rafmacalaba/data-use-mentions-extended huggingface dataset:
public schools, but 96% of private ones. A library is present in 20% of public schools, but 62% of private ones. This suggests why parents may pay for private pre-primary when public is free.

In the 2000 Census, about 32.8% of Brazilians were below the indigence line, defined as the income necessary to consume the FAO minimum daily caloric intake.<sup>13</sup> Based on the most recent household expenditure survey conducted in Brazil,<sup>14</sup> median _per child_ expenditure on private pre-primary education was 74% of the indigence line income. The 5th percentile of _per child_ expenditure was 15% of the indigence line income. This suggests that the indigent (and probably many above this line) could not afford formal, private pre-primary education.

At higher incomes, parents are less likely to send their child to public pre-primary school, less likely to keep their child out of school, and more likely to send their child to private preprimary school than at lower incomes. Figure 3 illustrates this with child-level data from the 2000 Brazilian Census. I examine the school enrollment status of 4-6 year-olds (preschool children), and observe how this varies with per capita household income. From all enrollment and income variables, I net out the effects of many potentially-relevant demographic characteristics.<sup>15</sup> I then plot the residuals and fit a quadratic function. The function is positively-sloped when I consider a dummy for enrollment in private pre-primary, or a dummy for enrollment in any pre-primary. It is negatively-sloped when I consider a dummy for enrollment in public pre-primary. This will be important later, as it supports my assumption that private pre-primary is a normal good.

# **3.2 Municipal governments and their finances**

Brazil has over 5,000 municipal governments, with remarkable financial and political autonomy. They are led by an elected mayor and an elected city council. Legislative, budgetary, and administrative authority are concentrated in
**_Experience_** : More experienced entrepreneurs obtain higher returns. Every additional year of experience (proxy by age) increases the probability of the firm being in the top performance distribution.

**_Skills_** : Educational attainment of the entrepreneur is the single most important characteristic for revenue and profit maximization. Returns to education are high and increase with each additional level. Completing the basic level is associated with 40 percent higher revenues, while completing the highest level is linked to 67 percent higher revenues per worker.

**_Inputs_** : Availability of electricity and, to a lesser extent, water is important for better firm performance. Access to power is associated with higher revenues per capita of about 27 percent, and a higher chance of being among the top performers.

**_Finance_** : In the absence of information on access to finance or credit by firms, the analysis uses access to domestic and foreign private transfers as a proxy. There is no clear or significant association between revenues or profits with being the recipient of private transfers.

**_Gender_** : Although the self-employed are mostly women, firms run by men perform better in terms of revenues and profits per worker. The correlation remains when controlling for type of firm, being household head, and sector of focus. Men attain 40 percent higher revenues and are 13 percent more likely to make it to the top 30 performing businesses.

The authors also note that the dominance of survival entrepreneurs puts a limit on the analysis of growth entrepreneurs.<sup>17</sup>

> 17 Another challenge to the analysis is the presence of outliers among the “growth entrepreneurs.” Given the small sample of this category of highly performing entrepreneurs, their exclusion (the option elected by the team) further reduces the sample while their inclusion leads to results highly biased by those few outliers. This issue further speaks to the difficulty of labor surveys capturing the full spectrum of employers in settings such as Haiti.

22
protection. Section IV presents an analytical model where the government maximizes social welfare that features behavioral elements. In Section V, we modify this model to incorporate into the influential lobbying model of Grossman and Helpman. In Section VI we analyze the observed policy patterns and tools in light of our analytical predictions. Section VII concludes.

# **II. Loss Aversion in Trade Policy: An Experiment**<sup>**3**</sup>

Numerous empirical studies from the behavioral literature find that the value people assign to a loss is significantly larger than the value assigned to an equivalent gain. In particular, for small or moderate gains and losses of money, the estimated value assigned to a loss is about twice as large as a similar gain (see Kahneman, Knetsch, and Thaler (1991) for a survey of the literature). Losses are also by definition time dependent, and reflect a particular reference point. For example, a firm accustomed to a high profit level will regard a low profit level as unacceptable, while a firm accustomed to a low profit level will find maintaining that level to be satisfactory.

An implication of loss aversion and reference dependence is that the value assigned to foregone gains is lower than that assigned to losses. This is reflected in many judicial outcomes, where the weight assigned to unrealized profits is typically well below the weight assigned to incurred losses. Survey evidence also bears this out. For example, faced with excess demand for a particular car model, a company that discontinues a discount of $200 is considered fair; but, a company that raises the price by $200 is considered unfair by a majority of respondents (Kahneman, Knetsch, and Thaler, 1991). This implies that imposing a surcharge (perceived as a loss) is considered less acceptable than eliminating a discount (perceived as a gain).

