rafmacalaba/datause-encoder-data huggingface dataset:
# **5 CONCLUSION**

Using new rich data sets on shocks, social protection/transfers and consumption expenditure records, we explore several hypotheses within the framework of social protection in Niger. To do this, we devise novel empirical frameworks. We estimate both consumption and poverty responses to global realization of household shocks. Our results indicate that there is a tremendous spatio-temporal variation in households’ exposure to negative shock realizations which highlights potential vulnerability of households to local shocks. To first order, nonpoverty status and household consumption expenditures decline remarkably when exposed to extreme shocks, an indication of the population’s sensitivity and vulnerability to adverse shocks. In response, households employ several strategies to deal with realized shocks ranging from the use of previous savings and livestock holdings to doing nothing. There is strong evidence of substitution across the shock-strategy set over time. Engaging in migration as a coping mechanism leads to worse household outcomes. In particular, our results suggest that migration of one or more members of the household as a coping strategy implies more sensitivity of households’ consumption and poverty to shock realizations. These results are consistent with theories of vulnerability, avoidance behavior and adaptation.

Next, we examined the time differentiated impacts of household shocks by asking whether the _timing_ (i.e., month or quarter) of disbursing transfers shape the extent to which social assistance help households to absorb shocks. Our analyses document that social transfers or schemes are crucial _only_ in second quarter of the calendar year, _conditional_ on transfers in other quarters. In particular, the transfers do very little to shape vulnerabilities of beneficiary households to shocks aside the second quarter. On average, social protection schemes provided within the second quarter appear to be useful and significantly dampen the impacts of shocks on households’ consumption. This result is an evidence against the long standing incentive-hypothesis that providing social assistance to households can create incentives effects /asymmetric information and cause households to become more sensitive to external shocks for behavioral reasons. Our results also re-affirm old arguments that favor targeting of social protection schemes along gender dimensions. These indicate that social transfers can be effective if household transfers are managed by females.

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22. Using a 10-point scale where 1 represents “not a serious problem at all” and 10 represents “an extremely serious problem”, respondents were asked how serious a problem they considered climate change to be: as Figure 7 shows, average scores by respondents in Kazakhstan (5.7) and Tajikistan (4.7) were considerably lower than in EU17 countries (7.9).<sup>15</sup>

**Figure 7: Seriousness of climate change problem (1-10 point scale)**

<!-- Start of picture text --> EU27 countries 7.9<br>Kazakhstan 5.7<br>Tajikistan 4.7<br>0 2 4 6 8 10<br>Average score (1‐10 scale)<br><!-- End of picture text --> Source: Authors’ calculations based on P-LITS data; Special EU Barometer 300/wave 69.2 23. Within Tajikistan, average ratings of the seriousness of the climate change problem vary quite a bit across different groups. Amongst those reporting themselves as being “very well informed” about the consequences of climate change, average scores were considerably higher than amongst those that were “not at all informed” (5.7 vs. 3.3 respectively). Similarly, average scores among respondents in Dushanbe (6.0), those reporting sale/barter of farm products as their main source of income (5.3), and the poorest one-third (5.0) were also higher than the rest of the population.

**Table 3: Ordered Probit Results**

|**Respondent Characteristics**|**Coefficient**|**Std. Err.**|**z**|**P>z**|
|---|---|---|---|---|
|**1. Income group:**|||||
|Poorest one-third of population|0.3512|0.1299|2.70|0.01|
|Middle one-third of population|0.1175|0.1237|0.95|0.34|
|Richest one-third of population||**Referenc**|**e Category**||
|**2. Region of Residence**|||||
|Dushanbe|0.8281|0.1832|4.52|0.00|
|GBAO|-0.3183|0.2526|-1.26|0.21|
|Soghd|-0.2578|0.1262|-2.04|0.04|
|Khatlon||**Referenc**|**e Category**||
|RSS|-0.3038|0.1337|-2.27|0.02|
|**3. Sale/barter of farm products is**|0.5226|0.1548|3.38|0.00|
|**household’s main source of livelihood**|||||
|**4. Awareness of main consequences of CC**|||||
|Not at all informed||**Referenc**|**e Category**||
|Not very well informed|0.5018|0.1832|2.74|0.01|
|Fairly well informed|1.1215|0.1829|6.13|0.00|
|Verywell informed|1.5978|0.2341|6.83|0.00|

**Source:** Authors’ estimates based on P-LITS data. n=465, Log likelihood = -846.52, Pseudo R2 = 0.0747

> 15 Among EU27 countries, the lowest reported scores were in the Netherlands (6.8) and the highest in Cyprus (9.4).

