rafmacalaba/data-use-mentions-extended huggingface dataset:
Van der Klaauw and Wolpin (2008) who study how the design of U.S. social security rules affects decision-making within a collective household model. As in their model, we allow households to accumulate private savings in addition to pension benefits for consumption in retirement. One important difference is that we focus on gender aspects of pension design, so we incorporate into our model divorce and separation. These events are major financial risks for women with low private and pension savings and low attachment to the labor force. We do not incorporate health or health insurance, in part because Chile has a public health insurance program. Also, we allow only women and not men to work part-time. Another important difference is that our model allows workers to make a choice about being employed in the informal sector, in which they do not contribute to their pensions or make progress towards qualifying for a minimum pension. This is a crucial margin from a public finance point of view in Chile and a majority of countries around the world. Other recent collective models of joint retirement include Blau and Gilleskie (2006), who focus on retirement incentives related to spousal health benefits, and Casanova (2010), who investigates the timing of retirement between two married individuals. Because these papers are not concerned with the difference in financial risk borne by each spouse individually, they model divorce and death through a terminal value, instead of following, as we do, individuals after separation or death has dissolved the household.<sup>7</sup> Many individuals, especially women, spend a significant part of their retirement as widows, so following them after their spouse dies is important to understanding old age poverty.

This paper also builds on a previous study by Joubert (2015) of the relationship between pension design and labor force participation decisions with regard to informal/formal sector work using the same Chilean data that we use. This study extends Joubert (2015) by using a collective household model and by allowing for divorce and fertility, which
One of the results we find out of the matching estimation method is that almost all of the estimated ATET are not statistically significantly different from zero. The only exception is the effect of government support on the percentage change of full-time employees estimated by kernel matching based on mahalanobis distance. While this estimation is significant at the 10% level, we note that balancing of covariates in El Salvador’s matched sample was not successful compared to that of the unmatched sample.

Another result of the matching estimation is the sign of the ATET. Although almost all of them are not statistically significantly different from zero, all of them have positive signs except for two: the effect of government support on the total number of temporary workers in matching with mahalanobis distance in El Salvador and on anticipating falling in arrears on outstanding liabilities within next six months in matching with propensity score in Georgia.

To sum up, while the balancing of covariates in Georgia’s matched samples seem better than that of its raw sample, the ATET is not significantly different from zero, neither in Georgia nor in El Salvador. One possible reason for the El Salvador case is the long gap between ES and COV-ES. The latest El Salvador ES was conducting in 2016, referencing the 2015 fiscal year. This may also partly explain the poor performance of balancing of covariates in El Salvador. Another possible reason why we have insignificant coefficients varying is a small sample size. While ES and COV-ES contain 719 and 581 observations in El Salvador and Georgia, respectively, the samples we use for matching estimation consist of only 169 and 235 observations in El Salvador and Georgia, respectively, because of the restrictions mentioned at the beginning of this section.

# **6. Panel Estimation**

This section uses both rounds of the COV-ES implemented in El Salvador and Georgia, along with the baseline ES. Consequently, the sample within each country is different from the samples we used in Section 5. We use the fixed effect panel regression method to help identify the causal link between
of the relationship between pension design and labor force participation decisions with regard to informal/formal sector work using the same Chilean data that we use. This study extends Joubert (2015) by using a collective household model and by allowing for divorce and fertility, which make the model suitable for studying the differential impact of the 2008 pension reform on men and women.

Lastly, this study is more broadly related to a literature initiated by Auerbach and Kotlikoff (1987) that examines the welfare effects of pension reform using life cycle and

> 7Other notable papers are Gustman and Steinmeier (2000, 2002) who consider a non-cooperative game solution to the household joint decision, and Gallipoli and Turner (2011) who compare non-cooperative and collective models of joint retirement. In the macroeconomic literature, Nakajima (2011) calibrates an equilibrium collective model of joint retirement, focusing on spousal and survivor benefit policies.

5
- Hall, Bronwyn H, Adam B Jaffe, and Manuel Trajtenberg. The NBER patent citation data file: Lessons, insights and methodological tools. 2001.

