rafmacalaba/datause-encoder-data huggingface dataset:
The attrition between the two telephone survey rounds was mostly due to not being able to reach the respondents, possibly because of nonfunctioning phone numbers. Only 3 percent of respondents refused to answer the food insecurity telephone survey. To adjust for this attrition, weights were designed for this sample specifically, which we use in all our estimations. In addition to accounting for the sampling and nonresponse issues in the conflict telephone survey, they also adjust for the attrition noted in the food security telephone survey.

The results are presented in appendix C, table C.4, and they can be considered as a robustness check for tables 2 and 3 (columns 1–3 in table C.4 correspond to columns 1–3 in table 2, and columns 4–8 in table C.4 correspond to columns 2, 4, 6, 8, and 10 in table 3). We can see that the results are similar with this smaller sample of households that extends to 2017. Table C.4 also confirms the finding that property-related events are more detrimental to food insecurity than are violent events, and that events perpetrated by insurgents as well as bandits and criminals are statistically significant. Magnitudes of the coefficient estimates are similar across the different specifications.

# 5.4.3.   Unweighted results

In our main specifications, we have used sample weights to adjust for the oversampling of the conflict-affected LGAs as well as for the fact that not all targeted households were reached. To check for any biases the sampling strategy might have induced, we also run the same results without the weights.

The results are presented in appendix E, and they consistently show that they are robust to omitting the weights. Tables E.1 and E.2 present robustness checks for the results on food insecurity (tables 2 and 3) and consumption (tables 4 and 5), respectively. The results are similar across both magnitude and significance when weights are not used.

# 5.4.4.   Alternative lag structure of conflict

In our main specification, the number of years—and therefore the time periods of conflict exposure—vary across different waves because the outcome variables used are not measured at even time intervals. This can be problematic because food insecurity and consumption are measured with the same recall period at each time point. Due to this discrepancy, we run a robustness check with a specification where the lag structure is uniform across the waves. This model takes the form 25
Policy Research Working Paper 5091

# **Abstract**

This paper uses district level data to estimate the general relationship between climate, income and life expectancy in Peru. The analysis finds that both incomes and life expectancy show hump-shaped relationships, with optimal average annual temperatures around 18–20°C. These estimated relationships were used to simulate the likely effects of both past (1958-2008) and future (2008– 2058) climate change. At the aggregate level, future climate change in Peru is estimated to cause a small reduction in average life expectancy of about 0.2 years.

This average, however, hides much larger losses in the already hot areas as well as substantial gains in currently cold areas. Similarly, the average impact on incomes is a modest reduction of 2.3 percent, but with some districts experiencing losses of up to 20 percent and others gains of up to 13 percent. Future climate change is estimated to cause an increase in poverty (all other things equal), but to have no significant effect on the distribution of incomes.

This paper—a product of the Social Development Division, Sustainable Development Department—is part of a larger effort in the department to address climate change. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at dverner@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<mark>Table C.5: Degree of homogeneity of classes</mark>**

||Count|Share|
|---|---|---|
|Relations with suppliers|||
|Pure bilateralism|8|67%|
|Bilateralism with private support|12|100%|
|Bilateralism with legal support|8|67%|
|Strong comprehensive governance|10|83%|
|Relations with customers|||
|Pure bilateralism|12|100%|
|Bilateralism with private support|12|100%|
|Bilateralism with weak support|8|67%|
|Weak comprehensivegovernance|10|83%|

**<mark>Table C.6a: Degree of separation of classes, relations with suppliers</mark>**

||AAPc|OC<br>c|mcmPc|class<br>membership<br>probabilities<br>(π�c)<br>95%<br>the|CI of<br> π�c|
|---|---|---|---|---|---|
|Pure bilateralism|0.963|13.730|0.665|0.657<br>0.609|0.705|
|Bilateralism with private support|0.883|37.990|0.159|0.166<br>0.123|0.209|
|Bilateralism with legal support|0.940|81.934|0.159|0.160<br>0.127|0.194|
|Strong comprehensive governance|0.977|10016.817|0.016|0.017<br>0.008|0.025|
|Rule-of-thumb minimum|0.7|5||||

Note: See Appendix C.2 for definitions of the statistical measures.

