rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix Table 1:

||Military|Military|Military|Military|
|---|---|---|---|---|
||Expenditures per<br>Soldier, Fixed|Expenditure per<br>Soldier, Pooled|Expenditure per<br>Capita, Fixed|Expenditure per<br>Capita, Pooled|
||Effects Panel|Panel|Effects Panel|Panel|
|GDP per Capita|4.219***|2.211***|.028***|.023***|
||(1.11)|(0.45)|(0.01)|(.003)|
|Democracy|307.2*|28.51|2.96***|-.545**|
||(158.9)|(39.78)|(1.07)|(0.27)|
|Democracy<sup>2</sup>|-14.297*||-.142***||
||(7.53)||(0.51)||
|Year|-166.4***|-170.9***|-.774***||
|(after 1990)|(32.06)|(54.34)|(0.21)||
|Area||0.001***||1.712e-06**|
|||(0.00006)||8.388e-07|
|ln(Population)||||-1.59|
|||||(1.20)|
|Landlocked||||-6.72**|
|(dummy)||||(3.31)|
|Constant|657.84|3044.48***|-6.24|34.86*|
||(1139.2)|518.14|(7.69)|(21.05)|
|N (Observations)|1090 (91)|1090|1097 (91)|1097|
|R<sup>2</sup>|.13|.14|.25|.27|

Significance: *<5%, **<2%, *<1%.

GDP per capita, Population and Area (sq km) from the World Development Indicators (WDI, 2007). Here democracy is the composite polity score from the Polity IV index (Democracy-Autocracy+10). In addition to the variables above, we included control variables in earlier models accounting for scale of the conflict (battledeaths), size of the peacekeeping force (SIPRI peacekeeping data), share of value of exports due to oil and minerals (WDI), and squared variables for population and area. None of these latter variables were significant determinants of military expenditure per soldier or per capita, and were therefore left out of the parsimonious models reported above. For fixed effects regressions, the number of countries in the panel is included in parentheses.

49
22 reduction in Laos with policies and programs aimed at reducing household vulnerability. Given the pattern of shocks, programs focused on income protection in the face of agriculture shocks (e.g. public works programs) and on reducing out‐of‐pocket expenditures associated with illness (e.g. direct or indirect subsidies to reduce the cost of health care services) would seem particularly pertinent.

From a methodological perspective, the paper has highlighted the value of comprehensive risk and vulnerability surveys. These surveys provide comparable data across several types of shocks, including on prevalence, risk management strategies, and impacts. The Lao survey also included notable innovations to capture pre‐ and post‐shock measures of subjective welfare and health status, which allowed for an analysis of subjective welfare impacts, and also provided evidence on the extent to which health (and other) shocks result in a long‐term loss of human capital. The use of retrospective data (collected after a health shock) on health status before and after the shock ought to reduce reporting biases. Just as one ought to worry about different people having different thresholds affecting whether they report their health as, say, “good” or “excellent” so one ought to worry about people’s thresholds varying over time; this would limit the scope for getting accurate estimates of the impacts of health shocks using self‐assessed health status data collected at different points in time. By contrast, there is no doubt that our ability to accurately get at the consumption consequences of shocks would have been increased by having consumption data measured through a survey undertaken 12 months earlier.
**Table (3)** Panel Retention and Compliance

||Ever found<br>Midline|Endline|Follow-up|
|---|---|---|---|
|**Panel A: Refu**|**gee sample retention**|||
|All|86.4%<br>80.3%|76.1%|75.7%|
|Treatment|90.5%<br>83.3%|80.4%|80.1%|
|Control|82.4%<br>77.3%|71.9%|71.5%|
|p-value (T-C)|0.000<br>0.014|0.000|0.000|
|N|2,017<br>2,017|2,017|2,017|
|**Panel B: Trea**<br>|**tment take-up among th**<br><br>|**e surveye**<br>|**d**<br>|
|All|17.8%<br>16.8%|17.6%|17.9%|
|Treatment|32.8%<br>33.0%|33.5%|34.0%|
|Control|0.0%<br>0.0%|0.0%|0.0%|
|p-value (T-C)|0.000<br>0.000|0.000|0.000|
|N|1,729<br>1,619|1,466|1,460|
|**Panel C: Neig**<br>|**hbors sample retention**<br>|||
|All|72.1%<br>–|–|–|
|Treatment|74.7%<br>–|–|–|
|Control|69.5%<br>–|–|–|
|p-value (T-C)|0.421<br>–|–|–|
|N|2,017<br>–|–|–|

