rafmacalaba/datause-encoder-data huggingface dataset:
# **IV. Results**

Tables 2 and 3 report our key results. We show the results for both small and large conflict onset. The coefficients on the control variables have broadly the expected signs. Political instability increases the risk of both small and large conflict. More populous countries also face a higher chance of civil conflict. The estimated coefficient for states with noncontiguous territory is positive and consistent with Fearon and Laitin (2003). Ethno-linguistic fractionalization (ELF) increases the risk of small scale war, while it does not appear to be associated with larger scale conflicts. Democracy reduces the risk of large scale conflict, while it has an ambiguous effect on small scale war. Finally, economic development, operationalized as income per capita is not predicting civil conflict in our models.

**Table 2 Conditional Effect of Oil Wealth on Conflict Onset**

||Model 1<br>Small conflict<br>onset|<br>Model 2<br>Large conflict<br>onset|<br>Model 3<br>Small conflict<br>onset|<br>Model 4<br>Large conflict<br>onset|Model 5<br>Small conflict<br>onset|<br>Model 6<br>Small conflict<br>onset|
|---|---|---|---|---|---|---|
|Oil-Gas Value per capita|0.17|-0.52|0.2|-0.08|-0.08|-0.51|
||(0.124)|(0.381)|(0.149)|(0.236)|(0.166)|(0.3570)|
|Government Expenditure|0.016<br>|0.004<br>|||||
||(0.01)|(0.02)|||||
|Gov Expenditure*Oil|-0.013*|0.01|||||
||(0.008)|(0.016)|||||
|Military Expenditure per capita|||0.0012***<br>(0.00)|0.002***<br>(0.00)|||
|Military Expenditure*Oil|||-0.001***|-0.001***|||
||||(0.0004)|(0.0003)|||
|Welfare Expenditure|||||-0.109***|-0.008|
||||||(0.0340)|(0.046)|
|Welfare Expenditure*Oil|||||(0.0003)|0.02|
||||||(0.0175)|(0.019)|
|ELF|1.609***|0.508|1.508***|0.517|1.484***|0.257|
||(0.48)|(0.95)|(0.44)|(0.93)|(0.51)|(0.915)|
|Religious Fractionalization|-0.704|0.672|-0.23|0.603|-0.3|0.778|
||(0.46)|(0.71)|(0.50)|(0.81)|(0.54)|(0.817)|
|Political Instability|0.686***|0.715**|0.741***|0.739***|0.792***|0.716**|
||(0.19)|(0.30)|(0.17)|(0.29)|(0.22)|(0.29)|
|Non Contiguity|1.166***|1.286*|1.171***|1.325**|1.293***|1.678***|
||(0.38)|(0.68)|(0.33)|(0.60)|(0.28)|(0.54)|
|Polity IV|0.038|-0.115**|0.026|-0.127***|0.0529*|-0.107**|
||(0.03)|(0.05)|(0.02)|(0.04)|(0.03)|(0.05)|
|Logged Population|0.362***|0.417***|0.348***|0.397***|0.219***|0.318***|
||(0.07)|(0.11)|(0.06)|(0.09)|(0.07)|(0.10)|
|Logged GDP per capita|-0.009|0.155|-0.091|0.025|-0.286*|-0.211|
||(0.09)|(0.12)|(0.10)|(0.13)|(0.16)|(0.23)|
|Constant|-12.43***|-14.46***|-11.61***|-13.59***|-6.015***|-10.27***|
||(1.54)|(2.33)|(1.30)|(2.00)|(2.107)|(2.90)|
|Psedo R squared|0.126|0.114|0.123|0.118|0.14|0.12|
|Log pseudolikelihood|-611.51|-253.83|-692.3|-288.59|-503.73|-254.78|
|Wald Chi-squared|355.51***|98.17***|414.25***|125.25***|175.81***|96.23***|
|Observations|5,190|5,190|5,618|5,618|4,108|4,226|

Note: ***p<0.01, **p<0.05, *p<0.1. The estimation method is logistic regression. Models 5 and 6: Welfare spending is imputed using Taydas and Peksen (2012) data. Regional dummies, half decade dummies, peace years and cubic splines are included in all models. Country clustered standard errors in parentheses.