**<mark>Table C.6b: Degree of separation of classes, relations with customers</mark>**

||AAPc|OC<br>c|mcmPc|class<br>membership<br>probabilities<br>(π�c)<br>95%<br>the|CI of<br> π�c|
|---|---|---|---|---|---|
|Pure bilateralism|0.915|8.310|0.576|0.565<br>0.503|0.626|
|Bilateralism with private support|0.874|21.833|0.241|0.242<br>0.184|0.299|
|Bilateralism with weak support|0.810|25.188|0.136|0.145<br>0.096|0.193|
|Weak comprehensive governance|0.965|3412.999|0.047|0.050<br>0.034|0.065|
|Rule-of-thumb minimum|0.7|5||||

Note: See Appendix C.2 for definitions of the statistical measures.

32
households that have migrant children from those households which do not. While this exercise can only demonstrate the current risks associated with reliance on transfers, ongoing changes in the rural demographic structure make it likely that any risks will become more important in the future.

# **3. How Do Transfers Respond to Household Income of the Elderly?**

## **A Framework for Analysis**

Much early research on intra-family transfers used data from developed countries and focused on efforts to distinguish motives for transfers.<sup>9</sup> Such distinctions may be important as they have implications for how transfers respond to exogenous income received by elderly households. Becker (1974) and Cox (1987) emphasize that transfers may reflect altruistic or exchange motives, or some mixture of the two. We follow Cox et al (2004) and frame our analysis in the context of a utility function that captures the potential for both altruistic and exchange motives in transfers between parents and children. We assume that the utility of individual �, ��, may incorporate the well-being of her relative, ���: �� and ��� are consumption levels for individual � and her relative, ��, respectively. Services provided by relative �� to � as part of an exchange relationship are denoted by �. If individuals care about each other, then we would expect ����⁄ �0 and ����⁄ �0, and this would raise the likelihood of observing altruistic transfers. Exchange motives may be present as well if �

> 9Barro (1974), Becker (1974) and Cox (1987) made important theoretical distinctions highlighting different motives for transfers.  Much of the empirical research in the US has suggested that inter-generational inter-vivos transfers are driven by an exchange motives (e.g., Cox and Rank, 1992; McGarry, 1999) rather than based on altruistic motives. It is important to remember, however, that in the US, the social security safety net provides substantial insurance against poverty in old age, and thus it is not as surprising to find an emphasis on the flow of resources from older to younger generations.

12
25

# **IV.** ESTIMATION RESULTS

# (i) The Nigerian Sample

The project's establishment survey included 179 firms of various sizes. It was found that 7.8% of the surveyed firms were captive to NEPA and did not produce any power on their own. Another 2.8% were self sufficient and used their own generated power exclusively. 89.4% of the sampled firms (160 firms) used both, public power and internally generated power. As an aggregate, these firms internally generated about 32% of their power needs. The proportion of total electricity that is internally generated varies across firms from 0.14% to 96%. The present study is limited to a sample of 131 firms which produce and purchase non-negligible amounts of electricity and for which we have clean data.

# ii) Indonesian Sample

The project's establishment survey included 290 firms of various sizes. 41 % of the surveyed firms were captive to PLN and did not produce any power on their own; none used their own generated power exclusively. The remaining 59% of the firms (171 firms) used both public power and intemally generated power. In aggregate, these firms internally generated about 47% of their power needs; the average ratio of intemally generated electricity to total electricity use was 28%. The percent proportion varied across firms from 0.1% to 99.9%. The present study is limited to the sample of 118 firms which use both public and embedded electricity in non-negligible amounts and for which we had clean data.

# (iii) The Thai Sample

The project's establishment survey included 300 Thai manufacturing firms, only 18 of which had their own generating capacity. Another 32 observations were lost due to missing
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE unified license (fixed, mobile, and Internet) to Sudatel Senegal in 2007.<sup>12</sup> Consequently, as of 2007, there were three companies providing cellular telephone services: Orange, with two-thirds of the market; Tigol, which was rapidly gaining market share; and Sudatel. The Internet market was served by SONATEL and Sudatel. In addition, an independent regulatory agency for the sector—the Agency for Telecommunications and Postal Regulation (ARTP)—was created in early 2002.

