rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.4:** Balance Checks on Citizen Demographics

||Female|Age|Disability|TertiaryEduc.|Not Working|MonthlyIncome|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|New village head|-0.051|-0.330|0.001|0.025|-0.016|-85.046|
||(0.080)|(1.646)|(0.011)|(0.049)|(0.057)|(341.049)|
|Observations|8880|8816|8873|8850|8856|6536|
|Control mean|0.47|34.9|0.025|0.20|0.26|2198|
|Robust p-value|0.451|0.860|0.988|0.609|0.749|0.892|
|MSE-opt. bandwidth|19.4|17.2|26.5|20.3|17.6|18.3|
|Effective obs.|4196|3767|5195|4318|3879|2961|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is: in column 1, a dummy equal to 1 for female citizens; in column 2, the age of citizens in years; in column 3, a dummy for whether the citizen reports having a disability; in column 4, a dummy for whether the citizen has a tertiary level education; in column 5, a dummy for whether the citizen reports not working; in column 6, the citizen’s average monthly income in the last 6 months (conditional on working) and winsorized at the top 1% and reported in thousands of Indonesian rupiah.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

**Table A.5:** Effects on Bureaucrats’ Demographic Characteristics

||Age|Years of education|Gender (female)|
|---|---|---|---|
||(1)|(2)|(3)|
|New village head|1.055|-0.521|-0.152<sup>_∗_</sup>|
||(1.797)|(0.439)|(0.090)|
|Observations|1061|1066|1067|
|Control mean|38.6|13.6|0.28|
|Robust p-value|0.338|0.191|0.055|
|MSE-opt. bandwidth|17.8|20.5|16.1|
|Effective obs.|474|523|437|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). Units of observation are bureaucrats in all columns. The dependent variable is: in column 1, the age of bureaucrats in years; in column 2, years of education; in column 3, a dummy equal to one for female bureaucrats. See Section 4 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors clustered by village in parentheses.

45
Table 4: Patents, Yearly Average per Decade

||**1963-1969**|**1970-1979**|**1980-1989**|**1990-1999**|**2000-2004**|
|---|---|---|---|---|---|
|**Japan**|1147.4|5391.4|12240.2|23709.2|34048.6|
|**USA**|46856.9|46053.1|39359.4|60286.3|86362.2|
|**Israel**|39.4|80.4|179.7|450.1|1002.8|
|**Singapore**|0.6|3.0|6.6|64.7|360.0|
|**Australia**|113.7|220.8|339.8|498.4|858.6|
|**Republic of Korea**|1.3|6.8|52.2|1425.6|3802.0|
|**N**ew **Zealand**|13.6|25.9|49.6|62.1|129.6|
|**China**|4.6|6.9|13.9|57.1|260.8|
|**Scandinavia**|603.1|991.9|1068.0|1415.9|2617.8|
|**OECD**|2109.2|2432.0|2493.0|3847.4|5581.0|
|**EAP**|0.5|1.3|8.6|95.5|281.8|
|**ECA**|5.2|16.3|11.3|7.2|10.0|
|**MENA**|0.5|0.5|0.6|1.0|1.9|
|**SHA**|1.2|1.7|2.0|2.4|2.5|
|**SA**|1.2|1.8|1.3|5.1|28.4|
|**LAC**|76.7|91.0|86.0|161.7|258.4|

_Source: Author’s calculations based on USPTO statistics._

**Note:** LAC: Latin America and the Caribbean; Scand.: Scandinavia, EAP: East Asia and the Pacific; ECA: Europe and Central Asia ; MENA: Middle East and Noth Africa (excluding Israel); SA: South Asia; SHA: Sub-Saharian Africa . All previous groups exclude high income countries..

