rafmacalaba/datause-encoder-data huggingface dataset:
Latin America. The surveys were conducted between 2009 and 2014 in the various countries and include manufacturing and services sector firms. Table A1 provides the distribution of the sample by country along with the survey year. A somewhat similar survey instrument and methodology was used across countries.<sup>5</sup> Given the cross-sectional nature of the data, each firm and country are surveyed once at a particular point in time. Thus, it cannot be guaranteed that responses from firms would be similar at other points in time.<sup>6</sup> It is important to note that there is much debate about the definition of informality. The IES data we use defines informal firms as those that are not registered with the relevant authorities. This is consistent with many other studies in the related literature (Amin and Okou, 2020; Loayza and Roginlini 2011; Gindling and Terrell 2005; Rand and Torm 2012; Mead and Morrisson 1996). Another important issue relates to the representativeness of the sample. A nationally representative sample of informal firms would require a complete list of all informal firms in the country. However, such a list does not exist for most countries at the national or even the regional and city level. Hence, most studies using data on informal firms tend to rely on random sample of informal firms taken from one or two main urban centers of the country (see for example, Harris 2014, Rand and Torm 2012). These random samples are not necessarily representative of the underlying informal economy, although it is not clear if the sample bias systematically affects the results. Likewise, the sample we use is also random, restricted to 55 regions/cities across 14 countries, and not necessarily representative of the informal economy in the city or the country. This point notwithstanding, the informal surveys we use have been used extensively in the literature to study the sector (see Amin and Islam 2015, Islam 2019, La Porta and Shleifer 2014).

> 5 Details of the methodology can be accessed through the following link: http://www.enterprisesurveys.org/methodology

> 6 The difficulty in accounting for time periods is one of the limitations of cross-sectional data sets.

9
**Table 3. Determinants of fiscal policy volatility, by type of commodity**

||PrimaryEx<br>|penditures<br>|Government<br>|Consumption<br>|
|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|
|EMDE|2.900***|1.794**|2.613**|1.539|
||(1.047)|(0.867)|(1.121)|(1.045)|
|Agriculture exporters|0.712|-0.592|2.197*|1.178|
||(0.983)|(1.036)|(1.294)|(1.350)|
|Metal exporters|2.236*|-0.136|2.984**|1.224|
||(1.337)|(1.353)|(1.395)|(1.328)|
|Energy exporters|6.185***|-1.125|7.250***|1.139|
||(1.625)|(1.252)|(1.561)|(2.004)|
|GDP per capita|-1.243**|-1.114**|-1.885***|-1.855***|
||(0.523)|(0.453)|(0.644)|(0.608)|
|Resources rents||0.377***||0.307***|
|||(0.089)||(0.0657)|
|Constant|17.081***|16.017***|22.318***|22.23***|
||(5.593)|(4.818)|(6.855)|(6.466)|
|Observations|177|177|166|166|
|R-squared|0.292|0.450|0.407|0.503|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘EMDE’ is a dummy variable that takes the value “1” if a country is classified as an EMDE, and “0” otherwise. ‘Agriculture’, ‘Metal’, and ‘Energy’ exporters are dummy variables that take the value “1” if a country is a net exporter of the respective commodity group and “0” otherwise.

33
As DeShazo and Fermo (2002) suggest, when conjoint analysis is applied, several design issues and options such as service definition and payment method need to be considered at the outset (see Pattanayak et al., 2001 and 2004 for a full discussion of design choices including survey and elicitation methods). This includes: (a) defining the commodity primarily involved (b) determining the type of WSS offered and how it differs substantively from current options, and (c) characterizing the institutional setting that is providing the service. Usually a careful review of existing levels of service and alternatives can identify and define the appropriate commodity for the study. In general, preparatory activities and focus groups can uncover important features of the selected service option(s) such as metering, hours of supply, and water quality. These can then be presented in detail in the descriptive/informational parts of the conjoint survey.

The scenario, or institutional setting, must also be adapted to local conditions. In general, if certain elements of the scenario are expected to substantively affect household demand for improved services, these elements become design features that vary across the surveyed households. Surveys with these different versions would be administered and econometric modeling could be used to detect whether the private versus public institutional context affects household demand and choices.

