rafmacalaba/datause-encoder-data huggingface dataset:
- 26 -

other obligations in the village generally preclude migration to the United States, which typically entails a large commitment of both time and capital. Household members' schooling (ED) has a significant positive effect on the probability of internal migration but is negatively related to Mexico-to-U.S. migration. Not surprisingly, better-educated villagers are much more likely to migrate to destinations in Mexico, where returns to schooling are likely to be high, than to low-skill undocumented immigrant labor markets in the United States.

Household members' experience as migrants in the U.S. and experience as migrants in Mexico have a positive association with the probability of migration to both destinations. However, the estimated coefficient on U.S. migration experience in the U.S. migration equation (0.487, significant at below the 0.05 level) is more than three times the coefficient on U.S. experience in the Mexico migration equation (0.141, significant at the 0.10 level). Similarly, although experience as an internal migrant is positively related to both types of migration, it has a larger and more significant effect on internal migration than on international migration. On the one hand, these findings suggest that migration experience has a general positive effect on migration propensities and that some migration work experience may be transferable across migrant destinations. On the other hand, they indicate that destination-specific migration experience plays a powerful role in shaping migration decisions. These general and destination-specific migration experience effects are analogous to the differential effects of general training and firm-specific training in employment and earnings studies. Several other variables in Table 3 stand out as significantly
Next, we compare the characteristics of IDHs and non-IDHs. Table 4 shows that while both groups are similar in several indicators, they are statistically different on other dimensions as indicated by the results from the t-test<sup>17</sup> in the last column. Displaced households, on average, are slightly older, have an older household head who is also less educated, have lower dependency ratio, and are more likely to be in urban areas. One observation we can draw from these statistics is that internal displacement may not be exogenous to some of the household innate characteristics. These characteristics may drive any empirical relationship we may find between our main variables of interest. Hence, it would be necessary to account for this potential endogeneity with an IV analysis.

**Table 4** . IDHs and non-IDHs general statistics

||**IDHs**|**Non-IDHs**|**P-value**|
|---|---|---|---|
||N=2,027|N=23,119||
|Average household size|7.07|6.99|0.1741<br>|
|Average age|23|22|0.0000|
|Head of household||||
|age|48|46|0.0000|
|Married (%)|88.41|89.35|0.1899|
|Male (%)|89.59|89.96|0.5911|
|Highest certificate (%)||||
|Postgraduate level|0.54|0.66|0.5229|
|Undergraduate level|10.36|12.47|0.0055|
|Intermediate|14.01|15.07|0.2021|
|Elementary|26.05|28.08|0.0505|
|No certificate|49.04|43.72|0.0000|
|Household dependency ratio|41.67|44.07|0.0000|
|Household environment (%)||||
|Rural (area of residence)|26.84|42.03|0.0000|
|Urban (area of residence)|73.16|57.97|0.0000|
|Household’spatially deflatedmonthly<br>consumptionpercapita (Dinars)|176,122|186,625|0.0000|

> 17 A p-value higher than 0.05 means the difference of the mean value of the characteristics between the two cohorts is not different from zero.

19
# **Table 9. Effects of Standards on Firm Export Entry: Role of Firm Size and Network** **<u>Effects</u>**

||<br>De|<br>pendent Varia|<br>ble:|
|---|---|---|---|
||Export Entry<br>Dummyc,i,k,d,t<br>(1)|Export Entry<br>Dummyc,i,k,d,t<br>(2)|Export Entry<br>Dummyc,i,k,d,t<br>(3)|
|Rel_restrictivenessc,k,d,t|‐0.190<br>[0.025]***|‐0.152<br>[0.050]***|‐0.186<br>[0.026]***|
|Log Agro_exportsc,i,t‐1|0.051<br>[0.001]***|0.050<br>[0.001]***|0.049<br>[0.001]***|
|Rel_restrictivenessc,k,d,tX Log Agro_exportsc,i,t‐1|0.010<br>[0.002]***|0.009<br>[0.002]***|0.009<br>[0.002]***|
|Number_agro_firmsc,d,t‐1||0.110<br>[0.010]***||
|Rel_restrictivenessc,k,d,tX Number_agro_firmsc,d,t‐1||‐0.007<br>[0.010]||
|Number_product_firmsc,d, k, t‐1|||0.079<br>[0.005]***|
|Rel_restrictivenessc,k,d,tX Number_product_firmsc,d,k,t‐1|||0.007*<br>[0.006]|
|Log 1+Tariffc,k,d,t|‐0.020<br>[0.004]***|‐0.016<br>[0.004]***|‐0.015<br>[0.004]***|
|Log GDP per capitad,t|0.030|‐0.100|‐0.009|
||[0.042]|[0.038]***|[0.038]|
|Log Total Exportsc,k,t|0.041<br>[0.002]***|0.041<br>[0.002]***|0.038<br>[0.002]***|
|Exporting Firm‐Importing Country‐Product Fixed Effects|Yes|Yes|Yes|
|Year Fixed Effects|Yes|Yes|Yes|
||Linear|Linear|Linear|
|Estimation method|Probability<br>Model|Probability<br>Model|Probability<br>Model|
|Observations|297,409|297,409|297,409|
|R‐squared|0.379|0.381|0.382|

