rafmacalaba/datause-encoder-data huggingface dataset:
the perpetration of physical IPV in the last year (the most common form of violence), a 3 percentage point (16.7 percent) increase for emotional violence and a 3.4 percentage point (42.5 percent) increase in sexual violence. Sole decision making by the wife, compared to joint decision making, is associated with a 10 percentage (62.5 percent) point increase in physical IPV, a 12 percentage point (66.7 percent) increase in emotional IPV and a 6.7 percentage point (83.7 percent) increase in sexual IPV. Using the same DHS data, we construct indicators representing the four blocks of alternate theories that may constitute important mediators for the relationship: attitudes towards violence, alignment of preferences, marital capital and bargaining. None of these covariates dampens the magnitude of the observed relationship between joint decision making and IPV, and in some cases they even strengthen the association.

Next, we re-run the analysis considering both wives’ and husbands’ views of who makes decisions. We find that the husband making decisions is associated with increased IPV compared to joint decision — making even when both spouses agree that the husband makes the decisions (i.e., in the absence of any contestation of power by the woman). This empirical finding contrasts with IPV theories emphasizing a divergence of preferences or contestation of power by the woman as a source of violence perpetration by the husband. Overall, we detect three broad categories of decision making patterns and their relationship with IPV. The first category, among which the highest levels of physical, emotional and sexual IPV are observed, are couples where the wife reports that she makes decisions (regardless of the husband’s view). The second category, with lower IPV compared to the first, are couples that disagree over decision making roles (but where the wife does not report making sole decisions) and couples that agree that the husband makes decisions. The third category, when spouses both report making joint decisions, is associated with the lowest levels of all forms of IPV. This finding is consistent with existing qualitative research that highlights the importance of shared responsibility for couples’ harmony.

4
The distribution is highly skewed, with PA rents comprising 1% or less of GDP in 55% of the countries in the sample, and 2% or less of GDP in 76% of the countries in the sample. With some exceptions, therefore, PA rents tend to be a small share of GDP. This leads to the next analytical question: How do these shares of GDP vary across income classes of countries?<sup>12</sup> _PA rents as a share of GDP across income classes_ Annex II Tables A1 – A3 present the detailed figures on GDP per capita, PA asset values per capita and PA rents as a share of GDP for each of the countries in the sample, classified by income category. Using 1% and 5% of GDP as thresholds, Table 1 summarizes the findings.

The striking result in Table 1 is the high proportion of developing countries where PA rents exceed 1% of GDP, often by a large amount. Only four OECD countries – Sweden (1.1%), Slovak Republic (1.2%), Canada (2.1%), and New Zealand (4.5%) – had shares of GDP above 1%.

**Table 1.  Countries with PA rents > 1% of GDP, by income class, 2005**

||Low income|Middle income|OECD|
|---|---|---|---|
|# of countries in class|41|65|29|
|# of countries in class with<br>PA rents > 1% of GDP|24|34|4|
|% of countries in class with<br>PA rents > 1% of GDP|59%|52%|14%|
|Which countries had<br>PA rents > 5% of GDP?|Benin (6%)<br>Kenya (7%)<br>Lao PDR (7%)<br>Tajikistan (8%)<br>Nepal (9%)<br>Ethiopia (10%)<br>Uganda (11%)|Dominica (6%)<br>Thailand (7%)<br>Cameroon (8%)<br>Honduras (9%)<br>Belize (11%)<br>Bhutan (12%)<br>Ecuador(23%)||

Source: Author’s calculations based on World Bank (2011) _Explaining the high PA rents as a share of GDP in developing countries_ Table 2 lays out three potentially relevant indicators to explain this high share of PA rents in the GDP of developing countries. The first two are physical measures – hectares of protected area per capita and protected area as a percent of total land area. The third is a measure derived from

> 12 We use the standard income classes of the World Bank, low and middle income, as well as OECD countries.

