rafmacalaba/datause-encoder-data huggingface dataset:
# Table **I.** All Firms **(Cont.)**

||Supply|Side Only|Joint Supp|ly and Demand|
|---|---|---|---|---|
|Parameter|CRTS|Non-CRTS|CRTS|Non-CRTS|
|_bat_|1.95*<br>(0.47)|2.25*<br>(0.53)|-10.59*<br>(0.97)|-8.39*<br>(1.23)<br>|
|_b,_||-74.6<br>(30.5)||-2.12E+2*<br>(93.8)|
|||<br>54.74<br>(13.64)||1.75E+2*<br>(45.2)|
|P.||3.62E-4||-4.91E-4|
|||(2.65E-4)||(6.3E-4)|
|ye||-0.06<br>(0.06)||-0.08<br>(0.14)|
|_bdm_|||0.00016<br>(0.0002)|-3.75E-4-*<br>(0.0002)|
|_bdt_|||0.0016<br>(0.0010)|0.0021*<br>(0.0009)|
|_bd_|||-2.92E+3|-2.29E+4-<br>|
||||(1.44E+4)|(1.17E+4)|
|**_ad_**|||-2.57E+2*<br>(99.91)|-2.21E+2-<br>(84.8)|
|**_Pd_**|||-6.01E-8|4.4E-7*|
||||(2.76E-7)|_<br>(2.24E-7)|
|--d|||0.0012<br>(0.0004)|0.0010*<br>(0.0004)|
|**_bmt_**|||-011*<br>|-0.24*<br>|
||||(0.06)|(0.06)|
|_bm_|||2.56E+6-<br>(1.08E+6)|1.62E+6<br>(1.13E+6)|
|am|||1.19E+4*<br>(6.20E+3)|2.92E+4-<br>(6.89E+3)|
|10m|||<br>4.93E-5*<br>(2.06E-5)|3.12E-5<br>(2.16E-5)<br>|
|m|~-0.|05*||-0.14*|
||||(0.03)|(0.03)|
|Objective*N<br>N|546.00<br>139|530.00<br>139|239.34<br>139|211.95<br>139|

Standard errors are in parentheses. ***** Significant at the 5 percent level. Significant at the 10 percent level.

- 16 -
# **Table 3. Percent Underemployed by Gender and Education**

|**Education**|**Male**|**Female**|**Total**|**Share of Population**|
|---|---|---|---|---|
|None|8.4|10.1|9.4|15.1|
|Some SD|10.3|11.6|10.9|28.7|
|SD Graduate|12.6|14.2|13.2|40.5|
|SMP General|14.2|16.6|14.9|8.5|
|SMP Vocational|11.5|13.9|12.3|1.3|
|SMA General|15.6|19.9|16.8|3.6|
|SMA Vocational|11.5|24.2|15.2|2.0|
|Diploma I/II|10.5|19.8|14.1|0.1|
|Academy|13.2|17.9|14.6|0.2|
|University|11.2|14.0|11.9|0.2|
|**Total**|11.7|13.0|12.2|100|

Given the fact that rural workers experience higher rates of underemployment, it is not too surprising to find that agriculture-related sectors of the economy are characterized by above average rates of underemployment. Table 4 below presents the incidence of underemployment by primary sector of employment. The first four categories are what would typically be considered to be agricultural activities and account for nearly twothirds (64.5 percent) of the work force in the informal sector; all four categories have higher than average rates of underemployment. The only other category that represents an important share of the population is trade and retail, and this category is characterized by very low rates of underemployment. While there are differences across gender, it is interesting to note how similar the pattern is for both men and women.

A breakdown of underemployment by primary sector and location (not shown) demonstrates that these workers in the first four categories account for most (81.9 percent) of the underemployment in rural areas, as would be expected. A somewhat more surprising result, however, is that 16.9 percent of urban workers claim agricultural activities as their primary form of employment, and these workers account for 30.8 percent of urban underemployment. In other words, almost one-third of urban underemployment in the informal sector can be accounted for by workers who are employed in sectors that are normally considered rural activities.

