rafmacalaba/datause-encoder-data huggingface dataset:
Scores for five subjects are analyzed: Spanish, mathematics, biology, chemistry, and physics. Socioeconomic variables at the student level are included in the database from 1998 to 2003<sup>6</sup> . These variables include birth date, gender, level of education of parents, their occupation, number of people in the family, number of siblings, household income, dwelling ownership, and whether the students works. Information on tuition paid is available from 2000 to 2006.

Because our analysis relies on controls and matching techniques to assess the performance of Fe y Alegría schools, we use the data from 1998 to 2003. We restrict the sample using the following criteria: (1) Schools in cities where Fe y Alegría has at least one school in the current year; (2) Students who graduate from high school in the current year or the year before; (3) Students between 15 and 20 years of age; and (4) Students who attended both morning and afternoon sessions of the test.

The data for 2001 seems to be problematic because the number of observations is much smaller than in other years. This then gives us five years of data to work with, for 1998-2000 and 2002-2003. Table 1 contains the total number of observations for Fe y Alegría and other schools for each year in the sample, after imposing the four constraints above. We would have liked to explore different control groups, but there is not enough information in the database to identify, for example, public or private schools, or other school level variables like number of students, number of teachers, and qualification of teachers, among others. That is the reason why our control group includes all non Fe y Alegría schools. The percentage of students in the sample attending Fe y Alegría schools increases from 1.1 in 1998 to 1.5 in 2003; the share of Fe y Alegría schools grows from 1.8 percent in 1998 to 2.5 percent in 2003.

We rely on both traditional propensity score matching and CEM (coarse exact matching) techniques for the estimations (Iacus et al., 2009; King and Stuart, 2007). CEM is a recent

> 6 Socioeconomic information was not collected from 2004 to 2006.
Organisation for Economic Cooperation and Development. 2004. Informal Employment and Promoting the Transition to a Salaried Economy, in _OECD Employment Outlook 2004_ , Paris.

Palmade, Vincent, Industry Level Analysis: The Way to Identify the Binding Constraints to Economic Growth, _World Bank Policy Research Working Paper No. 3551, March 2005_ .

Rogerson, Christian. 2004. Pro-Poor Local Economic Development in Post-Apartheid South Africa, The Johannesburg Fashion District, _International Development Policy Review_ , 26(4).

Small Business Council. 2004. _Informal Economy: Small Businesses in the Informal Economy: Making the Transition to the Formal Economy_ , available at <u>http://www.sbs.gov.uk/SBS_Gov_files/sbc/informaleconomy.pdf</u> (accessed July 2006) Small Business Project. 2005. _Counting the Cost of Red Tape for Business in South Africa: Regulatory Costs as a Burden for South Africa’s Business_ , available at <u>http://www.eldis.org/static/DOC19238.htm</u> (accessed August 2006).

Taussig, Markus, _Private Enterprise Formality and the Role of Local Government_ , Asian Development Bank Discussion Paper #2, November 2004.

Tendler, Judith. 2002. Small Firms, the Informal Sector, and the Devil’s Deal, _IDS Bulletin,_ Vol. 33, No. 3, July 2002.

Tenguez, Alper and Rainer Klump. 2005. Strategies of the State towards the Informal Economy, available at <u>http://www.wider.unu.edu/conference/conference-20053/conference-2005-3-papers/Teng%FCz%20&%20Klump.pdf</u> (accessed July 2006) USAID. 2005. _Removing Barriers to Formalization: The Case for Reform and Emerging Best Practice_ , Washington DC.

Wahl, Martin and Ralf Meier. 2005. _How a Business Membership Organization can Contribute to Improving the Business Environment in Northeast Brazil_ , Deutsche Gesellschaft fur Technische Zusammenarbeit (GTZ).

Wallace, Claire and Rossalina Latcheva. 2006. Economic Transformation Outside the Law: Corruption, Trust in Public Institutions and the Informal Economy in Transition Countries of Central and Eastern Europe. _Europe-Asia Studies_ , vol. 58(1).

