rafmacalaba/datause-encoder-data huggingface dataset:
In 1986 the NRM government inherited a trade regime that included extensive nontariff barriers, biased government purchasing, and high export taxes, coupled with considerable smuggling. The nontariff barriers have gradually been removed since the introduction in 1991 of automatic licensing under an import certification scheme. Similarly, central government purchasing was reformed and is now subject to open tendering without a preference for domestic firms over imports.

By the mid-1990s, the import tariff schedule had five _ad valorem_ rates between 0 and 60 percent. For more than 95 percent of imported items the tariff was between 10 and 30 percent. During the latter half of the 1990s, the government implemented a major tariff reduction program. As a result, by 1999 the tariff system had been substantially rationalized and liberalized, which gave Uganda one of the lowest tariff structures in Africa. The maximum tariff is now 15 percent on consumer goods, and there are only two other tariff bands: zero for capital goods and 7 percent for intermediate imports.

The average real GDP growth rate was 6.3 percent per year during the entire recovery period (1986-99) and 6.9 percent in the 1990s. The liberalization of trade has had a marked effect on export performance. In the 1990s export volumes grew (at constant prices) at an annualized rate of 15 percent, and import volumes grew at 13 percent. The value of noncoffee exports increased fivefold between 1992 and 1999.

These cases provide persuasive evidence that openness to foreign trade and investment— coupled with complementary reforms—can lead to faster growth in developing countries.

However, individual cases always beg the question, how general are these results? Does the typical developing country that liberalizes foreign trade and investment get good results? Crosscountry statistical analysis is useful for looking at the general patterns in the data. Cross-country studies generally find a correlation between trade and growth. To relate this back to the discussion in Section 1: among developing countries, some developing countries have had large increases in trade integration (measured as the ratio of trade to national income), while other have had small increases or even declines. In general, the countries that have had large increases in trade integration, have also had accelerations in growth. The group of developing country globalizers identified by Dollar and Kraay (2004) had population-weighted growth of 5 percent per capita in the 1990s, compared to 2 percent for the rich countries, and –1 percent for the rest 35
we conduct (i) country-level time-series regressions, with data aggregated across all banks and municipalities, (ii) bank panel regressions, with data aggregated for each bank over all municipalities, and (iii) bank-municipality panel regressions, with data for each bank within each municipality and each quarter. We discuss each specification in turn.

We investigate changes in outreach for the overall Mexican banking system by running the following specification: where Y is one of our four outreach measures – share of municipalities with bank branches, bank branches per capita, number of deposit accounts per capita, and number of loan accounts per capita – measured in quarter t. Foreign Share, our measure of foreign bank presence, is the percentage of deposits held by foreign-owned banks. GDP per Capita, measured in constant pesos, is introduced to control for changes in economic conditions that might affect the demand and supply of financial services. The coefficient α indicates whether there is a positive, negative, or insignificant association between foreign bank participation and outreach over time at the country level. Since the deposit and loan account data for some banks show unexplainable large jumps, we control for these outliers by including quarterly dummy variables for these periods. Further, in the deposit and loan accounts per capita regressions, we introduce step variables that equal one after Bancomer (later merged with BBVA) changed its classification of deposit and loan accounts.<sup>15</sup> We estimate three versions of equation (1): one where the outreach indicators are aggregated for all banks in the system, one where we add up the outreach measures for the domestic banks only and, finally, one where we consider only the outreach indicators for the five

> 15 Bancomer started including passbook savings accounts in their deposit account numbers in the second quarter of 2002, giving rise to a jump in deposit numbers. Also, there was a significant change in the classification of loan accounts in the first quarter of 1998.

14
**Figure 5. Skilled emigration rates by country group**

<!-- Start of picture text --> A. Average rate by region<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>America<br>Europe<br>Africa<br>Asia<br>Oceania<br>1990 2000<br>2000<br>1990<br><!-- End of picture text -->

# **_B. Average rate by income group_**

<!-- Start of picture text --> 0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>High<br>Upper<br>Lower<br>Middle Low<br>Middle<br>1990 2000<br>2000<br>1990<br><!-- End of picture text -->

# **_C. Average rate by country size_**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Large<br>Upper<br>Lower<br>Middle Low<br>Middle<br>1990 2000<br>2000<br>1990<br><!-- End of picture text -->
one-percent level (see columns 3 and 4).