- Han, Pengfei, Wei Jiang, and Danqing Mei. “Mapping US-China technology decoupling, innovation, and firm performance”. _Innovation, and Firm Performance_ , vol. 2, 2020.

- Holmes, Thomas J, Ellen R McGrattan, and Edward C Prescott. “Quid pro quo: Technology capital transfers for market access in China”. _The Review of Economic Studies_ , vol. 82, no. 3, 2015, pp. 1154–93.

- Hu, Albert Guangzhou, and Gary H Jefferson. “A great wall of patents: What is behind China’s recent patent explosion?” _Journal of Development Economics_ , vol. 90, no. 1, 2009, pp. 57–68.

- Jaffe, Adam B, Manuel Trajtenberg, and Rebecca Henderson. “Geographic localization of knowledge spillovers as evidenced by patent citations”. _the Quarterly journal of Economics_ , vol. 108, no. 3, 1993, pp. 577–98.

- Jeon, Yongbok, Byung Il Park, and Pervez N Ghauri. “Foreign direct investment spillover effects in China: Are they different across industries with different technological levels?” _China Economic Review_ , vol. 26, 2013, pp. 105–17.

- Jia, Ruixue, Margaret E Roberts, Ye Wang, and Eddie Yang. _The impact of US-China tensions on US science_ . National Bureau of Economic Research, 2022.

- Jiang, Kun, Wolfgang Keller, Larry D. Qiu, and William Ridley. “International joint ventures and internal technology transfer vs. external technology spillovers: Evidence from China”. _Journal of International Economics_ , vol. 150, 2024, p. 103939.

- Jinji, Naoto, and Shunya Ozawa. _Impact of Technological Decoupling between the United States and China on Trade and Welfare_ . Research Institute of Economy, Trade / Industry (
Joshi and Schultz 2013; Miller and Babiarz 2016), as Ashraf et al. (2014) show, it can also lead to spousal conflict among couples with discordant fertility preferences. In the Indian context, married couples exhibit significant misalignment in their self-reported ideal number of sons. When the husband desires more sons than the wife, the wife’s take-up of modern contraception might lead to backlash from dissatisfied and aggrieved husbands. Indeed, we find that our IPV results are driven by couples in which the husband has a stronger son preference than the wife.

We note upfront that using self-reported data on IPV has potential disadvantages. If female leaders increase female constituents’ sense of self-worth, they might start recognizing violent acts by their spouses as IPV and might be more likely to report IPV. While we cannot entirely rule out the presence of such reporting bias, we find no evi2
5|18.5|19.6|3.1|6.9|9.4|6.4|
|Developed econ (48)|World|82.9|79.9|72.2|62.5|6.8|5.8|7.2|6.6|
|Developing econ (ROW)|Developed econ (48)|11.4|11.8|15.5|15.8|7.6|9.7|9.0|8.8|
|Developing econ (ROW)|Developing economies|5.6|8.2|12.3|21.7|11.3|11.2|15.2|12.5|
|Developing econ (ROW)|World|17.1|20.1|27.8|37.5|8.9|10.4|12.1|10.5|
|World|Developed econ (48)|67.2|73.3|69.2|58.7|8.1|6.2|7.0|7.1|
|World|Developing economies|32.8|26.7|30.8|41.3|5.0|8.4|12.0|8.4|
|World|World|100.0|100.0|100.0|100.0|7.2|6.8|8.8|7.6|

Source: Based on UN COMTRADE Statistics.
and can have practices that are detrimental to the environment as opposed to formal firms which presumably adhere to environmental regulations (e.g. see Basbay et al (2016) or Biswas et al (2012)). To study these effects, the paper builds a theoretical model wherein the economy consists of formal and informal workers and the impact on countries’ total emissions depends on the difference in emissions per worker between the formal and informal sectors and the share of informal workers in total.