15
<!-- Start of picture text --> 6<br>25<br>£<br>a<br>o<br>§<br>=4<br>8<br>£<br>3<br>2>3 socialHigh supply goods of +[> > —*——*— DistrictDistrict withwith abovebelow medianmedian supplysupply<br>é -<br>co) 2 Moving up one<br>= income decile<br>=<br>o<br>Qa<br>©<br>a .1<br>0<br>rrssrs rs rs es a |<br>1 2 3 4 5 6 7 8 9 10<br>Objective household decile<br><!-- End of picture text -->
(less than 5% of total activity) _Source:_ ASYCUDA system.

The profession is highly concentrated. The 264 importers are accustomed to working with only 50 customs agents. Three major brokers deal with 120 different importers (accounting for 47 percent of the declared value).<sup>7</sup> Their values per kilogram vary from 480 CFAF to 542 CFAF. A difference of 60 CFAF (which is less than 10 percent) in the clothing value has important implications for taxes and duties, because it is approximately 1.5 million CFAF per container or equivalent. The amount 1.5 million CFAF “saved” on the value is approximately 750,000 CFAF in taxes and duties “saved” for the importer or 25 percent of the customs taxes and duties paid per container. Of these three major brokers, two have focused their activity on secondhand clothing (representing 40 percent of the value), and one devotes 21 percent of his activity to secondhand clothing. All three brokers deal with secondhand clothing and one other major product: motorcycles (the minimum value of which is informally set by agreement), cloth (the value of which is set by agreement, but this value is double the minimum value of secondhand clothing), workbooks, or milk.

An exclusive relationship between an importer and an agent is rare: we found only seven cases of such a relationship for a total value share of 1.3 percent of secondhand clothing imports. However, we could not confirm whether the same importer used several identification numbers.

One assumption was that there are connections among some brokers, informal brokers, and informal traders. To check this assumption, we examined more closely 32 brokers who process approximately 20 tons per transaction (which means one container per operation).

The three major official brokers (representing 47 percent of the secondhand clothing market) rely on secondhand clothing for their main product, but the value share treated is less than 50 percent (40 percent, 20 percent, and 10 percent). The shares are more or less similar when calculated in terms of the number of operations. Only two brokers among the 33 have a share of secondhand

> 7 Of 264 importers, 144 (18 PM and 126 PP) deal with just one agent (and represent 45 percent of the value treated). The maximum is one PP who has 9 agents. Sixteen customs agents (of the 50 who work in secondhand clothing) work with just one secondhand clothing importer.

15
13 significant impact on all three measures of poverty: headcount, poverty gap and squared poverty gap. As was the case with the migration model, the size of the elasticity of poverty with respect to remittances is small. On average, the point estimates for the poverty headcount measure using survey mean income suggest that a 10 percent increase in the share of remittances in country GDP will lead to a 1.6 percent decline in the share of people living on less than $1.00 per person per day. Controlling for the level of income and income inequality, the more sensitive poverty measures – the poverty gap and squared poverty gap – suggest that international remittances will have a slightly larger impact on poverty reduction. The point estimates for the poverty gap and squared poverty gap suggest that, on average, a 10 percent increase in the share of remittances will lead to about a 2.0 percent decline in the depth and/or severity of poverty.