_Notes:_ This table shows panel retention and compliance with treatment assignment. The p-values are obtained by estimating _yic_ = _β_ 0 + _β_ 1Treatment _c_ + _εic_ . Standard errors are clustered at the locality-level. Retention is defined as survey retention, meaning that the participant was located and surveyed in that round. These numbers only include people who were successfully surveyed. Panel A shows retention of the 2,017 households that were sampled for the study. Panel B shows treatment take-up for the sub-sample of households that were interviewed at midline (1,619), endline (1,466), and follow-up (1,460), and Panel C shows retention for the neighbors of the 2,017 refugees sampled. In this case, retention is an indicator for whether a Jordanian neighbor was located near the pre-randomization home location of the refugee household. The neighbor was selected using a randomized algorithm starting from the original home of the refugee participant, with a median distance between the selected neighbor and the original refugee participant’s home of 63 meters. Cases of non-retention could result from an inability to identify the refugees’ home location based on midline data, or the inability to locate Jordanian neighbors during field visits to the location.

42
Another issue to take into account is the amount of transfer to be allocated to each child. Our BFL model is based on the assumption that the decision to enroll (or not) every child in school is made (by the child or the household) independently, taking into account the transfer amount for the individual child. But the BDH program transfers US$15 per household, independent of the number of enrolled children in the household, which makes the decision of sending or keeping every child in school essentially a household level decision.

On the one hand, if we set $15 as the direct transfer amount, we suppose that each child will add to its utility an amount of _$15.αj_ (for all j= 1, 2),<sup>34</sup> which will lead to an overestimation of the effects of the program. This can then be considered as the upper-bound of the estimated impact of BDH from the BFL model. On the other hand, the BFL model does allow us to construct alternative scenarios about how the $15 is distributed within the household, namely the size of the de facto transfer to every child when there are multiple children in the household. We propose three alternative scenarios. The first scenario is one where the transfer received by each child is equal to $15 divided by the number of children aged 6-17 years in the household, with a minimum of $3 per child. In the second scenario, we set an individual transfer of US$5 because the average number of potential beneficiaries is close to three per household. In the third scenario, the individual transfer is set at $7.5. In order to account for the cost of the program and average size of transfer per household, we enforce the official household maximum of $15 as defined by the BDH program and also compute its impact _without_ any household maximum.

Table 6 shows all _ex post_ estimators obtained by SA and the results of the different scenarios proposed using the BFL model. As expected, the _ex ante_ or simulated ITT estimator under the assumption of US$15 transfer per child (IIT<sup>1</sup> ), at 14.58 percent, is almost double the size of the _ex post_ impact measured by SA at 10 percent. The annual cost of the program under scenario 1 (US$15 per household, despite individual decision) is US$166,000 if we set the household ceiling at US$15. Allowing for different amounts of transfer per household would increase the average transfer to US$32.33 and the total annual cost would more than double. However, the effect on enrolment rate generated by scenario 1 is unlikely, as a child living with its siblings will never make a decision on program participation based on a US$15 transfer.

> 34 Refer to Equation (4) earlier.

28
**Figure 9.3. Percent of those visiting agents who experienced a problem, by gender**

<!-- Start of picture text --> 100%<br>88%<br>90% 85% 86%<br>82% 83%<br>79%<br>80%<br>70%<br>62%<br>60%<br>48% Male<br>50%<br>Female<br>40%<br>30% 31%<br>30% 27% 26% 27%<br>21%<br>20%<br>10%<br>0%<br>Kenya Uganda Tanzania India Bangladesh Nigeria Pakistan<br><!-- End of picture text --> Note: Financial Inclusion Insights data 2016, weighted.

# **_References_**

<mark>Agarwal, Sumit; Alok, Sashwat; Ghosh, Pulak; Kanti Ghosh, Soumya; Piskorski, Tomasz; and Seru, Amit. 2018. “Banking the Unbanked: What do 255 Million New Bank Accounts Reveal about Financial Access?" Working Paper.</mark> <mark>Agent Network Accelerator. 2016. “Successful Agent Networks” Helix Institute of Digital Finance.</mark> Ahlin, Christian and Godwin Debrah. Forthcoming. “Group Lending in Theory and Practice.” In _Handbook of Microfinance, Financial Inclusion and Development_ , edited by Valentina Hartarska and Robert Cull. Elgar Handbooks in Development Series.

Allen, Franklin, Elena Carletti, Robert Cull, Jun QJ Qian, Lemma Senbet, and Patricio Valenzuela, 2021. “Improving Access to Banking: Evidence from Kenya.” _Review of Finance_ March 2021: 403-447.