The literature analyzing the relationship between the informal sector and emissions is very scarce and is limited only to a few papers. For example, Elgin and Oztunali (2014) use cross-country panel data to analyze this relationship by applying fixed effects regressions and system estimations. They find an inverted-U relationship between the size of the informal economy and environmental pollution: small and large sizes of the informal economy are associated with lower environmental pollution and medium levels of informality are associated with higher levels of environmental pollution. This inverted-U shape is also called the environmental Kuznets curve (e.g. see Cole et al. (1997), Dinda (2004), or <mark>Stern (2004)).</mark> Estimating the relationship between the environmental quality and informality for a sample of 58 countries, Swain et al. (2020) find that the informal sector measured as a share of value added positively influences the emission of local pollutants but has no impact on global pollutants such as CO2 per capita and CO2 intensity. The contribution of our study to the literature is three-fold. First, it adds to the literature by studying the impact of informality on emissions across developing countries using more recent data and helps understand to what extent the differences in production technologies and compliance with the environmental policies drive the differences in the emissions. Second, it uses a new measurement of informal economy – the share of informal workers in total employment which is based on data and not on estimates which are sensitive to approaches and assumptions that could potentially bias the
at an older age, maternal age at the time of the survey is included as an additional outcome variable. Thus, these regressions mimic the main regressions, except the outcome variables are now maternal characteristics. The results clearly show that the program did not lead to any change in maternal characteristics. Thus, it is unlikely that there is an over-representation of individuals with educated mothers in the sample so as to confound the main results. The conclusions do not change if this exercise is done separately for male and female samples (results not shown in the paper).

A more explicit assessment of the parallel pre-trends assumption is explored by examining the impact of placebo reforms.<sup>41</sup> For this test, consider the cohort born between 1974 and 1984 and assume that a midwife started working in the community 10 years before she actually started working. For example, if a midwife started working in a community in 1990, it is assumed that the midwife started working in 1980. Based on the assumed placement year and actual birth year, one can now create the exposure measures. In the absence of non-parallel trends, it is unlikely one will find estimates of the impact of midwives that are similar to the ones found earlier. Table 8 presents the results—the new estimates are much smaller and are usually insignificant at conventional significance levels. Thus, there is not sufficient evidence to reject the assumption of parallel trends between program receiving and non-receiving communities.

One important concern with the analyses involving long-term outcomes is selective attrition. While the IFLS team puts a great effort in following up households and respondents, not all the individuals, who were born between 1984-1994 in the IFLS households, are interviewed in the IFLS 5, conducted in 2014. Thus, it is obligatory to examine if there is any selective attrition with respect to the program. The panel structure of the IFLS can be used to test for this possibility. First, consider the entire sample of births for IFLS between 1984-1994 from IFLS 1 and IFLS 2
significant, however.

The treatment offer reduced total monthly earnings among female participants in garment making by almost 11 GHS (15 percent) compared with the control group, although the p-value is just outside the threshold for marginal significance. The reduction in total earnings was driven by noisy reductions in wage earnings and self-employment income. Although the estimated coefficients on farming and apprenticeship income are positive, the magnitudes are negligible and statistically insignificant.

# **4.2.2 Material Well-Being**

We present results on durable household assets, personal consumption expenditure on a limited set of items (such as phone calls), savings, loans, and migration in Table 6. These indicators enable us to examine broad measures of individual welfare. Our durable household assets index measured whether the household in which the respondent lived owned any of the following items: a radio, television, car, motorbike, and refrigerator, with the restriction that these items had to be functioning at the time of the endline survey (Column 1). Previous studies constructed similar indices based on householdowned durable assets for measuring poverty and conducting welfare analysis (Booysen, Van Der Berg, Burger, Von Maltitz, & Du Rand, 2008; Filmer & Scott, 2008). Although the overall (full-sample) treatment effect on durable household assets is positive, it is not statistically significant. For male participants in construction, however, the intention-totreat estimate is negative and insignificant. Among female participants in cosmetology the program offer increased assets by 0.1 _σ_ (p-value _<_ 0.05), but this is not very robust to multiple testing adjustments. The estimated effect among female participants in garment making is small (0.03 _σ_ ) and insignificant. Overall, these results could be driven by the greater propensity for cosmetologists to finish training and shift into self-employment, leading to greater asset accumulation.