Table 5: Product Innovation Rates by Region

|Region|Quality-Innovations|Variety-Innovations|
|---|---|---|
|Sub-Saharan Africa|41%|52%|
|East Asia and Pacifc|60%|39%|
|Europe & Central Asia|59%|43%|
|Latin America & Caribbean|33%|56%|
|Middle East and North Africa|36%|16%|

Note: This table shows the composition of innovations per region across two dimensions: quality and variety innovations 26
.}~ **~** ~~~~1 <!-- Start of picture text --> PRIVATE, PUBLIC AND FOREIGN SAVING.<br>tin 1980 pesos as % of GOP)<br>0.25_ ..<br>0.2-<br>0.15 ..<br>IDI_ 0.~~~~~~~e.<br>1980 19B1 1982 1983 1184 19B5198861987 19S881989 1990<br>-m PrivatoWavng -4- PiiEo Sm*g -'K- Foredgi So'hg<br><!-- End of picture text -->

**Figure** 1

<!-- Start of picture text --> PRIVATE, PUBLIC AND FOREIGN SAVING.<br>(in current pesos as X of GDP)<br>0.25<br>0.2-<br>0.1I5<br>0.15<br>50.05.<br>-0.0<br>-0.I<br>19801981 1982 1983t84 195519B6 19871988 1989 1990<br>ye".<br>1- Prtrvoh Stea Pk S4ing -i-Fr.Im Saing<br>Figure 2<br><!-- End of picture text -->
- 5 -

# Table ls SEQUENCE OF THE PARTS OF A COUNTERVAILING DUTY OR ANTIDUMPING CASE IN THE UNITED STATES

|Even<br>Prgli|t<br>minary<br>determinations|RE-'.LT|
|---|---|---|
|1.|Injury<br>Negative|Case ands|
||Positive|Case<br>continues|
|2.|Subsidy or dumping||
||Negative|Case continues|
||Positive|Came continues and suspension of<br>liquidation|
|Fin|al determinations||
|3.|Subsidy or dumping||
||Negative|Case ends and suspension of<br>liquidation is lifted|
||Positive|Case continues, suspension of<br>liquidation continued or initiated|
|4.|Injury||
||Negative|Case ends and suspension of<br>liquidation is lifted|
||Positive|Case ends, CVD or AD order is<br>issued|
- [8] Benabou, R. (1993) Workings of a City: Location, Education and Production, _Quarterly Journal of Economics_ , 108, 619-52.

- [9] Bertinelli, L. and D. Black (2004) Urbanization and Growth, _Journal of Urban Economics_ , 56(1), 80-96.

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- [16] Brueckner, J. and S. Lall (2015) Cities in Developing Countries: Fueled by Rural-Urban Migration, Lacking in Tenure Security, and Short of Affordable Housing, in _Handbook of Regional and Urban Economics_ , vol 5., chapter 21, in G. Duranton, J.V. Henderson, and W. Strange eds., 1399-1455.

- [17] Brueckner, J. and H. Selod (2009) A Theory of Urban Squatting and Land-tenure Formalization in Developing Countries, _American Economic Journal: Economic Policy_ , 1, 28-51.

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33
Table D1: Adjustment in Firm Exit to GDP Decline in Firms’ Destinations Mediated by Firm Characteristics in 2007

