rafmacalaba/datause-encoder-data huggingface dataset:
# 1  Introduction

Social insurance policies affect individual behaviors and can have non-trivial effects on the supply side of the labor market. The existence of mandatory pensions, for instance, affects retirement decisions. Often, benefit formulas that are not actuarially fair and/or minimum pension guarantees induce early withdrawals from the labor force, which increase the cost of the pension system and reduce employment levels (see, for instance, Bodor et al. 2008; Jiménez-Martin and Sánchez-Martin, 2006; Blundell et al. 2002; Anderson et al. 1999; Gruber and Wise, 1998; Samwick, 1998; Lumsdain and Wise, 1994; and Fields and Mitchel, 1988). Badly designed retirement income transfers can also reduce incentives to contribute to the social security and promote informal work (see Valdez, 2008; and Piggot et al., 2009).

Unemployment benefits schemes affect behaviors as well. Unemployment insurance systems, for example, can create moral hazard reducing incentives to search for or keep jobs. The literature usually finds a positive correlation between the level of the benefit and its duration, and the length of the unemployment spell (see, for instance, Layard et al., 2006; Card and Levine, 2000; Calmfors and Holmlund, 2000; Anderson and Mayer, 1993; and Mayer, 1991). Even funded mandatory unemployment savings accounts which are expected to be incentives neutral can, under some circumstances, have unintended consequences and induce more frequent separations and higher turnover (see Vodopivec et al., 2009).

More generally, the structure of the bundle of social security benefits and the financing mechanism determine the incentives facing individuals to take formal sector jobs (see Perry et al., 2007; and Levy, 2006). When the bundle includes benefits that are not valued by individuals or there is an important redistributive component, part of the social security contribution acts as a tax that can promote evasion and informal sector work.<sup>1</sup> At the same time, benefits that are too generous relative to contributions create implicit subsides that can reduce incentives to work and save (i.e., self-insure).

The standard analysis of the economic impacts of policy changes in the social insurance system, however, generally ignores these effects or simply makes assumptions about possible behavioral responses (see for instance World Bank, 2006). One reason is that there are no econometric models linking the complex set of rules of a given system (say pensions) to behaviors; there is never enough variation in system parameters to estimate these models. Thus, the econometric models that we have tell us how the _presence_ of a social insurance program

> 1 For a general review of some of the linkages between the social insurance system and the labor market see Krueger and Mayer (2002).

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**4**

Table 1: Main Exports of the Baltics in **1990**

||Intra-Republ|ic|E|xtra-Repu|blic|
|---|---|---|---|---|---|
||Product|**%**|Pro|duct|**%**|
|Estonia|Machinery|29|Food||31|
||Chemicals|19|Machin|ery|22|
||Light Industry|16|<br>Light In|<br>dustry|15|
||Food|_15_|Metals||13|
||Machinery|40|Machin|ery|53|
|Latvia|Food|_15_|Food||16|
||Chemicals|14|Wood||7|
||Other (Communic.)|9|LightJ|ndustry|6|
||Machinery|44|Oil||41|
|Lithuania|Oil|12|Machin|ery|37|
||Light Industry|10|<br>Food||8|
||Food|8|Wood||5|
|Source: Worl<br>able 2: Structure|d Bank (1992a).<br>of Baltic exports in 199<br>Estonia*)|2 or 199<br>Latvia*|3(%)<br>**)|Lith|uania**)|
|Products|West<br>-ast|West|East|West|East|
|Food|15<br>18|9|20|34|15|
|Wood|14<br>6|18|1|2|2|
|Textiles|24<br>11|9|16|11|21|
|Metals|16<br>2|15|3|7|3|
|Machinery|7<br>20|3|34|9|31|
|Other|24<br>43|46|26|37|28|
|Total|100<br>100|100|100|100|100|
|Memo:||||||
|Value Mio.US$|240<br>212|_518_|619|225|580|

Table 2: Structure of Baltic exports in 1992 or 1993(%)

*) HS Classification, last 6 months of 1992. **) SITC Classifiction. ***) HS Classification.