Note that Grade 6 test scores explain a larger share of the variation in future test scores than of the variation in on-time graduation. The R-squared in the graduation regressions is 0.059 and 0.069 (in Grades 9 and 12, respectively) while the R-squared in the test score regressions is 0.362 and 0.342 (in the same grades). This implies that the variables not included in this regression (such as family background) are relatively more important explaining on-time graduation than future test scores.

We move now to study the degree to which the relationship between test scores and future outcomes is driven by family background, which, very likely, affects both 6th grade test scores and future education outcomes. To do so, we exploit the sample of twins in the ENLACE Panel and run a specification including a vector of twin (family) fixed effects. This allows us to study how variation in test scores at Grade 6 predicts variation in future student outcomes holding constant all family characteristics (observed and unobserved). We first estimate the same model used in Table 3 to verify that any change in the coefficients of interest is due to the introduction of the family fixed effects and not due to sample variation between the population of twins and the total population of ENLACE takers. Columns 1-4 in Table 4 report the results from a model restricted to the sample of twins and not including family fixed effects. Reassuringly, the estimated coefficients are similar to those reported before. A 1 SD increase in Grade 6 test scores is associated with a 8.2 and 10.5 percentage points higher probability of on-time graduation from Grades 9 and 12 (versus 10.3 and 11.9 in the full sample), and with 0.63 and 0.61 SD higher test scores in Grades 9 and 12 (versus 0.61 and 0.62 in the full sample). For details, see columns 1-2 and 3-4. Columns 5-8 in Table 4 report the results of the estimations that include the family fixed effects. Qualitatively, the findings are the same as the ones from previous specifications: test scores at Grade 6 predict on-time graduation from Grades 9 and 12, and conditional on this, test scores in Grades 9 and 12. In the four cases, the results have a high statistical significance. However, as expected, the coefficients from the family fixed-effects specification are smaller than those estimated using the main specification 16
For the female, the model predicts that, as her net cost of risky sex increases, she becomes less willing to say yes to risky sex and more likely to say no and exit the relationship. Whether she is more or less likely to say no and stay in the relationship relative to exiting depends on her continuation value for staying in the relationship. If her continuation value is low, she has a higher likelihood of exiting and escaping violence. In this case, the model predicts that violence will decrease as the females’ cost of risky sex increases. If her continuation value is high (i.e., it is costly to exit, such as in marriage), she has higher likelihood of say no and staying in the relationship, putting her at increased risk of violence. In this case, the model predicts that violence from the male can increase as the female’s cost of risky sex increases.

There is evidence suggesting that the empowerment of women can, in some cases, lead to a “male backlash.” In our case, female empowerment can lead to more IPV due to her increased propensity to say no to risky sex. Most of the previous IPV research has been conducted in the context of married couples, where exit costs are relatively high, and female empowerment is along easier to observe economic dimensions (Angelucci (2008); Bobonis, Gonz ́alez-Brenes and Castro (2013); Erten and Keskin (2018)). The intuition is that greater income potential for women can precipitate more IPV either instrumentally, to control the additional resources, or as an expressive response, aimed at restoring men’s threatened beliefs about their masculine identity. In this case, female empowerment may raise the need to also treat men with complementary interventions, at least in the short term before male attitudes towards women adjust to the new equilibrium (Doepke and Tertilt, 2009).

# **6.1 Setup**

We now present the model in detail.<sup>15</sup> The timing of the model is as follows. First, nature draws a type vector _R_ = ( _Rm, Rf_ ), where _Ri_ is partner i’s net cost of risky sex (can be positive or negative) and _i_ = _male, female_ , which we index as _m_ and _f_ . _Ri_ is

> Haushofer et al. (2019) develop a general model where violence can be an instrument to extract resources from the female, and can be either pleasurable or distasteful in terms of its non-pecuniary returns.

> 15Inspiration for this model comes from conversations with Debraj Ray and from Aizer and Dal Bo (2009).

