rafmacalaba/datause-encoder-data huggingface dataset:
develops and life insurance becomes attractive for life cycle planning, including pension-like products. In many developed countries, the life sector plays a critical role in social protection, as public schemes mature and fiscal pressures mount. (Annex Table 3 provides a stylized description of the stages of development).

**The breakdown of insurance premiums in MENA by business lines only partly conforms to**

**the typical pattern of development.** Table 3 provides a breakdown of non-life premiums, showing that transportation related insurance (Marine, Aviation and Transit or MAT) accounts on average for a small share of the totalnon-life premium and is broadly unrelated to income levels.  Motor insurance account for the largest share of the insurance business in most countries but also bears little relationship to incomes. Construction related insurance tends to be higher in the oil and gas rich countries, but there is no consistent pattern.  Life insurance (not shown in Table 3) is undeveloped, especially in the higher income countries, also implying a deviation from usual patterns.

**Table 3: Breakdown of Non-Life Premiums by Major Line of Business, 2008**

|||**Breakdown of Non-Li**<br>|**fe Premium (% of total)**<br>||
|---|---|---|---|---|
||**Motor**|**Property and**<br>**Construction**|**Maritime, Aviation**<br>**and**<br>**Transit(MAT)**|**Other**|
|**Algeria**|47|42|9|1|
|**Bahrain**|48|39|7|7|
|**Egypt**|33|25|32|10|
|**Jordan **|60|20|10|10|
|**KSA **|46|26|18|10|
|**Kuwait**|40|15|16|29|
|**Lebanon **|51|19|9|21|
|**Libya**|31|41|21|6|
|**Morocco**|56|12|7|25|
|**Oman **|54|19|9|18|
|**Qatar **|21|31|14|34|
|**Syria**|65|19|12|6|
|**Tunisia**|59|9|10|22|
|**UAE**|35|13|15|37|
|**Yemen**|33|20|22|25|
|**MENA**|45|23|14|17|
|**GCC**|41|24|13|23|
|**Non-GCC**|48|23|15|14|
|**Oil**|43|27|13|16|
|**Non-Oil**|49|18|15|19|

Source: AXCO Reports

**Motor insurance accounts for the largest share of the non-life premium, but MENA countries still collect small aggregate motor vehicle premiums considering the size of their car fleets.** As shown in Figure 7, motor premiums are well below the levels that would be predicted by the number of cars (scaled by the population), with the exception of Jordan, Morocco, and Tunisia.  All but the least developed MENA countries have introduced mandatory Motor Third Party Liability Insurance – MTPL (Annex Table 3).

10
# **References**

- _Poor Economics: A Radical Rethinking of the Way_

- _to Fight Global Poverty_ . Public Affairs. 1

- Bennett, D. (2012). Does clean water make you dirty? water supply and sanitation in the philippines. _Journal of Human Resources 47_ (1), 146–173. 1

- Bertrand, M., S. Mullainathan, and E. Shafir (2006). Behavioral Economics and Marketing in Aid of Decision Making among the Poor. _Journal of Public Policy & Marketing 25_ (1), 8–23. 1

- Cohen, J. and P. Dupas (2010). Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment. _The Quarterly Journal of Economics 125_ (1), 1–45. 1

- Cutler, D. M. and J. Gruber (1996). Does public insurance crowd out private insurance? _The Quarterly Journal of Economics 111_ (2), 391–430. 1

- Das, J., S. Dercon, J. Habyarimana, P. Krishnan, K. Muralidharan, and V. Sundararaman (2011). School Inputs, Household Substitution, and Test Scores. _National Bureau of Economic Research (NBER) Working Paper 16830_ . 1

- De Paula, A., G. Shapira, and P. Todd (2011). How Beliefs about HIV Status Affect Risky Behaviors: Evidence from Malawi, Seventh Version. _Penn Institute for Economic Research (PIER) Working Paper 11-033_ . 1

- Delavande, A. and H.-P. Kohler (2009). Subjective Expectations in the Context of HIV/AIDS in Malawi. _Demographic Research 20_ , 817–874. 1

- Dupas, P. (2009). What Matters (and What Does Not) in Households’ Decision to Invest in Malaria Prevention? _American Economic Review 99_ (2), 224–30. 1

- Dupas, P. (2011a, January). Do teenagers respond to hiv risk information? evidence from a field experiment in kenya. _American Economic Journal: Applied Economics_ (3), 1–34. 1

23
# **4.3 Global Financial Cycle and Capital Flow Measures**

Rey (2015) suggests that “global financial cycles” exist whereby monetary and financial conditions in major economies, most notably the United States, are transmitted internationally to advanced, emerging market, and developing countries. The phenomenon raises important questions about the mechanisms through which the transmission occurs; the role of exchange rates in insulating the economies from the impact of these cycles; scope for monetary policy (and fiscal policy) autonomy; and the role of capital controls and prudential measures in ensuring economic and financial resilience in the face of capital flows induced by such global financial cycles.