**Table 15. Benchmarking ICT indicators**

||Unit|Low-income,<br>nonfragile<br>countries|**Sen**|**egal**|Middle-income<br>countries|
|---|---|---|---|---|---|
|||2005|2005|(2007–08)|2005|
|GSM coverage|% of population under<br>signal|48|82|85|97|
|International bandwidth|Mbps/capita|6|68.7|237.5|30|
|Internet|Subscribers/100 people|0|0.2|0.4|2|
|Landline|Subscribers/100 people|1|2.4|1.9|9|
|Mobile phone|Subscribers/100 people|15|15.3|44.1|87|
|||**Senegal**<br>2007|Countries<br>without<br>submarine<br>cable|Countres with<br>submarine<br>cable|<br>Other<br>developing<br>regions|
|Price of monthly mobile basket|US dollars|15.5|11.1|11.1|9.9|
|Price of monthly fixed-line basket|US dollars|21.6|13.6|13.6|n.a.|
|Price of 20-hour Internet package|US dollars|27.7|68.0|47.28|11.0|
|Price of a call to the United States per minute|US dollars|1.0|0.86|0.48|0.66|
|Price of an inter-Africa call per minute|US dollars|0.3|1.34|0.57|n.a.|

_Source_ : Senegal 2005 data together with benchmarks are taken from the AICD database.

_Note_ : Numbers reported in the table as of 2007. GSM = global system for mobile communications; Mbps = megabits per second. n.a. = Not applicable.

Growth in telecommunications has resulted in a countrywide expansion of these services. Currently, 82.7 percent of the population has access to a GSM signal, and it is estimated that up to 97 percent of the population could gain access to it on a commercial basis without the need for any government subsidy (figure 18).

In the mobile market, existing subscriptions went from 0.9 subscribers per 100 people in 2000 to 44.1 in 2007 (table 15). This translated into an average growth of 44 percent per year (table 16), a level comparable to its neighbors. In 2008 Senegal’s mobile service penetration, excluding that of Nigeria, was the highest in West Africa; GSM coverage was almost double that of other low-income countries (table 15), though below the levels of middle-income countries.

12 SONATEL began to be privatized in 2006, when Senegal’s government sold a 51 percent stake to Maroc Telecom. As of 2009, the government was selling a further 20 percent of SONATEL’s capital in a public offering and an additional 6 percent was expected to be sold to employees.

31
**Table 8**

||**Separ**<br>|**ating the Imp**<br>|**act of the 20**<br>|**11 Export Ba**<br>|**n**<br>||
|---|---|---|---|---|---|---|
||**Central**|**Coastal**|**Lake**|**Northern**|**Southern**|**National**|
|**Estimate reported in**<br>I|**Table 6 (sixth**<br>-2.78***|**row)**<br>-3.49***|-3.83***|-2.75***|-2.07***|-3.09***|
|BAN|(15.77)|(6.93)|(17.23)|(11.13)|(2.75)|(12.72)|
|**Specification separat**<br>I_ldi 2011_|**ing the impac**<br>-2.27***|**t of the 2011 exp**<br>-2.80***|**ort ban**<br>-3.30***|-2.01***|-1.62***|-2.52***|
|BAN_, excung_|(14.43)|(4.27)|(18.22)|(9.48)|(2.36)|(10.46)|
|_2011 BAN_|-7.94***|-10.57***|-9.00***|-9.17***|-7.28***|-8.87***|
||(10.28)|(4.09)|(10.92)|(12.47)|(3.31)|(12.25)|

**Notes:** The dependent variable is the change in the logarithm of the nominal price in market _i_ . All regressions employ a (market) fixed effects methodology with bootstrapped standard errors (1,000 replications). Robust absolute _z_ -statistics in parentheses; significance level, * = 10 percent, ** = 5 percent, *** = 1 percent; significance levels are different than typical due to clustering adjustment of the standard errors. The bootstrapped standard errors are clustered at the market level. The top row shows the parameter estimates of the export ban reported in Table 6. The second and third rows provide estimates of the bans prior to 2011 and the 2011 ban, respectively, which were estimated together with a similar specification to that of the model reported in Table 6.