Notes: Robust standard errors in brackets, clustered at the exporting country-importing country-product-year level. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. The dependent variable is defined as follows: entry equals one if firm _i_ exports product _k_ to destination _d_ in year _t_ but did not do so in year _t-1_ and zero if the firm did not export product _k_ to destination _d_ in year _t-1_ and does not start to do so in year _t_ .

37
Table 7: First-Stage Specifications - Albania (2002 - 2012) Dependent Variable: Fertility (Number of children ever born) (1) (2) (3) (4) (5) (6) (7) _Panel A: Mothers aged 20 to 49 years old with 2 or more children younger than 18 years old_

|Boy frst|—|—|—|-0.2461***|-0.4655***|-0.0255|-0.0283|
|---|---|---|---|---|---|---|---|
|||||(0.0178)|(0.0267)|(0.0227)|(0.0234)|
|Boy second|—|—|—|-0.2175***<br>(0.0177)|-0.4370***<br>(0.0267)|0.0031<br>(0.0226)|—|
|Two girls|—|—|0.4723***<br>(0.0254)|—|—|0.4412***<br>(0.0346)|0.4381***<br>(0.0267)|
|Two boys|—|-0.1621***<br>(0.0209)|—|—|0.0029<br>(0.0226)|—|0.0024<br>(0.0226)|
|Same sex|0.2289***<br>(0.0192)|—|—|0.2202***<br>(0.0173)|—|—|—|
|R-squared|0.1197|0.1090|0.1569|0.2982|0.2665|0.2982|0.2982|
|Observations|7480|7480|7480|7404|7404|7404|7404|
|Mean dep. var.|2.60|2.60|2.60|2.60|2.60|2.60|2.60|
|F-stat excl. inst.|140.1|61.2|343.3|161.7|0.02|268.7|134.4|
|Survey FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|No|No|No|Yes|Yes|Yes|Yes|
|_Panel B: _<br>Boy frst|_Husband’s of t_<br>—|_he married w_<br>—|_omen with 2 _<br>—|_or more child_<br>-0.2393***|_ren younger _<br>-0.4619***|_than 18 year_<br>-0.0115|_s old_<br>-0.0153|
|||||(0.0204)|(0.0309)|(0.0262)|(0.0277)|
|Boy second|—|—|—|-0.2240***<br>(0.0205)|-0.4465***<br>(0.0304)|0.0057<br>(0.0266)|—|
|Two girls|—|—|0.4703***<br>(0.0280)|—|—|0.4591***<br>(0.0400)|0.4465***<br>(0.0304)|
|Two boys|—|-0.1615***<br>(0.0235)|—|—|-0.0003<br>(0.0268)|—|-0.0014<br>(0.0269)|
|Same sex|0.2252***<br>(0.0216)|—|—|0.2225***<br>(0.0201)|—|—|—|
|R-squared|0.1160|0.1059|0.1530|0.2743|0.2415|0.2743|0.2743|
|Observations|5737|5737|5737|5460|5460|5460|5460|
|Mean dep. var.|2.60|2.60|2.60|2.60|2.60|2.60|2.60|
|F-stat excl. inst.|113.9|43.12|275.3|131.4|0.07|220.2|110.2|
|Survey FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls|No|No|No|Yes|Yes|Yes|Yes|

_Notes:_ Standard errors clustered at the PSU level are in parentheses. Other covariates in the models are the following: _Age, Age Squared, Age at First birth, Years of Education_ and also indicators for _Boy 1st_ and _Boy 2nd_ . Each model is estimated with survey and district fixed effects. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%.

40
# Cash and Conflict

Large-Scale Experimental Evidence from Niger Patrick Premand<sup>1</sup> , Dominic Rohner<sup>2</sup>

> 1Development Impact Evaluation (DIME) Department, World Bank

> 2University of Lausanne and CEPR

_JEL classification_ : D74, I38, 017.

