rafmacalaba/datause-encoder-data huggingface dataset:
to conflict take place. The non-conflict episodes begin in the first year for which it is possible to identify five consecutive years with no conflict. As with the conflict episodes, we also require the non-conflict episodes to be preceded by three years of no conflict. Figure 3 illustrates how we transform the annual observation on conflict indicators into conflict episodes, using the case of Central African Republic as an example. For this country, we have three non-conflict episodes in 1980, 1985 and 1990, and two conflict episodes, beginning in 1998 and 2006, and shaded gray. In 1998 the conflict episode is triggered by the presence of a UNPKO, whereas in 2006 the conflict episode is triggered by the battle death count criterion crossing the threshold.

We construct the conflict and non-conflict episodes using annual data for 114 countries over the period 1977-2014 for which our core explanatory variables -- discussed in more detail below -- are available.<sup>14</sup> Through this method, we arrive at a sample of 69 conflict events, and 422 non-conflict events. Our dependent variable in the empirical work that follows is a dummy variable taking the value one for conflict episodes, and zero for non-conflict episodes. The 69 conflict episodes identified are listed in Table 1, which identifies the country name and the start year of each episode. The list is dominated by Sub-Saharan African countries, but a number of other conflicts from other regions appear as well. The conflict episodes vary in length, with a median length of two years and an interquartile range from one to six years. Some conflict episodes are very prolonged: the 90<sup>th</sup> percentile of conflict length is 17 years. In the empirical work that follows, we do not seek to account for this variation in the duration of conflict once it has begun, but rather focus on the prediction of conflict conditional on being in a non-conflict state.

# **3.2 Correlates of Conflict**

We work with a selection of variables that have been emphasized in the existing literature as important correlates of conflict (Fearon & Laitin (2003); Collier & Hoeffler (2004); Hegre and Sambanis (2006); Besley and Persson (2011); Esteban, Mayoral, and Ray (2012); Hegre, Karlsen, Nygard, Strand, and Urdal (2012)). Our goal here is not to innovate relative to this existing literature by isolating novel drivers of conflict. Rather, we wish to have a set of covariates of conflict that is broadly representative of those used in the existing literature, that we can use as a benchmark from which to assess the

> 14 The key variable that limits the sample considerably is the World Bank’s Country Policy and Institutional Assessment (CPIA) ratings, which starts in 1977 and is available only for developing-country clients of the World Bank.

19
capital, and concentration in low wage jobs. Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services.

One widespread claim mentioned in some media reports is that Venezuelan migration led to an increase in crime (Freier et al., 2021). We test whether this claim is supported by the data in Table 6, where we use administrative information on the number of non-violent and violent crimes reported in each municipality, the personal security index from Gallup, and reports on whether crime is perceived as a major problem in ENAHO. The structure of this table is the same as the previous with Panel C our preferred specification.

Consistent with the idea that Venezuelan inflow lead to labor market conditions improving, we observe that locations that received a larger number of immigrants have lower number of reported non-violent crimes (columns 2). This effect is large with a double of Venezuelans in a province leading to a 42% decline in reported non-violent crimes. Individuals are also less likely to report that crime is a major national problem, this is true for both men and women.

Finally, in Table 7 we examine the effects of immigration on local communities. Consistent with our previous findings, Peruvians living in areas with a higher share of Venezuelan immigrants report that the quality of local services and community quality are higher and that they have a greater trust in their neighbors. However, in these locations they also report that the community is less likely to value diversity. In general, these findings are stronger for men than for women. It is important to note that these findings could be driven by the positive impact that Venezuelans have on the labor market outcomes of Peruvians.

Overall, we find that increased Venezuelan migration leads to improvements in both objective and subjective measures of the lives of Peruvians living in the same locations. This may explain why, even though some media has discussed Venezuelans in a negative light, there has been little political backlash against them in Peru up to this point in time.

# **6 Conclusion and Policy Implications**

In this paper, we study the economic underpinnings of hostility and discrimination against immigrants. In our analysis, we first use a specialized survey of Venezuelan immigrants in Peru to identify the causal effect of local labor markets on discrimination against immigrants. Then, we studying the flip-side of this analysis, namely, how does the presence of Venezuelan 18
from Dasgupta and Kanbur (2007): Conditional on household income, as the supply of religious and social goods increases, perceived household decile increases. A precise test is made difficult by the lack of fine-grained geographic data on the supply of group-specific public goods. We utilize data on household expenditures on social and religious obligations. In the CPHS, spending on “religious obligations” includes the expenditure by a household during a month towards religious ceremonies, donations to places of worship, payments to religious leaders, and contributions towards religious events. Spending on “social obligations” refers to expenditures on religious ceremonies, gifts, weddings, funerals, social causes and events, and contributions to creation of local public conveniences. We sum all household expenditures on social and religious obligations in a district and use this as a proxy for the supply of social and religious goods in the district. Approximately half the sample across economic strata reports expenditures on social and religious obligations, and the amount of spending increases with household income (see Section F of the supplementary materials).