Loss aversion and reference dependence also have implications for people’s perceptions of trade policy. Tharakan (1996, p. 1562) notes that
# Figure 14. Policies to improve resilience

# A. EMDEs with flexible exchange rates

# B. EMDEs with inflation targeting

<!-- Start of picture text --> Number Percent Number Percent<br>Number of EMDEs Number of EMDEs<br>30 80 70 60<br>Share of EMDE GDP (RHS)<br>Share of EMDE GDP (RHS)<br>60 50<br>50<br>20 60 40<br>40<br>30<br>30<br>10 40 20<br>20<br>10 10<br>0 20 0 0<br>1999 2017 1999 2017<br>C. Central bank transparency in EMDEs D. EMDEs with fiscal rules<br>Index Number Number of EMDEs Percent<br>6 70 Share of EMDE GDP (RHS) 60<br>5 60 50<br>50<br>4 40<br>40<br>3 30<br>30<br>2 20<br>20<br>1 10 10<br>0 0 0<br>1999 2014 1999 2015<br><!-- End of picture text -->

# C. Central bank transparency in EMDEs

Source: Dincer and Eichengreen (2014), International Monetary Fund, World Bank. A.B.D. Share of EMDE GDP based on respective year constant 2010 U.S. dollar GDP.

A. An economy is considered to have a flexible exchange rate if it is classified as “Floating” or “Free Floating.” C. Central bank transparency based on the Dincer/Eigengreen Transparency Index. Larger numbers reflect greater transparency. Last observation is 2014.

D. An economy is considered to implement a fiscal rule if it has one or more fiscal rules on expenditure, revenue, budget balance, or debt. Last observation is 2015.
‐0.394|‐5.735|
|P‐value|0.126|0.761|0.718|0.279|0.412|0.258|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,769|3,766|3,766|3,951|3,948|3,948|
|R‐squared|0.512|||0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Assets are measured by the first principal component from an index of household assets including bed, wall clock, watch, player, radio, tv, sewing machine, fan, air conditioning, fridge, freezer, gas stove, shovel, landline telephone, cell, bike, motorbike, car, computer, livestock, farm implements, generator, land; then normalized to have mean zero and standard deviation one. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
information on household income, geographic location, household demographics, dwelling characteristics, and economic activities, among others. The size of the dataset is especially important, as it allows us to draw repeated samples that are sufficiently large and diverse. In addition, the fact that the survey gathered detailed income information on all households allows us to reliably calculate the required poverty measures that serve as the basis for our simulation experiments.<sup>5</sup> Prior to sampling from the Intercensal Survey, we make three modifications to the data. First, as a large number of households reported earning no income, we randomly remove 90 percent of these households.<sup>6</sup> Second, to ensure that all municipalities are sufficiently large, all municipalities with less than 500 households are removed. Finally, to ensure that all primary sampling units (PSUs) are also sufficiently large for sampling purposes, we combine several neighboring PSUs such that all include at least 300 households. Our final census dataset consists of 3.9 million households in 1,865 municipalities and 16,297 PSUs. The resulting census is used to draw 500 survey samples that serve as the basis for our simulation experiments (Tzavidis et al., 2018). In what follows, we describe our approach to constructing these samples.

Our sampling procedure is intended to reflect standard design elements used in many face-to-face household surveys conducted in the developing world (Grosh and Muñoz, 1996). Mexico’s 32 states comprise the intended domains of the sample and the indicator of interest is household income per capita. For each sample, we seek to achieve a relative standard error (RSE) of 7.5 percent for average household income in each state.<sup>7</sup> We use a two-stage clustered design wherein PSUs or clusters are selected within each domain and then a sample of 10 households is selected within each cluster. Our decision to select 10 households within each cluster is consistent with the design of other large-scale
.5 4.5 6.5 8.5 10.5 12.5 14.5<br>Wealth Index<br>% of Patients in Arm<br><!-- End of picture text --> _Notes._ Kolmogorov-Smirnov test p-value = 0.325. Index range is observed range. Wealth index is estimated following the methodology of the Kenya Demographic and Health Survey based on household ownership of selected assets.