<mark>Alliance for Financial Inclusion. 2012. Agent Banking in Latin America. AFI Discussion Paper, March.</mark> <mark>Annan, F. (2019). Gender and Financial Misconduct: A Field Experiment on Mobile Money.</mark> _<mark>Available at SSRN 3534762</mark>_ <mark>.</mark> <mark>Anderson, S., & Baland, J. M. (2002). The economics of roscas and intrahousehold resource allocation.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>117</mark>_ <mark>(3), 963-995.</mark> 19
captured by the concentration coefficients), and if it is large relative to their incomes (as captured by the size).

**Zooming in to the elements of the fiscal system across the income distribution allows to better understand the distributional effects** . Figure 9 shows the share of broad categories of taxes and transfers in pre-fiscal income (market income plus pensions), by decile. The shares for the bottom first decile are very large because the denominators (pre-fiscal incomes) in this decile are small. This decile of the distribution is a net receiver of social benefits, as shown by the positive net cash position.<sup>37</sup> Once in-kind health and education benefits are included, though, the net benefit for this decile is very large, increasing real incomes by about 40 percent. Contrarily, starting at the second decile, households in Albania are net payers into the fiscal system. Direct transfers represent a non-sizable share of household income outside the first two deciles, reflecting a weak targeting. In-kind health and education benefits are sizable for most deciles, declining gradually as we move along the income distribution. Direct taxes and contributions also represent a much smaller share of income in the bottom deciles, reaching 21 percent in the richest decile. The composition of taxes gradually changes from indirect to direct taxes as we move upward in the income distribution.

**Figure 9. Distribution of taxes and benefits across deciles of market income (plus pensions)**

**Figure 10. Size of Fiscal System Elements (with respect to market income plus pensions)**

<!-- Start of picture text --> 80% 20%<br>18%<br>60%<br>16%<br>40% 14%<br>20% 12%<br>10%<br>0% 8%<br>‐20% 6%<br>4%<br>‐40%<br>2%<br>‐60% 0%<br>‐80%<br>‐100%<br>1 2 3 4 5 6 7 8 9 10<br>Transfers (excl pensions) Direct taxes and contributions<br>Indirect taxes Health<br>Education Net cash position<br>Total position<br>Source: Own estimates using the Albania HBS 2015. Source: Own estimates using the Albania HBS 2015.<br>Survivor Pension Tertiary education Energy transfers Social insurance Health insurance Disability Pension Excises Basic education Health PIT VAT Pension benefits Total taxes<br>Unemployment Benefit Social care services... Ndhima Ekonomike Secondary education Total benefits (excl pensions) Total in-kind transfers<br><!-- End of picture text --> In the next subsection, we take a closer look at the effect of specific fiscal elements summarized in Table 3. The analysis relies on the previously defined concepts of progressivity (Kakwani index) and marginal

> 37 The net cash position captures the difference between market income plus pensions and consumable income (equivalent to all payments of taxes and cash benefits), as a share of market income plus pensions.

22
7a

# lo. EXCHANGE RATE TYPF5, NO. OF DEVALUTIONS, AVG DEVALUATION, AND INFLATION (1955.71)

|COUNTRY|ER<br>TYPE|YEARW/<br>DEVAL.|DEVALUATION<br>AVEGE|<br>MEDIAN|AVERAGE<br>INFLATION|
|---|---|---|---|---|---|
|Atgentina|fix;d w/adjusntnt|10|47.80|23.00|27.80|
|Bolivia|fixed v/adjustn-i|3|1344.50|39.00|27.50|
|Brazil|<br>fixntmil<br>68 whou csAwllng persa|3|335.00|69.37|37.56|
|Chilo|fx until 73 withadjustment|14|42.50|29.19|30.94|
|Colombia|fix unil 67 when crawUing peg tats|9|29.32|16.74|10.04|
|Costa Rica 1/|fixed w/adjustment|1|17.75|17.7S|2.23|
|DominicanRepublic|<br>fullyfixed|0|0.00|0.00|1.44|
|<br>ElSalvador|<br>fullyfixed|0|0.00|0.00|0.77|
|Ecuador|fixed w/adjustment|2|29.44|29.44|3.17|
|Guatemuai,|<br>fhUyfixed|0|0.00|0.00|,.S2|
|Haiti|<br>filUy fixed|0|0.00|0.00|2.09|
|Hondums|fWUy fixed|0|0.00|0.00|1.75|
|Mexico|<br>fikUy fixed|0|0.00|0.00|4.61|
|Nwareg"|fully fixed|0|0.00|0.00||
|Panama|fulyfixaed|0|0.00|0.00|0.83|
|Paaguay/2|fixed w/adjustment|6|30.70|10.67|7.29|
|Petu|fixed w/adjustment|3|28."6|13.11|8.62|
|Uruguay/|fixed_w/_adjustment|7|78.03|49.36|37.00|
|Venezela/4|fulily fixed|1|38.20|38.20|2.11|