Because of time and budget constraints, we could not field a full detailed consumption module. Rather, we measured personal consumption expenditure as the sum of the
while raking skills related to knowledge of contemporary issues, system design, and design of experiments low. However, some skills are ranked differently, such as lifelong learning (valued by Indian employers and less so by US employers). We cannot test whether the rankings are statistically significantly the same since we do not have the underlying data for the US employer survey.

**Table 5 Ranking of Importance of Skills in India compared to the US**

||**India**|**US**||
|---|---|---|---|
|% of<br>employers<br>respond ―Very<br>or Extremely<br>Important‖|Skill|Skill|% of<br>employers<br>respond ―Very<br>or Extremely<br>Important‖|
|94%|Lifelong-learning|Communicate effectively|91%|
|93%|Understand professional and<br>ethical responsibilities|Engineering problem solving|86%|

8 Nevertheless, there is also the possibility that the formulation of the questionnaire in English implied that an English speaking person filled out the questionnaire. This could potentially have introduced a bias towards increased importance of English.

9 There is a logical explanation why the two surveys asked feedback on a set of skills that were almost identically formulated. The US survey sought feedback on the ABET EC2000 (a)-(k) criteria. These criteria formed the basis for the formulation of the expected attributes and competences of an engineer graduate in the Washington Accord. The NBA criteria used in this Indian employer survey were formulated to be consistent with the Washington Accord.

15
sex.

<!-- Start of picture text --> g!(R!) g!(R!)<br>V NV V NV<br>R! R!∗ R! R!∗<br><!-- End of picture text -->

- (a) No change in the share of males (b) Decrease in the share of males proposing risky sex proposing risky sex

**Figure IV** Shift _gm_ ( _Rm_ ) to the right (increase net cost of risky sex)

Both channels predict an unambiguous reduction in IPV. Through the expressive channel, males are less likely to play V when females play N, so risky sex does not occur when males propose it. Through the instrumental channel, males are again less likely to play V when females play N; however, males may also be less likely to propose risky sex in the first place.

# **Empirical evidence for the** _Boys_ **treatment**

We have shown that the _Boys_ treatment reduces female reports of IPV, suggesting that the soccer intervention is shifting either the expressive or instrumental (or both) motivations for violence. If the soccer intervention increases the cost of violence for males, _Vm_ , we should see male attitudes toward IPV improve, lower perpetration of IPV by males, and a decrease in sexual activity among males and females. If the soccer intervention shifts the distribution of _Gm_ ( _Rm_ ), then we would expect to see an improvement in SRH attitudes and a reduction in sexual activity, along with a reduction in IPV perpetration. Whether the reduction in sexual activity is of similar magnitude or is greater than the reduction in violence depends on whether the shift in _gm_ ( _Rm_ ) is as depicted as in IV(a) or IV(b).

We now turn to the male survey data and estimate the following specification to test 25
12</sup> Appendix Table 1 lists the identified turning points at the start of tightening and easing phases. The near symmetry of the blocks for each country—with each tightening phase followed by an easing phase (and vice versa) and a roughly equal number of tightening and easing phases for each country—reflects the way in which these cycles are defined.

The 24 economies in our sample averaged 5 tightening and 4 easing phases, but some countries have had substantially more rate cycles than others. For example, the United States has had 17 distinct easing and tightening phases, and Canada and Sweden 15, while Ireland and Portugal had only 3 and 4, respectively. The smaller number of cycles for the individual euro area countries, however, reflects their shorter time series—as any cycles after 1998 are included as the euro area and not for the individual countries. Of the countries with data on nominal policy rates from 1970 through the end of the sample, Japan has the fewest turning points, with only 4 tightening and 4 easing phases—about half those of several non-euro area economies.

More interesting than this list of turning points, however, is seeing these cycles in the context of the evolution of the policy rate and any QE or QT announcements for each economy. Chart 1 shows these graphs of policy rates for three major economies: the euro area, Japan, and United States. (Appendix Charts 1 and 2 show the resulting cycles for other economies with the individual euro area countries broken out separately on one page). In each graph, the start of tightening phases is denoted by dashed purple lines and easing phases by dashed red lines. The start of QT and QE programs are in dashed blue and orange lines, respectively.