Next, we analyze the use of CFMs over the global financial cycle. We ask if the use of CFMs, or other policies coincides with global economic cycles. We proxy the global economic cycle by the cyclical component of GDP of G7 countries (US, Japan, Germany, France, UK, Italy and Canada) and analyze the response of policy variables to global economic conditions, using the regression equation in (1).<sup>26</sup>

**Table 12: Global Economic Cycle and Policy Tools**

||(1)<br>**C**|(2)<br>**apital Flow**|(3)<br>**Measures o**<br>Non-|(4)<br>**n**<br>Non-|(5)|(6)|(7)|(8)<br>Real|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Resident<br>Inflows|Resident<br>Outflows|Resident<br>Inflows|Resident<br>Outflows|Interest<br>Rates|Macroprud<br>Regulation|Exchange<br>Rates|<br>effective<br>exch rate|FX<br>Reserves|
|Cyclical comp. of|0.243|0.602|0.483|0.070|14.030***|3.059***|-1.784***|0.635**|2.082***|
|G7 real GDP|[0.71]|[1.41]|[1.53]|[0.28]|[8.03]|[3.17]|[7.48]|[2.46]|[5.01]|
|Constant|0.021***|0.038***|0.019***|0.007***|-0.086***|0.068***|0.001***|-0.000**|-0.001***|
||[192.44]|[281.64]|[194.30]|[91.13]|[227.09]|[385.63]|[7.48]|[2.46]|[5.01]|
|Observations|3290|3290|3290|3290|2379|1680|3132|2479|3177|
|R-squared|0.000|0.001|0.001|0.000|0.060|0.007|0.066|0.013|0.027|
|# of countries|47|47|47|47|39|28|46|36|47|

_Notes_ : Dependent variables in Columns 1-4 are indices of CFM on respective type of flow. The indices take value of -1/0/1 (Tightening/no change/Easing). The dependent variable in Column 5 is a binary variable of policy rate changes. A value of -1/1 indicates a reduction/increase in policy rates by at least 25bps over previous quarter. Dependent variable in Column 6 is a measure of macroprudential regulation from Cerruti et al. (2017). A value of 1 indicates a tightening of restrictions and a value of -1 an easing. The dependent variables in Column 7-9 are the cyclical components of log nominal exchange rates, log real effective exchange rate and log foreign reserves, respectively. The RHS variable is the cyclical component of G7 countries’ real GDP. Regressions include country fixed effects. Robust t statistics are in parenthesis. *, ** and *** indicate that coefficients are significant at the 10, 5 and 1 percent level.

> 26 Results are very similar when we use the cyclical component of US GDP instead. The correlation of the output gap of G7 countries with the US output gap is 0.92.

23
- 27 -

independent of employment in return for a "low" wage will present problems for the union in terms of being able to allocate the lump-sum payments to its members.

- 8/ See Oswald (1982) for a formulation that includes leisure in the union's utility function.

- 9/ This is in constrast to Ballard et al. (1985) whose own elasticities were all unity and where cross-substitution effects between composite commodities are not allowed. In addition, our formulation allows

labor supply (including the parameter MAXHOURS) to be written in terms of econometrically estimated elasticities rather than arbitrarily assuming a value for MAXHOURS. Ballard et al. (1985), who chose the latter approach, noted that their results were heavily dependent on assumed value for MAXHOURS, for which there is little data.

- 10/ Elasticity estimates draw on Pencavel (1986) and especially Abbott and Ashenfelter (1976) for males and Killingsworth (1983), Killingsworth and Heckman (1986) and especially Mroz (1987) for females.

- 11/ For steel we draw on Tarr (1984) and for autos on Winston and Associates (1987) and Fr;edlander, Winston and Wang (1983). The corresponding costdisadvantage ratio (CDR) values are reported in table 1.