19
Figure 2: Cashew Nut Channels In Tanzania (1992/ 93 Season) <!-- Start of picture text --> Farmers<br>(Appmox. 280.000)<br>1<br>Production<br>[Marketed<br>(34,384 Tons)<br>Private BuyersAgents<br>(14.908 Tons)<br>Cooperative | _ __ _5956 Tons 8952 Tons<br>4275<br>Tons<br>12,54i _Privat Exporters<br>- ~~~~~Tons (I (322!7 Tons)<br>TCMB<br>(18.540 Tons<br>Sale to Foreign Sale to Foreign Sale to Foreign<br>Buye/Broker by Buyer/ Brkokr uer Bmkoer by<br>Private Treaty by Tender 8uy Treaty<br>Authos Field Interviews EKANS4313<br><!-- End of picture text -->

<!-- Start of picture text --> Sorce: Authos Field Interviews<br>Through March 10. 1993<br><!-- End of picture text -->
> DATA AND METHODS babe

using the equations = .00379 + .723 ro .000254 so for females, and .01159 + .885 ro - .000318 so for males.

Percent urban Is used in a few cases, with a different equation (see Bulatao et al., 1989), where secondary enrolment is not available, and in fewer cases still the rate of change in the previous decade is used instead of the rate of change in the previous quinquennium when the latter appears to have been affected by exceptional circumstances. Limits are imposed on the rate of change for the flrst quinquennium (and for all other quinquennia), such that it cannot be greater than -.017 (which would give slow mortality decline) or less than -.053 (which would give rapid mortality decline).

For the second quinquennium, the rate of change is estimated as a function of the rate of change for the flrst quinquennium:

> r2 -0.007 + 0.7 rl.

|Table A|l Assu|med annu|al incre|ments to li|fe expec|tancy|The rate of change for the third<br>quinquennium is estimated from<br>the rate of change for the<br>|
|---|---|---|---|---|---|---|---|
|Initial<br>Lifexpe<br>expec-<br>tancy<br>|c<br>Minfium<br>|Males<br>Mediun<br>|Maxima<br>|un<br>Nininun<br>|Female<br>Nediun|s<br>Naximun|second quinquennium in the<br>samemanner.<br>This equation<br>allows rates of change to<br>convergetoward the uniform<br>pattern imposed for subsequent<br>quinquennia.|
|40<br>45<br>s5<br>60<br>|0.14<br>0.15<br>0.16<br>0.15<br>|0.34<br>0.38<br>0.39<br>0.37<br>|0.55<br>0.61<br>0.63<br>0.59<br>|0.14<br>0.16<br> <br>0.17<br>0.17<br>|0.36<br>0.40<br>0.43<br>0.43<br>|0.58<br>0.65<br>0.69<br>0.68<br>|Forthesesubsequent<br>quinquennia<br>for all countries,<br>the<br>rate<br>ofchangeinlife<br>|
|65<br>70<br>75<br>80<br>5|0.13<br>0.10<br>0.07<br>0.03|0.32<br> <br>0.26<br>0.18<br>0.08|0.52<br> <br>0.42<br>a 0.28<br>0.12|0.16<br> <br>0.14<br>0.12<br>0o09<br>|0.40<br> <br>0.36<br>0.27<br>0.21<br>|0.69<br>0 57<br>0.47<br>0.34<br>|expectancy is constant at -.035<br>forbothsexes.<br>Atthis rate, the<br>annual<br>increments<br>to<br>life<br>|
|8|--|-||0.05|0.11|0.18|expectancyvarybyinitial levels|
||||||||as indicated by the medium<br>|

pattern in Table Al. Minimum and maximum increments corresponding tO the slow and rapid limits imposed on rates of change in the frst three quinquennia are also shown.

# _Future trends in infant mortalty._

Infant mortality is projected using a similar logistic function. The rate of change for each of the first three quinquennia is obtained from the equation r, = .0275 + _.5_ r,.,.

with the restriction that this rate must be in the range [.024, .1301. These limits provide schedules of minimum and maximum annual decrements to infant mortality, varying by the initial rate, shown in Table A2. A set of medium decrements is also provided, representing the typical schedule of improvements toward which rates of change converge when the preceding equation is applied successively. A minimum attainable level of infant mortality of 3 per thousand was used instead of the previously assumed 6 per thousand. This level is roughly equivalent to the level attainable under
Table 2. Determinants of Parental Consanguinity, Linear Probability Model