It is useful to speculate on the reasons why international migration and remittances have such a small – albeit statistically significant – impact on poverty reduction. As noted at the outset, both of these variables are probably underestimated with respect to their true values. The variable “migrants as a share of country population” is underestimated because it does not include the large number of people who illegally migrate to the United States or the OECD (Europe); also, this variable does not include the large number of migrants who go to work in other labor-receiving regions (like the Arab Gulf or South Africa). Similarly, the variable “remittances as share of country GDP” does not include the large (and unknown) amount of money that is remitted through private, unofficial channels. Since workers who migrate illegally are more likely to be poor and to remit through unofficial channels, it is likely that the variables used in this study underestimate the true impact of international migration and
**Table A. 2: Distribution of Per Capita Expenditure by Decile (in %)**

||**Household**|**Budget Su**|**rvey (1997)**|**Household**|**Budget Su**|**rvey (2000)**|
|---|---|---|---|---|---|---|
|**Decile**|**National**|**Urban**|**Rural**|**National**|**Urban**|**Rural**|
|1|2.9|2.5|3.9|2.4|1.5|6.2|
|2|4.5|3.8|6.7|4.1|2.9|8.6|
|3|5.7|5.2|7.4|5.2|4.3|8.8|
|4|6.7|6.4|7.7|6.3|5.5|8.9|
|5|7.7|7.5|8.3|7.3|6.9|9.1|
|6|8.8|8.9|8.5|8.4|8.2|9.5|
|7|10.1|9.6|10.9|9.8|9.7|10.0|
|8|11.8|12.1|11.8|11.7|11.9|11.2|
|9|14.8|14.6|15.4|15.4|16.1|12.3|
|10|26.9|29.4|19.4|29.3|33.1|15.3|

**Table A. 3: Estimates of Income Inequality by Geographical Region**

||**Household B**|**udget Survey (1997)**|**Household**|**Budget Survey (2000)**|
|---|---|---|---|---|
|**Regions**|**Gini**|**E0**|**Gini**|**E0**|
|Riga|35.23|21.80|37.29|23.89|
|Kurzeme|32.06|17.67|34.87|20.76|
|Vidzeme|30.91|16.12|35.88|25.12|
|Zemgale|32.78|18.54|34.62|20.38|
|Latgale|31.25|17.01|34.49|20.45|
|Urban|34.25|20.16|36.58|22.98|
|Rural|32.09|17.51|35.38|21.43|
|**National**|**33.8**|**19.67**|**37.3**|**24.12**|

37
Ravallion and Lokshin (2007) tested for RD effects on subjective welfare in Malawi using the 2004 round of the Malawi Integrated Household Survey (MIHS), covering a sample of over 11,000 households. MIHS collects data on consumptions of a wide range of food and nonfood items, detailed information about the socio-demographic composition of the household, labor status of the household members, health, educational achievements, various sources of household income including income in-kind, and individual wages. A section of the questionnaire asks the opinion of the household head about the household’s standard of living, including questions about satisfaction with life.

The measure of overall welfare is provided by answers to the _SWL_ question (Section 2). The majority of the sample were not satisfied with their lives; 62.5% answered either (1) or (2) (24.4% gave (1)). Slightly less than one-quarter were satisfied; 18.3% gave (4) as their answer and 5.6% gave (5). (The remainder, 13.6%, reported that they were neither unsatisfied nor satisfied.) These numbers indicate a much lower level of satisfaction with life than found in Western Europe where 80-90% of the population report that they are satisfied with their lives when asked the same question in surveys (Delhey, 2004), as compared to one-quarter in Malawi.

While SWL is the natural choice for identifying a measure of utility, how should we measure economic welfare, as one argument of utility? Ravallion and Lokshin used alternative objective and subjective measures. The most widely-used objective measure of economic welfare in developing countries is consumption expenditure per person ( _C_ ) given by total household expenditure, including spending on food (purchased and home-produced and food received as gifts), non-food items, estimated flow of services from consumer durables and actual or self-estimated rental cost of housing.<sup>25</sup> Although this is a comprehensive consumption aggregate, there are well-known concerns about how well it reflects welfare, even when this is thought of as the narrower concept of ‘economic welfare.’<sup>26</sup> In that light, one cannot rule out the possibility that the results obtained using consumption as the measure of economic welfare will be biased by correlations between the perceived economic welfare of friends and neighbors and the errors in measuring own welfare using consumption.

> 25 For details on how the consumption aggregate was formed see World Bank (2005b).