||||Firm|exit|||
|---|---|---|---|---|---|---|
||RAIS|||PIA|||
|Firm characteristic base|Firm size<br>(log)|Firm size<br>(log)|Firm labor<br>productivity (log)|Firm TFP<br>L-P (log)|Firm TFP<br>ACF (log)|Firm TFP<br>Wooldridge (log)|
|GFC frm shock*2009|-0.0001|0.0023|0.0170**|0.0107|0.0088**|0.0106*|
||(0.0012)|(0.0020)|(0.0080)|(0.0061)|(0.0038)|(0.0060)|
|GFC frm shock*2010|0.0032**|0.0035**|0.0061|0.0119***|0.0096**|0.0044**|
||(0.0010)|(0.0015)|(0.0046)|(0.0045)|(0.0039)|(0.0044)|
|GFC frm shock*2011|0.0010|0.0038**|0.0171|0.0197*|0.0114*|0.0100*|
||(0.0012)|(0.0017)|(0.0095)|(0.0102)|(0.0061)|(0.0100)|
|GFC frm shock*2012|0.0003|0.0038|0.0074*|0.0101*|0.0075**|0.0052*|
||(0.0013)|(0.0019)|(0.0042)|(0.0053)|(0.0034)|(0.0052)|
|GFC frm shock*2013|0.0027**|0.0019|0.0201**|0.0232**|0.0103**|0.0055***|
||(0.0011)|(0.0019)|(0.0087)|(0.0084)|(0.0050)|(0.0055)|
|GFC frm shock*2014|-0.0014<br>(0.0013)||||||
|GFC frm shock*2015|0.0005<br>(0.0013)||||||
|GFC frm shock*2016|0.0019<br>(0.0012)||||||
|GFC frm shock*2009*Firm characteristic base 2007|-0.0000|-0.0002|-0.0014**|-0.0007|-0.0013**|-0.0008*|
||(0.0002)|(0.0004)|(0.0007)|(0.0005)|(0.0006)|(0.0005)|
|GFC frm shock*2010*Firm characteristic base 2007|-0.0003<br>|-0.0007***<br>|-0.0006<br>|-0.0009***<br>|-0.0016**<br>|-0.0009***<br>|
||(0.0002)|(0.0003)|(0.0004)|(0.0003)|(0.0006)|(0.0003)|
|GFC frm shock*2011*Firm characteristic base 2007|0.0000|-0.0007***|-0.0015*|-0.0015**|-0.0018**|-0.0015*|
||(0.0002)|(0.0003)|(0.0008)|(0.0008)|(0.0009)|(0.0008)|
|GFC frm shock*2012*Firm characteristic base 2007|-0.0000|-0.0008**|-0.0007*|-0.0008**|-0.0013***|-0.0008*|
||(0.0002)|(0.0003)|(0.0004)|(0.0004)|(0.0005)|(0.0004)|
|GFC frm shock*2013*Firm characteristic base 2007|-0.0004**|-0.0005|-0.0019**|-0.0018***|-0.0018**|-0.0013***|
||(0.0002)|(0.0003)|(0.0008)|(0.0006)|(0.0008)|(0.0004)|
|GFC frm shock*2014*Firm characteristic base 2007|0.0002<br>(0.0002)||||||
|GFC frm shock*2015*Firm characteristic base 2007|0.0002<br>(0.0002)||||||
|GFC frm shock*2016*Firm characteristic base 2007|-0.0002<br>(0.0002)||||||
|Observations|107,541|26,054|26,787|26,178|25,981|26,244|
|R-squared|0.0169|0.0129|0.0144|0.0148|0.0120|0.0139|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. All firm characteristics in base year 2007 enter in logarithms. The specification controls for a vector of firm characteristics as of 2007 described in Section _C._ . L-P refers to Levinsohn & Petrin (2003). ACF refers to Ackerberg et al. (2015). Wooldridge refers to Wooldridge (2009). FE stands for fixed effects.

50
_Central America and NAFTA_ 17 higher prices for its exports which are then completely directed toward the Mexican market.

61. For Leather Products the reduction in ROW earnings in column (1) shows a 9 percent loss, with a strong jump in the Mexican share of U.S. imports. Column (2) already shows a bifurcated equilibrium with Central America. A modest increase in the size of Mexico beyond the column (2) size is enough to make Central America better off. Glass Products also exhibits in column (1) a loss of earnings for ROW on the order of 9 percent, but column (2) now displays both a bifurcated equilibrium and a better off ROW with earnings increases of 21.8 percent.

62. For Textiles and Beverages the 1985 data imply a bifurcated equilibrium with ROW earnings increases on the order of 6 percent. This comes basically from initial Mexican production levels that are more than enough to satisfy U.S. import demand. Actually, the effective U.S. tariff for Beverages is reduced to zero.