Winterberg, Susan J., _Still on the Other Path? The Impacts of Business Formalization in Peru and their Lessons for SME Development Policy,_ available at - <u>http://web.mit.edu/sigus/www/NEW/Peru_Website/SIGUS/linked%20files/Winterberg SIGUSPeruPaper.pdf (accessed August 2006).</u> 16
inputs, such as chemicals, could be realized through cooperation, and do not require consolidation.

# _<u>Government-Wide Economies</u>_

The potential costs savings from consolidating governments extend beyond the size economies for each service to include the potential for economies of scope (or diversification). Scope economies refer to the economies, to the extent they exist, from delivering multiple services together. Such economies, or diseconomies, could arise for a variety of reasons such as shared inputs in the production of some services or one service being partially a byproduct of another. For example, information obtained through collection of utility fees could be used in enhancing police and fire protection or in collecting taxes. Also, computer and engineering capabilities may be useful for numerous functions. The expected economies from circularly integrated services may be scope economies as well as size economies.

Scope economies are important to a consolidation decision if they grow with the size of government or if larger local governments can be assigned a greater set of responsibilities (perhaps because a certain size must be attained before it is reasonable to consider transferring some responsibilities to local governments) that would add additional cost savings. Both Zimbabwe and Sudan appear to have assigned greater expenditure responsibilities to the new, consolidated governments, perhaps suggesting an implicit notion of economies of scope.

Unfortunately, very little research has been conducted on the presence of economies of scope and there is nothing in the empirical literature on scope economies that adds any light to whether consolidated governments will gain even greater scope economies. Grosskopf and Yaisawarng (1990) provide one rare piece of literature in this area. The authors found scope economies between police and fire protection, evidencing that it is lower cost to produce these services together than to produce them independently.

Two other types of research have directly sought to determine whether costs are lower across all government services. First, a relatively limited set of work has focused on the costs of delivering all services across different sized governments. One likely reason for the paucity of research in this area is that it is difficult to separate demand side and supply side effects. For example, larger governments may potentially choose to deliver higher service levels than smaller governments because the need for police, fire, water, sewer and other services grows with population size and density. These demand side effects should not affect decisions on whether consolidation will be cost reducing.

Burnham et al. (1993) provide one example of research across multiple government services. The study based on UK data finds that population size was not an important factor compared with others in affecting government performance. Some services were found to have economies of scale while others had diseconomies. The 16
that of the poorest lottery losers—on average, the difference in food shares between the two groups for households in the lowest quartile is 2.8 percentage points. We return to this point below.

If BDH transfers were treated like other sources of income, households might move along their Engel curves in response to the program, although the fact that the program did not have an effect on total expenditures suggests that movement along the curves would be small. Under this scenario, the curves for lottery winners and lottery losers at follow-up would look very similar to those at baseline. The lower panel of Figure 1 shows that this is not the case. Instead, the curves for both lottery winners and lottery losers have shifted down over the two-year period. However, the shift for lottery winners is noticeably smaller.

The downward shift in the food Engel curves over time is surprising, although it has been observed elsewhere—see Deaton and Dreze (2007) for a long time-series in India, and Maluccio and Flores (2004) in their analysis of the effect of the _Red de Protección Social_ program in Nicaragua. To gain a better understanding of this change, we make use of the 1999 and 2006 Ecuador _Encuestas de Condiciones de Vida_ . We limited the sample to households in the rural sierra, to make the coverage of the surveys comparable to that in the evaluation data, and calculated nonparametric regressions of the food share on log per capita expenditures. On average, the food Engel curve using the _Encuestas de Condiciones de Vida_ in 2006 is 3.9 percentage points below that in 1999. Reassuringly, the downward shift in the food Engel curves in rural areas of the sierra in Ecuador we observe in the BDH evaluation sample is also apparent in other data sources.