Payment methods, in other words, how individuals are asked to pay for the services, is also an important consideration. In the water and sanitation sector, the main approach is to ask the valuation question(s) from an _ex ante_ perspective such as increments to current bills or new bills. For households without a connection, the payment method may be a connection charge plus an increment to monthly bills. The ranges of the connection charge and monthly bill should be sufficiently wide to estimate demand and to capture relevant policy alternatives (e.g., cross-subsidization). The relative advantages of different payment method alternatives depend importantly on the target population and on the policy context. If cash income is a binding constraint for the poor, households could be asked to consider paying for service access and improvements in terms of labor as suggested by Estache et al. (2002). The preference for one alternative over another should also depend on which payment scenario is most realistic from a policy perspective.

When a high degree of uncertainty surrounds the policy design for WSS delivery, conjoint methods offer a flexible way to evaluate several institutional, service quality, tariff structure, and subsidy options simultaneously. Estache et al. (2002) provide a long list of policy instruments to address access and 6
4 On December 20, 1994 the Mexican peso was devalued by 15% and two days later it was allowed to float. At the end of December the Mexican government was forced to cancel a Tesobono refinancing auction because investors were no longer willing to bear the sovereign risk at rates acceptable to the Mexican authorities<sup>2</sup> . The perception of sovereign risk - as reflected for example in the Brady bonds prices (see the plots of the Mexican and Polish Par and Discount bond prices in figure I and 2) - jumped dramatically during the crisis. Even though the Mexican crisis reflected mainly internal problems, it immediately affected the perception of the sovereign risk in other debtor countries. In particular the yield on most emerging market bonds jumped along with Mexico's. As an example, we report in figure 3 a plot of the _stripped spreads_<sup>_3_</sup> of the Mexican and Polish Brady Discount bonds, and in figure 4 an analogous plot for the Par bonds.

_Mexican and Polish Discount Bond Prices Mexican and Polish Par Bond Prices_ <!-- Start of picture text --> I100 70<br>90_ 60<br>80-5<br>60~ 30<br>7/2 i94 1 Si21i 6/9 4 g/O8I~5 ~I25 9 i2i0~6 '~I96 7129/94 1Ji6/94 v/23W5 5*05/95. :I W 6.6J2 /96<br>- MEXDIS.POLDI - MEXIPAR ---- POLPAR|<br><!-- End of picture text -->

_Figure I_ _Figure 2_ In 1981, the choice not to differentiate among different debtors might have been a rational response to the crisis - considering the political and international dimension that that crisis soon reached. This statement could certainly be questioned at end-1994. As long as the international community and the traders believe in the process of debt securitization one would expect that, over time, only country specific elements or international shocks should affect the risk premium of a sovereign country. This paper originated from the initial observation that for the 1994 Mexican crisis there is evidence of _spillover effects_ in the assessment of the sovereign risk premium for different countries. The questions we will try to address are the following: Are there comovements (not due to international shocks) in the sovereign risk premium of different debtor countries? If there are, what is the nature and the dimension of these comovements? To what extent can a leader country like Mexico affect the risk perception on 2 For an assessment of the Brady deal on Mexico the reader can see Claessens, S., D. Oks and S. van Wijnbergen (1993). For an account of the 1994 Mexican debacle see Calvo, G.A. (1996).

3The reader not familiar with this term can refer to the Appendix.
e¤ects of a change in the level of the standard, respectively, on the number of ...rms and ...rms’output. Assessing the relative strengths of these two e¤ects helps us determine the overall impact on imports of any regional initiatives that deal with standards.