63. All the calculations are to some extent out of date, especially because they do not embody economic growth and product mix changes in Mexico as a result of the Mexican liberalization. Leamer's (1993) estimates of the potential effect of the Mexican liberalization on the levels of Mexican output produce output figures that make Mexico large enough to reduce the U.S. effective tariff to zero in all these product categories. In this sense, NAFTA amounts to a general lowering of U.S. trade barriers, not a preferential lowering in favor of Mexico. And Central America will gain, provided that it is willing and able to redirect its products from the U.S. market to Mexico.

# **Destination of Central American Exports**

64. The partial equilibrium simulations that are presented here predict diversion of Central American exports away from protected North American markets, which will be supplied instead from North American sources which can sell free of the trade barriers applicable to non-members. This would radically alter the geographic pattern of Central American exports, particularly for the labor-intensive manufactures which are most likely to be subjected to trade diversion, because of their growing importance for Central America and because these are the manufactured products that are most protected in the U.S.

65. In the last two decades, the geographic pattern of Central American exports of labor-intensive manufactures (principally Apparel and Footwear) has shifted dramatically away from other Central American countries toward the U.S. Almost 94 percent of Central American exports of labor-intensive manufactures in 1970 were shipped to other Central American countries; 4.2 percent went to the U.S. and Canada; and only 0. 1 percent went to Mexico. In 1990 only 23 percent of Central American labor-intensive exports stayed in the region, and 63.3 percent were shipped to the U.S. and Canada.
<!-- Start of picture text --> the question ―Are you allowed to decide how daily work is organized?" (self-assessment,<br>where 0 means ―I have no influence‖, and 10 means ―I have complete control‖). The data do<br>not support the hypothesis that a substantial proportion of ESS respondents which classify<br>themselves as informal self-employed are in fact employees. Their median self-assessed<br>autonomy is 10 in all countries but PT where it is 9, and mean self-assessed autonomy in all<br>countries is well above that of formal employees. Figure 2 shows that the same is true also for<br>the 25th percentile of the autonomy variable, thus excluding the possibility that even a quarter<br>of informal self-employed are in fact dependent workers.<br>Allowed to decide how daily work is organised (self-assessment, p25)<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>RO UA CZ RU HU PL SK BG LT EE LV SI SE DK FI NO GR IT PT ES IL CY IE NL AT CH DE BE FR UK<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>points in 0-10 scale<br><!-- End of picture text -->

**Figure 2 Worker autonomy: Informal self-employed vs. formal and informal employees.**

**2004-2009**

_Notes:_ The Figure displays the 25<sup>th</sup> percentile of the self-assessed autonomy for each of the three groups. _Source:_ Calculation with ESS data When both size and composition of informal workforce are taken into account, all countries considered can be arranged in 11 clusters, as shown in Table A4 (in the Annex).

An important finding from Table 3 and Figure 1 is that median country in the East features a substantially higher proportion of employees without contracts than median country in the West. In fact, in 5 out of 8 Western European countries (and in 7 out of 12 countries when the Nordic countries are added) workers without contracts account to less than 3% of extended labor force, while among 12 Eastern European countries this is the case only for four countries, and the median is about 4%. This provides at least some support to an ̳intuitive‘ belief that there ̳should‘ be more informality in the East.

16
headcounts across all groups included in this analysis. This results in a 9% increase in poverty headcounts (and 8% increase in the poverty gap) between market and consumable income. This increase is slightly more pronounced for boys than for girls (10% vs. 8%). The highest increase in relative terms can be found in urban areas, where the poverty headcount increases by 21% and the poverty gap by 25% between market and consumable income. Also noteworthy is a significant increase of the poverty gap for girls (17% increase between market and consumable income). This contrasts with small decreases in monetary inequality for almost all groups, with an average decrease of 2.5% in the Theil index.