7
groups: the innovators, who adopt the new technology or product based on exogenous information, and the imitators, who adopt based on endogenous information (learning from the adopters).

While empirical studies show that different diffusion models fit different situations effectively, this paper employs both logistic and Gompertz models to examine the aggregate diffusion of the CDM. In the case of CDM, the analogy to the epidemic model is that exposure to the opportunity and learning from the experience of the countries that have already adopted the mechanism lead new countries to adopt the mechanism. Also, the ability of investor countries to invest in several host countries leads to increased learning and adoption.

# **Mitigation Potential, Model Predictions and the CDM Pipeline**

Drawing on a combination of top-down and bottom-up studies, the IPCCC [24] concluded that most of the economic potential for emission reduction is in developing countries (table 1). Studies reviewed by the Panel suggest that upwards to 3.3 gigatons of that potential represents possible win-win improvements in efficiency that would pay for themselves in the long run. However, even at a low carbon prices of less than $US 20 per ton of CO2 equivalent (tCO2e), a consensus view is that roughly half of a global mitigation potential of 7.4 gtCO2e can only be brought into the Kyoto trading system via the Clean Development Mechanism. Moreover, as prices increase developing countries’ share of global mitigation potential increases.

At the same time, recent experience suggests the CDM project cycle is better suited to some types of mitigation projects than others. Because developing countries have not pledged emission reductions, CDM projects rely on counter-factual business-as-usual scenarios, or baselines, to calculate the number of offsets that enter the Kyoto trading system. The methodology for determining the project baseline and the plan for monitoring project outcomes is reviewed by the CDM Executive Board case-by-case and each project must be approved before it can go forward. Outcomes from the project are reviewed as well prior to the issuance of CERs. The approach is intentionally conservative and came about because of conceptual uncertainties about how to construct baselines and a deep suspicion by some treaty negotiators that project monitoring would be weak unless closely watched, 6
**Figure 2: Power supply gap in the Dominican Republic**

<!-- Start of picture text --> 2,500.0<br>2,000.0<br>1,500.0<br>1,000.0<br>500.0<br>-<br>2003 2004 2005 2006 2007 2008<br>Maximum Electricity Demand Maximum supplied power<br>Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct<br><!-- End of picture text --> Source: National Interconnected Electric System Coordinating Organism, published at the www.sie.gov.do In the same way some factories have chosen to privately generate electricity in order to avoid sudden production stops, households cannot fully rely on this faulty system either. Since electricity blackouts are frequent in the Dominican Republic, buildings increasingly count with alternative electricity supply systems. The so-called “inversores” (electrical inverter) are used to supply energy stored in batteries (direct current) to alternating current. They are the cheapest and less sophisticated private solution to electricity shortfalls in the country. On the other hand, “generadores” (generators, normally using fuel-oil) are normally found in newer and more expensive blocks of flats and houses.

Given this context, it would be interesting to calculate auto generation capacity installed by private hands (both companies and families) and also expenditure on auto generation, which can illustrate the substitution of public goods.

In 2001, a survey conducted under the supervision of the National Commission for Energy concluded that auto generation capacity (2,715 MW at the time) was already similar to installed power in the national interconnected system (see BID, 2010). In order to update these figures, and given that electricity generators are not manufactured in the Dominican Republic, we have used customs statistics to estimate added imported capacity in the last decade. Private

[6]
# **Data Sources**

CEPEA (Center for Advanced Studies on Applied Economics), ethanol market price, data available at <u>http://cepea.esalq.usp.br/english/ethanol/ accessed April 2012.</u> _Contribution to Social Security Financing_ — _COFINS_ data available <u>http://www4.planalto.gov.br/legislacao/legislacao-por-assunto/impostos-taxas-e-contribuicoesteste/impostos-taxas-e-contribuicoes#contribuicoes</u> _Contribution to the Social Integration Program_ — _PIS_ data available at <u>http://www.planalto.gov.br/ccivil_03/Leis/LCP/Lcp07.htm</u> ICMS-ST _substituição tributária_ , available at <u>http://www.fazenda.df.gov.br/aplicacoes/legislacao/legislacao/TelaSaidaDocumento.cfm?txtNu mero=404&txtAno=1999&txtTipo=7&txtParte and at http://www.brazil-help.com/taxes.htm</u> _Estrutura de Forma_ _<mark>ção</mark> dos_ _<mark>Preços</mark>_ Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (ANP) (National Agency of Petroleum, Natural Gas and Biofuels), data available at <u>http://www.anp.gov.br/?pg=58058&m=&t1=&t2=&t3=&t4=&ar=&ps=&cachebust=133711076 5166</u> accessed March 2012.

