rafmacalaba/datause-encoder-data huggingface dataset:
Table 8 presents the drivers of continued off-farm employment in urban areas. Compared to rural areas, we observe that 15-24-year-olds are much less likely to continue employment. This is particularly the case in Nigeria, and for women in Ethiopia, Malawi and Tanzania. This suggests that (female) youth’s unstable employment is more problematic in urban areas than rural areas. Education plays a more important role in ensuring continued employment, but the effect is still quite limited. Surprisingly, wealth is not associated with stable employment (even the opposite effect in Ethiopia) while landholdings stimulate continued employment for men in Nigeria and women in Tanzania. Shocks do not influence people’s transitions out of employment, suggesting that off-farm employment in urban areas is not used as a risk reduction strategy but rather as a stable source of income. Only in Ethiopia extreme weather events cause men to drop out of employment. Unlike rural areas, self-employment is not necessarily more conducive for stable employment than wage employment (it is even worse for women in Malawi and Tanzania), indicating that wage employment in urban areas offers more job security than wage employment in rural areas. Employment in industry and services leads to more stability for men in Ethiopia, and women in Tanzania. Surprisingly, men’s work in services in Nigeria leads to an exit from employment. Being full-year employed has a positive influence on remaining employed for women in Ethiopia, Tanzania and Uganda, and for men in Nigeria and Tanzania, while full-time employment does not result in more stability.

# **6. Conclusion**

This paper provides recent empirical evidence for women’s and men’s participation in off-farm employment across five countries that represent 40 percent of the total population in Sub-Saharan Africa and investigates the drivers of entry into and continued participation in off-farm employment. We use the national, longitudinal and multi-topic household survey data from Ethiopia, Malawi, Nigeria, Tanzania and Uganda, generated as part of the World Bank Living Standards Measurement Study (LSMS-ISA) initiative. Since these surveys include extensive questionnaire modules on off-farm wage and employment; solicit individual-level data; track individuals over time; and allow for cross-country comparable analyses, they are particularly wellsuited to investigate the nature, dynamics, and drivers of off-farm employment, with a strong focus 20
Table 2 lists the major importers of raw sugar for the period 1990-93. Former USSR, until 1991, and Russia, since 1992, are the largest importers of raw sugar. USA, UK and Japan and Korea are other large importers. On average, each of them imported over 1 mln MT per annum during 1990-93. In the 1960s and 1970s the USA used to be the largest importer, importing over 3 mln MT, about a quarter of world imports.

Table 2: **Principal importers of raw sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than 1.7 mln MT|Former USSR / Russia|
|1.1-2 mln MT|USA, Japan, Korea, UK|
|0.5-1 mln MT|Algeria, Canada, Malaysia, China|
|0.3-0.45 mln MT|France, Sri Lanka, Morocco, Portugal|

Source: See Data S6 for details Data S6 shows the principal importing countries and their imports for the years 1965, 1975, 1985, and 1990-93. The countries listed accounted for over 95% of the total world imports during 1990-92. The developed countries accounted for 67% of total raw sugar imports.

The major exporters of refined sugar (including plantation white sugar) are given in Table 3. In the 1990s the EU was by far the largest exporter, on its own accounting for about 38% of the world's exports. In 1992 and 1993 there were large increases in exports of refined sugar from Brazil and China, which caused total world exports to jump about 50 percent above the levels prevailing during most of the 1980s. In the 1960s, in addition to being the largest exporter of raw sugar, Cuba was also the largest exporter of refined sugar, accounting for about 15 percent of world exports. By 1975 France had taken the lead, and Cuba's share dropped to less than 2% by the early 1990s. Data S7 shows exports by country for the six years 1986, 1988, 1990, and 1991 - 1993. The countries listed accounted for about 95% of world exports of refined sugar during the period 1986-93.

Table 3: **Principal exporters of refined sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than_5_mln MT|EU|
|1-2 mln MT|Brazil, China|
|0.31-0.9 mln MT|Thailand, USSR/FSU, USA, India, Korea|
|0.2-0.3 mln MT|Cuba, Guatemala, Malaysia, Poland, Turkey|

