rafmacalaba/datause-encoder-data huggingface dataset:
- Batista, C., Lacuesta, A., and Vicente, P. C. (2012). Testing the ‘brain gain’ hypothesis: Micro evidence from Cape Verde. _Journal of Development Economics_ , 97(1):32–45.

- Beine, M., Docquier, F., and Rapoport, H. (2008). Brain drain and human capital formation in developing countries: Winners and losers. _The Economic Journal_ , 118(528):631–652.

- Besley, T. and Kudamatsu, M. (2008). Making autocracy work. In Helpman, E., editor, _Institutions and Economic Performance_ . Harvard University Press.

- Borjas, G. J. (2014). _Immigration Economics_ . Harvard University Press.

- Brookings Institution (2023). Syria’s dissolving line between state and nonstate actors.

- Card, D. (1990). The Impact of the Mariel Boatlift on the Miami Labor Market. _Industrial and Labor Relations Review_ , 43(2):245–257.

- Card, D. (2001). Immigrant Inflows, Native Outflows, and the Local Labor Market Impacts of Higher Immigration. _Journal of Labor Economics_ , 19(1):22–64.

Carter Center (2024). Venezuela: Monitoring elections.

- Chiovelli, G., Michalopoulos, S., Papaioannou, E., and Regan, T. (2023). Illuminating Africa. _Institute for International Economic Policy Working Paper Series_ , 3(5).

- Chiovelli, G., Michalopoulos, S., Papaioannou, E., and Regan, T. (2026). Illuminating the Global South. _The Economic Journal_ , page ueaf134.

- Chiovelli, G., Michalopoulos, S., Papaioannou, E., and Sequeira, S. (2021). Forced displacement and human capital: Evidence from separated siblings. _National Bureau of Economic Research Working Paper Series_ , (w29589).

- Colombian Spatial Data Infrastructure (2023). Colombia shapefiles.

- Consejo Nacional Electoral (2023). Resultados electorales 1998–2018.

- Datosmacro (2024). Venezuela - producci ́on de petr ́oleo.

44
<!-- Start of picture text --> 2<br>rey<br>oO2<br>2<br>Es<br>fy=c<br>“<br>°<br>Feb'20 Apr Jun Aug Oct Dec —Feb'21 Apr Jun<br>—e— Estimated mean for urban refugees<br>——®— Estimated mean for camp refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> ©&<br>2&<br>2Beo<br>2So5°<br>=<br>rr<br>°<br>Jun'20 Aug Oct Dec Feb'21 Apr Jun<br>—e®— Estimated mean for urban refugees<br>——®— Estimated mean for camp refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>oe2<br>2<br>£<br>=y8c<br>“<br>Jun'20 Aug Oct Dec Feb'21 Apr Jun<br>—e— Estimated mean for urban refugees<br>——®— Estimated mean for camp refugees<br>—®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> e2<br>2°?3o<br>2Lo3<br>=<br>6<br>Feb'20 Apr Jun Aug Oct Dec Feb’21 Apr Jun<br>—e— Estimated mean for urban refugees<br>——®— Estimated mean for camp refugees<br>—®— Mean of urban nationals<br><!-- End of picture text -->
differences in the dependent variable, time frame, and strategies adopted to address the important selection bias problems inherent in these analyses.

As for the dependent variable, many studies, especially those focusing on previous generations of policy-based support, have considered the impact of the World Bank DPLs on broad macroeconomic and public finance indicators.<sup>3</sup> In a recent paper, Smets and Knack (2014) discuss the impact of DPLs on the quality of economic management, as measured by the World Bank’s Country Policy and Institutional Assessment. Like them, we focus on the most recent generation of policy-based support (i.e., all operations approved after the 2004 reform) and refrain from assessing DPLs’ effect on variables representing final outcomes, such as economic growth, macroeconomic stability or poverty reduction. Instead, we look at the impact of DPLs on the business environment.

The _Doing Business_ survey, carried out by the World Bank’s Research Department, provides one of most widely used measures of the ease of doing business. It produces objective indicators of business regulations and the protection of property rights that can either enhance or constrain business activity for firms in about 190 economies going back to 2004. The measures are obtained by surveying experts in each country about the legally required time and costs of regulatory compliance for various aspects of private enterprise, such as starting a business, dealing with construction permits, getting credit, and enforcing contracts. According to these data, OECD high-income economies have on average the most business-friendly regulatory systems, followed by Europe and Central Asia. The Sub-Saharan Africa region continues to be home to the economies with the least business-friendly regulations on average, despite the relatively large improvement recorded for these economies in recent years.