_Table 6: Monetary child poverty across income concepts, by gender and location_

||**_Income concept_**|**_All children_**|**_Girls_**|**_Boys _**|**_Rural_**|**_Urban_**|
|---|---|---|---|---|---|---|
|**_Poverty_**|Market income|0.33|0.36|0.31|0.39|0.14|
|**_headcount_**||(0.004)|(0.006)|(0.006)|(0.005)|(0.005)|
||Disposable income|0.34|0.36|0.31|0.39|0.15|
|||(0.004)|(0.006)|(0.006)|(0.005)|(0.005)|
||Consumable income|0.36|0.39|0.34|0.42|0.17|
|||(0.004)|(0.006)|(0.006)|(0.005)|(0.005)|
||Final income|0.26|0.27|0.24|0.30|0.12|
|||(0.004)|(0.005)|(0.005)|(0.005)|(0.004)|
|**_Poverty gap_**|Market income|0.12|0.12|0.11|0.14|0.04|
|||(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|
||Disposable income|0.11|0.12|0.11|0.13|0.05|
|||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
||Consumable income|0.13|0.14|0.12|0.15|0.05|
|||(0.002)|(0.003)|(0.003)|(0.002)|(0.002)|
||Final income|0.08|0.09|0.08|0.10|0.04|
|||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|**_Poverty_**|Market income|0.06|0.06|0.05|0.06|0.02|
|**_severity_**||(0.001)|(0.002)|(0.001)|(0.002)|(0.001)|
||Disposable income|0.05|0.06|0.05|0.06|0.02|
|||(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
||Consumable income|0.06|0.06|0.06|0.07|0.03|
|||(0.001)|(0.002)|(0.002)|(0.002)|(0.001)|
||Final income|0.04|0.04|0.04|0.04|0.02|
|||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|

**_Source:_** _Authors’ calculations; Data from ESS 2018/19._ **_Note:_** _Standard errors in parenthesis._

In other words, the combined effect of fiscal policies in Ethiopia (taxes and direct transfers) increases poverty among children when comparing market and consumable income. While this finding is similar to the observation made for the Kenya (Save the Children 2021), it is relatively uncommon when compared to most other countries in which similar studies have been carried out.<sup>9</sup> Finally, these combined effects do not incorporate benefits from education or health care, as those cannot be

> 9 While comparable data for children is missing, we see a similar pattern for the whole population in only a small number of countries where CEQ assessments have been conducted (e.g., Tanzania, Ghana).

19
Learning from Developing Country Power Market Experiences: The Case of Colombia

# 8 References

- Arango, S., Dyner, I., & Larsen, E. R. (2004). Lessons from deregulation in Colombia: successes, failures and the way ahead. _Utilities Policy_ , _32_ (15), 1767–1780.

- Castro, L. De, Oren, S., & Riascos, A. J. (2013). _An Evaluation of CREG 051 – 2009 Regulatory Intervention in Colombian Electricity Mark_ .

- Contraloría General de la República. (2016). _Informe de resultados de la actuacion especial de fiscalización sobre el manejo de recursos del cargo por confiabilidad_ .

- Cramton, P., Ockenfels, A., & Stoft, S. (2013). _Capacity Market Fundamentals_ . Retrieved from http://stoft.com/wp-content/uploads/2013-05_Cramton-Ockenfels-Stoft_Capacity-marketfundamentals.pdf

- Cramton, P., & Stoft, S. (2007). Colombia firm energy market. In _Proceedings of the Annual Hawaii International Conference on System Sciences_ (pp. 1–11). https://doi.org/10.1109/HICSS.2007.133

- CREG. (1995). Código de redes. Retrieved from http://apolo.creg.gov.co/Publicac.nsf/Indice01/Codigos1995-RES.025-1995.COD..REDES.-.COD..OPERACION?OpenDocument

CREG. (2006a). Resolución 071-2006.

CREG. (2006b). Resolution 065-2006. Retrieved from http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/68320ef6fdb033c305 25785a007a6d65?OpenDocument

- CREG. (2007). _Reliability Charge: Regulatory Scheme to Guarantee the Reliability in the Supply of Electric Energy in Colombia, A Long-Term Vision_ . Retrieved from http://www.creg.gov.co/cxc/download/documentos/ReliabilityCharge.pdf

CSMEM. (2014). _Informe No 94 – 2014_ .