Using Equation (2), we explore whether district-level spending on these goods is correlated with perceived household income decile. Here, i ii<sup>refers to the perceived</sup> income decile of household _i_ in district _j_ in state _k_ . i refers to the district-level mean PPT household expenditure on social and religious obligations in district TT _j_ in state _k,_ with 1 being the quantity of interest. Our estimate of 1 is conditional on i , household _i_ ’s own spending ππ ii on religious and social goods, as well asππO i , the household’s decile based on income data. HH ii Standard errors i are clustered at the district level. To ensure that all districts are weighed OO ii equally in our analysis, each observation (i.e., household) is weighted by the inverse of the ηη district sample size in the CPHS. This guards against the possibility that our estimates are driven by districts with a larger sample. Note that expenditure data is missing for 16,268 households in the June-August 2021 round of the CPHS. These missing values are spread evenly across income deciles as shown in Table S6, distributed across all states, religions and 14
(2009) casts doubt on the robustness of these findings with respect to the functional form specifications adopted and the validity of the instrumental variables. An RD analysis of the same data finds less support for positive program impacts and provides new evidence that providing loans to men has reduced the labor supply of women. In another study based on a fairly simple comparison of villages that do or do not have microlending programs, Khandker, Samad, and Khan (1998) find that these programs have had positive impacts at the village level on income, production and employment. They also find that the Grameen Bank has had positive spillover effects on people not participating in the program.

On the other hand, de Mel, Duresh, McKenzie and Woodruf (2008) question the efficacy of targeting programs toward women. Their experimental evaluation of a program that randomized grants in Sri Lanka finds much lower rates of return on capital for female-owned enterprises in comparison to male-owned enterprises, which they attribute in part to differences in the returns for female and male dominated industries. They also show that the female entrepreneurs tended to come from less poor dual-earner families and that loans targeted at women may not have targeted the poorest families. de Mel et al. (2008) do not find any benefits of the program on children‟s school-going, but do not examine the full set of outcomes examined in the Pitt and Khandker study. Nevertheless, their evidence calls into question the justification for targeting credit programs at women. Whether micro-lending programs yield higher rates of return when targeted at women is likely to depend on the country context and the types of enterprises in which women are engaged.

Hussein and Hussein‟s (2003) survey of Pakistani microcredit programs similarly shows that microcredit programs do not necessarily benefit women or the poorest families, unless there is an explicit targeting mechanism in place to reach these groups. Even when the loans are targeted at women, it is not uncommon for women to relinquish control of the loans over to male household members. Hussein and Hussein (2003) provide some evidence, however, that microcredit programs provide some nonpecuniary benefits for women, such as increasing reported levels of confidence and self-esteem.

Kaboski and Townsend‟s (2007) study of a Thai microcredit program that was not gender targeted and did not impose joint liability finds relatively large impacts of the program on consumption. Their estimates indicate that consumption increased by substantially more than the initial amount of credit due to the productive use of funds. Interestingly, using the dynamic 38
-35-

STEP 1 ADJUSTED BALANCE SHEET, Period 0 <!-- Start of picture text --> The balance sheet<br>ASSETS Ao' LIABILITIES Lo' (A0 ,1,') is obtained<br>by revaluing the NMA<br>and NW items by the<br>MA0' 0 500 MLO' inflation factor.<br>NHA0' NMLO Notice that this<br>Equipment 600 600 Indexed Debt revalued sheet does<br>Building 1200 NWO' not balance because<br>600 Net Worth accrued income is not<br>yet recognized.<br>Ao' 1800 1700 Lo'<br><!-- End of picture text --> STEP 2

|ASSETS|A1'|L1'|LIABILITIES|STEP3<br>Income in Period 1|
|---|---|---|---|---|
|MA1'|1640|500|ML1'|- NW1'- NWo'|
|NKA1'<br>Equipment<br>Building|<br>450<br> <br>1080|600<br>2070|NML1'<br>Indexed Debt<br>NW1'<br>Net Worth|-<br>2070- 600<br>- 1470|
|||||Tax liability|
|A1'|3170|3170|<br>L1'|-<br>0.5 (1470) -735|

STEP 4

|ASSETS<br>|A1|L1 LI|ABILITIES|Net Income in Period 1|
|---|---|---|---|---|
|MA1|905|500|ML1|_-_ NW1<br>- _NWO'_|
|NMAl<br>Equipment|<br>/450|600|NMLl<br>Indexed Debt<br>NW1|- 1335- 600<br>-<br>735|
|Building|1080|1335|Net Worth||
|Al|2435|2435|Ll||
# **Bibliography:**

- Acemonglu, D., S. Johnson, and J. Robinson (2002), “Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution”, _Quarterly Journal of Economics_ , November, Vol. 117, 1231-1294.