Source: See Data S7 for details The major importers of refined sugar (inclusive of plantation white sugar) in the 1990s are listed in Table 4. USSR/FSU was the largest importer, accounting for around 22% of the world's imports. Algeria (5.3%), Iran (4.6%), Mexico (4.3%) and Nigeria (4.1%) were other large importers. During the 1960s Iraq was a large importer, accounting for around 8-9% of world imports, but its share in early 1990s was only about 2.8%. The EU countries were also important 9
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# **Table 16A:  Annual value added per worker by enterprise age and size group: UBI 2002 and UBI 2009**

|**Size Group**|**_A. Annu_**<br>|**_al value_**<br>All|**_addedper_**<br>|**_worker(m _**<br>|**_illion LCU) by ente_**<br>|**_rprise sizegr_**|**_oup_**<br>||
|---|---|---|---|---|---|---|---|---|
||sectors||Primary|sector|Manufacturing||Services & o|ther industries|
|**(number of workers)**|2002|2009|2002|2009|2002|2009|2002|2009|
|less than 5|2.9|6.7|2.8|3.2|1.7|7.8|3.0|6.7|
|5‐20 workers|7.9|16.7|3.2|3.6|3.4|17.9|9.2|17.8|
|21‐100 workers|13.9|64.6|1.3|18.4|14.9|28.6|14.5|76.6|
|More than 100|9.0|18.3|1.5|7.3|11.0|17.6|14.2|20.9|
|Total|6.4|14.9|1.7|6.1|7.8|15.8|6.8|15.2|
|**Age Group**|**_B. Annu_**<br> <br>sectors|**_al value a_**<br>All|**_ddedper_**<br>Primary|**_worker(m _**<br>sector|**_illion LCU) by ente_**<br>Manufacturing|**_rprise agegr_**|**_oup_**<br>Services & o|ther industries|
|**(years)**|2002|2009|2002|2009|2002|2009|2002|2009|
|< 5|3.1|18.7|6.2|9.5|3.4|20.8|3.0|18.9|
|5 to 10|3.7|24.4|2.8|6.5|3.9|38.3|3.7|24.3|
|More than 10|3.4|16.6|1.3|11.4|3.9|22.3|3.6|16.5|
|Total|3.2|18.6|2.6|10.0|3.6|24.9|3.2|18.6|

52
and other types of violence.<sup>11</sup><sup>_,_12</sup> Over this time period, the entire survey includes 18,078 separate households that were interviewed at least once, with 45 percent ever displaced. In the baseline empirical specifications, we compare how violence is changing at and around the time of displacement for the 1,116 displaced households on which we focus in origin and migration districts to the violence occurring in the district of residence at and around the time of the latest survey of the 9,992 households that never became displaced in the WFP survey.<sup>13</sup> Importantly, RDD mobile phone surveys have limitations. Specifically, they are only potentially representative of the mobile phone-using population and might suffer from sample selection biases based on who has access to mobile phones and which households choose to respond to the survey. However, all available evidence suggests that access to mobile phones (85 percent prior to the conflict) has remained high, and the survey is capturing changes in the population that are independently corroborated by other sources, including the timing and general evolution of the humanitarian and forced displacement crises (e.g., WFP 2019; Almoayad et al. 2020; Tandon and Vishwanath 2020; Tandon and Vishwanath 2021; D’Souza et al. 2022; etc.).

Figure 1 reports the number of violent incidents by types of violence- ground battles, remote violence (e.g., airstrikes), and all other violence (e.g., terrorism, violence against civilians, etc.). During the analysis period, 60 percent of all fatalities were from ground battles, with 38 percent from remote violence, and the remaining 2 percent were from other types of violence. However, despite there being significantly more fatalities from ground battles, the most common type of violence in the conflict was remote violence. Approximately two-thirds of violent incidents were instances of remote violence, 11We are only able to identify these groupings when using the ACLED data from 2016 and on. Although the Uppsala Data Program has some information on types of violence, they are not very compatible with the ACLED groupings. Furthermore, the vast majority of violence that occurred during our period of analysis occurs after 2015.

> 12Other types of violence are mostly violence attributable to terrorism or violence against civilians. 13As mentioned in the introduction, 34 percent of these non-displaced households were only interviewed once. And of the ones that were interviewed more than once, the largest share were interviewed only twice (15 percent of the total non-displaced sample), and all households that answered more than one survey tended to respond to surveys that were close in time and the choice of the exact survey is not important to the results. Importantly, all results are robust to using the first survey to which non-displaced households respond.

10
initial (wheat) sales earnings shares suggests that they will lose from WTO accession, whereas, after adjustment (to maize), they may end up boosting their income, possibly even lifting them out of poverty.

Robilliard, Bourguignon, and Robinson (RBR, 2003) sacrifice some of the detail attained by Ravallion _et al._ , but dig more deeply into the households’ labor market responses to external shocks. Their work highlights a different set of benefits of the two-step, micro-macro approach to poverty analysis. As in the Friedman/Levinsohn paper, the authors study the effects of the 1997 Indonesian crisis on poor households. The methodology here, however, involves linking the output of a GE model with post-simulation analysis based on a micro-simulation model. The micro-simulation model is estimated with a 33,000 person sub-sample of the 1996 Susenas survey, and offers a detailed representation of the way households generate their income.<sup>12</sup> Earnings and occupational choice equations are a function of personal characteristics, so when there is a decline in the aggregate demand for wage labor, the model predicts which individuals are forced into the informal sector. Such a shift can mean a dramatic decline in income for an individual, and indeed the entire household. Capturing this phenomenon is the real strength of the RBR micro-simulation model.