The comparability of quantitative indicators across a large number of economies and over time makes the _Doing Business_ survey a potentially ideal source of data for empirical work aiming to investigate the impact of business regulation both within and across countries. However, the ability of these indicators to capture the real investment climate that most firms actually experience has been questioned on several grounds. For example, Hallward-Driemeier and Pritchett (2015)

> 3 Previous generations of policy-based support addressed areas of macroeconomic stabilization, trade integration, privatization and tariff reforms, among others. Over the past decade DPF in a number of countries has evolved to sector-specific operations focusing on critical areas such as energy, governance or social protection.

2
# **<mark>Table 5: Soil Improvement and Tree Planting</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impac|t by Round|(OLS)|Pooled|Impact|by Round|(SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N<br>(1)|R3<br>(2)|R1<br>(3)|R2<br>(4)|R3<br>(5)|Impact<br>(6)|R1<br>(7)|R2<br>(8)|R3<br>(9)|
|**_Panel A: Plots Controlled by Women_**||||||||||
|Any part of plot fallowed [yes=1]|1,715|.133|-.044<br>(.032)|.026<br>(.026)|-.020<br>(.030)|-.012<br>(.017)|.013<br>(.036)|.062*<br>(.036)|-.047<br>(.048)|
|Any irrigation investment [yes=1]|1,702|.003|-.002<br>(.002)|.002<br>(.006)|.002<br>(.007)|.002<br>(.003)|-.009<br>(.041)|.041<br>(.042)|.021<br>(.044)|
|Any soil investment [yes=1]|1,699|.005|.006<br>(.004)|.040**<br>(.017)|-.004<br>(.006)|.016**<br>(.007)|-.029<br>(.052)|.073<br>(.049)|.043<br>(.053)|
|Trees planted in past year [yes=1]|1,452|.202|.052<br>(.040)|.039<br>(.028)|.031<br>(.051)|.044*<br>(.023)|-.059<br>(.039)|-.001<br>(.031)|-.032<br>(.034)|
|**_Panel B: Plots Controlled by Men_**||||||||||
|Any part of plot fallowed [yes=1]|2,503|.113|-.026<br>(.028)|-.046**<br>(.022)|.031<br>(.044)|-.013<br>(.014)|-.0047<br>(.032)|-.040<br>(.032)|.015<br>(.033)|
|Any irrigation investment [yes=1]|2,463|.005|-.005<br>(.005)|-.0003<br>(.006)|.009**<br>(.004)|.002<br>(.004)|-.000<br>(.009)|.001<br>(.009)|.007<br>(.010)|
|Any soil investment [yes=1]|2,479|.005|.002<br>(.007)|.004<br>(.007)|.013<br>(.014)|.006<br>(.005)|.002<br>(.009)|.006<br>(.010)|_<br>|
|Trees planted in past year [yes=1]|2,116|.267|<br>.012<br>(.042)|<br>.061<br>(.040)|<br>-.002<br>(.041)|<br>.029<br>(.020)|<br>.009<br>(.036)|<br>.082**<br>(.036)|_<br>.001<br>(.044)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

27
_leakage_ using the 80<sup>th</sup> percentile of the per capita consumption distribution. The key qualitative insights of our analysis are robust to alternative thresholds, and a nonparametric regression approach is used to provide visual evidence of the full distributional incidence.

We assess the targeting performance of the UDB against the baseline targeting performance of Jamkesmas and the BLT. More specifically, we consider the performance expected from the use of lists of eligible beneficiaries from the UDB. Focusing on predetermined eligibility based on the UDB rather than on reported receipt of benefits allows us to emphasize the potential for the newly established UDB to improve targeting outcomes, setting aside other program implementation issues that may affect benefit delivery. However, some discrepancy between the expected and actual UDB targeting errors may occur depending on the degree of compliance with the beneficiary lists printed in the capital and provided in the field. This discrepancy should be limited for BLT and Jamkesmas, but we revisit the implications of targeting adherence in our concluding discussion.