||Parents ar|e Related|F-coeffi<br>Inbre|cient of<br>eding|Parents are|1st Cousins|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Grandfather Owned/Owns|0.113***|0.064|0.013***|0.008|0.105**|0.068|
|land|(0.039)|(0.047)|(0.005)|(0.005)|(0.043)|(0.044)|
|Maternal Grandfather Died|-0.144***|-0.129***|-0.016***|-0.014**|-0.117**|-0.113**|
|before Age 65|(0.043)|(0.041)|(0.006)|(0.006)|(0.050)|(0.049)|
|Age|0.010**|0.009**|0.001|0.001|0.009|0.008|
||(0.005)|(0.005)|(0.001)|(0.001)|(0.005)|(0.005)|
|Male|0.011|0.012|0.001|0.002|0.017|0.018|
||(0.023)|(0.023)|(0.003)|(0.003)|(0.026)|(0.026)|
|Wealth index|-0.066***|0.004|-0.007**|0.000|-0.053*|-0.006|
||(0.027)|(0.025)|(0.004)|(0.003)|(0.030)|(0.028)|
|Father's Years of Education|0.005|0.006|0.001|0.001|0.005|0.004|
||(0.006)|(0.005)|(0.000)|(0.001)|(0.005)|(0.005)|
|Dummy for Sindh Province|-0.119||-0.003||-0.021||
||(0.100)||(0.011)||(0.081)||
|Dummy for KPK Province|-0.164**||-0.030**||-0.233***||
||(0.076)||(0.009)||(0.073)||
|Father's years of|-0.014|-0.019**|-0.002*|-0.003**|-0.015|-0.016|
|education*Sindh|(0.011)|(0.009)|(0.001)|(0.001)|(0.010)|(0.008)|
|Father's years of|-0.011|-0.016|-0.000|-0.001|0.006|-0.006|
|education*KPK|(0.015)|(0.016)|(0.001)|(0.002)|(0.013)|(0.016)|
|District Fixed Effects|No|Yes|No|Yes|No|Yes|
|F-Test on Instruments|8.27|5.30|6.18|3.90|5.04|3.25|
|F-Probability|0.001|0.007|0.003|0.025|0.01|0.046|
|Number of observations|1411|1411|1411|1411|1411|1411|

Notes: *: statistically significant at the 10% level; **: statistically significant at the 5% level; ***: statistically significant at the 1% level. Standard errors clustered at the village level are reported in parenthesis. The F-statistic, corrected for clustering at the village-year level, tests the hypothesis that the estimated coefficients on the instruments (Grandfather Owned/Owns Land and Maternal Grandfather Died before 65) are zero.

21
use the GTOPO20 (USGS 2011a) data set. The interaction of year dummies, _lt_ , with the river gradient variables is intended to capture country-wide, time-varying effects of river gradients that may affect dam construction, whereas _Mi_ is a vector consisting of riverbasin-specific, time-invariant measures of elevation, gradient, river length, and river basin area.<sup>7</sup> To derive the effect of own basin and upstream dams on cropland productivity, we estimate the following specification: where _Z it_ represents the right-hand-side explanatory variables in (2), except for the _RGr_ ( _k_ ) _i_ * _Pit_ interaction terms. _Z itU_ are the same control variables as _Z it_ , but they are calculated in terms of a river basin’s upstream basins. _X it_ is a vector of other available control variables. To estimate (3), we follow Duflo and Pande (2007) and use the ^ estimated number of large dams from (2), _D_ ( _LARGE_ ) _it_ , its upstream equivalent, ^ ^ _UD_ ( _LARGE_ ) _it_ , and the estimated number of small dams, _D_ ( _SMALL_ ) _it_ , as instruments. We thus have three instruments for three endogenous variables.