24
3.52|0.84|1.55|2.40|3.90|
|26|Other Non-Metallic Mineral Products|1.49|0.76|1.39|1.50|2.76|
|27|Basic Metals|4.88|33.52|41.64|1.57|8.07|
|28|Fabricated Metal Products|3.37|2.05|2.51|1.95|6.04|
|29|Machinery and Equipment, not elsewhere classifed|16.36|4.11|6.59|4.30|7.22|
|30|Ofce, Accounting and Computing Machinery|2.82|0.16|0.23|0.60|0.28|
|31|Electrical Machinery and Apparatus, not elsewhere classifed|5.42|1.44|2.69|2.78|5.85|
|32|Radio, Television and Communication Equipment|4.92|0.68|0.67|4.50|3.80|
|33|Medical, Precision and Optical Instruments|4.12|1.55|1.22|1.15|0.79|
|34|Motor Vehicles, Trailers and Semi-Trailers|20.14|2.35|1.37|5.93|12.23|
|35|Other Transport Equipment|3.94|5.10|4.42|4.78|5.72|
|36/37|Manufacturing not elsewhere classifed; Recycling|1.89|2.77|0.51|4.03|7.98|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. Table reports export shares by sector over the period 1994-2004 for sectors are classified according to NACE Rev. 1 (2-digit) classification
Source:** Land Use Plan, East Java.

**Table 2: Production of Major Crops (1000 metric tons)**

|**Location**|**1970**|**1980**|**1990**|**2000**|
|---|---|---|---|---|
|**_Rice_**|||||
|Indonesia|26.39|29.77|45.18|51.90|
|East Java|4.66|6.28|8.23|9.22|
|Brantas Basin|1.43|2.29|2.43|2.99|
|**_Maize_**|||||
|Indonesia|2.60|4.01|6.73|9.68|
|East Java|1.05|1.69|2.58|3.49|
|Brantas Basin|0.03|0.40|0.49|1.12|
|**_Cassava_**|||||
|Indonesia|10.69|13.53|15.83|16.09|
|East Java|3.33|4.03|3.71|3.62|
|Brantas Basin|0.09|1.03|0.86|0.95|

**Source:** Statistical Year Book-2001, Central Bureau of Statistics.

9
> . One solution is to use panel data as it is generally done in the literature devoted to linear representations of the productivity of infrastructure. In a seminal paper, Hansen (1999) proposed the ...rst procedure to estimate and to test the threshold e¤ects in nondynamic panels. His Panel Threshold Regression (PTR) model allows for dividing the individual observations into classes according to an observable variable. In this case, the time series and cross sections are used in order to identify the regimes. Based on his procedure, it is then possible to test and to estimate the threshold e¤ects in the marginal productivity of infrastructures in a panel of countries, without assuming the homogeneity of the aggregated production function. This model assumes a transition from one regime to another based on the value of a threshold variable, the infrastructure stock for instance: in a model with two regimes, if the threshold variable is below a certain value, called the threshold parameter, the productivity is de...ned by one model, and it is de...ned by another model if the threshold variable exceeds the threshold parameter.

It is important to note that the existence of threshold e¤ects in the productivity of infrastructure is compatible with the three major criticisms addressed to the linear speci...cations of the production function in the huge literature devoted to this topic (see Gramlich, 1994; Sturm, 1998 or Romp and De Haan, 2005 for a survey). Indeed, when it comes to estimate the rates of return on infrastructure (or public capital) with an augmented production function in a panel, three major problems are generally raised. The ...rst one is the potential reverse causation. If public investments depend on income,

> 1 This is the main reason why these models have not been used even in the case of OECD countries. To the best of our knowledge no study devoted to the productivity of public capital and based on threshold models has yet been proposed
. less than two standard deviations below the mean of the reference group) are by convention classified as “underweight” (WAZ) or “stunted” (HAZ). We present results for three age groups: children aged between zero and 4 inclusive in 1992/93 and therefore between 5 and 9 at the time of the second wave; children aged between 5 and 23 in 1992/93; and all children between the ages of zero and 23 in 1992/93. In the second and third cases, in addition to matching on the propensity score, we match on the child’s age bracket (the brackets being in five-yearly intervals).<sup>11</sup> Table 5 shows the results.<sup>12</sup> VHI produces a significant effect on the height and weight of young children (children aged between 0 and 4 in 1992/93) but not of older children. In the case of weight-for-age but not height-for-age, the effect rises with per capita household consumption; indeed, there is no evidence of any beneficial effect of VHI on weight-for-age among the poorest quintile.

> 10  We standardize the anthropometric data using the 1990 British growth reference data (Cole, Freeman and Preece 1998) which - - are available for newborns up to the age of 23.  A convenient Stata routine <u>http://www.stata journal.com/software/sj4</u>

> <u>1/dm0004/zanthro.hlp: is available to undertake these and the more familiar US reference standardizations (Kuczmarski et al.</u> 2000).  The latter are available for young people only up to the age of 20.