Notes: 11 Cos Riea had only one devalustion in 1961 (n th period 1955-71) 2/ End of period not availabe, ued average 3/ Baedondataaning 1961 (vs. 195)-end of peiod exrxnot vailablebefore 1961.

4/ Venezuela had only one devaluation in 1964 (m the period 1955-71)

# lb. EXCCHANGE RATE TYPES, NO. OF DEVALUTIONS, AVG DEVALUATION, AND INFLATION (1975-89)

|COUNTRY|EXR<br>TY-PE|_YE_<br>W/<br>DEVAL.|DEVALUATION<br>AVEAGE|<br>NEDIAN|AVEAGE<br>INIATION|
|---|---|---|---|---|---|
|**Agentina**|fexible|IS|1186.90|263.76|452.99|
|Bolivia|fix butshiftin '82|9|2416.80|22.SS|923.45|
|Brazil|Crawlingpeg**st**<br>'68|13|301.80|103.30|226.83|
|Chile|crawlingpeestarts'73|13|201.28|21.42|64.37|
|Colombia|erwling peg sti<br>'67|IS|18.14|8.99|23.56|
|CostaRica|fixed urnil 1980, then erawling peg|9|45.68|12.6S|20.7S|
|Dominica Republi|fix w/adj.fiom 85 onwards|4|71.92|27.82|17.63|
|El Salvador|fix w/ oneadj.in '86|1|100.00|100.00|16.74|
|Ecuador|fix until 83, thencrawlingpeg|8|31.49|31.19|26.71|
|Guatemal|fix w/adj.in 1986, '88,and'89|3|61.30|25.69|1l.7|
|Haiti|fixed|0|0.00|0.00|7.13|
|Hondura|fixed|0|0.00|0.00|7.57|
|Mexico|crawng peg**sine,**1976|11|76.0B|49.18|52.40|
|Nicaragua/I|fixw/majoradj. in1985,86,88,&89|<br>S|263720.00|150.00|867.C0|
|Pauay|fix w/ adj. in 84-88, c.p. as 89|4|79.28|71.88|17.61|
|Peru|crawling peg st<br>1976|14|238.98|93.21|333.52|
|tuguay|crawling peg since 1972|IS|35.29|3S.29|S8.50'|
|Venezuela|fix w/ adj. in 84,86,89;<br>floating sKar 1989.|3|121.62|93.33|1838|

1/ Costa Rica: fixed exr until 1980, fully flexible threafter.

_21_ NicrUa had ha end of period develuadon of 1,314,18S.71 %in 88 and 4046.74 in 89, avevrge devaluations for these 2 ye were 262,673.34% **and** 5,704% respectively.
# Table 16: Mechanism testing: differential mortality rates

||(1)<br>Any children|(2)<br>Number of|(3)<br>Child ae|(4)|(5)|(6)<br>Age|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|under 5|children under 5|g|0 - 1|1 - 2|2 - 3|3 - 4|4 - 5|
|HWWS|-0.014|-0.022|0.009|-0.003|0.002|-0.001|-0.000|0.002|
||(0.014)|(0.028)|(0.018)|(0.006)|(0.006)|(0.006)|(0.006)|(0.007)|
|TSSM|0.011|0.020|-0.015|-0.004|0.007|0.001|0.006|-0.010*|
||(0.014)|(0.030)|(0.017)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|
|HWWS + TSSM|-0.013|-0.034|0.010|-0.005|0.002|-0.001|0.009|-0.004|
||(0.014)|(0.029)|(0.018)|(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|
|Observations|50,885|50,885|34,092|34,092|34,092|34,092|34,092|34,092|
|R-squared|0.068|0.100|0.003|0.002|0.004|0.004|0.002|0.006|
|Control Mean:|0.444|0.666|1.928|0.205|0.209|0.214|0.196|0.176|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects. All results based on listing data.