<mark>ANP</mark> Agência Nacional do Petróleo, Gás Natural e Biocombustíveis _<mark>Levantamento de Preços e de Margens de Comercialização de Combustíveis,</mark>_ data available at <u>http://www.anp.gov.br/preco/</u> accessed March 2012.

SINDICOM _Sindicato Nacional das Empresas Distribuidoras de Combustíveis e Lubrificantes_ data available at <u>http://www.sindicom.com.br/pub_sind/cgi/cgilua.exe/sys/start.htm?tpl=home</u> accessed March 2012.

_Taxes on Goods and Services_ — _ICMS_ — _Impostos Sobre Circulação de Mercadorias e Prestação de Serviços_ (Merchandise and Services Circulation Tax) data available at <u>http://www.planalto.gov.br/ccivil_03/Leis/LCP/Lcp87.htm</u> 44
As a weather indicator, we use the Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index developed by Beguería et al. (2014).<sup>10</sup> The SPEI jointly considers precipitation, potential evaporation, and temperature, which provides a distinctive advantage over simple rainfall or temperature indicators as it incorporates the interaction between these variables in determining farmers' agricultural outcomes (Bertoli et al., 2022; Harari & La Ferrara, 2018). It is increasingly used in the literature to capture agriculture-relevant weather shocks and has been found to outperform other indicators in predicting crop yields (Vicente-Serrano et al., 2012). The SPEI is obtained by taking the difference between precipitation ( _P_ ) and potential evapotranspiration ( _PET_ ): _Di = Pi – PETi_ . Then, _Di_ is standardized so that the indicator represents the deviation from the normal water balance (a SPEI of 0 indicates a value corresponding to 50% of the cumulative probability of _D_ , according to a log-logistic distribution). A negative SPEI value is associated with dry events (lower rainfall) while positive SPEI values capture wet events (higher rainfall).

To capture weather conditions that matter for agricultural outcomes and, in turn, migration decisions, we focus only on growing-season SPEI (GS-SPEI), defined as the average monthly SPEI during the growing season months in the period between two waves. The rationale for this choice is provided by previous literature investigating outcomes and settings where impacts tend to be driven by the agricultural channel (Cai et al., 2016; Cattaneo et al., 2019; Harari & La Ferrara, 2018), which is particularly sensitive to weather variability during the growing season. To identify growing periods, we rely on crop calendars—differentiated by region—provided by the Famine Early Warning System Network (FEWS NET) of the United States Agency for Agricultural Development (USAID).<sup>11</sup> Ideally, one could argue that we should attribute to each migrantsending household only the average SPEI during the growing season months between the last wave and the month in which the individual migrated. However, this would imply that migration is an

> anonymized survey datasets with publicly available remote-sensing weather products, since the current spatial resolution of geospatial data is not fine enough for spatial anonymization to result in mismeasurement of weather events that are experienced by sampled households.

> 10 The SPEI data are based on gridded data produced by the Climatic Research Unit of the University of East Anglia and can be downloaded here. The level of resolution is 0.5 x 0.5 degrees.