The RBR GE model relies on standard neoclassical assumptions and is linked to the microsimulation model through (a) the wage rate in each labor market, (b) income per worker in the informal, self-employed sector, (c) the number of wage workers and self-employed workers, by labor market segment, and (d) consumption prices. The micro-simulation model is solved to generate values for these linking variables that are consistent with the general equilibrium model.

The authors validate their model against the historical facts via two steps. First, they set aside the CGE model and simply impose observed historical changes in the linking variables that drive the micro-simulation model. They then compare the changes in poverty produced by the latter to changes actually observed over the crisis period. Results indicate the model captures the qualitative flavor of the impacts across population segments, but over-predicts the total increase in poverty (+239% estimated vs. +67% observed in the poverty headcount).

Second, they add the CGE component, which permits them to relate the poverty changes to the underlying factors driving this crisis, including the: foreign credit crunch, domestic credit crunch, real devaluation, and El Nino drought. They find that the financial crisis did indeed contribute the majority 19
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Figure 5: Trend of Rate of Retum on Assets.
At the same time, there are reasons to question the efficacy of this aid-for-policy program. The World Bank might pick policy conditions that do not reflect local needs and priorities, demand too many conditions, or set arbitrary deadlines that short-circuit the domestic political processes needed to secure the buy-in of various parties with disparate interests (Boughton and Mourmouras 2004; Koeberle et al. 2006). It might also face internal and external pressures—for example, to reward the allies or punish the enemies of powerful shareholders, or to “push money out the door” to achieve professional prestige and budget maximization goals—that undermine the credibility of its policy conditionality (Collier 1997; Dollar and Svensson 2000; Easterly 2005; Kilby 2009).

Notwithstanding these concerns, there is some evidence that DPLs may instigate changes in development policy. After accounting for self-selection bias, Smets and Knack (2016) and Moll and Smets (2018) demonstrate that the cumulative number of World Bank economic policy DPLs contracted by a borrower government is strongly and positively correlated with the quality of its economic policies.<sup>4</sup> Similarly, Giordano and Pagano (2017) provide econometric evidence that DPLs positively correlate with the adoption of business environment reforms in World Bank borrower countries. Rather than focusing on DPLs, Smets and Knack (2018) examine the effects of the PAs that are nested within World Bank DPLs and find evidence of a concave relationship between the cumulative number of PAs and the adoption of public sector governance reforms in World Bank borrower countries: low to moderate levels of conditionality hasten the adoption of public sector governance reforms, while high levels of conditionality have the opposite effect. There are also several studies that suggest World Bank policy lending and conditionality might be more effective in conjunction with the provision of analytical and advisory services (Deininger et al. 1998; Wane 2004; World Bank 2004; IEG 2008a: 40; Fardoust and Flanagan 2011).<sup>5</sup>

> 4Self-selection bias—the fact that an unobserved factor or set of factors might influence a government’s propensity to pursue DPL-related policy changes—is a major impediment to causal inference. World Bank borrowers might contract DPLs to justify policy changes or insulate themselves from the consequences of policy changes that they would have pursued in the absence of the DPLs (Vreeland 2003; Chwieroth 2007; Parks and Davis 2019).

> 5In a 1998 study entitled _Assessing Aid: What Works, What Doesn’t, and Why_ , Dollar and Pritchett

4
347–366.

Xiao, J.J., Serido, J., and Shim, S. (2012). Financial education, financial knowledge and risky credit behavior of college students. In: Lamdin, D.J. (editor) _Consumer Knowledge and Financial Decisions: Lifespan Perspectives_ . New York: Springer: 113–128.

Yetter, E.A. and Suiter, M. (2015). Financial literacy in the community college classroom: A curriculum intervention study. _Federal Reserve Bank of St. Louis Working Paper 2015-001._ 75
We note that the true estimate is unlikely to be close to this bound for two reasons. First, the set of controls that include village fixed effects is quite comprehensive so that omitted variables are unlikely to be just as well correlated with biogas as the controls. Second, the bound is reached under the assumption that _all_ the variance in firewood collection is explained by the explanatory variables once _v_ is included (Oster 2013). This is also very unlikely. We conclude that the true effect is likely to be closer to the estimate than to either the upper or lower bound.

We therefore proceed to the next stage of the analysis by using a reduction of 1,000 kg/year as a best guess of the true effect of biogas adoption.<sup>10</sup> Since all subsequent calculations are linear in this quantity, it is easy to examine how they will change under alternative estimates. Before proceeding to the next stage we report a comparison with the existing literature.