We are particularly interested in exploring the possible asymmetric e¤ects of regional agreements on standards in a heterogeneous world. We assume that the world consists of two types of countries: type K and type L, which di¤er in terms of their ...rms’costs of complying with standards, i.e. ci and Fi. Two broad cases are possible: (i) cK < cL and FK > FL or (ii) cK < cL and FK 6 FL. Our analysis focuses on case (i) for several reasons. First of all, case (i) is more analytically challenging, and once the implications in this case are established, those in case (ii) can be worked out quite straightforwardly. More importantly, case (i) may more accurately depict situations where ...rms from both rich and poor countries produce the same product. It is likely that ...rms from richer capital-abundant countries (type K) tend to adopt a more capital-intensive technology that requires a relatively large Fi, while ...rms from poorer labor-abundant countries (type L) tend to adopt a more labor-intensive technology that requires a relatively large ci. In fact, the World Bank’s (2004) Technical Barriers to Trade Survey reveals that a ...rm’s percentage of compliance cost spent on product re-design for each export market (corresponding to Fi in our paper) is positively correlated with the GDP per capita of the country in which it is located, whereas that spent on hiring additional labor for production and testing (corresponding to ci in our paper) is negatively correlated with the GDP per capita of the country. This limited evidence does not, however, provide the basis for an a priori assertion that type K countries correspond to developed countries and type L to developing countries. Rather, we treat this correspondence as a hypothesis to be tested in the empirical section.

In the rest of this section, we examine the impact on both intra-regional trade and trade with excluded countries of two types of regional initiatives: upward harmonization and downward harmonization (with or without rules of origin).

11
With perfect competition for land, free entry and exit will ensure that excess profits are driven to zero. Land rents will consequently be equal to net income per hectare (Ricardo 1817; MNS, 1994).

The Ricardian function is intended to be a locus of the most profitable crops with respect to each exogenous variable such as temperature. The net income function does not include less profitable alternatives. It consequently does not look like the response function for any single crop but rather as a flatter function across all choices. Figure 1 depicts a theoretical set of crop specific net income functions with respect to temperature as well as the overarching Ricardian function. For example, at cool temperatures, farmers would choose to grow wheat ( _Triticum aestivum_ L.). As temperatures rise, farmers would no longer want to grow wheat because it would become less profitable. They instead would shift to maize ( _Zea mays_ L.). As temperatures increase further, they might want to shift to fruit ( _Panicum miliaceum_ ) or vegetables which are more heat tolerant. The Ricardian function, Equation (2), captures the locus of maximum profits for each temperature or precipitation level. It is estimated across crops and across inputs, revealing the net effect of changing the exogenous variable. Because farmers are assumed to make adaptations that are profitable, the method automatically captures the adaptation inherent in the market (MNS, 1994).

The Ricardian model was developed to explain the variation in land value per hectare of cropland over climate zones (MNS, 1994). In repeated studies in the United States, Brazil, Sri Lanka and South America, the land value per hectare of cropland has been found to be sensitive to seasonal precipitation and temperature (Mendelsohn and Dinar 1999; 2003; Seo et al. 2005; Seo and Mendelsohn 2007). Similar results have also been found for crop net revenue in India, Africa, South America, and Israel (Mendelsohn and Dinar 1999; Kurukulasuirya et al 2006; Seo and Mendelsohn 2007; Fleischer et al. 2007). Because the response is nonlinear, a quadratic functional form has been used in every Ricardian study.

There have been a number of criticisms of the Ricardian approach since it was first developed. There was initially a concern about irrigation (Cline 1996; Schlenker et al 2005). This study and other analyses (Mendelsohn and Dinar 2003; Kurukulasuriya and Mendelsohn 2006; Mendelsohn and Seo 2007) address this concern by examining the differences in the response to warming between irrigated and rainfed land. A related concern is the importance of 5
<!-- Start of picture text --> East Asia Financial Crisis**<br>@ before<br>after<br>Latin America Financial Crisis<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text --> <!-- Start of picture text --> In_GDP***<br>above avg<br>@ belowbel ave<br>In_pop***<br>0 0.05 0.1 0.15 0.2 0.25<br><!-- End of picture text --> <!-- Start of picture text --> East Asia Financial Crisis*<br>@ before<br>@ after<br>Latin America Financial Crisis<br>0 10 20 30 40 50 60 70<br><!-- End of picture text --> <!-- Start of picture text --> In_GDP***<br>Maboveavg<br>@ below avg<br>In_pop***<br>ie} 50 100 150<br><!-- End of picture text -->
> the banking industry is in long-run equilibrium, but a separate test exists whether this condition

> is satisfied. ' As we have access to bank-level information and as we want to study differences

> among banks, we choose for the PR model (the empirical specification we use is explained in section two).