- Acemoglue, D., and S. Johnson (2005), “Unbundling Institutions”, _Journal of Political Economy_ , October, Vol. 113, 949-995.

- Acemoglu, D., S. Johnson, and J. Robinson (2005), “Institutions as the Fundamental Cause of Long-Run Growth”, _Handbook of Economic Growth (Philippe Aghion and Stephen Durlauf, eds.)_ North Holland, December.

- Alesina A., and D. Dollar (2000),”Who Gives Foreign Aid to Whom and Why?” _Journal of Economic Growth_ , Vol 5. No.1, 33-63.

- Alesina, A. and B. Weder (2002), “Do Corrupt Governments Receive Less Foreign Aid?” _The American Economic Review_ , Vol. 92, 4, 1126-1137.

- Alonso-Borego, C., and M. Arrellano (1999), “Symetrically Normalizad Instrumental Variable Estimation Using Panel Data”, _Journal of Business and Economic Statistics_ , Vol. 17.

- Arellano, M. and S. Bond (1991), "Some Tests of Specification for Panel Data: Montecarlo Evidence and an Application to Employment Equation ", Review _of Economic Studies_ , Vol. 58, 277-297.

- Arellano, M. and O. Bover (1995), “Another Look at the Instrumental Variable Estimation of Error- Components Models”, _Journal of Econometrics,_ Vol.68, 1.

- Banerjee ,S.G.and D.A Rondinelli. (2003), “Does Foreign Aid Promote Privatization? Empirical Evidence from Developing Countries”, _World Development Report._

Barro, R. and , X. S. Martin. (2004), Economic Growth, MIT Press.

- Blundell, R. and S. Bond (1997), "Initial Conditions and Moments Restrictions in  Dynamic Panel Data Models", _Journal of Econometrics_ , Vol.118, 115-143.

- Bond, S., Hoeffeer A., and J. Temple (2001), "GMM Estimation of Empirical Growth Models _", CEPR Discussion Paper 3048_ .

- Burnside, C. and D. Dollar (2000), “Aid, Policies, and Growth”, _American Economic Review_ , Vol. 90, 4, September, 847-868.

35
**Table D.3:** Health Outcomes

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Health outcomes index**(SUR)||0.021<br>(0.023)<br>[0.322]|0.055<br>(0.025)<sup>_∗∗_</sup><br>[0.094]<sup>_∗_</sup>|_−_0.010<br>(0.027)<br>[0.463]|5053|
|(1) U5 Child death per HH|0.039|_−_0.009|_−_0.015|_−_0.004|5053|
||(0.193)|(0.005)<sup>_∗_</sup>|(0.005)<sup>_∗∗∗_</sup>|(0.006)||
|||[0.544]|[0.039]<sup>_∗∗_</sup>|[1.000]||
|(2) Maternal death per HH|0.001|0.000|0.001|_−_0.001|5053|
||(0.035)|(0.001)|(0.001)|(0.001)||
|||[1.000]|[0.462]|[1.000]||
|(3) Illness/injury in HH|0.579|_−_0.003|_−_0.009|0.004|5053|
||(0.494)|(0.016)|(0.018)|(0.019)||
|||[1.000]|[0.462]|[1.000]||
|(4) Child weight for length|0.546|0.133|0.156|0.109|1991|
||(1.682)|(0.081)|(0.093)<sup>_∗_</sup>|(0.093)||
|||[0.544]|[0.252]|[1.000]||
|(5) Vaccine completion index (Under 2)|3.085|0.032|0.303|_−_0.209|1457|
||(2.560)|(0.152)|(0.184)|(0.163)||
|||[1.000]|[0.252]|[1.000]||
|(6) Child birth complication index|0.000|_−_0.026|_−_0.126|0.061|856|
||(1.000)|(0.077)|(0.086)|(0.087)||
|||[1.000]|[0.277]|[1.000]||
|(7) Child illness index|0.002|0.024|0.031|0.018|4993|
||(0.995)|(0.102)|(0.115)|(0.115)||
|||[1.000]|[0.513]|[1.000]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets.Maternal death is defined as death relating to either pregnancy complications or childbirth. The Pentavalent Vaccination targets diphtheria, tetanus, whooping cough, hepatitis B, and haemophilus influenza type B. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A17
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Variable annuities are exposed to investment risk and complete reliance on them would not be advisable. Like term annuities and phased withdrawals, they may be included in product combinations once minimum levels of inflation-protected life annuitization are secured and the regulatory framework is sufficiently robust.