13
effect of MR on the estimated comparative static is very minor for SS agreements – the coefficient value of 0.528 becomes a value of 0.528*0.985=0.520. For NS agreements, MR has a moderate dampening effect.

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|Any FTA|0.458***|||||||
|NN||0.471***|0.468***|0.355***|0.178***|0.228***|0.316***|
|NS||0.320***|0.307***|0.337**|0.183*|0.186*|0.448***|
|SS||0.528***|0.533***|0.360***|0.371***|0.311***|0.273*|
|NN(-1)|||||0.422***|0.194***|0.242***|
|NS(-1)|||||0.294**|0.339***|-0.023|
|SS(-1)|||||0.367**|0.355**|0.354*|
|NN(-2)||||||0.350***||
|NS(-2)||||||-0.13||
|SS(-2)||||||-0.0791||
|NN(+1)|||||||-0.0826|
|NS(+1)|||||||-0.126|
|SS(+1)|||||||0.094|
|n|48235|48235|47081|40108|45262|41656|37960|
|R<sup>2</sup>|0.31|0.31|0.189||0.285|0.263|0.265|

Table 2: Gravity regression results. Dependent variable is log of real bilateral trade except in column 3, where trade is divided by the product of importer and exporter GDP. Constant, pair dummies and country-time dummies included but not reported. Standard errors clustered by country pair, except column 4, where Baltagi & Wu (1999) standard errors are used. * 5% ** 1% *** 0.1%.

|Table 2 column reference:|Scenario:|SS|NS|Difference|Std Error|p|
|---|---|---|---|---|---|---|
||No MR|0.528|0.320|0.209|0.111|0.061|
|2|2 countries|0.526|0.308|0.217|0.109|0.046|
||4 countries|0.520|0.286|0.235|0.105|0.025|
||No MR|0.533|0.307|0.226|0.112|0.044|
|3|2 countries|0.531|0.296|0.235|0.11|0.033|
||4 countries|0.525|0.274|0.251|0.106|0.018|
||No MR|0.36|0.337|0.023|0.146|0.877|
|4|2 countries|0.358|0.325|0.033|0.143|0.817|
||4 countries|0.354|0.301|0.053|0.136|0.696|
||No MR|0.737|0.477|0.261|0.16|0.103|
|5|2 countries|0.734|0.459|0.274|0.157|0.081|
||4 countries|0.726|0.426|0.300|0.152|0.049|

Table 3: Comparative static effects of FTAs. SS refers to South-South agreements and NS refers to North-South agreements. Difference is the difference between the estimated effects of the agreements while the std error and p values refer to this difference. No MR is taken directly from the applicable coefficient in table 2 and ignores multilateral resistance. 2 countries and 4 countries adjust the coefficients for the impacts of multilateral resistance.

13
Policy Research Working Paper 5086

# **Abstract**

This paper describes important trade-offs that microfinance practitioners, donors, and regulators navigate. Drawing evidence from large, global surveys of microfinance institutions, the authors find a basic tension between meeting social goals and maximizing financial performance. For example, non-profit microfinance institutions make far smaller loans on average and serve more women as a fraction of customers than do commercialized microfinance banks, but their costs per dollar lent are also much higher. Potential trade-offs therefore arise when selecting contracting mechanisms, level of commercialization, rigor of regulation, and the extent of competition. Meaningful interventions in microfinance will require making deliberate choices—and thus embracing and weighing tradeoffs carefully.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the development and impact of microfinance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rcull@worldbank.org or ademirguckunt@ 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
|PSU dummies|Yes|Yes|Yes|
|---|---|---|---|
|Year dummies|Yes|Yes|Yes|
|Women controls|No|Yes|Yes|
|Partner controls|No|No|Yes|
|Observations|2,366|2,315|2,025|
|R-squared|0.120|0.146|0.161|
|The dependent variable is the female|decision-making index (FDI). Standa|rd errors clustered at the|district level|

In Table 2 and Table 3, we unpack the FDI index into its sub-indices: Table 2 includes all women in the sample, while Table 3 is restricted to couples. Results show that the observed change in the FDI index is driven by significant improvements in women’s participation in household decision making related to household purchases and children’s education in medium conflict districts. The estimated coefficients indicate a 0.25 - 0.29 point increase in household purchases decisions compared to 0.19 - 0.24 point increase in children education decisions. There is no significant change with regards to participation in decisions regarding medical purchases, children’s marriage, or other decisions.