> 11  Similar results were obtained when we matched on the propensity score and restricted matches to the same age group. 12 The treatment effects reported throughout are the average treatment on the treated.  The standard errors underlying the z- statistics were computed treating propensity score as fixed using the Stata add-in ‘
Panels E and F of Figure 12 show the first-year exporter survival rates in sectors with and without comparative advantage in each country during 2005-2007 and 2008-2009. Survival rates were higher in sectors with comparative advantage in all LAC countries except Costa Rica during the steady growth period. The differences were in some cases very large: e.g., in Ecuador a 22 percentage point difference separated the survival rates in sectors with and without comparative advantage. Again this was not a LAC specific pattern, as it was verified also in Bangladesh, Cambodia, and South Africa. In absolute the magnitudes are striking: whether a sector has comparative advantage or not, more than 75% of new exporters in that sector do not make it beyond their first year. Panel F shows that the global recession did not affect exporter survival rates across sectors with and without comparative advantage in LAC countries.

These patterns suggest that exporters in the LAC region are engaging in a high degree of experimentation in ‘non-traditional’ sectors where they do not have comparative advantage. However, the longer-lasting new export relationships were observed in sectors with revealed comparative advantage, which may be linked to learning spillovers due to the presence of networks of established exporters in each country. These patterns are, however, not a feature of the LAC region per se since they are also observed in comparator countries.

# **_8. Conclusion_**

Using novel exporter-level data from customs for 11 countries in the LAC region – Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru – covering the period 2005-2009, we uncover a wealth of new findings on export entrepreneurship in the LAC region.

A few of the findings are worth highlighting. First, the region appears to be no less entrepreneurial in terms of the extensive margins of exports relative to other countries, and relatedly, entry rates across sectors are quite similar but survival rates appear to be highest in agriculture sectors. Second, the size of new exporters tends
breaking out the indicator for whether a particular trading partner-product combination ( _ih_ ) was _excluded_ from the safeguard into one of the three categories described in Table 3.

While the average import growth differential across all excluded products was 52 percentage points in 2002 (column 3), column (4) now reveals that import growth in steel-affected products from PTA partners Canada, Mexico, Israel and Jordan grew fastest (95 percentage points), followed by developing countries that were excluded (73 percentage points), and then firm-product exclusion petitions (30 percentage points). While each of these three differentials is statistically significant, there are two likely contributing explanations for the relatively smaller estimate for the firm-product exclusions: First, the estimation is on _country-product_ level data, and the USTR granted _firm-product_ level exclusions in a way that would not necessarily have applied to all varieties of product _h_ from all firms (or even the same firm!) in the home country of the firm that filed the exclusion petition; see the discussion in Section 2.3.3. Furthermore, many of the excluded products were granted in June, July and August of 2002 and thus would only have an impact on the last six months of trade data for 2002; see Table 2.

Before considering our robustness checks in columns (5) through (8), consider the rows of estimates in the lower third of Table 5. These estimates identify the trade effects of the applied safeguard and exclusions in 2003: the second (and last) year that the policy application was in place. Columns (2) through (4) begin by indicating that by 2003 there was already no longer a statistically significant differential between the import growth of non-investigated products and products that had been part of the original investigation but that had not faced the safeguard at all.

However, column (3) provides additional evidence of the negative effect in 2003 of the applied safeguard for the products and trading partners that were actually hit with the import restrictions. Even though the applied safeguard
also wage kernels comparing the two sources in supplementary online appendix S5.

> 24In addition to the focus on formality and the definition of formality, these two types of data differ in several ways. First, whereas household surveys collect information on a sample of workers, administrative data cover the universe of formal workers. Second, in household surveys earnings are reported by the respondent in response to the question: “what are your monthly wages for the job worked last week?” In the administrative data sets, earnings are reported by firms and used by the government to compute social security benefits. This may imply that administrative data depict earnings in the formal sector with less measurement error (see Mayer and Mittag 2015). In both types of data, wages are typically reported as monthly take-home pay, after social security taxes have been paid. They include bonuses that workers receive on a regular basis (monthly or more frequently), but exclude bonuses paid less frequently than monthly. Because working hours are typically contractual hours in the administrative data, the wage measure used in this section is monthly wages.

25Subperiods of four (three) years are considered in Brazil and Ecuador (Costa Rica), this paper tested the robustness of the findings to longer subperiods, as in Alvarez et al. (2018). Results indicate a larger variance of the residual, which is offset by worker effects explaining a few percentage points less of the overall variation in wages. The share contributed by firms remains roughly the same. Results available upon request.

> 26As in Alvarez et al. (2018), our specification does not include an index of time-varying observable characteristics of workers, as data are not available for all the countries. This exclusion avoids identifying the time-varying effects of changes within workers and firms during the limited time frame of each subperiod. For brevity, the analysis also excludes year effects, the covariance between worker and year, and firm and year effects. The results are available from the authors. They show that, as