Reverse mortgages have many advantages for owner-occupying retiring workers. However, like variable annuities, they require a robust regulatory framework to provide effective protection to pensioners from aggressive selling.

Deferred annuities (with or without refunds), which are purchased at the time of retirement and are payable 10, 15 or 20 years later, are an attractive option in most countries. Because they have greater exposure to the tail end of the age distribution, they are more difficult to price than immediate annuities. In countries with sophisticated insurance markets and reliable mortality data, they may be used in combination with term annuities, phased withdrawals or even reliance on self-annuitization during the deferment period.

Countries that offer a constrained choice to retiring workers and do not mandate the use of a single retirement product for all should also specify the product that will be used as the default option. This will help workers who are unable or unwilling to make a decision on their own and will protect them from abusive selling practices of brokers and selling agents of providers. The use of centralized electronic quotation systems and offer of guidance and advice by regulatory agencies will also contribute to greater consumer protection.

Unlimited lump-sum distributions and the implied complete reliance on self-annuitization should be avoided, unless there are strong cultural factors in their favor. Selfannuitization requires considerable financial savvy by retired workers and is very difficult to manage in advanced old age.

The level of permitted lump-sum withdrawals may be determined either as excess balances once the targeted level of annuitization is achieved or as an upper limit, normally between 25 and 33 percent, of accumulated balances.

The targeted level of annuitization reflects country preferences. A sensible approach is to have a public pension equal to between 25 and 30 percent of economy-wide average earnings for single pensioners (between 40 an 50 percent for married couples) and a targeted integrated replacement rate from the zero, first and second pillars of between 50 and 70 percent of a worker's own average real earnings over the preceding ten years. However, countries that favor the development of voluntary savings may adopt somewhat lower targets.

These integrated replacement rates may also be seen as regulatory thresholds that will determine the level of excess balances that can be withdrawn as lump sums. Their main role is to ensure an adequate level of income over the whole of a person's retirement, while avoiding excessive annuitization.

4
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they are. So everyone with a BPL card gets the same amount under the counterfactual, and those without the card get nothing.

We find that this alternative counterfactual would reduce the poverty rate from 51.4% to 49.8% in R1, while in R2 it would reduce the poverty rate from 42.3% to 40.0%. In both cases, the poverty rates are lower than under BREGS. If we make a 10% allowance for leakage in the transfers, then the poverty rates fall to 49.9% and 40.2% in R1 and R2, respectively.<sup>29</sup> It is notable that, in terms of its impact on poverty, the BPL ration-card counterfactual is no better than the BIS. This confirms findings from other studies that the targeting of the BPL cards might be improved.

It should be noticed that with 10% leakage the poverty rate attained by either of these counterfactual transfer schemes is almost identical to BREGS in R1 (Table 7). This implies that the workfare scheme would dominate at slightly more than 10% leakage. The gap is somewhat larger in R2, so greater leakage would be needed to tilt the balance in favor of workfare.

# **7. Conclusions**

While work requirements can ensure good targeting, this probably comes at a cost to poor participants, notably their forgone earnings. Little is known about these hidden costs. We have employed a novel survey-based method of measuring these costs at the individual level and used this in estimating the poverty impacts of a major workfare scheme in rural Bihar, with impacts assessed against various counterfactuals.

We find that workfare participants are not drawn solely from the pool of the unemployed. Many report forgone earnings, though mostly a good deal less than the market wage. On average about one-third of the workfare wage rate is forgone. But the cost of participation varies greatly, with three distinct groups evident: those with roughly zero forgone income (the largest group), those for whom the forgone income accounts for around half their workfare wage and those for whom it is much higher; only for the latter group (the smallest of the three) is the opportunity cost of workfare labor well approximated by the wage rate for casual market labor.

Factoring in these household-specific opportunity costs, we find that the extra earnings from this large scheme in Bihar had less impact on poverty than either a basic-income scheme—

> 29 Again, applying estimated leakage in BREGS yields very similar results. Poverty rates fall to 50.1% and 40.3% in R1 and R2 respectively with a BPL-targeted uniform transfer.

23