32
seekers from Haiti, 38,232 migrants from Colombia, 9,063 asylum seekers from Cuba and 3,986 refugees from the Syrian Arab Republic.

Figure 1: Migration Trend in Brazil <!-- Start of picture text --> 2016 2017 2018 2019 2020<br>World Venezuela<br>Haiti Bolivia<br>Colombia Argentina<br>China<br>1,500<br>1,000<br>Total(’000)<br>500<br>0<br><!-- End of picture text --> Source: Author’s calculation from SISMIGRA and STIMAR.

Figure 2 shows the trend of Venezuelans migrants and asylum seekers in Brazil. Although the number of Venezuelans increased quickly from about 1100 in 2017 to 118,000 by the end of 2018 to about 265,000 in the first quarter of 2020, the stock started dwindling down after the onset of the COVID-19 pandemic. As of October 31, 2020, there were about a total of 261,441 Venezuelan refugees and migrants in Brazil. The figure shows that half of the Venezuelans, who entered Brazil, exited Brazil either to go back to the Rep ́ublica Bolivariana de Venezuela or to remigrate to other countries. When the pandemic hit, the movement across borders was restricted and more Venezuelans were leaving than coming in and as a result the total number started falling. The Federal Police database reveals that half of the Venezuelan migrants and asylum seekers entered Brazil with regular visa, while the other half registered as asylum seekers. As of October 2020, there were 145,462 Venezuelan migrants, 96,556 Venezuelan asylum seekers and 46,647 Venezuelans refugees, who had migrated to Brazil since July 2017. Most Venezuelan migrants and asylum seekers enter through the municipality of Pacaraima in Roraima, which is not surprising given its proximity to the Rep ́ublica Bolivariana de Venezuela at the North (FGVDAPP, 2020). Brazil went through a recession in 2015 and 2016, which saw the economy contracting by about 7%, but the economy grew at about 1.1% annually in 2017 and 2018.

The total stock of registered Venezuelans in Roraima according to Federal Police was about 72,000 as of October 2020. Figure 2b shows that the net inflow of Venezuelans in the first quarter of 2018 was about 19,500, which is about 3.7% of the population in Roraima and increased consistently to reach a peak of 33,000 in the second quarter of 2019, which is about 6.3% the population of Roraima at the time. The population of Roraima in 2017 was about 0.5 million, the lowest among the states in Brazil and contributing only about 0.2% of the Brazilian GDP. Consequently, after the 4
benchmark. Table 6 provides the sample size one would need under three impact scenarios: 5%, 10%, and 15% reduction in child labor.<sup>19</sup> For small impacts on child labor (5%), the top panel shows that one would need a large number of household observations to detect meaningful impacts according to the self-reported (n=4988) and proxy-reported (n=6769) statistics of a baseline measure of child labor. For expected moderate impacts (10%), the middle panel of Table 6 show the sample size requirements shrink to n=1295 and n=1684 for self and proxy reporting, while for relatively large impacts (15%) the bottom panel shows one would need sample sizes of n=583 and n=765, respectively. Thus, across these three different impact scenarios, the sample differences account for 1781, 389, and 182 additional observations if one uses the proxy reported child labor statistics as the baseline measure, relative to the self-reported child labor statistics.

