rafmacalaba/datause-encoder-data huggingface dataset:
41 Table 8. Average effect of the Venezuelan migration shock on educa– tional outcomes Urban areas (marginal effects)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent Var.|Ln(Enrollment)|Promotion Rate|Dropout Rate|Math Std.<br>Score|Language Std.<br>Score|
||**P**|**anel A: Without **|**Controls**|||
|Foreign Males|0.0041***|-0.0205*|0.0112|0.0014|0.0036***|
||(0.0010)|(0.0108)|(0.0078)|(0.0011)|(0.0011)|
|Native Males|-0.0006|-0.0164|0.0079|-0.0004|-0.0000|
||(0.0011)|(0.0113)|(0.0089)|(0.0007)|(0.0005)|
|Foreign Females|0.0038***|-0.0103|0.0079|0.0008|0.0013|
||(0.0010)|(0.0115)|(0.0080)|(0.0015)|(0.0010)|
|Native Females|-0.0008|-0.0130|0.0086|0.0000|0.0002|
||(0.0012)|(0.0113)|(0.0089)|(0.0004)|(0.0004)|
|R-Squared|0.8683|0.2497|0.2033|0.8051|0.7836|
|||**Panel B: With C**|**ontrols**|||
|Foreign Males|0.0040***|-0.0201*|0.0106|0.0014|0.0036***|
||(0.0009)|(0.0105)|(0.0077)|(0.0011)|(0.0011)|
|Native Males|-0.0007|-0.0160|0.0074|-0.0004|-0.0000|
||(0.0011)|(0.0112)|(0.0089)|(0.0007)|(0.0005)|
|Foreign Females|0.0037***|-0.0099|0.0073|0.0008|0.0013|
||(0.0009)|(0.0114)|(0.0080)|(0.0015)|(0.0010)|
|Native Females|-0.0010|-0.0126|0.0080|-0.0000|0.0002|
||(0.0012)|(0.0112)|(0.0089)|(0.0004)|(0.0004)|
|R-Squared|0.8684|0.2502|0.2037|0.8052|0.7837|
|Native Males Mean|4.631|93.74|3.309|0.387|0.214|
|Native Males SD|1.627|10.50|8.572|1.024|0.981|
|Native Females Mean|4.561|95.24|2.847|-0.00142|0.227|
|Native Females SD|1.660|9.275|8.158|0.934|0.974|
|Foreign Males Mean|1.138|91.13|5.880|0.897|0.603|
|Foreign Males SD|1.137|21.80|18.32|1.870|1.755|
|Foreign Females Mean|1.119|92.42|5.545|0.311|0.470|
|Foreign Females SD|1.120|20.45|18|1.694|1.747|
|Observations|253,107|253,107|253,107|95,866|95,866|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
# Table I.13. Robustness Analysis for the Reservation Wage Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.029|-0.223|0.025|0.011|0.015|0.016|0.029|-0.223|0.025|0.011|0.015|0.016|
||(0.068)|(0.224)|(0.070)|(0.065)|(0.086)|(0.064)|(0.068)|(0.224)|(0.070)|(0.065)|(0.086)|(0.064)|
|Obs. left|110|80|108|99|82|103|110|80|108|99|82|103|
|Obs. right|173|92|170|269|187|276|173|92|170|269|187|276|
|Mean values (Non-RAMV)|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.096|-0.133|0.132|0.059|0.104|0.065|0.097|-0.122|-0.051|0.074|0.036|0.086|
||(0.071)|(0.234)|(0.101)|(0.072)|(0.076)|(0.076)|(0.087)|(0.197)|(0.178)|(0.076)|(0.100)|(0.083)|
|Obs. left|123|80|100|105|80|100|123|80|100|105|80|100|
|Obs. right|207|90|135|277|254|279|207|90|135|277|254|279|
|Mean values (Non-RAMV)|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|
|_Panel C: Polynomial of order two_|||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.112|0.089|-0.113|0.099|0.018|0.053|-0.847|0.722|-0.051|0.023|-0.201|-0.065|
||(0.201)|(0.202)|(0.301)|(0.097)|(0.129)|(0.114)|(1.129)|(3.349)|(0.178)|(0.107)|(0.243)|(0.158)|
|Obs. left|105|100|83|105|83|83|105|100|100|105|83|83|
|Obs. right|153|136|105|353|343|359|153|136|135|353|343|359|
|Mean values (Non-RAMV)|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|0.564|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
currently enjoy in terms of access to the U.S. market under the CBI. In addition, Mexico may obtain preferential access to the North America market for products such as textiles and apparel, which are excluded from the CBI. This would leave CA (and others) with negative preferences on those items in the US market.