_Table 2: Results for sub-indices – All sample_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Purchases of<br>food & clothing|Medical<br>purchases|Children<br>marriage|Children<br>education|Others|
|Medium x 2021|0.293**|0.049|-0.026|0.242**|0.072|
||(0.119)|(0.077)|(0.111)|(0.100)|(0.098)|
|Strong x 2021|0.135|-0.083|0.026|0.003|-0.051|
||(0.095)|(0.064)|(0.114)|(0.093)|(0.116)|
|PSU dummies|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|Yes|
|Partner controls|No|No|No|No|No|
|Observations|2,311|2,240|1,402|1,794|2,160|
|R-squared<br>The dependent variables are t|0.180<br>he sub-indices of the female de|0.182<br>cision-making ind|0.192<br>ex (FDI). Stand|0.173<br>ard errors cluster|0.130<br>ed at the district|

The dependent variables are the sub-indices of the female decision-making index (FDI). Standard errors clustered at the district level.

_Table 3: Results for sub-indices – couples’ sample_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Purchases of<br>food &<br>clothing|Medical<br>purchases|Children<br>marriage|Children<br>education|Others|
|Medium x 2021|0.251**|-0.002|-0.041|0.186*|0.016|
||(0.103)|(0.055)|(0.115)|(0.097)|(0.100)|

15
||**Log of hourly**<br>**wage **|**Employed**|**Wage worker**|**White‐collar**<br>**worker**|**Formal**<br>**worker**|
|---|---|---|---|---|---|
||(0.085)|(0.034)|(0.045)|(0.040)|(0.048)|
|Father has incomplete higher education|0.524**|0.099|0.033|0.002|‐0.005|
||(0.234)|(0.079)|(0.097)|(0.091)|(0.114)|
|Father has complete higher education|0.135|‐0.060|0.048|0.005|0.093|
||(0.105)|(0.045)|(0.063)|(0.056)|(0.068)|
|Dummy for taking less than 1/2 hour to go to school|0.038|‐0.047|‐0.002|‐0.021|‐0.046|
||(0.083)|(0.040)|(0.050)|(0.046)|(0.055)|
|Constant|‐0.209|‐0.111|1.430***|0.662***|0.390|
||(0.450)|(0.189)|(0.244)|(0.244)|(0.279)|
|Observations|770|1,293|809|809|809|
|R‐squared|0.245|0.235|0.255|0.354|0.123|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

**22 |** P a g e
# **Statistical Table 8 Overall performance indicator and components (East Caribbean)**

||Results-focus<br>from Statistical<br>Table 5|Accountability<br>from Statistical<br>Table 6|Employee Morale<br>from Statistical<br>Table 7|Performance|
|---|---|---|---|---|
|Antigua and Barbuda|3.6|5.4|8.0|5.7|
|Grenada|_5.1_|5.7|7.8|6.2|
|St. Kitts and Nevis|5.2|6.2|8.5|6.6|
|St. Lucia|_5.0_|5.9|7.6|6.2|
|St. Vincent And Grenadines|5.2|_5.7_|8.2|6.4|
|East Caribbean|4.9|5.5|8.0|6.1|

No measure of vertical solitude was calculated<sup>in the East Caribbean. The very</sup> considerable differences in the perceptions of managers<sup>prevented the establishment of</sup> any single reference point against which other<sup>staff s perceptions could be tested.</sup> However, in other country reports where indicators<sup>could be calculated at organizational</sup> level, an indicator of vertical solitude was constructed.