> 11 The Nigeria FEWS NET crop calendar is available <u>here.</u>

14
parents chose the initial location before the imposition of _Hukou_ system, the location can also be viewed as a summary statistic of unobserved heterogeneity (ability and preference) of parents and grandparents.<sup>21</sup> Given the restrictions on geographic mobility, it is also likely that the children with rural residence live in the same village (or the same county) as their parents, and thus the county fixed effects capture common labor market opportunities due to initial economic endowment of a county (for example, mineral resources, land productivity, climatic conditions etc.).<sup>22</sup> The estimates of the effects of parental schooling on children’s schooling reported in Table 2 tell a story which is largely consistent with the correlations reported in Table 1; the strength of the intergenerational persistence in schooling for sons is lower than that for daughters in 1988, but they have become comparable in 2002.<sup>23</sup> The estimates in the last column of Table 2 (column 7) show that the intergenerational persistence in educational attainment remains both numerically substantial and statistically significant in 1988 and 2002 even after we control for a rich set of individual, parental, household, village level variables along with county fixed effects. The controls used include age of child and both mother and father, and parents’ occupation, are thus likely to pick up a good measure of the genetic endowment of children. As pointed out before, the changes in the educational persistence from 1988 to 2002 are not likely to be due to changes in genetic correlations, as genetic traits do not change in any significant way in a decade and a half. We provide a more complete treatment of the possible role played by common unobserved factors such as genetic transmissions in the estimated intergenerational educational persistence below.

> 21 The adult children sample used in the paper consists of individuals born after 1967.

> 22 Before 1998, a child would inherit the _Hukou_ of his/her mother. After 1998, it can be either mother’s or fatehr’s _Hukou_ if they are different.

> 23 This conclusion does not depend on the set of controls used across different columns in Table 2.

16
of a promising model of childcare provision, with scope for future work to offer further validation.

The monthly cost of operating the childcare centers was USD 16.6 per child if used at the full capacity of 50 children per center. This is lower than our estimated treatment on the treated (TOT) effect on monthly earnings, with increases of FCFA 13,253.96 (approximately USD 23) for the full sample and FCFA 14,447.78 (approximately USD 25) for the sample of women with a child under the age of two. In practice, the childcare centers typically operated below maximum capacity and had 33 children on average, so the operating costs per child were higher (at USD 25.2). Nonetheless, the intervention potentially had broader impacts beyond the monetary benefits of income increases if the training provided to the childcare center attendants generated positive development outcomes for other children in their communities. Although operating costs are comparable to the earnings increases observed for program participants within our treated sample, these are higher than costs in other settings. For example, Mart ́ınez A. and Perticar ́a (2017) report that community-based preschools in Mozambique cost USD 3.09 per child per month and increased child development scores by 0.33SD (implying they were three times as cost effective as the Burkina Faso childcare intervention, which had an estimated child development TOT effect of 0.88SD with a cost of USD 25.2 per child). The key differences are higher provider to child ratios, with the Mozambique preschools and other community-based models typically having a ratio of 1:15, instead of 1:5 ratio of the mobile childcare centers. Relatedly, the Burkina Faso childcare centers catered to children aged 0 to 6 whereas the Mozambique preschools and other similar models typically focus on children aged 3 to 5. Almost 60% of women in our sample had at least one child aged between 0 to 2. Given that the take-up rates and treatment effects for children in this age group were similar to those for the older age-group, the added costs of caring for younger children appear to be driving the decreased cost-effectiveness. Thus, the program’s expansive scope of serving the youngest children (typically excluded from childcare programs) comes with the trade-off of increasing costs, which is an important factor to consider in the prioritization of program objectives.<sup>19</sup>

> 19This cost-effectiveness comparison comes with the caveat that attendance at the mobile childcare centers was irregular for many children. We unfortunately cannot link individual attendance records to survey data for children to adjust our cost-effectiveness estimates.