We calculate _baseline targeting errors_ by comparing reported (pre-UDB) program receipt to household per capita consumption. These estimates reflect the targeting outcomes expected from a “business-as-usual” policy of continuing to allocate social programs to those households deemed eligible through the previous program-specific targeting system. We then calculate the _expected UDB targeting errors_ by comparing, for households registered in the UDB, actual per capita consumption (from SUSETI) with the PMT scores (from the UDB) used to produce beneficiary lists based on each program’s eligibility threshold. Any household in the SUSETI sample not found in the UDB through the matching process is considered to be a non-recipient.

The comparison of baseline and expected UDB targeting errors reveals the change in targeting performance due to the transition from program-specific targeting to using a single registry for beneficiary selection. Switching to the UDB implies changes not only in which households will receive program benefits but also in the total number of beneficiaries (i.e., program coverage). Therefore, we first present standard undercoverage and leakage measures to assess the overall change in targeting performance between baseline and with the UDB. We then isolate the change expected 14
Contractual savinFs amounted to MUR 54 billion in 2001, equivalent to 41 percent of GDP (Table 1). In 2001, the sector covered the National Pensions Fund, the National Savings Fund, 22 active insurance companies and 1007 occupational pension funds created by statutory bodies and private sector companies (of the latter, 37 were self-administered and registered with the Registrar of Associations; the remainder were either insured or administered by insurance companies).

Funded occupational pension funds accounted for 26 percent of total net assets of contractual savings institutions, the NPF and NSF together represented 46 percent of total net assets, while the non-pension assets of insurance companies accounted for the remaining 28 percent. Public sector institutions, including the NPF, NSF and SICOM were responsible for managing 57 percent of the total, although the operations of SICOM are no different from those of any private sector manager.

The main types of contractual savings institutions exhibit significant differences in their asset allocation policies (Table 2). As a group, contractual savings institutions favor government securities (mostly two-year treasury bills) and housing loans. The NPF invests more heavily in government securities, but is underweight in company shares and foreign assets. The NPF provides substantial indirect support to the financing of the housing market, through its sizable loans to the Mauritius Housing Corporation.

**Table 2: Asset Allocation of Contractual Savings, 2001**

|Percentoftotalassets|Pension<br>Funds|NPF|Insurance<br>Companies|All|
|---|---|---|---|---|
|GovernmentSecurities<br>|21|58|_15_|35|
|Non-GovernmentBonds<br>|3|5|8|6|
|CompanyShares<br>|15|7|17|12|
|Bankdeposits<br>|6|11|9|10|
|HousingLoans<br>|27|8|23|17|
|LoanstoSponsors<br>|4||||
|RealEstate<br>|8|2|6|_5_|
|ForeignInvestment<br>|12|4|9|7|
|Other<br>|4|_5_|13|7|
|Total|100|100|100|100|

Source: Estimated on the basis of data collected by the FSC, NPF and Registrar of Associations Pension funds and insurance companies are more heavily involved in extending direct housing loans and in holding company shares and foreign assets.<sup>2</sup> However, there are large differences in asset allocation policies among individual pension funds and insurance companies, which tend to deviate significantly from the average pattern of their respective sectors.

' The reported data aggregate statistics of individual pension funds. Most funds have financial years ending in December but several report at the end of June and some use other months.

2 The asset allocation of insurance companies differs from the data published by the FSC. This is because official statistics do not divide clearly the various types of assets. The figures reported in Table 2 were estimated from a perusal of the annual reports of all operating companies. The high level of "other assets" is worth noting. To some extent, it reflects "loans to shareholders" among the more closely held companies 3
<!-- Start of picture text --> Election turnout Share of votes for left<br>Year Year<br>Share of votes for center Share of votes for right<br>Year Year<br>Electoral competition<br>Year<br>Figure 3. Parallel Trend Assumption – Mayoral Elections<br>Discrete Treatment Variable<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019 2000 2003 2007 2011 2015 2019<br>2000 2003 2007 2011 2015 2019<br>.15 .15<br>.1 .1<br>.05 .05<br> (95% CI) (95% CI)<br>β β<br>0 0<br>-.05 -.05<br>.15 .15<br>.1<br>.1<br>.05<br>.05<br>β (95% CI) β (95% CI) 0<br>0 -.05<br>-.05<br>.15<br>.1<br>.05<br> (95% CI)<br>β<br>0<br>-.05<br><!-- End of picture text --> _Notes:_ The figure illustrates the results of an event study estimation. We interacted the discrete variable PEP, which takes the value of one in municipalities where there are PEP holders, with mayoral electoral years excluding the 2015 election—the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent a 95% confidence interval.