_55_
margin (Table 2, Panel A, column 1) as 95% of control individuals already have an activity. A small but significant impact on entry into self-employment is found (+2pp, Table 2, Panel A, column 2). It is offset by a small decrease in participation in wage employment (-3pp, Table 2, Panel A , column 3). Similar results are found for hours worked (Table 2, Panel A, columns 5-7), with no significant change in total hours, and a positive effect on hours in self-employment (not statistically significant, p=0.195) offset by a decrease in hours in wage employment.

The pooled treatment induces more substantial changes at the intensive margin by significantly increasing the number of independent activities by 0.24 per individual (Table 2, Panel A, column 4). This means that, on average, one out of four individuals added a new activity to his/her portfolio, representing a 8% increase relative to the control group. Yet it appears that a substantial share of beneficiaries did not launch the activity they proposed in their business plan. While agricultural activities account for 80% of independent activities in control, 70% of grant beneficiaries prepared a business plan for a non-agricultural activity (often retail trading). The impact on entry into non-agricultural activity is only 0.08.<sup>36</sup> To better understand the dynamics of activity creation, we also compared the business plans developed during the program with the activities listed by respondents in the follow-up survey. Only 40% of the activities in the business plans could be matched to activities reported as active in the 12 months preceding the followup survey. This is broadly consistent with the magnitude of the observed impacts on the net number of activities, and suggests that a large share of individuals either did not launch their proposed activity or stopped it quickly.

Importantly, we do not find differences in impacts on employment or time worked in wage or self-employment between treatment modalities (Table 2, panel B). We cannot reject that the treatment effects are equal between the three arms for any variable, aside from cash grant modalities leading to fewer hours in wage employment. There is no significant difference in the impact on the number of independent activities across treatment modalities either (p=0.6, panel B, column 4).

> 36The point estimate for the increase in independent activities at endline is 0.15 (not statistically significant) for agriculture, and 0.08 (significant at 10 percent) for non-agricultural activities (Table A10, Panel A, columns 1 and 2).

18
The situation of Venezuelan migrants displays many similarities to the situation of refugees (i.e., those deserving the legal status). Mindful of Venezuelans’ conditions of vulnerability, in 2017 the Colombian government created a new migratory status called the ‘ _Permiso Especial de Permanencia_ ’ (Special Permanency Permit), known as PEP. This permit has had different versions: the 2018 iteration was initially valid for up to two years and renewable every 90 days. Moreover, Venezuelans who held that version of the PEP were able to join the labour market and access public services.<sup>4</sup> While the permit regularised a significant proportion of Venezuelans, the number of unauthorised Venezuelans in the country remained high. In fact, until late 2020, nearly 50% of Venezuelans did not have regular status as shown in Figure 2 (Migración Colombia 2020). However, given the large level of informality in the Colombian labour market—estimated at over a third of all employment in Colombia (Departamento Nacional de Estadística 2021)—the lack of regularisation and formal work authorisation might not have represented a major impediment for Venezuelans to find employment.<sup>5</sup> <!-- Start of picture text --> Figure 2 - Number of Regular and Irregular Venezuelan migrants since 2018<br>Regular Irregular<br>2018m7 2019m1 2019m7 2020m1 2020m7 2021m1<br>Year<br>1000000<br>800000<br>600000<br>Number of Venezuelan migrants<br>400000<br><!-- End of picture text --> _Source_ : Authors' calculations using data published by the Ministry of External Relations 2020.

> 4 The PEP was initially rolled out in 2017 and came to be known as the PEP-I. Three resolutions were enacted in 2018 with three different forms of the PEP. Current evidence shows negligible effects of these amnesties on the Colombian labour markets (Bahar et al. 2021).

> 5 There is evidence that, even after being eligible for regularisation, some migrants chose to keep working in the informal sector (Bahar et al. 2021).