> 26 On the identification problem in consumer demand-based measures see Pollak (1991). Slesnick (1998) surveys alternative empirical approaches. Subjective welfare measures have been seen as a promising route to avoiding the identification problems in deriving a metric of welfare from observed demand behavior; for overviews of this approach see van Praag (1991) and Kapteyn (1994).

16
|P145361|2014|2015|9/3/2014|1 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P145361|2014|2015|12/16/2014|2 Closed|Yemen,Republic of|
|P145361|2014|2015|6/16/2015|3 Closed|Yemen,Republic of|
|P145361|2014|2016|2/5/2016|4 Closed|Yemen,Republic of|
|P145361|2014|2017|11/8/2016|5 Closed|Yemen,Republic of|
|P145391|2015|2016|12/10/2015|1 Closed|Philippines|
|P145391|2015|2016|1/7/2016|2 Closed|Philippines|
|P145391|2015|2017|8/3/2016|3 Closed|Philippines|
|P145391|2015|2017|2/9/2017|4 Closed|Philippines|
|P145391|2015|2018|10/25/2017|5 Closed|Philippines|
|P145391|2015|2018|5/8/2018|6 Closed|Philippines|
|P145391|2015|2019|11/30/2018|7 Closed|Philippines|
|P145410|2015|2016|12/8/2015|1 Closed|Guatemala|
|P145410|2015|2017|12/22/2016|2 Closed|Guatemala|
|P145410|2015|2018|10/23/2017|3 Closed|Guatemala|
|P145410|2015|2019|9/21/2018|4 Closed|Guatemala|
|P145410|2015|2019|6/25/2019|5 Closed|Guatemala|
|P145419|2015|2016|11/13/2015|1 Closed|India|
|P145419|2015|2017|12/21/2016|2 Closed|India|
|P145419|2015|2019|10/5/2018|3 Closed|India|
|P145420|2014|2015|11/16/2014|1 Closed|Pakistan|
|P145420|2014|2016|10/23/2015|2 Closed|Pakistan|
|P145420|2014|2017|12/19/2016|3 Closed|Pakistan|
|P145420|2014|2018|5/23/2018|4 Closed|Pakistan|
|P145434|2013|2013||0 Closed|Yemen,Republic of|
|P145434|2013|2014|1/11/2014|1 Closed|Yemen,Republic of|
|P145434|2013|2014|6/15/2014|2 Closed|Yemen,Republic of|
|P145434|2013|2015|12/5/2014|3 Closed|Yemen,Republic of|
|P145434|2013|2016|7/8/2015|4 Closed|Yemen,Republic of|
|P145439|2014|2014|6/24/2014|6 Active|Mongolia|
|P145439|2014|2015|12/10/2014|7 Active|Mongolia|
|P145439|2014|2015|6/14/2015|8 Active|Mongolia|
|P145439|2014|2016|12/17/2015|9 Active|Mongolia|
|P145439|2014|2016|4/20/2016|10 Active|Mongolia|
|P145439|2014|2017|11/21/2016|11 Active|Mongolia|
|P145439|2014|2017|6/1/2017|12 Active|Mongolia|
|P145439|2014|2018|12/17/2017|13 Active|Mongolia|
|P145439|2014|2018|6/19/2018|14 Active|Mongolia|
|P145439|2014|2019|12/27/2018|15 Active|Mongolia|
|P145439|2014|2019|6/20/2019|16 Active|Mongolia|
|P145458|2013|2014|6/24/2014|1 Closed|Morocco|
|P145458|2013|2019|5/9/2019|2 Closed|Morocco|
|P145468|2018|2020|3/18/2020|1 Closed|Haiti|
|P145482|2014|2016|6/24/2016|1 Active|Maldives|
|P145482|2014|2017|6/13/2017|2 Active|Maldives|
|P145482|2014|2018|6/29/2018|3 Active|Maldives|
|P145482|2014|2019|6/13/2019|4 Active|Maldives|
_Table 12: Effect of CCT on Incidence of Non-Domestic Violence (Ranking PMT)_

||(1)<br>Incidence of<br>physical NPDV|(2)<br>Incidence of<br>sexual NPDV|(3)<br>Incidence of<br>emotional NPDV|(4)<br>Incidence of<br>economic NPDV|
|---|---|---|---|---|
|RD Effect|0.012|-0.004|-0.123**|-0.060|
||(0.69)|(0.27)|(0.01)|(0.13)|
|FDR q-val|0.69|0.36|0.05|0.26|
|Observations|564|564|564|564|
|Control mean|0.043|0.000|0.114|0.075|
|P-values in paren|theses: * p<0.1 ** p|<0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NPDV regressions.