13
# **Figure 5: Dropout rate from private schools**

<!-- Start of picture text --> Unadjusted differences between treatment and comparison districts<br>All other schools<br>2 3 4<br>Survey Round<br>Lower bound Comparison Group<br>Treatment Group Upper Bound<br>.08<br>.06<br>.04<br>.02<br>0<br>Percentage of students dropping out<br><!-- End of picture text --> Note: Thick solid line represents sample average for the treatment district. Thick dashed line represents sample average for the comparison districts. Confidence intervals are marked using small dashes (lower bound) and lines that alternate dashes and dots (upper bound). Data exist for all four rounds. The y-axis is the percentage of children dropping out. The mean at round 2 for the treatment district is 0.062 and the mean at round 2 for the comparison districts is 0.056.

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||[0.006]|[0.022]|[0.000]|[0.000]|
|---|---|---|---|---|
|Household was affected negatively by some income Shock During the last 12 months (0/1)|0.112***|0.107***|-0.274***|-0.147***|
||[0.000]|[0.000]|[0.000]|[0.002]|
|Household has a migrant network (0/1)|0.019|0.030|0.023|-0.001|
||[0.465]|[0.296]|[0.754]|[0.977]|
|Rain - EA level CoV of Dec-Jan rainfall from 1983/84 - 2012/13|0.425||-0.531||
||[0.270]||[0.616]||
|Average 12-month total rainfall (mm)|0.001|0.001|-0.001|-0.001|
||[0.291]|[0.184]|[0.613]|[0.399]|
|Annual Mean Temperature (∞C * 10)|-0.000|0.001|0.006|0.003|
||[0.832]|[0.784]|[0.330]|[0.450]|
|HH Distance in (KMs) to Nearest Road|0.006+|0.004|0.001|0.005|
||[0.123]|[0.293]|[0.919]|[0.331]|
|HH Distance in (KMs) to Nearest Population Center with +20,000|0.002|0.000|-0.004|-0.003|
||[0.337]|[0.909]|[0.404]|[0.444]|
|Price of fertilizer in the EA|0.000|0.000|0.000|0.000|
||[0.222]|[0.203]|[0.740]|[0.895]|
|Price of maize grains in the EA|0.002***|0.002***|-0.002**|-0.001*|
||[0.000]|[0.002]|[0.026]|[0.078]|
|year_2011|0.101***|0.122**|-0.114|-0.066|
||[0.009]|[0.036]|[0.263]|[0.335]|
|Constant||-2.209*||2.298+|
|||[0.061]||[0.115]|
|Observations|5,317|5,321|5,321|5,321|
|R-squared||0.058||0.044|
|Number of Households||2,764|2,764|2,764|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination. ***, **, *, and + indicate that the corresponding regression APE is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The table presents APEs except in the ordered Probit cases. The APEs from the CRE Probit models are estimated using GEE approach. The coefficients form the CRE ordered Probit are from pooled ordered Probit. The linear FE results are presented for comparison purpose. P-values between brackets are based on cluster robust standard errors.

57
ˆ coefficients ( <sup>ˆ</sup> ,  ) and the disturbance terms, both of which affect undernutrition estimates and the level of their accuracy. In ELL, a simulated z-score is calculated for each census child by a predicted z-score as in (2): These simulations are repeated a large number of times (100 in the case of this application), with new values for  ,  , and _uih_ drawn from their distributions, in each repetition. The distributions of  ,  are obtained through GLS, while the distribution of _uih_ is obtained through a non-parametric approximation following Elbers and van der Weide [27]. All the estimation is automated within the PovMap program.

For any given location (such as a woreda), each simulation is used to calculate the share of children underweight or stunted (a z-score below negative two standard deviations). The mean across the simulations of an undernutrition statistic provides a point estimate of the statistic, and the standard deviation provides an estimate of the standard error.

In most applications of ELL, the error term _uih_ is decomposed into two independent components: _uih_  _c_   _ih_ , where  _c_ is a cluster-specific effect and  _ih_ an individual effect. In this application, the error term has not been split into a cluster and individual effect, as no cluster effect was found. The lack of a cluster effect is likely closely related to the sampling design of EMDHS, as it is designed with few EAs within each region and relatively large samples within each EA.