_Keywords_ : Conflict, Terrorism, Cash Transfers, Sahel.
the sample was randomly excluded and the model from Table 10 estimated. The process was repeated 1,000 times and Figure A.4 shows the results for the in‐sample and out of sample R<sup>2</sup> of this validation.

**Table 9: Final model to predict urban poverty.**

|**Coefficients**|**Coefficient estimate**|**Standard error**|**p‐value**|
|---|---|---|---|
|(Intercept)|‐1.946|0.355|0.000|
|Distance to bare areas|0.165|0.028|0.000|
|Distance to cultivated areas|0.000|0.008|0.969|
|Distance to dry areas|0.001|0.000|0.017|
|Distance to major roads|0.084|0.026|0.002|
|Distance to medical sites|0.062|0.024|0.011|
|Distance to schools|0.083|0.028|0.003|
|Distance to unsafe areas|0.002|0.001|0.001|
|Distance to urban areas|‐0.057|0.021|0.009|
|Distance to water sources|0.001|0.001|0.147|
|Distance to waterways|0.001|0.001|0.437|
|Population density|0.000|0.000|0.001|
|Temperature|0.084|0.013|0.000|
|Distance to bare areas x Distance to waterways|0.000|0.000|0.000|
|Distance to bare areas x Temperature|‐0.006|0.001|0.000|
|Distance to cultivated areas x Distance to waterways|‐0.001|0.000|0.012|
|Distance to dry areas x Distance to schools|0.000|0.000|0.001|
|Distance to major roads x Distance to schools|‐0.010|0.003|0.001|
|Distance to major roads x Distance to unsafe areas|‐0.003|0.001|0.000|
|Distance to major roads x Distance to urban areas|‐0.090|0.024|0.000|
|Distance to medical sites x Distance to water sources|‐0.001|0.000|0.015|
|Distance to schools x Temperature|‐0.002|0.001|0.025|
|Distance to urban areas x Distance to water sources|0.002|0.000|0.000|
|**Model statistics**||||
|Unit of observation|Enumeration areas|||
|Observations|252|||
|Degrees of freedom|229|||
|R‐squared|0.56|||
|Adjusted R‐squared|0.518|||
|Root mean squared error|19.8|||
|F‐Statistic|13.3|||

Source: Flowminder / WorldPop.

The model’s relatively low predictive power is likely because the explanatory variables do not vary at a high enough spatial frequency relative to urban poverty estimates, which can vary significantly across a small space. Furthermore, distance explanatory variables could result in relatively smooth predictions across space and not accurately capture small geographical clusters of low/high consumption.<sup>27</sup> For example, in urban settings, poverty levels may be quite different in two EAs which are only several hundred meters apart. In contrast, the same two EAs will have very similar levels of precipitation or,

> 27 The same issue can arise with non‐distance variables computed within a buffer, such as precipitation, temperature and conflict density.

27
has no e¤ect irrespective of self-control. If we are willing to regard baseline pro...ts as proxy for ability �, this suggests that self-control is an impediment to growth only for more able female entrepreneurs, who have more potential for growth.

Next we examine whether the pattern of heterogeneous treatment e¤ects in the sample is consistent with external pressure. We ...rst investigate capture by spouses. Unmarried individuals are not, by de...nition, subject to capture by a spouse. Consequently, if capture by a spouse is the main reason why cash treatments do not raise pro...t, this should not a¤ect unmarried entrepreneurs –especially women. To test this hypothesis, we interact treatment with a married dummy and expect the cash treatment e¤ect now to be positive and the interaction term to be negative, especially for female entrepreneurs. The percentage of unmarried individuals in our sample is 34% for females and 35% for males. Results, presented in columns (1) and (2) of Table 8, show that the cash treatment remains non-signi...cant for female entrepreneurs. None of the interaction terms is statistically signi...cant but point estimates for females are positive for cash and negative for in-kind, which is the opposite of what the external pressure hypothesis predicts. Married status is also not (negatively) correlated with baseline capital stock, as should be case if marriage leads to more capture.

Next we examine the determinants of bargaining power between married entrepreneurs. The hypothesis is that less of the cash and in-kind treatment is taxed away from the business when a married entrepreneur has more bargaining power with his or her spouse. We expect the e¤ect of bargaining to be stronger for female entrepreneurs, given their subordinate status in traditional society. The ...rst proxy for bargaining power is the share of assets brought to marriage by the owner. This information is available for 397 married respondents.