11
**Figure 2.** Changes in the Composition of Global Per-Capita Daily Calorie Demand For Selected Commodities

<!-- Start of picture text --> 111 1140<br>109 1130<br>107 1120<br>105 1110<br>103 1100<br>101 1090<br>99 1080<br>97 1070<br>95 1060<br>Beef, Veal, & Poultry Fruit Wheat and Rice<br><!-- End of picture text -->

**<u>Source</u>** : FAO (http://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en).

Many farm workers who leave the farm remain within the broader food supply chain. In many low-income countries, off-farm work in AFS already makes up about 25 to 33 percent of overall off-farm work (see Table 1) (Allen, Heinrigs, and Heo 2018). It is still relatively small as a share of total employment (7 percent in Eastern and Southern Africa, Table 1); however, it rises to 25 percent when expressed in full-time equivalent employment (hours worked) as opposed to the number of people employed (Dolislager et al. 2019).<sup>5</sup> The importance of off-farm employment (food manufacturing and food service) in the overall agri-food system (AFS) rises with income, from 9 percent (7/80) of total AFS employment in Eastern and Southern Africa to 52 percent (16/31) in Brazil and 80 percent (8/10) in the United States (Table 1). The share of off-farm AFS employment in total employment first rises (from 7 percent in Eastern and Southern Africa to 16 percent in Brazil) and then falls (to 8 percent in the United States).

Asia’s experience shows that the more successful countries develop their agri-food system as they pass through the structural transformation, and that this leads to a more rapid reduction in poverty (Huang 2016). Non-farm AFS jobs are often easily accessible for poor workers leaving the farm as well as for women, given their proximity and low entry requirements in terms of capital and skills. A large part of employment opportunities within the AFS is happening in secondary cities and towns (Cazzuffi et al. 2017), and most of the poor live in the rural hinterlands of these intermediate centers (Ingelaere et al. 2018).

> 5 While many more people have agriculture as their primary sector of employment, the number of hours actually worked in the sector is sizably less given seasonality and underemployment (McCullough 2017).

6
<!-- Start of picture text --> 1<br>0.9<br>0.8<br>C07<br>S<br>foe<br>E 0.6<br>_=<br>£05<br>£<br>3= 0.4<br>© 0.3<br>0.2<br>0.1<br>0<br>Primary Secondary Interior<br>Location<br><!-- End of picture text --> <!-- Start of picture text --> 0.02<br>0.01<br>a<br>g 0<br>S<br>. -0.01<br>¢<br>i)<br>= -0.02<br>£<br>= -0.03<br>°<br>(5 -0.04<br>-0.05<br>-0.06<br>Primary Secondary Interior<br>Location<br><!-- End of picture text -->
||Overall|Treatment|Control|p‐value|
|---|---|---|---|---|
|_First Follow‐up Survey_|||||
|Completed Full Survey|0.65|0.70|0.60|0.066|
|Completed at least Short Survey|0.79|0.80|0.78|0.781|
|At least basic information|0.92|0.93|0.91|0.520|
|_Second Follow‐up Survey_|||||
|Completed Full Survey|0.64|0.66|0.62|0.282|
|Completed at least Short Survey|0.85|0.86|0.84|0.504|
|At least basic information|0.95|0.95|0.94|0.873|
|Sample Size|346|174|172||
|Note: p‐value is for test of equality<br>rates after controlling for randomiz<br>At least basic information denotes<br>is operating and whether it has rec|of treatme<br>ation strat<br>that inform<br>eived exter|nt and contr<br>a.<br>ation on whe<br>nal financing|ol complet<br>ther the f<br>is availab|ion<br>irm<br>le.|

Appendix Table 8.2 compares baseline observables for the treatment and control groups, conditional on completing at least the short survey. We cannot reject that these observables are orthogonal to treatment status for either definition of survey completion. Given the lack of significant difference in response rates by treatment status, and that the sample responding to at least the short survey is balanced on observables, we treat attrition as missing at random in our analysis of the survey data.

66
**Figure A6: Impact of Delegated Randomization on Tax Yield - Event Study**

<!-- Start of picture text --> .3<br>.15<br>0<br>-.15<br>t-6 t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t+6<br><!-- End of picture text --> _Notes_ : The graph presents the estimates from an event study of the impact of the introduction of the delegated randomization on tax yield per declaration. Month t denotes the start of the delegated randomization and the sample covers 6 months before and 6 months after the start of the delegated randomization on November 18 2017. An OLS regression of log 1 plus tax yield per declaration is estimated on dummies that define the position of the month relative to November 2017 as well as inspector, broker, source country, HS2-product, and calendar month fixed effects. Standard errors are clustered by inspector. The dots represent the point estimates and the vertical bars the 95 % confidence interval.

55