Connecting these sample differences to survey costs allows one to assess the trade-offs between survey precision and costs. In rural Ethiopia where communications infrastructure is mostly absent, the costs associated with surveys fieldwork are high as any local research organization needs to move its operations from the capital city of Addis Ababa to rural areas for several days or weeks. The overall costs of collecting information in the field through multi-topic survey questionnaires that comprise a standard number of sections and questions were approximately 40US dollars per household. As indicated in columns 4 and 5 in Table 6, a large share of these costs is fixed (around 25US dollars) and thus, the variable costs associated with the additional time it would demand seeking information from each child within the household is not proportional to the number of children in the house.<sup>20</sup> Since self-reporting involves, on average, two respondents per household, as opposed to one adult respondent in proxy-reporting households, and since children are less mobile and siblings tend to be together, based on a detailed breakdown 23
effects of Syrian refugees on Turkey’s local labor markets. This research was pioneered by two related papers, Ceritoglu et al. (2017) and Tumen (2016). Using a differencein-difference estimation strategy applied to labor market outcomes for natives, these studies find moderately negative effects on the employment rates of natives employed in the informal sector, though no effects on wages. Their analysis employs data from the Turkish Household Labor Force Survey (2010-2013). Specifically, they estimate a 2 percentage-point decline in the employment-to-population ratio in the informal sector tied to the inflow of refugees. Furthermore, they show that these adverse effects disproportionately affect women and lower educated workers.

The study by DelCarpio and Wagner (2015) extends the previous analyses by adopting an instrumental-variables estimation approach that controls for distance to the Syrian border, which helps purge confounding geographic factors. Their estimates suggests that the arrival of 10 refugees leads to a 6-native worker reduction in the informal sector, with a greater incidence on Turkish workers without formal education. In addition, the study also documents employment creation in the formal sector, by about 3 additional workers for every 10 refugees, mostly benefitting males with medium educational attainment (but less than a college degree). This study also provides an explanation for the lack of wage effects in Ceritoglu et al. (2017) and Tumen (2016). Namely, the arrivals of Syrian refugees affected the composition of employment in Turkey’s labor market by inducing lower productivity workers to leave the labor force. The authors argue that, when these compositional changes are accounted for, wages indeed fell in the informal sector (for low-educated and female domestic workers).<sup>7</sup> Recent studies have also found evidence of effects on costs and prices: using price data, Balkan and Tumen (2016) find that consumer prices of goods typically associated with informal labor declined following the influx of Syrian workers in those regions. Likewise, Tumen (2018) documents that the wages for young workers in new low-skill jobs have also declined in those regions.

Adopting a more structural approach, Loayza et al. (2018) also analyze the labor market effect of Syrian refugee flows on Turkey’s labor market. Their model considers both the location choices of refugees within Turkey and the decisions of companies to hire informal workers. Their analysis suggests that the inflow of refugees increases informal employment among low-skill native workers, but also increases the skilled-unskilled wage premium and induces regional migration of native workers to other regions.

> 7The negative effect on female workers is in line with the findings in Ruiz and Vargas-Silva (2018) and Ruiz and Vargas-Silva (2016). Using data for Tanzania, these authors showed that the arrival of refugees negatively affected the labor force participation of low-educated women.

5
third of the distribution did not grow at all. As a result, regional inequality increased, which is robust to the choice of inequality measure. This rise in regional inequality was driven by increasing inequality between countries, i.e. increasing dispersion in average living standards across countries. This contrasts with results at the global and East Asia level where within-country inequality increased.

The share of the regional population that is covered by a household survey that can be included in our database increased from 68% to 76% over this period. However, around a quarter of the population remains uncovered, and they are likely to live in some of the poorest countries in the region. Therefore, it is plausible that the estimates of inequality presented here are a lower bound on the true inequality. In addition, the latest available benchmark year is 2008, while GDP per capita has grown strongly since then. Thus it would be interesting to understand whether this growth at the macro level translated into gains in household consumption and what its distributional incidence was.<sup>53</sup> In order to move forward with this research agenda, we thus need more recent data and better coverage of the poorest countries, for which there is an emerging consensus among a number of actors (e.g. World Bank, 2015).

> 53 Between 2009 and 2014, regional real GDP in Sub-Saharan Africa grew at 5% on average (IMF, _World Economic Outlook_ 2015), compared with the average real growth of 4.8% during 1993 and 2008 (the period analyzed in this paper). In fact, during the latter period, Africa grew faster than other regions except Asia for which average real growth was about 6%. Furthermore, Sub-Saharan Africa avoided the slowdown experienced globally and in all regions except Asia. Nonetheless, it is important to note that population growth in Africa has been stronger than in other regions (about 2.4% per annum during 1993 and 2008, and 3% per annum during 2009 and 2014).

24