52
# **6. Conclusion**

Our paper deals with two critical issues in the literature of privatization: the lack of convincing identification of privatization effects due to the selectivity on time-varying firm characteristics, and the lack of understanding of the causal effects of privatization on productivity in the country where the scale of privatization has been the greatest. To address these issues, we take advantage of the bureaucratic rules surrounding the promotions of local politicians, wherein their chance of promotion drastically drops once crossing the age threshold of 58. After empirically confirming this pattern, we use the dummy variable of city party secretaries crossing the age threshold as the instrument to identify the effects of privatization in firm productivity regressions, while allowing the age of the city party secretaries to have direct effect. We find that, without controlling for time-varying selectivity and using the OLS, the firm fixed-effects and/or the firm random growth specifications, privatization in China is associated with productivity improvements within the range of effects found in earlier literature on Eastern European and CIS countries, on the relatively high end of the spectrum of those estimates. However, once addressing the time-varying selectivity of privatization with our RDD instrument, the effects of privatization increase to more than 170 percent, an order of magnitude higher than the workhorse fixed-effects estimate. Privatizations also have strong and positive effects on profitability. Privatizations thus drastically transform the loss-making SOE sectors. Moreover, we find the privatization effects to be much higher in regions with lower government activism, which implies that marketization and privatization are complements.

Our findings have several implications. First, in light of the large disparity between our RDD-based instrumental variable estimates and those of conventional fixed-effects-based specifications, the causal effects of privatization were likely substantially underestimated in the past. In other words, negative selectivity based on time-varying characteristics of SOEs could be of first-order importance. Future studies of the effects of privatization should seriously address the importance of selectivity on time-varying factors. This selectivity is indeed intuitive: SOE performances vary greatly over time, depending on the entry barriers to non-state firms, market competition, among other factors, all of which vary over time. Only _when_ SOEs ``` 22 ```
<!-- Start of picture text --> Table 5<br>Output gains/losses after labor and capital transfer from state to non-state sector<br>(as share of GDP)<br>1996 estimation<br>as a result of labor transfer as a result of capital transfer<br>(43% of labor transfer) (70% of capital transfer)<br>Share of total loss (-)/share of total gain(+)<br>State -15% -61%<br>Non-state 33% 118%<br>Net gain 2.56% 8.29%<br>1997 estimation<br>as a result of labor transfer as a result of capital transfer<br>(57% of labor transfer) (74% of capital transfer)<br>Share of total loss (-)/share of total gain(+)<br>State -16% -69%<br>Non-state 38% 114%<br>Net gain 3.42% 7.74%<br>1994-1997 estimation<br>as a result of labor transfer as a result of capital transfer<br>(12% of labor transfer) (70% of capital transfer)<br>share of total loss (-)/share of total gain(+)<br>State -6% -48%<br>Non-state 11% 117%<br>Net gain 1% 10%<br><!-- End of picture text --> 28
with monetary poverty and other indicators of well-being (e.g., Headey and Ecker 2013; Lain et al. 2022), and have been shown to be particularly sensitive to the many conflictrelated shocks that have occurred in the Republic of Yemen’s conflict (e.g., Tandon and Vishwanath 2020; Favari et al. 2021; D’Souza et al. 2022; Favari et al. 2022). A difference in pre-displacement food access measures of those who became displaced before violence began and those who became displaced after violence had begun would be consistent with either the humanitarian situation causing a significant amount of displacement or the lack of resources limiting how early some households might be able to become displaced.

However, there is little evidence of any difference between the two samples in Table 4. Signs on the differences vary between specifications and are low in magnitude. Additionally, only one variable was statistically significant at the 10 percent level, which is what would be expected by chance; and a joint test of significance of all the variables is not statistically significant at conventional levels (p-value 0.174).

The lack of any difference in food access measures is also consistent with a growing amount of evidence suggesting that sharp changes in the humanitarian situation are not driving forced displacement in the Republic of Yemen’s conflict. Food access was not becoming worse for displaced households leading up to displacement – which one might expect if worsening humanitarian conditions were forcing displacement (e.g., D’Souza et al. 2022); households that would have had the most incentive to become displaced due to the humanitarian situation- those with the worst pre-displacement food access- had an identical and large drop in food access following displacement as other households, which might not have been expected if these households were migrating for better economic opportunity (e.g., D’Souza et al. 2022); and, as mentioned above, key informant interviews with displaced households suggest that violence was pivotal to the displacement decision (e.g., TFPM 2016; OCHA 2019; etc.).

However, another potential explanation for a significant share of households becoming displaced prior to any surge in violence is that some households might have been especially intolerant of the potential of a poor security situation. Along with the lack of other explanations, this explanation is consistent with the violence patterns. The total 19