# **Application of SAE to Ethiopian data**

SAE estimates of stunting and underweight is done at woreda level. Woreda is the third administration layer after region and zone. Each woreda is also divided into kebeles, which are the smallest administrative units in Ethiopia. A woreda on average has around 12,000 children, with a standard deviation of 8,000, with around 2,000 children at the 5 percentile and 28,500 children at the 95 percentile. The estimates are based on regional z-score models. Regional models have the advantage of fitting data relevant to local circumstances, although they limit the 6
38

**Table 8:** When considering cash income separately, MPC from unearned income remains larger for non-basic consumption goods (non-staples, alcohol and tobacco, transportation and communication, clothing).

|Marg.Propensityto Cons.|Kilimanjaro||Ruvuma||Gansu||Inner Mong|olia|
|---|---|---|---|---|---|---|---|---|
||OLS|hh FE|OLS|hh FE|OLS|hh FE|OLS|hh FE|
|**Food**|||||||||
|**_Staple food_**|||||||||
|Earned income in cash|0.004<br>(1.335)|0.004<br>(1.274)|0.018*<br>(1.887)|-0.014<br>(-0.861)|0.008**<br>(2.135)|0.003<br>(0.775)|0.005**<br>(3.252)|0.005**<br>(2.412)|
|Earned income in kind|0.003|0.008**|0.040**|0.030*|0.007|0.002|0.011***|0.005*|
||(1.206)|(2.215)|(2.086)|(1.653)|(1.056)|(0.772)|(3.786)|(1.841)|
|Unearned income in cash|0.016|0.042|-0.112|0.090|0.012*|0.014*|0.011|0.012|
||(0.599)|(1.225)|(-0.849)|(0.543)|(1.755)|(1.884)|(1.419)|(1.622)|
|Unearned income in kind|-0.086|0.010|0.085|0.106|0.087*|0.115*|0.006|-0.005|
||(-1.055)|(0.102)|(0.372)|(0.358)|(1.826)|(1.914)|(0.212)|(-0.174)|
|**_Non-staple food_**|||||||||
|<br>Earned income in cash|0.025**|0.008|0.071***|0.027|0.060***|0.013*|0.039***|0.019***|
||(1.964)|(0.716)|(6.034)|(1.631)|(6.892)|(1.914)|(11.002)|(4.808)|
|Earned income in kind|<br>0.008|<br>0.006|<br>0.028**|<br>0.023*|<br>0.017**|<br>-0.01|<br>0.039***|<br>0.027***|
||(1.556)|(1.000)|(2.171)|(1.862)|(2.224)|(-1.217)|(6.718)|(4.737)|
|Unearned income in cash|0.197***|0.099**|-0.087|-0.029|0.070***|0.049**|0.108***|0.098***|
||(5.105)|(2.337)|(-0.658)|(-0.141)|(5.405)|(3.130)|(4.314)|(4.081)|
|Unearned income in kind|0.137|-0.002|0.318|0.422|0.098|0.007|-0.055|-0.073|
||(1.349)|(-0.025)|(1.594)|(1.431)|(1.565)|(0.081)|(-1.135)|(-1.483)|
|**_Alcohol and tobacco_**|||||||||
|Earned income in cash|0.001|0.001|0.003|0.000|0.018***|0.007**|0.010***|0.007***|
||(1.038)|(1.180)|(1.526)|(0.030)|(8.250)|(3.112)|(7.677)|(4.823)|
|Earned income in kind|0.000<br>(0.175)|0.000<br>(0.060)|0.001<br>(0.541)|-0.001<br>(-0.457)|0.000<br>(0.006)|0.000<br>(-0.036)|0.006**<br>(3.144)|0.005**<br>(2.538)|
|Unearned income in cash|0.019*|0.004|0.031|0.048|0.025**|0.018***|0.056***|0.056***|
||(1.742)|(0.290)|(0.871)|(1.324)|(3.244)|(3.601)|(4.201)|(4.876)|
|Unearned income in kind|0.005<br>(0.237)|-0.005<br>(-0.140)|-0.010<br>(-0.233)|0.067<br>(1.498)|0.023<br>(0.986)|0.009<br>(0.327)|-0.050*<br>(-1.850)|-0.045**<br>(-2.185)|
|**Non-food**|||||||||
|**_Housing and appliances_**|||||||||
|<br>Earned income in cash|0.028|0.025|0.029***|0.022*|0.078***|0.089***|0.066***|0.059***|
||(1.172)|(0.862)|(3.534)|(1.955)|(5.116)|(4.717)|(4.963)|(3.530)|
|Earned income in kind|-0.007*|-0.013*|0.004|0.008|0.063|0.067|0.021|0.018|
||(-1.657)|(-1.740)|(0.654)|(1.207)|(1.248)|(1.352)|(1.591)|(1.138)|
For consistency of household FE-IV estimates of , receptivity needs to fulfill two criteria. It needs to be correlated with refugee intensity. And it should affect the outcome variable(s) only ββ through its effect on refugee intensity.