_Table 13: Effect of CCT on Incidence of Non-Domestic Violence (Ranking PMT)_

||(1)<br>Incidence of<br>physical NDV|(2)<br>Incidence of<br>sexual NDV|(3)<br>Incidence of<br>emotional NDV|(4)<br>Incidence of<br>economic NDV|
|---|---|---|---|---|
|RD Effect|0.004|-0.001|0.008|-0.002|
||(0.56)|(0.41)|(0.81)|(0.91)|
|FDR q-val|0.91|0.91|0.91|0.91|
|Observations|564|564|564|564|
|Control mean|0.011|0.000|0.029|0.007|
|P-values in parent|heses: * p<0.1 ** p|<0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all NDV regressions.

_<mark>Table 14: Effect of CCT on Help-Seeking Behavior (Ranking PMT)</mark>_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Did you seek help<br>due to physical<br>violence?|Did you seek help<br>due to emotional<br>violence?|Did you seek help<br>due to economic<br>violence?|Did you seek<br>help due to any<br>violence?|How many<br>times did you<br>seek help due<br>to violence?|
|RD Effect|0.040|0.283**|-0.004|0.074|0.133|
||(0.77)|(0.05)|(0.97)|(0.43)|(0.46)|
|FDR q-values|0.96|0.25|0.97|0.77|0.77|
|Observations|132|159|141|263|263|
|Control mean|0.167|0.244|0.227|0.206|0.373|
|P-values in pare|ntheses: * p<0.1 ** p<|0.05 *** p<0.01||||

Note: All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all regressions in the table.

23
Figure 6. Mliteracy Rate of 15-24 year-olds im Selected Countries, 1999 <!-- Start of picture text --> : OMale<br>Youth Illiteracy by Sex, 1999 Female<br>50<br>40 &<br>x 35 t |<br>220 Pa :<br>4) ‘7] . Qe Kd<br>Lv a * © se<br>Bs)<br><!-- End of picture text -->

Source: DFID (2002) By this the Government has shown what can be achieved by prioritizing social sector investments. During the 1990s, primary enrolment rates in Vietnam improved for all expenditure groups and in all regions, for both the majority population and ethnic minority groups, and for both sexes. It is recognized, however, that the high enrolment rates for both girls and boys mask significant challenges in addressing disparities (socio-economic and geographic) in access, coverage and participation in education. For example, though the gap between the richest and poorest expenditure quintiles is closing, the net enrolment rate for the lowest income quintile is still 14 percentage points below the rate for the highest quintile (Figure 7).

Figure 7. Trends im Primary Net Enrolment Rates, 1993-1998, by imcome quintile <!-- Start of picture text --> Primary Net Enrolment Rates, 1993-1998 i1998<br>120<br>1005 g7_91 87 91 95 74 96 96i96<br>__ ae 82 ae * aa ES<br>40; | se Be ae ae o ae<br>204 |aa ‘2 a. = . =<br>Vietnam I H suet IV Vv<br>Income Quintile<br><!-- End of picture text -->

Source: DFID (2002) 16
level of education increases: while 60 percent of women with no education degree report household duties as the primary reason for staying home, only 31 percent of women with tertiary education make the same claim. The data indicate also that 17.9 percent do not want to work; and for 11.6 percent of inactive women, a close relative (father, husband or other) does not allow them to work.<sup>4</sup> Again, these numbers differ markedly from those reported by the International Labour Organization (op. cit.) for Northern Africa, which are 30.1 percent and 5.6 percent, respectively. In addition, 10.9 percent would like to find employment, which represents a potential of 1.1 million additional workers.<sup>5</sup> A formal empirical analysis by Attioui et al. (2017) has indeed documented the fact that the primary reason for inactivity among women in Morocco is related to household duties and child (as well as, in some cases, elderly) caring.