Source: Estonian Central Bank. COMTRADE. State Committee for Statistics of Latvia (1994).
Policy Research Working Paper 9495

# **Abstract**

How do inexperienced consumers learn to use a new financial technology? This paper presents results from a field experiment that introduced payroll accounts in a population of largely unbanked factory workers in Bangladesh. In the experiment, workers in a treatment group received monthly wage payments into a bank or mobile money account while workers in a control group continued to receive wages in cash, with a subset also receiving an account without automatic wage payments. The results show that exposure to payroll accounts leads to increased account use and consumer learning. Those receiving accounts with automatic wage payments learn to use the account without assistance, begin to use a wider set of account features, and learn to avoid illicit fees, which are common in emerging markets for consumer finance. The treatments have real effects, leading to increased savings and improvements in the ability to cope with unanticipated economic shocks. An additional audit study provides suggestive evidence of market externalities from consumer learning: mobile money agents are less likely to overcharge inexperienced customers in areas with higher levels of payroll account adoption. This suggests potentially important equilibrium effects of introducing accounts at scale.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ebreza@harvard.edu, mkanz@worldbank.org, and lklapper@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
# **4. Impact of the Crisis**

The crisis had a negative impact on the welfare of refugees, IDPs and returnees. There is a notable shift towards feeling poorer amongst the three sub-samples, which is corroborated by a decrease in the quality and number of meals consumed, a reduction in income and employment, decreases in assets and livestock owned and the fact that many children were taken out of school.

**Figure 5: Levels of poverty before the crisis and in June2014 (%)**

<!-- Start of picture text --> Before June 2014<br>78<br>64 65<br>56<br>52<br>49<br>42<br>35<br>25 27<br>20 18<br>16 17<br>11<br>5 4 1 6 3 7 1<br>Poorest Poor In the middle Rich Poorest Poor In the middle Rich<br>IDPs Refugees Returnees IDPs Refugees Returnees<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

Before the crisis, the majority of IDPs, refugees and returnees considered themselves as neither poor, nor rich (a likely understatement of their actual wealth status as they were better educated and owned more assets than the average person in the North). But after the crisis, the majority of respondents consider their household to be poor and hardly anyone considers their household as rich (Figure 5).

**Table 6: Comparing the quality of diet before the crisis and June 2014**

||**Better before the Crisis(%)**|**Same(%)**|**Better today (June) (%)**|
|---|---|---|---|
|IDPs|77|16|7|
|Refugees|93|5|2|
|Returnees|87|9|4|

Source: Listening to Displaced People Survey, 2014.

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categories: starters (ages 16–29), young families (ages 30–44), mature families (ages 45–59), and empty nesters (ages 60 and over). Four categories of dwelling status of the household were coded based on the household surveys: owned by the household (1), rented from the state (2), private rentals (3), and free or other (4). For each survey, the four categories were assigned based on the following information (Table 2).

**Table 2. Survey Specific Definitions of Dwelling Status**

|**Code**|**Romania**|**Serbia**|**Armenia**|**Poland**|**Russia**|
|---|---|---|---|---|---|
|1=Own|Personal<br>property|Owner|Owned|Ownership of house; ownership<br>of apartment in municipal<br>building or an apartment<br>building, membership in<br>cooperative, ownership;<br>membership in cooperative,<br>tenancy|Your household<br>(privatized, bought)|
|2=State<br>rental|Leased by<br>state|Protected<br>tenant|State or<br>municipality<br>rented|Rental of apartment - regulated<br>rent; social security apartment|Government or<br>municipal<br>authorities|
|3=Private<br>rental|Leased by<br>landlord|Paying guest,<br>rents out<br>whole<br>apartment|Private person<br>rented|Rental of apartment - free<br>market rent|Leased from a<br>private person,<br>private legal entity|
|4=Free or<br>other|Held for<br>free|Lives with no<br>charge in a<br>house one<br>does not own|Other|Sub-renting part of apartment;<br>at parents’ or other person’s;<br>other type of residence|Other form of<br>ownership, difficult<br>to answer|