We briefly discuss some possible reasons for the downward shift in the food Engel curves in Ecuador over time. First, there may have been changes in relative prices—for example, a change in the price of food, relative to other items in the household budget. Both the BDH evaluation surveys and the _Encuestas de Condiciones de Vida_ collect village-level information on a basket of food items, which are then used to deflate expenditures spatially. However, neither survey collects information on the prices of non-food items, so it is not possible to use these data to assess changes in relative prices.

Seasonal patterns could also explain the decline in the food Engel curve—recall that the baseline evaluation survey was collected between June and August, while the follow-up survey was collected between January and March. The 2006 _Encuesta de Condiciones de Vida_ collected data in every month of the year. The average food share in rural areas in the sierra is 0.473 in January-March period and 0.475 in the June-August period. Thus, it does not appear that seasonal effects are important.

Finally, measurement error also seems an unlikely candidate to explain the falling food Engel curve in the evaluation surveys, since the expenditure modules and recall periods were identical 9
-9 -

Table 2

# Excise Taxes as Percentage of Retail Prices, 1988

|Country|Cigarettes|Distilled<br>Spirits|Beer|Wine|Gasoline|
|---|---|---|---|---|---|
|Australia|32.3|17|35|15|49.4|
|Austria|53.0|40|36|31|62.5|
|Belgium|64.4|56|27|27|64.7|
|<br>Canada|N.A.|82|53|69|40.5|
|Denmark|69.2|83|50|48|75.3|
|Finland|N.A.|66|41|66|52.0|
|France|49.2|45|18|18|76.9|
|Germany|59.8|64|20|12|64.0|
|<br>Greece|36.8|N.A.|N.A.|N.A.|66.4|
|Iceland|N.A.|N.A.|N.A.|N.A.|N.A.|
|Ireland|53.8|66|64|51|70.7|
|Italy|56.8|27|20|8|78.3|
|<br>Japan|N.A.|23|47|22|47.0|
|Luxembourg|60.9|44|14|6|56.4|
|<br>Netherlands|54.8|72|34|25|70.4|
|New Zealand|N.A.|53|30|20|51.0|
|Norway|N.A.|91|54|59|66.6|
|<br>Portugal|58.0|8|14|8|66.0|
|Spain|32.8|47|15|11|65.2|
|<br>Sweden|N.A.|92|34|69|62.2|
|Switzerland|N.A.|31|14|5|64.7|
|Turkey|N.A.|N.A.|N.A.|N.A.|N.A.|
|<br>United Kingdom|<br>61.3|51|31|29|67.8|
|United States|30.1|45|15|12|31.5|

Source: Congressional Budget Office, Federal Taxation of Tobacco. Alcoholic Beverages and Motor Fuels (Washington: Congressional Budget Office, 1990.
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and aggregated to the county level.<sup>1</sup> To do so, we start with the daily measures of minimum and maximum temperature and total precipitation at each 2.5 _×_ 2.5 mile grid point. We then weight each grid point by the number of crop pixels of each type of crop (corn or soybeans) that are in each grid point’s unique catchment area. We then calculate the weighted average of each weather measure at the county level for each day.

We calculated the total growing degree days and extreme degree days for each crop in each county during the growing season (May 1 to September 30). We used bins from 10<sup>_◦_</sup> C to 29<sup>_◦_</sup> C and above 29<sup>_◦_</sup> C for corn and 10<sup>_◦_</sup> C to 30<sup>_◦_</sup> C and above 30<sup>_◦_</sup> C for soybeans. We calculated the total precipitation prior to the growing season and during the growing season for each county-year.

We measured cloud cover using data from the ERA5 re-analysis product that records the fraction of cloud cover each hour.<sup>2</sup> We calculated the daily average fraction of cloud cover from 6am to 8pm for each day during the growing season and the number of days the average total fraction of cloud cover falls into bins from 0–25, 25–50, 50–75, and 75–100 percent during the growing season.