As shown in columns (3) and (4) of Table 8, point estimates are insigni...cant and do not go in the expected direction: we would expect those who bring a higher share of assets into the marriage to have more bargaining power, yet the interaction of this with the cash treatment is negative. We also test (not shown) whether the share of assets brought to marriage is correlated with baseline capital stock, as should be the case if entrepreneurs with more bargaining power are less subject to spouse taxation and can accumulate more over time. We ...nd no evidence of this.

We investigate other possible determinants of intra-household bargaining power, such as di¤erences between spouses in age and education level. We expect older, more educated spouses to have more bargaining power. The results, shown, in columns (5) to (8) of Table 8, are once 34
3 jobs had Increased substantially. The Canadian survey of 1986 also shows that displaced workers are predominantly males In the 25 to 54 age bracket but wth the pre-layoff employer being less k.;ely to be in the manufacturing sector than in the U.S.

Information available for other OECD countries Is limited to data for job losers, a category of the unemployed which includes displaced workers but Is not limited to just the displaced. If the early 1980s, unemployed job losers were for the most part men between the ages of 25-44 who lost jobs In the manufacturing sector. As the decade wore on, however, worker displacement tended to spread across all industries and occupations.

For the U.S. and Canada, Table 1 summarizes the cost of displacement to Indivdual workers. The growth In job opportunHties in the U.S. during the 1980s helps to explain the Increase In the observed employment rate from 60% to 71% between the 1984 and 1988 surveys. Nevertheless, there was little change over the decade In the nearly half of reemployed workers who found new jobs only by being wiling to accept a lower wage. Compared to the adjustment costs incurred by American displaced workers, reemployed Canadian workers appearto have experienced a somewhat longer period of joblessness and a higher proportion of workers eaming a lower wage in their new jobs.

The OECD (1990:61) observes that the groups of workers for whom the probability of displacement is highest are not necessarily those that bear the highest costs of displacement. Drawing on the U.S. and Canadian national surveys, a number of characteristics can be identifed that are associated with increased costs of adjustment measured In terms of length of joblessness and loss in wages upon reemployment. These characteristics include membership in an ethnic minoriy, being over age 55, not possessing an academic diploma or vocational skills, and living in an area of high unemployment. Among workers In these high-risk groups, the unskilled, women, older workers, and those with little formal education are more likely to become long-term unemployed; while worker with skills and long service in their previous jobs are more likely to suffer a reduction In pay. Follow-up surveys of workers displaced from their jobs with selected companies in France, Italy, and Sweden provide evidence supporting these findings. These follow-up surveys sugges **t** hat the best educated and
economic activities in places or areas that are more likely to be negatively affected by climate shocks.

The data used in the analysis are from the nationally representative Rural Agricultural Livelihood Surveys (RALS) conducted in 2012, 2015, and 2019 in Zambia. The poverty metric used in this paper is based on total household income captured in RALS. For our poverty estimates to be comparable to those based on consumption expenditure,<sup>5</sup> we used the RALS 2015 data to determine an income threshold or “poverty line” that would make the incomebased poverty rate equal to the expenditure-based rural poverty rate estimated in the 2015 Living Conditions Monitoring Survey (LCMS) in Zambia.<sup>6</sup> Besides our focus on resilience and vulnerability, the use of an income-based poverty line that equates the 2015 poverty rate in RALS to the expenditure-based poverty rate for that year is another major difference between this paper and others that use RALS data to study poverty dynamics in Zambia, see Chapoto et al., (2011); Ngoma et al., (2019) and Diwakar et al., (2020).

The paper proceeds as follows. Section 2 briefly reviews the links between climate shocks, vulnerability, and resilience. Section 3 presents a conceptual framework on the linkages between climate change, vulnerability, and livelihood outcomes. Section 4 presents the data and methods. Section 5 presents our results, which we discuss further in section 6. Section 7 concludes the paper and offers some reflections.

2. Climate shocks, vulnerability, resilience, and rural livelihoods: A brief review The linkages between climate shocks, vulnerability, resilience, and livelihoods are complex. Climate change can increase poverty directly by reducing agricultural productivity and production, and by hindering asset accumulation and return on assets. Indirectly, climate change affects poverty through output prices, labor productivity and the availability of offfarm employment opportunities. This paper focuses primarily on the direct livelihood effects. A <mark>s in other mainly agrarian SSA countries, the incidence of climate shocks such as droughts,</mark> seasonal and flash <mark>floods, and e</mark> xtreme temperatures and dry spells is increasing in Zambia. <mark>These weather extremes</mark> have significant carryover side effects on rural farmers who depend on rainfed agriculture for their livelihoods.