Housing cost primarily included rent, heat, gas, and electricity. The survey in each country posed the questions differently, and in some cases, common utilities might include items that are impossible to separate.

|**Country**|**Costs included**|
|---|---|
|Armenia|Rent, heat, electricity, gas|
|Serbia|Rent, water supply, garbage disposal, central heating, building maintenance, electricity|
|Romania|Rent, water, sewerage and garbage collection, heat, electricity, other utilities|
|Poland|Rent, electricity, gas, district heat and other fuels|
|Russia|Rent,hot water, cold water,heat, gas, electricity|

In addition to survey analysis, the study also draws on published census results, official statistics, and data cited in secondary sources.

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- In contrast, the result for having an employed male member shows a 35-percentage points difference between male refugees and male hosts. This difference is observed in all refugee hosting regions ranging from 19 percentage points in Somali region, 29 percentage points in Tigray and Afar region, and to 46 percentage points in Benishangul-Gumuz region.

- Comparing the observed differences between refugees and hosts by gender, the results indicates that women refugees show a relatively smaller difference with their hosts (24 percentage points) compared to men refugees who show the largest difference with their hosts (35 percentage points). Unpacking the results further, we see that the differences among female refugees and host are driven by difference in employment in Benishangul-Gumuz region. In contrast, among men, the differences are observed in all refugee-hosting regions.

_Table 3: Employment participation: matched results_

||All|Fe|male|Male|
|---|---|---|---|---|
|Refugee|-0.461<sup>***</sup>|-0.238<sup>***</sup>||-0.348<sup>***</sup>|
||(0.033)|(0.034)||(0.037)|
|Refugee - Somali|-0.218<sup>***</sup>||-0.039|-0.193<sup>***</sup>|
||(0.068)||(0.060)|(0.073)|
|Refugee - Eritrea|-0.349<sup>***</sup>||-0.098|-0.294<sup>***</sup>|
||(0.066)||(0.064)|(0.070)|
|Refugee - Sudanese|<br>-0.651<sup>***</sup>||-0.413<sup>***</sup>|-0.463<sup>***</sup>|
||(0.043)||(0.052)|(0.052)|
|Number of Obs.|2002<br>2002|2002|2002|2002<br>2002|

Notes: The dependent variable is participation in employment. All regressions include camp fixed effects. Robust standard errors are reported in parentheses. Asterisks indicate statistical significance at 1% ***, 5% **, and 10% * levels.

Overall, the results suggest that Somali refugees have better employment opportunities than other refugee groups in Ethiopia. As noted above, refugees and hosts in the Somali region share both language and culture. This has allowed a peaceful coexistence and greater integration compared with other displacement settings, and better opportunities for socio-economic integration (Betts et al., 2019). Moreover, some Somali refugees are also benefited from crop irrigation schemes in Melkadid, which was implemented in line with CRRF pledge with the aim of benefiting refugees and hosts on equal basis (UNHCR. 2020b).

We check the robustness of our results by expanding the sample to include all households in the SPS. Table A4 shows that when we include all households, our estimated results remain robust.