Recent estimates indicate that, next to Brazil, CA would experience the largest amount of trade diversion from the preferential elimination of tariffs on Mexican goods entering the U.S. market -- nearly 20% of the total trade displacement for the whole of Latin America.14 While this effect is currently small relative to the amount of trade involved, the potential for trade displacement would grow over time, as new investment decisions (and hence future trade flows) would reflect the shift in preferential access to the US market in favor of Mexican exports implied by NAFTA.

Indeed, investment diversion is potentially amore serious problem for Central America than trade diversion per se. Even with preferential access to the US market and generous incentives for investment in export-oriented activities, these countries have had a hard enough time attracting foreign 5 direct investment from the US and elsewhere' . With Mexico now becoming a springboard for unrestricted entry into the US and Canadian markets, whatever locational advantage Central America had as an investment site to serve the North American market will disappear. To be placed at such competitive disadvantage at a time of global capital scarcity, is a matter of great concern to the region. Such concern is well justified. Already, before the NAFTA is even concluded, anecdotal evidence is beginning to accummulate of firms relocating to Mexico or changing their investment plans so as to be advantageously positioned by the time the deal is struck" . Such imminent, though unintended, fallout from NAFTA creates a strong incentive --and a strong case-- for the countries of Central America to seek to obtain assurances from the U.S. that their access to its market would be at least as favorable as that to be provided to Mexico.

In conclusion, the Uruguay Round and policy modifications related to EC 1992 are likely to offer some modest opportunities to CA countries for enhanced market access, especially regarding primary commodities, whereas the 14/ Refik Erzan and Alexander Yeats, "Free Trade Agreements with the United States: What's in it for Latin America?" in Sylvia Saborio, ed., U.S. - Latin America Trade Relations in the 1990s, ODC (forthcoming). The calculation assumes that whatever NTBs may exist would be relaxed so as to let the full effect of the tariff reduction play out.

IS/ USITC Annual Report on the Impact of Caribbean Basin Economic Recovery Act on U.S. Industries and Consumers. Sixth Report, 1990, Washington, D.C., Sept., 1991.

16/ FEDEPRICAP, "A Central American Private Sector Perspective on Some of the Main Issues as Regards NAFTA and the Formation of an FTA between CA and the US", Nov. 21, 1991.

13
# **The Use of Financial Incentives to Prevent Undesirable Behaviors**

Damien de Walque<sup>1</sup> Development Research Group, The World Bank

**Keywords:** Financial incentives, HIV/AIDS, smoking, illicit drugs, alcohol, obesity, early marriage **JEL codes:** I12, I15

> 1 <u>ddewalque@worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the</u> authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
whether directly provoked by changes in trade or not – cannot offset the potentially negative effects for territorial equality of increases in trade in the developing world.

By and large, countries in the developing world are characterized by a series of features that are likely to potentiate the spatially polarizing effects of greater openness to trade. Their higher existing levels of regional inequality, their greater degree of sector polarization, the fact that their wealthier regions often coincide with the key entry points to trade, and their weaker state all contribute to exacerbate regional disparities as trade with the external world increases. And countries in the developing world have a much greater scope for increases in spatial polarization, as their level of international market integration, while growing rapidly, is still a fraction of that of developed countries.