Climate shocks are likely to become more intense and more frequent. Ahmed et al. (2009) predict that by 2080, climate change will increase the intensity of dry spells and subsequently worsen the incidence of poverty among smallholder farmers by 4.6 percentage points in all agro-ecological regions in Zambia. Average temperatures are predicted to rise by between

> 5 From which the national poverty rate is derived.

> 6 At the time of writing, the 2015 LCMS is the data set that provides the most recent estimate of national, urban/rural, and provincial poverty rates in Zambia.

4
24

# **2.4 Early Retirement**

Early retirement options are now offered by most companies. For many companies this is simply an actuarially fair reduction of the conversion factor in the case of early withdrawals. Some companies offer more generous early retirement packages, including additional payments to make up for first pillar benefits up to the legal retirement age (refer to the appendix for illustrative examples). There are no legal restrictions on how the conversion factor has to be adjusted in case of early retirement. As it has been mentioned before, pension funds are allowed to deviate from the legal conversion factor, if they use the resources to finance other benefits for pensioners, including early retirement options.

Take-up rates for early second pillar benefits are very high. On average, the observed retirement in occupational plans is substantially below the statutory age even in funds that do not subsidize early retirement explicitly. On the other hand, take-up rates of early benefits in the first pillar have been low. Presumably this is due to the fact, that many second pillar pension plans allow an anticipation of benefits at actuarially fair rates (or better). This latter option is administratively more convenient for most beneficiaries.

In the past many beneficiaries (predominantly middle and high income) have received generous early retirement packages from their occupational pension provider, often with additional benefits until age 65/62 (64) to bridge the time to the legal retirement age. Figure 12 displays the distribution of retirement ages collected from 15 Swiss occupational pension funds. One clearly sees that over time the relative importance of the statutory retirement age has declined. For the period from 2000-2003 a triple–peak profile for men at ages 60, 62 and 65 and a double–peak profile for women at ages 60 and 62 is apparent. The peaks at 60 and 62 correspond to the lowest age for which early retirement packages are offered at relatively favorable conditions in occupational pension funds.
_54_ This is compounded **by** the fluctuations in Argentina's quarterly trade shares where, traditionally, a high proportion of total trade occurs in the third and fourth quarters. Together, they account for the large deviations of the merchandise-weighted conversion factor form the unweighted official exchange rate. **A** closer look at Table 14 reveals that the small deviation in MWXR in **1980** is the result of relative stability in exchange rates and the dispersion of total trade, **by** quarters, in **1980;** similar observations are seen in **1986.** The outcome of the high volatility in exchange rates and trade shares in the other years is obvious: large deviations between MTXR and official rate. It is obvious that while the official rate encompasses the unweighted exchange rate changes over the entire year, its suffers from the drawback of not encompassing the volumes of transactions at each of these fluctuating rates; hence the need for a weighted conversion factor based on shorter period (quarterly) data.
# **Obstacles to doing business**

Firms reported electricity, corruption, access to finance, and political instability as the four major or very severe obstacles to their operations in 2014 (Figure 4). The firms’ perception of the major obstacles shows a relative improvement, as the percentage of firms that ranked these factors as major obstacles declined over time, except corruption; while 30 percent of firms ranked corruption as a major obstacle, that fraction rose to 45 percent in 2014.

Figure 4. Top constraints to doing business in Nigeria <!-- Start of picture text --> 80<br>54<br>49<br>45<br>34 34<br>30 30<br>26<br>24<br>21<br>18 18 17<br>14 14<br>9<br>6<br>3 4<br>2008 2014<br>Electricity Corruption<br>Access to finance Political instability<br>Tax rates Transport<br>Tax administration Licencing and Permits<br>Crime Courts<br>Source: Authors' calculation based on data from Enterprise Surveys<br>80<br>60<br>40<br>20<br>0<br>%saying area is major or severe obstacle<br><!-- End of picture text --> There is a large heterogeneity in the firms’ perceptions of obstacles to doing business across regions. Electricity is perceived as the biggest obstacle to doing business in all the regions except in the North West, where corruption is reported as the major obstacle by a small margin (Table 2). Political instability appears within the top 3 major or very severe obstacles to doing business in the Northern regions (NC, NE, and NW), while it is ranked in the bottom three obstacles in the Southern regions (except the SE region, Table 2).

7
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