22
# **Table A.2. Government Spending and Oil Revenues for 1970-2005, levels in constant $**

||**EQ.1**<br>Total Government Expenditure<br>**EQ. 2**<br>Current Expenditure|**EQ. 3**<br>Capital Expenditure|
|---|---|---|
||Breusch-Godfrey Serial Correlation LM Test||
|F-statistic (Prob.)|3.11(0.06)<br>0.83(0.44)|9.82(0.00)|
|Obs*R-sq. (Prob.)|5.86(0.05)<br>1.78(0.41)|13.69(0.00)|
||Chow Breakpoint Test: 1984<br> <br>||
|F-statistic (Prob.)|16.31(0.00)<br>10.29(0.00)|17.70(0.00)|
|Log likelihood<br>ratio (Prob.)|25.30(0.00)<br>17.88(0.00)|26.82(0.00)|

_Note: Data source: IMF World Economic Outlook database. 2. p-values in parentheses._ We therefore ran the three regressions including a dummy term for the period up to 1984 (see Table A.3). We also include the asymmetric adjustment coefficients: a dummy that equals one in years of rising revenue and 0 in years of falling revenue is entered multiplicatively with the oil revenue term. With this definition one should expect a positive coefficient under the VE, and a negative coefficient under the DO hypothesis. The adjustment coefficient is negative and significant in all three equations, supporting the DO hypothesis.

**<u>Table A.3. Government Spending and Oil Revenues in Nigeria: VE versus DO</u>**

||**with Overha**<br>|**ng Variable and 1984 dummy**<br> <br>|
|---|---|---|
||**EQ. 1a**|**EQ. 2a**<br>**EQ. 3a**|
||<br>TotalGovernmentExpenditure|<br>CurrentExpenditure<br> <br>Capital Expenditure|
||1.49|0.88<br>0.56|
|Oil Revenue|(16.6)|(14.74)<br>(10.36)|
||2.69|1.45<br>0.97|
|Constant|(2.11)|(1.70)<br>(1.26)|
|Overhang|0.52|0.31<br>0.23|
|variable|(4.70)|(4.29)<br>(3.56)|
|Dum84*Oil|-0.63|-0.30<br>-0.37|
|revenue|(-5.93)|(-4.36)<br>(-5.82)|
|No. of Obs.|35|35<br>35|
|R-sq. overall|0.92|0.90<br>0.84|
|DW|2.22|2.24<br>1.38|

_Note: Data source: IMF World Economic Outlook database. 2. t-statistics in parentheses._ We then check again if there are any problems with serial correlation in the three regression equations (see Table A.4). The results again come out very strongly, but this time without indications of serial correlation (except for the investment equation).

29
In fact, free trade and imports may well be good substitutes for large numbers of domestic competitors: Scandinavian, and particularly Swedish, industrial policies have for a long time built on this assertion (Hjalmarsson, 1991), although the competition between the few remaining large firms has also been important (Porter, 1990; S6lvell, Zander, and Porter, 1991). The conclusion by Chen (1983, p. 90) from his study of Hong Kong manufacturing, where all industries are either export-oriented or import-competing, is consistent with these arguments: "There are indications that the presence of foreign investment in an industry may have the effect of eliminating wasteful competition ... [without introducing] damaging monopolistic elements into the industry".

Secondly, focusing more closely on technology, there is the classic "Schumpeterian Dilemma" of weighing the static allocative efficiency of competitive markets against the supposed dynamic efficiency of monopolistic and oligopolistic firms. The rate of technical progress can perhaps be higher in concentrated markets, since firms there have internally generated profits to use for R&D, and are generally larger and more able to enjoy economies of scale in R&D. It is also possible that market structure has some impact on what the R&D efforts aim to achieve.