## **4.1.4 Yield data**

For both corn and soybean, we downloaded annual data on yield in bushels per acre at a county level from the USDA’s National Agricultural Statistics Service (NASS) database. We also collected total bushels produced for each crop in each county and year in the sample.

# **4.2 Empirical approach**

## **4.2.1 Estimation of smoke’s present day marginal impact**

In order to identify the marginal impact of smoke plumes on yields, we rely on the assumption that the incidence of smoke days across counties in a given year is plausibly random conditional on county and year fixed effects. In other words, we assume the amount of a given county’s exposure to smoke after removing the portion of a county’s exposure in a given year that would be considered average for that county and the portion that is experienced by all other counties in the same year, or the portion that is due to a universal increase in smoke, is quasi-random. Some counties may be consistently more exposed to smoke than others; we show that counties in the upper Midwest generally experience more smoke days in a given year than the rest of our sample (Figure 1), but we expect differences in year-to-year exposure within a county is quasirandom. These differences are driven by weather conditions in the location of fires, generally the

> 1The original data is available here: `http://www.columbia.edu/~ws2162/links.html`

> 2Data available here: `https://cds.climate.copernicus.eu/cdsapp#!/dataset/ reanalysis-era5-single-levels?tab=overview`

17
|**Table 1: Base**|**Regression R**|**esults for Inc**|**idence of Pet**|**ty Corruptio**|**n**||
|---|---|---|---|---|---|---|
|Dependent variable: Financially|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Constrained (dummy)|||||||
||**Panel A: E**|**stimated log-**|**odds ratios**||||
|Incidence of Petty Corruption (Cell|0.635**|0.678**|0.704**|0.757**|0.834**|0.821**|
|average)|(0.323)|(0.325)|(0.327)|(0.328)|(0.337)|(0.323)|
|Annual Sales (2009 USD, logs)||-0.170***|-0.164***|-0.120***|-0.116***|-0.123***|
|||(0.037)|(0.036)|(0.035)|(0.036)|(0.037)|
|Age of Firm (logs)|||0.018|0.021|0.028|0.045|
||||(0.067)|(0.069)|(0.070)|(0.072)|
|Exports (proportion of sales)|||-0.592*|-0.510|-0.468|-0.450|
||||(0.309)|(0.324)|(0.349)|(0.363)|
|Firm Has Female Owners Y:1 N:0||||-0.180|-0.182|-0.194|
|||||(0.116)|(0.117)|(0.118)|
|Foreign Ownership (proportion)||||-0.021|-0.035|-0.047|
|||||(0.278)|(0.272)|(0.282)|
|Multi-establishment Firm Y:1 N:0||||-0.287|-0.295|-0.283|
|||||(0.181)|(0.189)|(0.194)|
|Firm Purchased Assets Last Fiscal Year||||-0.563***|-0.529***|-0.529***|
|Y:1 N:0||||(0.106)|(0.107)|(0.111)|
|Firm is Audited Y:1 N:0||||-0.238**|-0.237**|-0.186|
|||||(0.118)|(0.120)|(0.126)|
|Hours of Power Outages in a Month|||||0.000|0.000|
||||||(0.001)|(0.001)|
|Firm Experienced Losses from Crime|||||-0.461**|-0.486**|
|Y:1 N:0|||||(0.189)|(0.191)|
|Firm Inspected/Visited by Tax Officials||||||-0.114|
|Y:1 N:0||||||(0.119)|
|Informal Competition (||||||0.670**|
|average)||||||(0.312)|
|How Much of an Obstacle?: Courts||||||0.300**|
|(average)||||||(0.135)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|20,502|20,502|20,432|20,086|19,060|18,931|
||**Panel**|**B: Marginal**|**effects**||||
|Incidence of Petty Corruption (Cell|<br>0.069**|<br>0.073**|<br>0.075**|0.079**|0.090**|0.086**|
|average)|(0.035)|(0.035)|(0.035)|(0.034)|(0.036)|(0.034)|
|Other controls as above|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficients in Panel A are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country time industry in brackets. *** (1%), ** (5%), * (10%). Sample size varies due to missing data.