- 12/ A natural extension is to recognize that conjectures are themselves dependent on the number of firms. Sensitivity simulations with this extended formulation yield slightly larger benefits from trade liberalization but similar results for the pattern of wage distortions and are available from the authors on request.

- 13/ We assume there are three auto firms initially in calibrating conjectures.

- Given data on prices, costs and elasticities only the ratio of 0 to N is identified. Thus, an equivalent approach is to read in Cournot conjectures and calibrate for N, the Cournot equivalent number of firms. =

- This is the approach followed by Dixit (1988) who obtains a value of N 13.8 for 1983. Our calibration yields a value of N = 4.2, indicating a less competitive industry in 1984. An alternative -- but in our view less appealing -- approach is to solve for marginal costs or for the demand

values for 0, N and the zero profit condition. See Devarajan and Rodrik (1989) for another example along the lines followed by Dixit.

- 14/ The specification of foreign trade is as in de Melo and Robinson (1989). CES and CET formulations were developed by Armington (1969) and Powell and Gruen (1968). The assumption of product differentiation between domestic and export sales is of little consequence in view of the low export shares of the auto and steel industry. We choose the small country assumption. For an alternative large country formulation, see de Melo and Tarr (1990a).
Table 8: Fixed-effects regression results for employment elasticity of growth: diversification

|Variables|ECA|WE|PECA|PWE|EU10|BalTur|CIS|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Export diversification||||||||
|Output<br>Coeff.|0.081|0.519|0.046|0.313|0.110|-0.058|0.073|
|t-stat.|2.2|8.0|2.9|4.6|1.5|-0.3|2.2|
|Other variable<br>Coeff.|0.014|0.012|-0.003|0.011|0.012|-0.026|0.000|
|t-stat.|2.0|1.6|-1.2|1.1|0.6|-0.5|0.0|
|N|337|208|873|195|130|64|143|
|R2|0.25|0.60|0.22|0.60|0.35|0.26|0.53|
|Natural resource rent||||||||
|Output<br>Coeff.|0.125|0.408|0.029|0.037|0.234|0.027|0.084|
|t-stat.|3.8|7.1|2.4|1.8|3.7|0.2|2.7|
|Other variable<br>Coeff.|0.000|0.000|0.000|0.000|0.002|0.004|0.000|
|t-stat.|-1.3|0.4|0.5|0.2|0.7|0.9|-0.8|
|N|413|285|1435|304|150|83|180|
|R2|0.29|0.52|0.23|0.52|0.49|0.26|0.50|
|Agricultural employment||||||||
|Output<br>Coeff.|-0.007|0.282|0.959|0.569|-0.062|0.036|0.174|
|t-stat.|0.0|0.7|0.2|0.5|-0.3|0.1|0.8|
|N|283|281|425|177|143|47|93|
|R2|-0.02|0.00|-0.05|0.00|0.23|0.29|0.33|
|Industrial employment||||||||
|Output<br>Coeff.|0.328|0.899|0.762|0.512|0.624|0.034|0.057|
|t-stat.|2.4|6.6|4.0|3.2|6.9|0.0|0.3|
|N|283|281|425|177|143|47|93|
|R2|0.16|0.43|0.22|0.74|0.69|0.35|0.41|
|Service sector employment||||||||
|Output<br>Coeff.|0.649|0.153|0.166|0.241|0.292|-0.208|1.412|
|t-stat.|2.4|1.7|0.9|2.8|5.2|-0.6|1.9|
|N|283|281|425|177|143|47|93|
|R2<br>Note: The dependent variable is em<br>parameter estimate of output grow<br>fixed-effects.|0.00<br>ploymen<br>th. All reg|0.17<br>t growth<br>ressions|0.01<br>. The out<br>also incl|0.42<br>put coeffi<br>ude count|0.45<br>cient is the<br>ry and year|0.43|0.26|