- EIA. (2016). Colombia Country Analysis Brief. Retrieved December 22, 2017, from https://www.eia.gov/beta/international/analysis.cfm?iso=COL

Harbord, D. (2016). _CREG Expert Panel on Colombian Energy Market Reform_ .

- Harbord, D., & Pagnozzi, M. (2014). Britain’s Electricity Capacity Auctions: Lessons from Colombia and New England. _The Electricity Journal_ , _27_ (5), 54–62. https://doi.org/10.1016/j.tej.2014.05.004

- Keay, M., Letova, K., Yao, R., Davidson, M., Bialek, J., Robinson, D., ... Godinho, C. (2018). June 2018: ISSUE 114, (114), 1–58. Retrieved from https://www.oxfordenergy.org/wpcms/wpcontent/uploads/2018/06/OEF-114.pdf

- Maurer, L., & Barroso, L. (2011). _Electricity Auctions : An Overview of Efficient Practices_ . The World Bank. https://doi.org/10.1596/978-0-8213-8822-8

- Mcrae, S. D., & Wolak, F. A. (2016). _Diagnosing the Causes of the Recent El Niño Event and Recommendations for Reform_ . Retrieved from https://web.stanford.edu/group/fwolak/cgibin/sites/default/files/diagnosing-el-nino_mcrae_wolak.pdf

- Millán, J., von der Fehr, N.-H. M., Ayala, U., Walker, I., Benavides, J., Rufín, C., & Brown, A. (2003). Keeping the Lights On: Power Sector Reform in Latin America. Retrieved from http://publications.iadb.org/handle/11319/311

53
**TABLE 5. Impact of Nationalization on Access to Sewage Network in Maldonado at the Bottom and Top 25 percent of the Income Distribution**

||**A**<sup>**a**</sup>||**B**<sup>**a**</sup>||**C**|<sup>**b**</sup>|**D**|<sup>**c**</sup>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|
|Private provision|0.013|0.083|<br>0.022|0.084|−0.030|−0.005|−0.152***|−0.119**|
||(0.046)|(0.089)|<br>(0.047)|(0.091)|<br>(0.043)|(0.053)|(0.030)|<br>(0.048)|
|Renationalized provision|0.093*|0.143**|0.105*|0.146**|<sup>0.158***</sup>|<sup>0.164***</sup>|0.016|0.044|
||(0.056)|(0.063)|<br>(0.056)|(0.066)|<br>(0.045)|(0.053)|(0.035)|<br>(0.052)|
|**Private provision by Bottom 25 percent**|0.032|−0.134*|0.013|−0.139*|0.025|−0.112***|<sup>0.101***</sup>|0.013|
||(0.024)|(0.070)|<br>(0.024)|(0.072)|<br>(0.023)|(0.033)|(0.015)|<br>(0.043)|
|**Renationalized provision by Bottom 25**|0.109***|−0.047|0.091**|−0.049|0.108***|−0.028|0.177***|0.094**|
|**percent**|(0.036)|(0.041)|<br>(0.037)|(0.043)|<br>(0.022)|(0.033)|(0.013)|<br>(0.042)|
|Bottom 25 percent|−0.362***|0.111|<br>−0.348***|0.103|−0.364***|0.128|−0.415***|<sup>−0.185*</sup>|
||(0.018)|(0.086)|(0.018)|(0.093)|(0.018)|(0.089)|(0.009)|(0.110)|
|Controls|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|1470|1470|1160|1160|1404|1404|864|864|
|Log likelihood|−653.93|−597.64|−524.11|−482.46|−625.26|−568.37|−341.32|−338.75|
|Sampleperiod|1986–|2009|1993–|2009|1986–|2009|1986–|2009|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.

> <sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young.

> <sup>b</sup> Treatment Unit (one city): Maldonado; same Control Units as in A.

> <sup>c</sup> Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

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