21
**Table 1: Robustness checks: Spillovers from EM7**

||**On impact**|**1year**|**2years**|**3years**|
|---|---|---|---|---|
|**Aggregate model (baseline)**|**0.28**|**0.79**|**0.86**|**0.87**|
||[0.15 0.43]|[0.48 1.13]|[0.49 1.3]|[0.46 1.32]|
|**Global financial crisis dummy**|**0.26**<br>[0.14 0.4]|**0.78**<br>[0.52 1.07]|**0.84**<br>[0.53 1.18]|**0.85**<br>[0.54 1.19]|
|**Controlling for oil prices**|**0.28**<br>[0.15 0.4]|**0.87**<br>[0.58 1.18]|**1.34**<br>[0.93 1.81]|**1.55**<br>[1.04 2.17]|
|**Industrial production series**|**0.39**<br>[0.1 0.7]|**0.97**<br>[0.4 1.6]|**0.93**<br>[0.3 1.6]|**0.93**<br>[0.3 1.6]|
|**Country model**|**0.26**<br>[-0.07 0.59]|**0.81**<br>[0.26 1.41]|**0.88**<br>[0.23 1.58]|**0.89**<br>[0.24 1.62]|
|**Factor-augmented model**|**0.28**<br>[0.01 0.57]|**0.54**<br>[0.17 0.96]|**0.52**<br>[0.1 0.94]|**0.51**<br>[0.090.95]|
|**Spillovers from BRICS**|**0.20**<br>[0.08 0.34]|**0.69**<br>[0.390.98]|**0.80**<br>[0.43 1.16]|**0.80**<br>[0.42 1.2]|
|**Spillovers to other EFM plus Hong Kong,**|**0.59**|**1.14**|**1.06**|**1.05**|
|**Singapore, Korea Rep.**|[0.43 0.75]|[0.81 1.51]|[0.67 1.5]|[0.65 1.5]|

_Note:_ The table shows the cumulated impulse responses of growth in other EFM, for different horizons, due to a 1 percentage point increase on impact in EM7 growth. These are from the alternative specifications of the aggregate model. Bolded denote the medians and the numbers in the parentheses are the 16-84 percent confidence bands. **Global financial crisis dummy:** includes a dummy for the global financial crisis of 2008-09; **Controlling for oil prices:** oil prices are treated as an exogenous regressor in the model; **Industrial production series:** estimates are based on industrial production growth; **Country model** : spillover estimates for the individual EM excluding EM7 economies from the country models are aggregated by GDP weights; **Factor-augmented model:** estimates are based on the common factor (first principal component) each of G7, EM7, and other EFM economies; **Spillovers from BRICS:** estimates are based on spillovers from BRICS. The spillover destination in this case is other EFM minus South Africa; **Spillovers to other EFM plus Hong Kong, Singapore, Korea Rep.** : estimates for alternative definition of spillover destination that includes other EFM and Hong Kong, Singapore, Korea Republic.

22
raise IPV by reducing the opportunity cost of men’s time (Blattman and Miguel 2010)<sup>12</sup> , and increasing exposure of the woman to the man if he spends more time at home (Chin, 2002).<sup>13</sup> Similarly to this paper, but using Spanish data, Alonso-Borrego and Carrasco (2017) and TurPrats (2017) find that improved female employment opportunities increase violence against women for some (although not all) women.<sup>14</sup> Alonso-Borrego and Carrasco (2017) find that female employment reduces violence only if the female’s partner is employed as well. Tur-Prats (2017) finds that in Spanish territories with a historical nuclear family tradition a decrease in female unemployment relative to male unemployment increases IPV due to cultural norms pertaining to gender.<sup>15</sup> This study shows the critical role of women’s access to divorce in explaining this relationship across a number of developing countries covering a wide geographical area. It may be that access to divorce is itself influenced by historical cultural norms pertaining to gender. However given the wide geographical scope of our study it is not possible to use consistent controls for such historical cultural norms, so the study cannot tease out the separate influence of gendered norms vs access to divorce in this study.

Section 2 describes the data and Section 3 the methodology. Section 4 provides the estimation results, Section 5 the robustness checks, and Section 6 provides a discussion and conclusions.

# **2 Data**

> 12 The literature on civil conflict emphasizes the opportunity cost of time as a causal factor. It argues that economic shocks that leave men without jobs leave them vulnerable to being drawn into soldiering (Blattman and Miguel 2010).

> 13 The idea that exposure or inadvertent increases in time that the partners spend together increases IPV is analogous to the idea in the wider crime literature that incarceration reduces crime.

> 14 There is no similar evidence previous to this study for developing countries.

> 15 Specifically, she finds that, in areas in which deep-rooted social norms support the male breadwinner stereotype, decreases in female relative to male unemployment increase violence. In contrast, in areas in which cultural norms viewed both men and women as providers, she finds evidence consistent with the existing US and UK evidence, with increases in female unemployment raising IPV.

6