> 11 We only include programs involving government bonds (the vast majority of asset purchases programs), and do not incorporate adjustments to the speed, size or scope of the programs after announcement. 12 For an economy that has recently adjusted its policy rate,
**Table 1** . Estimation results from a mixed logit model to predict WTP. The model is estimated using data on all respondents (1,373 respondents), predicting the joint probability of the five choice made by each individual (total 6,865 choices). Note that both models also incorporate observed heterogeneity in the program cost coefficient and control for program misconceptions; see Supporting Information for full estimation results.

|||Model 1<br>Correlated<br>|: Mixed Lo<br>Random P<br>|git with<br>arameters<br>|Model 2:M<br>Progra<br>|odel 1 + C<br>m Misconc<br>|ontrols for<br>eptions<br>|
|---|---|---|---|---|---|---|---|
|Variable||Est.|t-value|p-value|Est.|t-value|p-value|
|Avoided forest<br>|Mean|5.27|7.39|0.00|4.97|6.78|0.00|
|loss, WTP in $ per 1 % point|Std.<br>dev.|8.67|2.95|0.00|8.57|3.03|0.00|
|Reduced species<br>|Mean|3.03|2.07|0.00|3.19|2.18|0.03|
|threats, WTP in $ per 1 % point|Std.<br>dev.|16.30|3.68|0.00|15.19|3.73|0.00|
|LF,S||-3.72|-3.95|0.00|-3.47|-3.63|
are "free on board", thus excluding any duties or shipping charges. The 2005 FTS data set comprises information on 16,541 exporting ...rms and 220 destination markets. Despite the above-mentioned constraint, the export transactions included in these data aggregate to 97 percent of the total value of merchandise exports reported in the o¢ cial national accounts.<sup>6</sup> Firm characteristics. Data on additional characteristics of exporting ...rms, namely labor productivity (gross value added per worker), employment, age and geographical location, come from 2005 Enterprise Integrated Accounts System (EIAS). This is a census of ...rms operating in Portugal run by the National Statistics Institute.<sup>7</sup> These data are

> 6 In the FTS data, Serbia, Montenegro, and Serbia and Montenegro are considered as three di¤erent destinations. We treat them as such in the analysis, but none of our results is in‡uenced by this decision.

> 7 In both the FTS and the EIAS, ...rms are uniquely identi...ed by their tax identi...cation number (NPC). Hence the mapping of the two data sets was straightforward. After merging the two datasets, the Statistics Institute applied a transformation of the NPC to the data that were made available to researchers in order

7
half of the average income level in sub-Saharan Africa, Rwanda remains a rather poor country.<sup>4</sup> For our analysis it is important to highlight that aggregate exports have increased rapidly over the last decade. Figure 1 shows the evolution of both exports and imports, calculated by aggregating over all firms in the firm-level sample that we use in our subsequent analysis (which is described in the next section). Both series are normalized at 1 in 2008, the starting period of the data in the firm-level sample. We use two alternative deflators to convert the nominal values that we observe in the firm-level data into real values.<sup>5</sup> Exports approximately tripled from 2010 to 2014 in real value. Imports also expanded rapidly, but they increased only by 120%.

**Figure 1: Evolution of aggregate exports and imports**

<!-- Start of picture text --> (a) exports<br>4<br>2<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total exports (nominal) Total exports (real - US deflator)<br>ratio (with WDI Goods exports) Total exports (real - WDI deflator)<br>ratio (with WDI Merchandise exports)<br>(b) imports<br>4<br>2<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Total imports (nominal) Total imports (real - US deflator)<br>ratio (with WDI Goods & Services imp.) Total imports (real - WDI deflator)<br><!-- End of picture text --> Source: Total exports and imports are constructed by aggregating over the firm-level custom’s data. The denominator in the ratios and the WDI deflator are constructed using series downloaded from the World Bank’s online World Development Indicators.

4 The average GNI per capita for all