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# **Table 29: IV Estimation (4)**

||||Bus for mu<br>|nicipal|||
|---|---|---|---|---|---|---|
||Bus for popul<br>IVProbit|ated area<br>|capital<br>IVProbit||Bus for hos<br>IVProbit|pital|
|D|1.015|(0.712)|1.298|(0.493) ***|1.675|(0.521) ***|
|lnHH size|-0.235|(0.195)|-0.008|(0.144)|0.034|(0.147)|
|Adult share|-0.659|(0.419)|-0.430|(0.302)|-0.252|(0.308)|
|HH head sex: Male|0.064|(0.245)|0.252|(0.165)|0.233|(0.175)|
|HH head education: Elementary|-0.066|(0.163)|-0.193|(0.120) *|-0.143|(0.124)|
|HH head education: Secondary|-0.437|(0.323)|-0.406|(0.211) *|-0.325|(0.218)|
|HH head education: University|||-0.311|(0.430)|-0.602|(0.542)|
|Appliance ownership: Refrigerator|-0.228|(0.236)|-0.073|(0.177)|-0.044|(0.184)|
|Appliance ownership: Color TV|-0.059|(0.251)|0.032|(0.179)|-0.045|(0.183)|
|Appliance ownership: Radio|0.146|(0.159)|-0.165|(0.113)|-0.120|(0.117)|
|Appliance ownership: Gas stove|0.084|(0.228)|-0.016|(0.172)|0.055|(0.179)|
|Appliance ownership: Washing machine|-0.293|(0.222)|-0.201|(0.144)|-0.237|(0.148) *|
|Gas use for cooking|0.204|(0.190)|0.028|(0.136)|0.035|(0.141)|
|Power use for lighting|-0.390|(0.239) *|<br>-0.296|(0.182) *|-0.022|(0.192)|
|Tap water for cooking|-0.017|(0.178)|-0.061|(0.125)|-0.169|(0.127)|
|Constant|-1.189|(0.987)|-1.146|(0.694)*|-1.781|(0.731)**|
|Obs.|911||911||911||
|Wald-stat|37.69||59.34||57.71||
|Amemiya-Lee-Newey overidentifying restriction|||||||
|test|1.39||9.36|***|14.89|***|
|Hausmanexogeneity test|4.81 *|*|12.22|***|9.77|***|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

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- 28 -

# V. SIMULATING TARIFF POLICY FOR INDONESIA

Our survey sample in Indonesia was conducted in 1992, five years later than in Nigeria. A total of 279 firms remained after routine data cleaning and of these only 171 reported using both power sources. Virtually all of the remaining firms relied entirely on PLN. This is an important difference from Nigeria where, as mentioned earlier, 87% of the firms used both power sources. In addition, the Indonesian sample includes a broader representation of the smallest firms whereas the Nigerian sample is strongly biased towards larger firms. In the Indonesian sample many firms which used both sources of power used small quantities of their own power. This is in part a reflection of the fact that the public power source is more reliable in Indonesia than it is Nigeria and that some of the sampled firms had, perhaps, virtually abandoned use of their generators by 1992, but it may also be due to the bias that smaller users appeared more frequently in the Indonesian sample.

Since we are simulating tariff policy we ought to consider, in principle, the potential response of exclusive PLN users when PLN's tariff is changed. More specifically, if some firms are charged a sufficiently higher marginal price for PLN power they might consider installing their own generators and using their own power at least part of the time. At the other extreme, firms for which PLN power is made sufficiently cheaper could greatly increase purchases from PLN. These two types of responses are important components of how the entire Indonesian population of firms would respond to PLN tariff changes. It is difficult to include in the simulations the behavior of the exclusive PLN users. The main reason for this is that we do not know what kind of embedded power technology such firms might install (if at all) and we also
an area-level model, predictions from a household “unit-context” model specified with area-level predictors were equally accurate in sample, but markedly more accurate out of sample. In sum, there is a clear ranking in accuracy across the different methods for incorporating the geospatial auxiliary data: The household model was usually more accurate, and never noticeably less accurate, than the two other methods. This was generally followed by the sub-area model estimates, and finally the area-level model estimates. There is no evidence in any of the settings considered that area-level models give estimates that are more accurate and efficient, or less biased, than household-level unit-context models.