The FE-IV approach exploits the interaction between a static geographic factor (inverse geographic distances) and a dynamic factor (temporal change in outflow of refugees from refugees’ countries of origin). Temporal change in the underlying causes of displacement and thus changes in the outflow of refugees in these countries are arguably exogenous to withinvillage/household temporal changes in village/household outcomes in Ethiopia, conditional on the 12 time-varying covariates. However, the static geographic factor may affect host outcomes on its own, but this potential relevance would be avoided by the household- and round-specific fixed effects. As a consequence, receptivity is arguably orthogonal to the residual of equation (1). Further, it is less likely for the interaction of these two factors (forming receptivity) to have a direct effect on household outcomes in Ethiopia. Thus, the second criterion plausibly holds.

As the first criterion is an empirical issue, I run alternative regressions of (logarithm of) refugee intensity on (logarithm of) receptivity, controlling for household- and round-specific fixed effects and numerous (12) time-varying covariates, and present the results, called first-stage results, in Table A8. As expected, receptivity has a positive and significant effect on refugee intensity. The estimated elasticity is about 0.84. Further, the first-stage F-statistic is well above the conventional value of 10 (Kleibergen-Paap rk Wald F > 10), implying that receptivity is a strong instrument.

In the second stage, I use the predicted refugee intensity from the first-stage regression to explore the effect of refugee intensity on household welfare and poverty outcomes considered in the main analyses. The results from two-stage least squares (2SLS) (household FE-IV) estimations are reported in Table A9, which reveal that the main findings are qualitatively robust to using this alternative empirical strategy. Quantitatively, however, there is sizable discrepancy in the estimates between these and those from the non-instrumented (household fixed effects) estimations. For instance, the 2SLS estimations show that the adverse consumption effects of hosting refugees are about twice as large as the corresponding effects from the non-instrumented estimations.

31
SWP villages participated, as compared to 58.8% of those with above median income and higher education, 50.0% of those with high income but low schooling, and 47.8% of those with both low income and schooling. (The program slightly favored better educated households both above and below median income.) Suppose that beneficiary selection had focused solely on the relatively well-educated poor, and saturated this group, with no change to conditional mean impacts by subgroup, which were zero for other groups (consistently with Table 6). Then the impact of the program as a whole would have risen substantially, from a mean impact of about 40 yuan per person to about 150 yuan.<sup>36</sup> To achieve this outcome, the program would have had to over-ride the community-based selection process, which evidently put too little weight on reaching the educated poor, even though this group was already favored in the selection process.

While we found no impacts on average remittances and out-migration, significant positive impacts were evident when we stratified by initial income and education; the impacts were significant for those who were initially above median income and (among those with above-median income) were larger for those with more schooling.

_5.4 Are we underestimating the impacts due to spillover effects?_ Biases in long-term impact estimates can arise from interference due to spillover effects, as discussed in section 4.2. Our results do not offer much support to the idea of trade-induced spillover effects. We have seen that there were no significant impacts on prices, although it might be argued that arbitrage eliminated any price differentials. More damaging to the notion that there were significant trade-spillovers across villages is the fact that we did not find significant impacts on cash income, even during the disbursement period; the short-term income gains were in kind, and mainly from animal husbandry. Since inter-village trade is likely to involve cash, there must be a presumption that such trade was affected rather little by SWP.

What about bias due to the responses of the local political economy? From the data on project activities, we counted the number of new non-SWP projects of each type that started between 1996 and 2001 (inclusive). (So this is the change in the number of non-SWP projects during the period.) For the loans made to households, the project data

> 36 This is based on an impact of about 200 Yuan for this group (Table 5), scaled down by 25% to reflect the number of households in this group, which would then represent 75% of the total number of SWP participants.