19
**Table 5. Statistical impact of SHG membership on capability indicators for scheduled castes/tribes**

|||Nearest-n<br>|eighbours<br>||Kerne<br>|l-based ma<br>|tching<br>|
|---|---|---|---|---|---|---|---|
|Outcome|AT|E|AT|T|ATE|A|TT|
||Coef.|z-stat|Coef.|z-stat|Coef.<br>|Coef.|t-stat|
|Health limits activities**|0.0899|2.43|0.0820|2.02|0.1090|0.1107|3.40|
|Able to meet socially|0.0297|1.03|0.0221|0.68|0.0398|0.0386|1.75|
|Lost sleep from worry (mental health)|0.0456|1.61|0.0422|1.37|0.0379|0.0367|1.48|
|Able to enjoy recreation**|0.0882|2.57|0.0903|2.37|0.0975|0.0934|3.44|
|Own home**|0.0684|2.35|0.0788|2.46|0.0434|0.0459|1.83|
|Accommodation suitable|0.0088|0.28|0.0058|0.17|0.0279|0.0296|0.91|
|Feel safe walking**|0.1004|2.52|0.1029|2.32|0.1229|0.1224|3.87|
|Risk of future assault|0.0016|0.04|-0.0045|-0.11|0.0276|0.0284|1.04|
|Risk of future discrimination|0.0265|0.79|0.0242|0.64|0.0222|0.0219|0.88|
|Influence local decisions***|0.1059|2.78|0.1073|2.56|0.0931|0.0948|2.90|
|Freedom of political and religious<br>expression**|0.0893|2.40|0.0815|1.99|0.1097|0.1108|3.39|
|Support of family and friends|-0.0279|-0.68|-0.0348|-0.76|0.0023|0.0016|0.05|
|Free to live life***|0.1374|3.39|0.1457|3.22|0.1189|0.1234|3.78|
|Freedom of creative expression***|0.1358|3.25|0.1206|2.59|0.1484|0.144|4.45|
|Interesting work**|0.0686|2.00|0.0606|1.63|0.1120|0.1156|3.65|
|Note: See notes for Table 5; n=1841||||||||

44
14-a FIGURE 5: UMOA: MONEY MARKET INTEREST RATE <!-- Start of picture text --> la -<br>la<br>12<br>10<br>9<br>a<br>o 7<br>a<br>4<br>.3<br>0. 2<br>-1<br>-2<br>i175 1a76 1977 1978 1979 19a0 1981 19i 1983 1984 19 19M 1987<br>TdM and Olffen*ial wRh Pari<br><!-- End of picture text -->
# **Table 7 Use of Consumer Credit**

||**Percentage**<br>**among the**<br>**banked**|**Percentage**<br>**among the un-**<br>**banked**|
|---|---|---|
|Have amajorcredit card|55.9|7.8|
|**In past 3 years, looked for a loan from a:**|||
|Bank|19.3|5.9|
|Finance company|6.0|2.8|
|Mortgage company|4.9|0.3|
|Familymember|6.7|16.6|
|Friend|4.5|8.4|
|Cardealer|6.5|2.3|
|Retailstore|1.7|1.9|
|Individualwho chargesinterest|0.7|0.4|
|Paydaylender|0.2|0|
|**In past 3 years, used a:**|||
|Credit card cashadvance|15.7|1.2|
|Installment or layaway purchase plan|10.0|9.9|
|Pawnshop|2.3|2.9|
|Rapid tax refund|5.1|4.2|
|Rent-to-ownstore|0.7|1.2|
|Loan fromarotating credit society|0.3|0.3|
|Could borrow $500for3monthsif needed|67.7|50.5|

A striking finding in Table 7 is the low percentages of individuals who reported that they used a pawnshop or payday lender.<sup>8</sup> This is likely a misleading indication of the use of these credit sources in lower-income urban areas generally. New York sets a severely binding interest rate ceiling on its pawnshops, so pawnshops in New York City are scarce and generally take only jewelry as collateral. In addition, New York regulations do not permit payday lending to be profitable. Although payday lenders were reasonably well-represented in Los Angeles in 1998, they have grown explosively in California and many other states since that time. Undoubtedly, a contemporary survey would find much heavier use of payday lenders than is suggested by the OCC survey.

> 8 Payday loan offices commonly make two-week loans for amounts between $100 and $500.  Typically, the borrower needs an advance to meet expenses until his or her next payday.  At the time of the advance, the borrower writes a personal check to the lender who agrees not to submit it for payment until the borrower’s next payday at which time the borrower’s account will presumably have sufficient funds to pay the check.  Frequently, borrowers choose not to repay the loan at the next payday and instead renew it.  In this way, a two-week loan can become a six- to eight-week loan.

