorporated quantities, not prices, so the FAO’s capacity to simulate the effects of food price increases was very limited. Therefore, the FAO relied on a USDA trade model (USDA 2009). A major shortcoming of the USDA model was that it did not include middle-income countries, including large ones such as China, Mexico, and Brazil. Headey (2011a) also shows that the USDA (2009) estimates are contradicted by the USDA’s own historical production and import estimates for 2007–08 (USDA 2011).

3 In addition to the two basic approaches described above (the World Bank poverty simulations and the FAO/USDA hunger simulations), several authors have taken mixed approaches to estimate calorie availability trends, including Anriquez et al. (2010) and Tiwari and Zaman (2010). Dessus et al. (2008) adopt the net benefit ratio approach, but only for urban areas. There are also

> many country-specific simulation exercises; a particularly good one is Arndt et al. (2008). See Headey (2011a) for a more

> extensive overview and critique.

2