36
role of interest rates in the aftermath of large devaluations that result in an undershooting of the real exchange rate. They ask whether high interest rates following a devaluation increase the likelihood that real exchange rate equilibrium is restored through a nominal appreciation rather than through higher inflation. They find that high interest rates are effective in this sense only in countries with strong banking sectors. Furman and Stiglitz (1998) examine daily data on interest rates and exchange rates in a sample of nine developing countries during the 1 990s to identify episodes of sustained high interest rates, and then ask whether these were followed by an appreciation of the domestic currency. They find little evidence that this is the case. In contrast Dekle, Hsiao and Wang (1 999a,b) study the relationship between interest rates and exchange rates using weekly data for Korea, Malaysia and Thailand during 1997 and 1998, and argue in favour of the conventional view. The main difficulty with all of these papers is that they simply document reduced-form (partial) correlations between interest rates and exchange rates. Without controlling for the endogeneity of the monetary policy response, it is difficult to infer anything regarding the effects of high interest rates from these papers. This paper makes a first attempt to take seriously the identification problem, drawing on a much larger sample of successful and failed speculative attacks.<sup>5</sup> The remainder of this paper proceeds as follows. In Section 2, I describe the data and the methodology used to identify successful and failed speculative attacks. In Section 3, I present some descriptive results, which provide scant evidence of any association between changes in interest rates and the outcome of speculative attacks. In Section 4, I develop a simple model to illustrate the endogeneity problem, and I use this to motivate a set of probit regressions expressing the probability that speculative attacks fail as a non-linear function of policies and fundamentals. After instrumenting for the endogeneity of policy, I again find no evidence of a significant impact of high interest rates on the outcome of speculative attacks. Section 5 offers concluding remarks.

shocks leading to appreciations in some countries and depreciations in others. Finally there is a large empirica **l** iterature documenting the properties of macroeconomic variables around speculative attacks (e.g. example Eichengreen, Rose and Wyplosz (1994,1995,1996)), which to date has not focused on the policy and non-policy determinants of successful and failed attacks.

This concern with the endogeneity of monetary policy is of course not new, and is a recurring theme in the literature on the effects of monetary policy during normal times (as opposed to periods of speculative pressures). See for example the discussion in Bemanke and Mihov (1998) and Christiano, Eichenbaum and Evans (1998).

5
in the that period, and 36 percent became displaced with zero violent incidents.<sup>19</sup> Along with the high violence experienced in large portions of the country, the significant share of households that became displaced prior to any violence occurring is one of the reasons that the average violence experienced by households before displacement was not significantly different than the violence experienced by non-displaced households before their latest survey in Tables 1 and 2.

However, even for households fleeing districts that were non-violent prior to displacement, the expectation of future violence might have still forced displacement. Figure 6 presents the average pre- and post- displacement violence for these households that fled districts with no violent incidents in the month of or six months prior to displacement and those that fled districts with one or more violent incidents. The figures demonstrate that although some households fled non-violent districts, the violence in those districts significantly escalated in the months after they became displaced. The increase was larger than the increase following the latest survey of non-displaced households; and for the number of violent incidents, the increase was larger than even for households being displaced from more violent districts.

There are a number of potential explanations for these patterns. First, it could be the case that the humanitarian situation, that was caused by the conflict, could have been driving a significant portion of displacement. Or second, it could have been the case that only households of sufficient means were able to either better predict violence or afford to become displaced prior to any violence actually occurring. But in either of these cases, there would be a significant difference in the financial well-being of households that became displaced prior to violence occurring and those becoming displaced only after significant violence occurred.

We empirically investigate this possibility, where we estimate the difference in all average food security outcomes collected in the most recent pre-displacement surveys for households that fled districts with no violence and for households that fled districts with violence. Food access measures- particularly diet diversity- are strongly correlated

> 19The timing of these displacements during periods of little violence occur proportionately across all years of the sample.

18