All geospatial small area estimates, however, remain substantially less accurate than those generated using survey and household census data from 2010. This suggests that municipal poverty patterns changed slowly in Mexico during this time, although the methodology used to generate the 2015 benchmark estimates may also not fully capture transient changes in welfare.<sup>24</sup> The correlation between the 2010 estimates and the 2015 estimates is very high: 0.91 in sample and 0.90 out of sample. Thus, if forced to use municipal poverty estimates to inform a pro-poor budget allocation in 2015, the most accurate source would be the 2010 census-based estimates. Analysts should therefore exercise healthy caution before jettisoning moderately older census-based estimates in favor of newer small area estimates based on geospatial data.

The high accuracy of older census-based estimates, however, may not generalize to other contexts, and more research is needed to better understand under what circumstances older census-based estimates give more accurate predictions than recent geospatial poverty estimates. This will depend on the extent to which regional poverty patterns have changed in the country since the last census, as well as the size of the sample survey and the predictive power of the satellite-based auxiliary data, among other factors. It is also likely that the predictive performance of geospatial and other big data will continue to improve as more and better indicators become available.

In addition, surveys can also be fielded in ways that better facilitate integration with geospatial data. For example, increasing the number of households interviewed in each enumeration area would likely further improve the predictive performance of the model and the accuracy of the estimates. Likewise, given the better performance of all small area estimates when predicting in-

> 24 This is partly because the model used to generate the official 2015 estimates of municipal poverty rates utilized data from the 2010 census as predictor variables.

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Arellano, M., O. Bover (1995), “Another Look at the Instrumental-Variable Estimation of ErrorComponents Models,” Journal of Econometrics, 68 (1): 29–52.

Bernard, Andrew B., Stephen J. Redding, Peter K. Schott (2006), “Multi-Product Firms and Product Switching,” American Economic Review, 100(2): 444-448.

Bertschek, Irene (1995), “Product and Process Innovation as a Response to Increasing Imports and Foreign Direct Investment,” Journal of Industrial Economics, 43(4): 341–357.

Besley, Timothy and Robin Burgess (2004), “Can Labor Regulation Hinder Economic Performance? Evidence from India,” Quarterly Journal of Economics, 119(1): 91-134.

Blundell, R. and S. Bond (1998), “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 87 (1): 115–143.

Breusch, Trevor and Adrian R. Pagan (1980), “The Lagrange Multiplier test and its application to model specification in econometrics,” Review of Economic Studies, 47(1): 239–253.

Chamarbagwala, Rubiana and Gunjan Sharma (2010), “Industrial De-licensing, Trade Liberalization, and Skill Upgrading in India,” forthcoming in Journal of Development Economics.

Chari, Amalavoyal (2009), “Industrial Licensing,” Oxford Companion to Economics in India, Kaushik Basu and Annemie Maertens (eds), Oxford University Press, New Delhi, India.

Chari, Amalavoyal (2010), “Identifying the Aggregate Productivity Effects of Entry and Size Restrictions: An Empirical Analysis of License Reform in India,” Working Paper, Department of Economics, Cornell University.

Chari, Anusha and Nandini Gupta (2008), “Incumbents and Protectionism: The Political Economy of Foreign Entry Liberalization,” Journal of Financial Economics, 88: 633-656.

Criscuolo, Chiara, Jonathan Haskel, and Mathew Slaughter (2010), “Global Engagement and the Innovation Activities of Firms,” International Journal of Industrial Organization, 28(2), pp.191-202 Cohen, Wesley and Steven Klepper (1996), “Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D,” Review of Economics and Statistics, 78(2): 232-243.

Davis, Steven and John Haltiwanger (1992), “Gross Job Creation, Gross Job Destruction, and Employment Reallocation,” Quarterly Journal of Economics, 107(3), pp. 819-863.

Debroy B. and A. T. Santhanam (1993), “Matching Trade Codes with Industrial Codes,” Foreign Trade Bulletin, 24(1): 5‐27.

Foster, Lucia, John Haltiwanger, and Chad Syverson (2008), “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?” American Economic Review, 98(1): 394-425.

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