> There were very few payday loan offices in the country prior to 1995, but by 2003 there were well over 10,000.  Many belong to large chains that operate across multiple states.  Some payday lenders also function as check-cashing outlets but others dedicate themselves only to payday lending.

20
|**Table 5.3. Multiple Convergences Est**|**imates**|LFPR, Pay-G|ap and Sectoral|Complete|Convergence|
|---|---|---|---|---|---|
|||7.1|7.2|7.3|7.4|
||Baseline|Closure|Closure+|Closure|Closure+|
|**1. GDP**||||||
|1.1. GDP (in USD)|$4,049,744,636|$4,514,124,396|$4,438,326,790|$5,007,472,629|<br>$4,943,569,613|
|1.1.1. Growth (in %)||11.47%|9.60%|23.65%|22.07%|
|1.2. GDP per capita (in USD)|$800.71|$892.53|$877.54|$990.07|$977.44|
|1.2.1. Growth (in %)||11.47%|9.60%|23.65%|22.07%|
|1.3. GDP per worker (in USD)|$2,125.44|$2,215.82|$2,178.61|$2,428.51|$2,397.52|
|1.3.1. Growth (in %)||4.25%|2.50%|14.26%|12.80%|
|**2. Women's Contribution**||||||
|2.1. in USD, bil.|$1,084,390,549|$1,624,414,351|$1,586,438,641|$1,873,707,498|<br>$1,841,654,912|
|2.2. of GDP (in %)|26.78%|35.99%|35.74%|37.42%|37.25%|
|2.2.1. Growth (in p.p. %)||9.21%|8.97%|10.64%|10.48%|
|2.3. of Labor Output (in %)|39.04%|50.10%|50.10%|50.16%|50.16%|
|2.3.1. Growth (in p.p. %)||11.06%|11.06%|11.12%|11.12%|
|**3. Sectoral Value Added**||||||
|3.1. traditional, female (in USD)|$647,951,018|$812,510,752|$833,158,308|$638,402,833|$657,565,149|
|3.1.1. Growth (in %)||25.40%|28.58%|-1.47%|1.48%|
|3.2. modern, female (in USD)|$436,439,531|$811,903,599|$753,280,333|$1,235,304,665|<br>$1,184,089,763|
|3.2.1. Growth (in %)||86.03%|72.60%|183.04%|171.31%|
|3.3 traditional, male (in USD)|$735,858,976|$809,219,818|$829,783,745|$634,376,428|$653,417,887|
|3.3.1. Growth (in %)||9.97%|12.76%|-13.79%|-11.20%|
|3.4. modern, male (in USD)|$957,620,009|$808,615,124|$750,229,301|$1,227,513,601|<br>$1,176,621,711|
|3.4.1. Growth(in %)||-15.56%|-21.66%|28.18%|22.87%|
|_Source:_Authors’ simulation based on exp|anded version of CKW|2017 model.||||

39
# **4. The empirical model**

In our empirical analysis we aim to quantify the effect of different factors affecting the satisfaction with the reform before and after its implementation. The analysis before implementation explores the role of three main factors of interest: (i) the level of information about the reform (variable ’Information’), (ii) whether the respondent trusts the government’s ability to deliver the subsidy (variable ’Delivery’), and (iii) the political partisanship of the individual (variable ’Partisanship’). ’Information’ is a dummy variable taking the value of ’1’ if the respondent declares to be ’informed’ or ’well informed’ about the upcoming reform. ’Delivery’ captures the expectations of getting the subsidy conditional on qualifying for it, in other words conditional on a level of monthly electricity usage below the threshold. Finally, ’Partisanship’ is a dummy variable taking the value of ’1’ if the respondent is a voter of FMLN, President Mauricio Funes’ party. In order to avoid endogeneity issues (the consensus towards FMLN could be endogenous to the satisfaction about the reform), we consider the preference expressed at the 2009 general elections, before the reform was announced.

Our dependent variable is a dummy taking the value of ’1’ if the respondent expressed a view that the proposed reform was either a ’very good’ or ’good’ idea and ’0’ otherwise. We do not consider individuals without a defined opinion of the reform (those answering ’NS/NR’) and drop them out of the regressions. This choice reduces - although only marginally - the statistical power of our estimates but it does not affect the significance of our results. In robustness checks we relax this assumption and show the results are insensitive to this choice. In the analysis after implementation we have a slightly different set of explanatory variables. One may expect that the level of information about the reform would increase significantly once it is implemented. Using the September 2013 survey we can observe directly how well-informed about the nature of the reform are individuals and whether this is relevant for the individuals’ satisfaction with the reform. We consider ’informed’ the respondents correctly reporting the electricity consumption threshold to qualify for the subsidy.<sup>8</sup> The variable ‘Delivery’ now captures whether the respondent effectively gets the subsidy or not. The variable ‘Partisanship’ remains unchanged. ‘Delivery’ and ‘Partisanship’ are available for all five surveys after implementation.