Finally, there is also evidence of persistent legal gender discrimination.<sup>6</sup> Women have fewer rights to economic assets (inheritance), social security benefits, and marital property. Despite the adoption of a new family code in 2004, divorced women continue to struggle to assert their alimony rights, and single mothers remain subject to discrimination.This affects women’s ability and capacity to engage in the labor market, as well as (regarding their limited rights to marital property, for instance) their bargaining power in the family.

The model that we present next does not aim to explain the low participation rate for women in Morocco. Based on the above evidence, we view women’s decision to participate in the labor market in the first place as essentially a dichotomous, all-ornothing choice in the presence of social norms that compel women to devote a significant share of their daily time to household chores and child rearing, and indivisibilities in the time that women must allocate to market work–should they choose to be employed. From that perspective, the secular fall in women’s participation rate reported earlier is consistent with the evidence presented by Ragbi et al. (2017, Figure 1.11), which shows

> 4This issue is not limited to Morocco. As noted in a recent issue of The Economist (May 4, 2018), in 104 countries around the word women are barred from certain jobs, and in 18 countries a husband can prevent his wife from working. These sex-specific restrictions do not relate to temporary and specific precautions (as in the case of pregnant and breast-feeding women when working with chemicals, for instance) but rather to a priori assumptions about weaknesses that women may suffer from.

> 5According to World Bank estimates, alleviating the obstacles in accessing the labor force by those 1.1 million women would bring an increase of 4.3 and 8.5 percentage points to the overall labor force participation rate (up to 51 percent compared to 46.7 percent) and to the female labor force participation rate (up to 31 percent compared to 22.4 percent). However, the implications for growth are not assessed.

> 6See World Bank (2013).

6
channel. As mentioned in Section 2.1, controlling for everything else, a reduction in import tariffs should increase domestic competition and hence boost labor productivity. The present study uses tariff variations to identify the link between NAFTA and labor productivity via the import-competing channel. Nevertheless, we complement this approach with a pseudo-experimental procedure that identifies all other tradeproductivity channels discussed in Section 2.

As discussed in Section 2, theoretical models with heterogeneous firms suggest that trade reforms will impact asymmetrically on different types of firms. We expect integrated firms to be positively affected by the reforms relative to non-integrated firms. Moreover, the impact within integrated firms could be different depending on a firm’s integrated status. This idea is not only based on theoretical considerations but also appears to emerge from the descriptive statistics presented in Section 3 suggesting that plants within different “integration status” show a different productivity evolution over time. Hence, a crucial identifying assumption behind the pseudo-experimental approach adopted in this paper is that the reforms introduced by NAFTA had a different effect on pre-reform integrated and non-integrated firms.

Our strategy builds on the previous work by Pavcnik (2002) and L ́opez-C ́ordova (2003) analyzing the impact of trade reforms in Chile and Mexico, respectively. While L ́opezC ́ordova (2003) exploits tariff variations Pavcnik (2002) uses a quasi-experimental approach (i.e. treatment versus control group). The mayor difference between these two closely related studies and the empirical approach followed in this paper are the following:

1. Pavcnik (2002) defined a firm as being integrated when it belonged to a “integrated” sector—at 4 digits of the ISIC classification—regardless of the firm’s integration status. Thanks to data availability, in this paper we define the integration status at the firm level.<sup>11</sup>

2. Within integrated firms, our approach allows for a heterogeneous impact of the reforms among firms with different integration status, i.e. exporters, importers or fully integrated.

3. Our econometric approach controls for possible endogeneity problems related to a firm’s decision to change integration status; and it also attacks the attrition problem present in the Mexican industrial survey (EIA).

> 11We evaluated the correlation between the definition used by Pavcnik (2002) and our definition and found out this is only about .30.

14