One should note that, as Duflo and Pande (2007) argue, the approach above, in contrast to standard IV methods, uses all available variation efficiently by using all basins to predict the relationship between basin geographic features and the number of dams (rather than only those upstream) and avoids averaging these features when there are 14
|Average age of|-0.006|0.057***|-0.090***|-0.022|-0.003|-0.204|0.000|0.002|-0.003|
|---|---|---|---|---|---|---|---|---|---|
|household members/10|(0.013)|(0.018)|(0.021)|(0.017)|(0.025)|(0.126)|(0.022)|(0.007)|(0.013)|
|Square of average age<br>|0.000|-0.001***|0.001***|0.000|-0.000|0.021|0.000|-0.000|0.000|
|of household<br>members/100|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.014)|(0.000)|(0.000)|(0.000)|
|Household dependent<br>|-0.269***|-0.250***|-0.381***|-0.209***|-0.285***|-0.312***|-0.226***|-0.067***|-0.126***|
|ratio|(0.0348)|(0.050)|(0.050)|(0.051)|(0.056)|(0.077)|(0.064)|(0.022)|(0.039)|
|Average years of<br>|0.059***|0.068***|0.047***|0.064***|0.050***|0.067***|0.014**|0.016***|0.010***|
|schooling|(0.004)|(0.005)|(0.005)|(0.006)|(0.005)|(0.009)|(0.006)|(0.002)|(0.004)|
|Urban/rural area|0.185***|||0.158***|0.190***|0.216***|-0.020|0.020|0.010|
|dummy (Urban=1)|(0.0312)|||(0.055)|(0.038)|(0.069)|(0.058)|(0.014)|(0.031)|
|West|-0.081|-0.065|-0.103||||-0.002|-0.003|0.015|
||(0.058)|(0.077)|(0.062)||||(0.069)|(0.025)|(0.047)|
|Central|0.011|-0.011|0.056||||0.125*|0.029|-0.021|
||(0.052)|(0.061)|(0.058)||||(0.067)|(0.018)|(0.038)|
|Constant|6.808***|6.484***|7.827***|6.572***|6.916***|7.711***|7.290***|8.595***|10.036***|
||(0.194)|(0.262)|(0.245)|(0.350)|(0.266)|(0.288)|(0.244)|(0.094)|(0.221)|
|Observations|10,138|4,709|5,429|4,055|3,224|2,858|2,062|5,180|2,896|
|Adjusted R-squared|0.501|0.472|0.540|0.469|0.520|0.530|0.756|0.887|0.760|
|F test|215.7|126|219.5|85.36|168.8|157.4|436|1751|330.9|

Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

The positive association between e-commerce and consumption growth is significant not only for total household consumption, but also for most consumption categories, ranging from basic consumption, such as food, clothes, and local transportation, to consumption of durable goods, travel, and entertainment. The magnitude of the association of e-commerce intensity is larger for the second consumption group than for the first. For most of the consumption categories, the association between e-commerce development and consumption growth is largest for the poorest quartile and declines as household income level moves up. While we have tried to control for as many factors as possible, some variables correlated with both the outcome variable and e-commerce indicator may have been omitted, which may bias the estimates of the e- commerce variable. Our findings imply only associations and not causality. More studies are needed to tease out the causal relationship between e-commerce development and consumption growth.

Developing e-commerce requires not only Internet, but also many other factors, such as infrastructure and logistics services, skills and entrepreneurship, and an overarching enabling business environment facilitating online and offline business. China has been on a fast track of e- commerce development in recent years. With the right policies, further development of e·· 24
Source LFSs 2007 and HBS 2007. : Logit regressions, dependent variable: FLP (1: in the labor force; 0: otherwise). Standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1).

To further investigate the question of age and participation, we plot the marginal probability of participation conditional on the variables used in Table 2 and plot the results in Figure 2 by age cohorts (top panel). The graphs confirm what we learn in the table in terms of the hump shape relation between age and FLP. They also highlight the urban-rural divide in terms of FLP and pinpoint when women really exit the labor force. In urban areas female participation is much lower than in rural areas for all age-cohort. In fact, this explains the low FLP rates in Morocco given that rural rates can reach up to 60%. It is also evident that in urban areas the exit age is in the 25-29 age cohort, which again explains what was found for Morocco as a whole.

If we further split the probability of participation by marital status (Figure 2, bottom panel) we can see that marriage has a major role in Urban areas in keeping FLP rates low. The probability of participation for married women in Urban areas is below 10% as compared to a probability for unmarried women of close to 40%. The peak of the two curves also shifts to the right of the age cohort scale as compared to figure 2, which means that marriage is what keeps the peak in Figure 2 around the age of 25-29. This marriage effect is not visible in rural areas where marriage can even increase participation.

If we consider that most women in rural areas are uneducated, we should conclude that low FPRs in Morocco are mainly explained by women in urban areas with secondary education. This group is not likely to participate mainly because of marriage and because they have an education level matching sectors that are not growing. In addition, educated women are likely to marry educated men who we know have done better than women in the labor market and may be able to support their families on their own. Married women with secondary education are often married to highly educated men working in the public sector and the public sector in Morocco is known for paying very good salaries. This last factor, in turn, contributes to reduce the likelihood for educated women to seek employment as amply shown by the literature on FLP in the US (Mincer, 1962, Gronau, 1977).

17