Policy-makers in the developing world – as well as international organizations – may thus need to tread carefully when thinking about the potential implications of greater market openness for their countries. While greater openness to trade is likely to yield rewards in terms of growth and the absolute welfare of local citizens, it may also bring the unwelcome consequence of greater territorial polarization. While, as pointed out in the WDR 2009, this may not necessarily be bad in the short term, enhancing territorial inequality in countries with already high levels of spatial polarization and where territorial differences may pile on top of pre-existent social, cultural, ethnic, and/or religious grievances, can contribute flare up tensions which could ultimately undermine the very economic benefits that trade is suppose to bring about. Hence, it is convenient to bring the territorial implications of trade into the trade policy equation. This may imply trade policies aimed at promoting growth not just focused on generating greater agglomeration, as these can have unintended effects that may ultimately limit their influence on development. A return to ‘spatially balanced growth’ policies may not be in the cards (World Bank, 2009: 5), but many growth policies based on trade may benefit from including a ‘spatially-sensitive’ dimension, if the potential economic benefits of greater openness to trade for countries in the developing world are to be maximized.

26
Table 3: ln(Wage) Regression Results for Pension Eligible and Non-Eligible Wage Earners

||Sample|of “older” wo|rkers|Sample|of “younger”|workers|
|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|
|Assigned to Treatment|0.072**|0.058*|0.072**|0.012|0.014|0.012|
||(0.033)|(0.033)|(0.034)|(0.027)|(0.026)|(0.026)|
|Lagged ln(Wage)|0.343***|0.301**|0.294**|0.649***|0.584***|0.575***|
||(0.123)|(0.123)|(0.121)|(0.049)|(0.051)|(0.051)|
|Individual controls|No|Yes|Yes|No|Yes|Yes|
|Firm level controls|No|No|Yes|No|No|Yes|
|City fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|381|381|381|705|705|705|
|R-squared|0.476|0.499|0.517|0.436|0.462|0.468|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: “Older” workers are those workers within fifteen years of retirement age (men over 45, and women over 40), and who cannot expect to receive the employer contribution as part of their pension. “Younger” workers (men and women who are under 45 and 40, respectively) can expect to receive full benefits at retirement. The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, age squared, gender, education, marital status, number of children), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head level are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

31
# **II. Province-Local** Fiscal **Transfer in Ontario, Canada**

Southern Ontario has a two-tier local government system. The upper-tier municipalities include regions (including Metro Ontario) and counties. The lower-tier municipalities include cities, towns, and townships governed by regions and counties. Northern Ontario has a single-tier local government system (regions and counties). The province is also divided into 10 school boards which are responsible for financing and operating prinary and secondary education. Similar to those **in** the United States, school boards **in** Ontario are independent from the regions and counties.

The province allocates unconditional and conditional transfers to municipalities (regions, counties, cities, towns, townships, and school districts) for both operating (current) and capital expenses. In 1994, transfers from the province accounted for about 32 percent of total municipal revenues. In the same year, the municipalities raised about 38 percent of their revenues from property taxes and 30 percent from fees and user charges (including 12 percent from user fees, 10 percent from special charges, and 8 percent from sewer and water fees).<sup>28</sup> The relative importance of transfer as a source of a municipality's revenue varies significantly depending on several factors.<sup>29</sup> The most important factor is the municipalities' responsibilities. For example, although counties and regions are both upper-tier municipalities, their responsibilities differ greatly (regions fund their own police forces and counties get free police protection). In addition to providing their own police forces, regions also tend to provide more comprehensive social services and health care than counties. The second factor is the revenue capacities of the municipalities. Generally, urban municipalities raise more of their own revenues (and therefore receive less transfers) than municipalities with a lower degree of urbanization. In 1988 counties received 37.8 percent of their current revenues from provincial transfers. Metro Toronto (with a high proportion of urban population), on the other hand, received just 23.1 percent of its revenues in the form of transfers.

The third factor affecting the distribution of transfers is whether a municipality is located in the north. Different patterns of provincial support are also evident in comparing the north with the south. For example, in 1988, transfers account for 27.4 percent of total current revenue for county cities (those in the south), but 42.6 percent in district cities (those in the north). Expressed in dollars per household, transfers to county cities were about $713 per household, while the corresponding amount to district cities was about $1,390 percent household.