Our estimates are much lower than the estimate of 3,000 kg/household/year obtained from a survey of 461 biogas users in 15 of the 75 districts in Nepal in 2007‐2008. The survey was conducted by a consulting firm for the Alternative Energy Promotion Center of the Government of Nepal and this result is reported in Katuwal and Bohara (2009). The AEPC survey also gave estimates for consumption of firewood both before and after biogas adoption that are roughly twice the estimates we obtain from the much larger nationally representative NLSS‐III.

The reasons for these differences are not clear, but could be in part due to the assumption of Katuwal and Bohara (2009) of a larger conversion factor of 40 kg/bhari than the average in the NLSS of 33 kg/bhari. However, to reconcile the estimates, it would have to be true that the correct conversion factor in the AEPC survey would be as low as 20 kg/bhari, which seems very low and highly unlikely.<sup>11</sup> It appears that the survey may not have been representative of Nepal.

> 10 We discuss in the next section the adjustments to our estimate that would be necessary when we take into account the possibilities that (a) some biogas users would substitute biogas for cylinder gas rather than firewood, and (b) some new adopters of biogas may be induced to hold more cattle than they would have done if there were no increase in the biogas subsidy.

> 11 Baland et al (2010) use a conversion factor of 35 kg/bhari for the 2003 round of the NLSS.

11
Table A.3. Heterogeneous effects on Host Attitudes Toward Rohingya Refugees

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_A. Heterogeneous Effects b_|_y Wealth Index_|||||
||My family<br>benefted<br>from Rohingya<br>presence|Rohingyas<br>presence<br>negatively impacts<br>neighborhood<br>safety|Rohingyas<br>should live<br>exclusively<br>in camps|Rohingyas<br>families are<br>poorer than<br>local<br>vulnerable<br>families|Rohingyas<br>basic needs<br>should just<br>be covered by<br>external<br>entities|
|SID_×_I( High Wealth)|0.001|0.016|-0.009**|0.008|-0.035***|
||(0.026)|(0.012)|(0.003)|(0.014)|(0.008)|
|SID|0.056*|0.040***|0.007**|-0.034|-0.011|
||(0.025)|(0.008)|(0.003)|(0.041)|(0.014)|
|I(High Wealth)|0.010|0.027|0.005|0.030|0.007|
||(0.008)|(0.015)|(0.004)|(0.017)|(0.008)|
|R-squared|0.034|0.014|0.003|0.011|0.012|
|Observations|3,276|3,674|3,674|3,674|3,674|
|height<br>_B. Heterogeneo_|_us Effects by Gender_|||||
|SID_×_I(Male)|0.004|-0.001|-0.006*|0.017*|-0.008|
||(0.006)|(0.007)|(0.003)|(0.007)|(0.010)|
|SID|0.056***|0.049***|0.006*|-0.038|-0.025**|
||(0.012)|(0.008)|(0.003)|(0.036)|(0.007)|
|I(Male)|0.002|0.036*|0.004|0.044**|0.000|
||(0.017)|(0.015)|(0.009)|(0.015)|(0.011)|
|R-squared|0.035|0.013|0.002|0.010|0.009|
|Observations|3,301|3,703|3,703|3,703|3,703|
|_C. Heterogeneous Effects b_|_y Age_|||||
|SID_×_I(Young)|0.001|-0.000|-0.000|-0.001|-0.001**|
||(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|
|SID|0.035**|0.053**|0.008|0.006|-0.002|
||(0.010)|(0.019)|(0.004)|(0.017)|(0.014)|
|I(Young)|0.008|-0.035**|-0.009|0.004|-0.054***|
||(0.017)|(0.013)|(0.012)|(0.012)|(0.012)|
|R-squared|0.036|0.014|0.002|0.006|0.011|
|Observations|3,301|3,703|3,703|3,703|3,703|

_Notes:_ The table presents results from equation 3 with estimates examining the interaction between SID and indicator variables of wealth, gender, and age. SID represents standardized values of the inverse distance from surveyor households to refugee camps. The estimates incorporate population weights. Dependent variables are available for the 2023 wave, and detailed explanations for their construction are provided in Appendix B. Panel A illustrates the heterogeneous effects for high wealth, where _I_ ( _High Wealth_ ) is an indicator set to one if the respondent’s wealth asset index exceeds the median of the overall sample. Panel B presents the heterogeneous effects for male respondents, where _I_ ( _Male_ ) is an indicator variable set to one if the respondent’s gender is male. Panel C showcases the heterogeneous effects for age, with _I_ ( _Y oung_ ) as an indicator set to one if the respondent is between 0 and 23 years old. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017. Wave fixed effects are included and clustered standard errors at the upazila level (i.e., subdistrict) are presented in parentheses. Significance levels:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.

54