In fact, empirical studies seem to show that market structure affects both the rate and type of technical progress. Looking at the overall rate of technical change, Kamien and Schwartz (1982) summarize a survey of research in industrialized countries by concluding that neither perfect competition nor perfect monopoly, but rather mildly oligopolistic markets, are most conducive to technical progress. Moreover, Katz (1984) and Teitel (1984) in studies of Latin America, and Lall (1980) for India, show that technical change in industries with limited competition largely aims to overcome supply bottlenecks, e.g. by substituting imported raw materials and components, while change in more competitive industries is characterized by costreducing and quality-improving innovations. In these cases, however, limited competition is intimately tied to import-substitution rather than concentration, although there is a certain overlap.

32
|of children age 1-5 in the household between|constraints|
|---|---|
|periods||
|children age 6-1 1: number of children in the<br>household age 6-11|Change in head wife's time<br>constraints|
|*change in school aged children: change in the<br>number of children age 6-11 between periods|Change in head wife's time<br>constraints|
|daughter age 12-17: number of girls in the<br>household age 12-17|more older females may substitute<br>for the mother in market or<br>household work|
|*change in daughters age 12-17||
|son age 12-17 household: number of boys in the<br>household age 12-17|more older males increase<br>household expenses, but they are<br>also potential market earners|
|*change in sons age 12-17||
|* additional child: dummy =1 if there are more<br>own children in the household in period 5 thanl|Additional constraint on head-wife's<br>time|
|fewer children: dummy =1 if there are fewer own<br>children in the household in period 5 than 1|Less constraints on head-wife's time|
|maid present: dummy =1 if a maid is present in<br>the household|less constraints on the head-wife's<br>time|
|number of adults in the hh|Substitution for market work|
|*change in number of adults in the hh|Change in potential substitution|
|**Labor demand**||
|Regional dummies|proxy for regional labor market<br>conditions|
|% LF female: calculated as the percentage of the<br>municipal level labor market that is female|ease of labor market entry for<br>women, crowding out of men|
|city size: calculated from the 1990 census data|proxy for the availability of jobs|
|**Search resources**||
|household income: level of household labor<br>income in period 1|proxy for either wealth or search<br>resources|
|<br>spouse was s-e- dummy: 1 if husband was self-<br>employed in the last job|the spouse has information about<br>employment in a previous job,<br>decreasing current search costs|
|spouse was informal wage:dummy = 1 if husband<br>was an informal wage employee in the last job|<br>the spouse has information about<br>employment in a previous job,<br>decreasing current search costs|
|spouse was formal wage: dummy = 1 if husband<br>was a formal wage employee in the last job|<br>the spouse has information about<br>employment in a previous sector,<br>decreasing current search costs|
|spouse was contract: dummy = 1 if husband was a<br>contract worker in the last job|the spouse has information about<br>employment in a previous sector,<br>I decreasing current search costs|

* a variable that measures change over the **five** periods

38
# **Figure 1: Brazil (PRI**<sup>**2**</sup> **)**

<!-- Start of picture text --> Brazil, 1985 (1) - 2004 (10)<br>20 PRI1 Mean PRI1<br>10<br>1985 1990 1995 2000 2005<br>Probabilities of Regime 1<br>1.0<br>0.5<br>1985 1990 1995 2000 2005<br>Probabilities of Regime 2<br>1.0<br>filtered smoothed<br>0.5<br>predicted<br>1985 1990 1995 2000 2005<br><!-- End of picture text --> Figure 1 depicts the regime probabilities based on PRI<sup>2</sup> for Brazil over high (regime 1) and low (regime 2) exchange rate intervention periods. The identified regimes correspond closely to Brazil’s macro economic performance since the mid-1980s and suggest three episodes of floating (regime 2) for Brazil: the hyperinflation period following the failed 1986 Crucado Plan, the hyperinflationary period preceding the 1995 Real plan and a brief period following the 2002 presidential election, when speculation about Brazil’s presidential succession generated a high degree of uncertainty about macroeconomic stability and pressure on Brazil’s currency.

The Real plan successfully used the exchange rate as an anchor for price stabilization and the peg to the US dollar was abandoned in 1999. While we do not identify a regime switch to floating at the end of this fixed regime, we register a moderate increase in PRI<sup>2</sup> relative to 1999.

Indonesia (Figure 2) shows a clear regime switch to floating following the 1997 Asia crisis and Chile (Figure 3) exhibits a preference for floating since 2000. Appendix 2 contains the graphs for the full sample, along with the dates for the regime changes. The large swings in the German cycle towards the end of the sample are largely due to transfer of reserves to the European central bank.

12