# **Conditional** Grants

28 Ernie Eve, Q.C.,"1995 Fiscal and Economic Statement," 1995, Ministry of Finance of Ontario.

29 Based on "Report of the Advisory Committee to the Ontario Minister of Municipal Affairs on the ProvincialMunicipal Financial Relationship," 1991.

68
period from the analysis. We find that the impacts of tariffs on average wages are positive, similar in magnitude, but not statistically significant; in contrast, the impacts of tariffs on the skill premium remain negative and statistically significant.

Non-tariff barriers were fully eliminated from 1988 to 1991. Indeed, the elimination of quantitative restrictions was a pre-requisite to the negotiations of the common external tariff of Mercosur (Berlinski, 1994; 2003). Thus, an additional robustness check of the link between tariffs and the skill premium is to run the model on the 1992-2001 sample. Active trade policy during this period comprises only tariff changes. Results in column (5) confirm our previous findings. Tariffs have a positive impact on average wages; this effect is highly significant during the 1990s. Further, tariffs impact negatively, and highly significantly, on the skill premium.

A final concern with the analysis is the potential endogeneity of sectoral tariffs to wages (as in a model of political economy or unionization). In our setting, the case for the endogeneity of tariffs is relatively weak because our regressions include a number of control variables that ameliorate this problem, namely time-varying returns to schooling and tenure, individual characteristics, industry effect, time effects, and sectoral trends. The temporal variation in our data is critical to support this claim. For instance, the endogeneity of tariffs caused by sectoral unionization, industry lobbies, or political economy is unavoidable in a cross-section but can be accounted for, to a large extent, with the industry dummies, the time dummies, and the sectoral trends in the pooled historical data. Once we control for all these variables, the level of protection is mostly determined by two factors: the worldwide trend towards trade liberalization and the initial level of protection (so that sectors with higher tariffs would face larger tariff cuts, on average).<sup>11</sup> We argue that these two factors can reasonably be thought of as exogenous to the level of current wages in our econometric models. Moreover, we claim that pursuing an instrumental variable approach would be necessarily weak given the impossibility of finding reasonable instruments due to the nature of our empirical exercise (which spans thirty years of Argentine recent history). Instead, we exploit here the comparison of the breaks in the trends in tariff reforms and the breaks in

> 11See Goldberg and Pavcnik (2005a)

15
Table 6: Robustness check for the effect on savings: Controlling for the proportion of sons

|**Dependent variable:**||**Househol**|**d Per Capita**|**Saving in **|**2008**||
|---|---|---|---|---|---|---|
||Saving in fna|ncial institutions|Plus cash|saving|Plus inter|est earned|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Firstborn girl_ * _Expected dowry_|415.241**|556.159*|388.075*|479.043*|497.124**|609.212*|
||(188.352)|(273.204)|(186.939)|(252.785)|(225.458)|(301.575)|
|_Expected dowry_|64.385||-91.194||-103.773||
||(305.886)||(314.327)||(335.142)||
|_Firstborn girl_|-489.111|-641.473|-1,206.605|-1,246.842|-2,162.101|-2,324.344|
||(1,691.462)|(3,291.391)|(1,555.138)|(3,074.922)|(1,923.838)|(3,480.122)|
|N|||2,840||||
|**X**_i_ + Proportion of sons|x|x|x|x|x|x|
|Caste FE|x|x|x|x|x|x|
|YOB FE|x|x|x|x|x|x|
|State FE|x|x|x|x|x|x|
|State*YOB FE|x|x|x|x|x|x|
|Caste*YOB FE|x|x|x|x|x|x|
|Caste*State FE|x|x|x|x|x|x|
|Firstborn girl*YOB FE|x|x|x|x|x|x|
|Firstborn girl*State FE|x|x|x|x|x|x|
|Firstborn girl*Caste FE|x|x|x|x|x|x|
|Caste*State*YOB FE||x||x||x|

NOTES: The only difference between this table and Table 3 is the addition of a new control: the proportion of sons.
of processed food crops remains unchanged over the course of this century. The consumption of ecosystem services declines in the near term decades as a result of deforestation. However, the consumption of ecosystem services subsequently increases with greater demand for recreation and continued growth in protected forest areas. Nonetheless, the consumption of ecosystem services in 2100 is still lower than their corresponding levels in 2004 an indication of the intense competition for land-based services in our baseline scenario.