> 8In robustness checks exercises (not reported in the paper but available upon request) we allow for a margin of error (up to 5US$) in the responses and show that our results are fully robust in this dimension.

18
Table 7: Simulated changes in poverty rates in four migration scenarios estimated under <u>the different assumption.</u>

|**Migration scenarios**|**Actual**|**No**<br>**migration**|**Level of**<br>**migration**<br>**as in 1995-**<br>**96**|**+10% point**<br>**increase in**<br>**domestic**<br>**migration**|**+10% point**<br>**increase in**<br>**international**<br>**migration**|
|---|---|---|---|---|---|
||||_Poverty ra_|<br>_te(%)_||
|Preferred secification||||||
|p<br>(from Table 5)|**30.0**|**33.6**|**31.9**|**27.3**|**29.5**|
|_Alternative specifications_||||||
|Assuming independence of<br>error terms in (1-3)<br>[Tables A3.1 and A4.1]|30.1|31.1|30.8|29.9|30.1|
|Including amounts of<br>remittances (not instrumented)<br>[Tables A3.2 and A4.2]<br>Including amounts of<br>remittances instrumented by<br>age of a sender<br>[Tables A3.3 and A4.3]|29.0<br>29.5|33.5<br>33.6|30.4<br>30.5|26.9<br>27.2|29.4<br>29.0|
|Unrestricted Sample<br>(3,874 households)<br>[Tables A3.4 and A4.4]|30.0|34.3|32.0|28.0|29.2|
|Treating India as domestic<br>destination<br>[Table A3.5 and A4.5]<br>Assuming equal returns in 3<br>states of migration in<br>consumption equations<br>[TableA3.6 andA4.6]|29.9<br>30.1|32.7<br>35.8|31.7<br>31.6|28.4<br>28.3|27.2<br>30.8|

Note: that the “actual” poverty rates are simulations based on the model with the initial values of instrumental variables. So, the alternative specifications produce different poverty rates for the simulated “actual” scenario.

38
between 2010 and 2019, including the 30A poverty reduction program (World Bank, 2022). The question of whether poverty alleviation programs truly assist the poor in escaping poverty in Viet Nam is thus of importance for policy makers and various aid donors, regarding both accurate evaluation of past programs and effective inputs for future policies.

Evaluating poverty alleviation programs that focus on disadvantaged areas, however, presents a challenge due to the nonrandom placement of such programs (Ravallion, 2007; Meng, 2013). In Viet Nam, the 30A Program covers the poorest districts that had a poverty rate exceeding 50% in 2006. This threshold enables us to employ a sharp Regression Discontinuity Design (RDD) method combined with the difference-in-difference method, together with district fixed effects regressions, on rich data from the Vietnam Household Living Standard Surveys spanning the period 2004-2020 to rigorously estimate the program effects on household welfare. Our study thus offers more rigorous evaluation of a large-scale poverty reduction program and covers the longest time period that such a program was in operation to date in Viet Nam.<sup>3</sup> Furthermore, beyond offering new evidence from Viet Nam, to our knowledge, very few existing studies on poverty reduction programs for other developing countries analyze a rich set of outcomes as we do. These outcomes are diverse, ranging from employment (including working rates and work sectors), wages (including incomes from different sources), health insurance (including numbers of health care visits), education (including school enrollment and receipt and amount of school subsidies), poverty measures to household access to credits and loan sizes, and availability of community public services and

> 3 Several other features distinguish our study from these existing studies. While Nguyen _et al._ (2015) employ a difference-in-difference model to analyze household-level outcomes, Phan _et al._ (2016) use a GMM model to analyze province-level outcomes. We employ a more rigorous, combined RDD and difference-in-difference model with district fixed effects and analyze a richer database consisting of household surveys, firm surveys, and nighttime lights data. The time period that we analyze is also (more than) twice longer and more updated than those in the other studies.

5