25
Figure 1. Government debt <!-- Start of picture text --> Percent of GDP<br>150<br>World<br>120 Advanced economies<br>EMDEs<br>90<br>60<br>30<br>0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Sources: Kose et al. (2017, 2020). Note: Aggregates are weighted averages using current GDP in U.S. dollars as weights.

# Figure 3. Sustainability gaps

<!-- Start of picture text --> Percent of GDP World<br>10 Advanced economies<br>EMDEs<br>5<br>0<br>-5<br>-10<br>-15<br>2000 2005 2010 2015 2020<br><!-- End of picture text --> Source: Kose et al. (2017).

Note: Sustainability gaps are computed with current output growth rates and long-term interest rates. Aggregates are weighted averages using current GDP in U.S. dollars as weights.

Figure 2. Private debt <!-- Start of picture text --> Percent of GDP<br>200<br>World<br>Advanced economies<br>EMDEs<br>150<br>100<br>50<br>0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Sources: Kose et al. (2017, 2020). Note: Aggregates are weighted averages using current GDP in U.S. dollars as weights.

Figure 4. Interest payments and interest rates in EMDEs <!-- Start of picture text --> Percent of GDP Percent<br>1.9 8<br>Interest payments<br>Interest rates (RHS)<br>1.8<br>7<br>1.7<br>6<br>1.6<br>1.5 5<br>2010 2012 2014 2016 2018 2020<br>Sources: International Monetary Fund, Kose et al.<br>(2017).<br><!-- End of picture text --> Note: Interest payments refer to net interest payments, which are computed as differences between primary balances and overall fiscal balances. Interest rates refer to long-term nominal interest rates (i.e., government bond yields with maturity of 10 years or close). Aggregates are weighted averages using current GDP in U.S. dollars as weights.

31
generally less resilient and more vulnerable. On the other hand, asset-rich households or those with savings might be less affected by climate shocks because they can liquidate their assets and/or use savings to smooth consumption and avert food and income insecurity. To achieve a more nuanced understanding of how climate shocks impact livelihoods, it is important to classify households into sub-groups that differ along dimensions that inform their vulnerability and resilience.

Climate shocks could increase household vulnerability and reduce resilience through various pathways. Weather and climate variability affect rural household consumption and incomes, more so for those who depend on rainfed agriculture for their livelihoods. In the event of disruptions to agricultural production, poor households are likely to intensify extraction of natural resources and to engage in non-farm employment (Mulenga et al., 2014; Angelsen and Dokken, 2018). Increased natural resources extraction, such as charcoal production and logging, speed environmental degradation, which disproportionately affects smallholder farmers. Moreover, intensification of environmentally degrading livelihood activities can aggravate poverty for households whose livelihoods depend on these natural resources.

Households with a higher asset base could liquidate some to help smooth income and consumption, but this is only a temporary coping strategy. If climate or weather shocks persist for a longer period, households may deplete their assets and savings, causing a further slide into poverty. Absent formal insurance or social protection, this creates a vicious cycle in which climate shocks increase poverty and environmental degradation that create more climate shocks. Households my also use incomes earned from non-farm employment to smooth consumption in places where such work is available. The outcome will either be vulnerability or resilience, depending on whether (i) a household stays or becomes poor, or (ii) was never poor or escaped poverty.

# 4. Data and methods

## 4.1 Data sources

We draw data from the three-wave, nationally representative RALS survey conducted by the Indaba Agricultural Policy Research Institute (IAPRI) in collaboration with the Ministry of Agriculture and the Central Statistical Office (CSO) of Zambia. The first round of RALS was conducted in May/June 2012, the second in June/July 2015, and the third in June/July 2019. This timing coincides with harvesting for the previous agricultural production season and the agricultural marketing season (from May year _t_ to April year _t+1_ ). The dekadal (10-day) rainfall data from the Climate Hazards Group Infrared Precipitation with Station database (CHIRPS) for the period 1981 to 2018 compliments these data, with the CHIRPS merged into the RALS data set using household geo-coordinates.<sup>9</sup> We use the dekad CHIRPS data to compute

> 9 CHIRPS is a quasi-global (50'S-50'N), gridded 0.05' resolution with satellite and observation-based precipitation estimates. It combines station based rainfall data with satellite data (Funk et al., 2014).

7
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