The lower right-hand panel of Figure 1 hightlights the results for consumption of biofuels. The consumption of rst generation biofuels grows slowly as oil prices and agricultural yields increase. However, along this optimal path, rst generation biofuels do not become a signicant source of energy consumption. In 2100 the consumption of rst generation biofuels is 13 Mtoe, considerably higher than in 2004, but still small in relative terms. Second generation biofuels become competitive around 2035 and rapidly expand reaching 300 million toe in 2050, and 500 million toe in 2100. The share of biofuels in total liquid fuel consumption steadily increases, reaching its maximum of 36 percent in 2075. Further expansion of biofuels is crowded by even greater expansion of animal feed, and the share of biofuels in total liquid fuel consumption declines to about 30 percent in 2100. This is of comparable magnitude to ndings in recent economic studies on bioenergy and land use (Gurgel et al. 2007, Chakravorty et al. 2011, Popp et al. 2011).<sup>17</sup>

# 4 Counterfactual Scenarios

Private and public land allocation decisions must be made despite signicant uncertainty about the future productivity of land in dierent uses, as well as the future valuation of environmental services from this land, including biodiversity and carbon sequestration. This uncertainty is particularly problematic in light of the fact that some of the decisions are irreversible (e.g., cutting down natural forests, extraction and combustion of fossil fuels) and others take considerable time to reverse (e.g., harvesting a mature forest). Though we do not explicitly incorporate uncertainty in the model's optimization stage, we do examine the ways in which global land use responds to changes in factors corresponding to the

> 17Direct comparison of model predictions of biofuels penetration is dicult due to considerable uncertainty in variety of factors, such as, e.g., evolution of biofuels' production technologies, land access costs, yield growth rates, and energy demand projections. We show model sensitivity to these factors in counterfactual simulations below, and in technical appendix, section E.1.

23
capital income by making use of technical reserves, which allowed not only financing current expenditures but also maintaining a positive financial result. However, this implies a reduction in the fund accumulated by the system. Based on IESS-provided information, technical reserves diminished from USD 9 billion in 2015 to approximately USD7 billion in 2017.

From the expenditures point of view, recently a steady increase has been noticed. This trend relates directly to two factors: Firstly, an increase in coverage, as described above, and secondly, a sustained increase in the benefit level.

In 2010, the approval of _Ley Cero_ [Zero Law] established an automatic mechanism for updating benefits, which resulted in increases exceeding the inflation rate (Table 2). For that reason, between years 2011 and 2015, the average increase in benefits climbed up to 9% (Figure 13). This, in addition to an increase in coverage, explains the increase in total expenditures. However, in 2015, the inflation rate directly replaced this mechanism for updating benefits. Based on this new rule, benefits experienced an annual increase on average of 3% between 2016 and 2018.

Table 2 Legal increase of contributory <u>pensions up to 2015</u>

|Benefit Amount<br>(as a percentage of basic<br>minimum wages)|Legal Variation<sup>(*)</sup>|
|---|---|
|Lower than 50%|16.16%|
|50-100%|12.41%|
|100%-150%|9.53%|
|150%-200%|7.31%|
|200%-250%|5.61%|
|250% and more|4.31%|

Source: IESS Note: (*) Variation of the benefits established by _Ley Cero_ Figure 13. Benefit on Average Years 2011-2018 <!-- Start of picture text --> 700<br> 600<br> 500<br> 400<br> 300<br> 200<br>2011 2012 2013 2014 2015 2016 2017 2018<br> Source: IESS<br>USD<br><!-- End of picture text --> Regarding social pensions, the cost of the non-contributory program is financed by means of income coming from general income or specific funds, and during 2016 it accounted for 0.3% of the GDP.

The financial sustainability of the social security system is an important challenge that has to be considered in the medium and long terms. The sum of resources that the Ecuadorian community allocates to social security (adding up IESS and BDH costs) accounts approximately for 4.6% of the 21