- D. Competition Testing: Empirical Results for Banking Systems

> A number of papers have applied either the Bresnahan or the PR methodology to the issue of competition in the financial sector, although mostly to the banking system specifically. Cetorelli (1999) provides more detail on these formal tests and reviews results of previous studies of

> empirical banking studies. One of the first Bresnahan test is Shaffer (1989). For a sample of US banks, he finds results that strongly reject collusive conduct, but are consistent with perfect competition. Using the same model, Shaffer (1993) finds that the Canadian banking system was competitive over the period 1965-1989, although being relatively concentrated. Gruben and McComb (2003) finds that the Mexican banking system before 1995 was super-competitive, that is marginal prices were set below marginal costs. Shaffer (2001) uses the Bresnahan model for

15 countries in North America, Europe, and Asia during 1979-91. He finds significant market power in five markets and excess capacity in one market. Estimates were consistent with either contestability or Cournot type oligopoly in most of these countries, while five countries were significant more competitive than Coumot behavior would imply.

Shaffer (1982) applied the PR model to a sample of New York banks using data for 1979 and found monopolistic competition. Nathan and Neave (1989) studied Canadian banks using the PR methodology and found results consistent with the results of Shaffer (1989) using the Bresnahan

> In case of short-ni, but not long-run equilibrium, the parameter H represents a one-tail test in the sense that a positive value rejects any form of imperfect competition, but a negative value is consistent with a variety of possibilities, including short-run competition (Shaffer 1983).

8
examine the effect of household wealth and student academic ability on the type of school attended, we use the junior secondary school sample to estimate a multinomial logit regression of school type attendance. The dependent variable is an indicator of whether the student attended public school, private secular school, private Muslimaffiliated school, private Madrassah, or a private non-Muslim religious school.<sup>9</sup> There are few students attending private non-Muslim religious schools; these schools include Christian and Catholic private schools that are considered to be of comparable quality to public schools.<sup>10</sup> Therefore, we focus on the determinants of attendance at private secular, Muslim-affiliated private schools, and Madrassahs. The entire set of household and student characteristics listed in the data section are included as control variables, but we report only the results from indicators of household wealth, the student’s prior academic achievement and parental education. Table 1 reports the marginal effects of each variable on the probability of attending a particular type of school, and whether the variable was statistically significant in the multinomial logit model. For dummy variables, the marginal effect is the sample average of the difference in the predicted probabilities when the dummy variable is set to one or zero. For the test score and consumption variables, we report the average of each observation’s marginal effects of the variable and its square. For these variables, the significance of the variable and its square were tested jointly.

> 8 The coefficients on the province dummies are as high as one standard deviation. These substantial provincial differences, which we do not attempt to explain, may be the topic of future research.

> 9 In this analysis, public Madrassahs are grouped with public secular schools for brevity. Only 7% of public school students attend public Madrassahs.

> 10  Three percent of the full sample and ten percent of the private-school attendees attend non-Muslim religious schools.   Of these schools, about 60 percent of this category are Catholic schools, while the remaining 40 percent are Christian, Buddhist, or other religious affiliation.  Christian schools are not restricted to Christian students.  Private Catholic and Protestant secondary schools often enroll Muslim

16
|Ouagadougou Water SupplyEngineeringCredit Project|Yao Badjo|
|---|---|
|Ouagadougou Water SupplyEngineeringCredit Project|Yao Badjo|
|Ouagadougou Water SupplyEngineeringCredit Project|Yao Badjo|
|Ouagadougou Water SupplyEngineeringCredit Project|Yao Badjo|
|Ouagadougou Water SupplyEngineeringCredit Project|Yao Badjo|
|BiodiversityConservation and Management Project|Noel Rene Chabeuf|
|BiodiversityConservation and Management Project|Noel Rene Chabeuf|
|BiodiversityConservation and Management Project|Noel Rene Chabeuf|
|BiodiversityConservation and Management Project|Noel Rene Chabeuf|
|BiodiversityConservation and Management Project|Noel Rene Chabeuf|
|Education Project(03)||
|Zapi East Integrated Rural Development Project||
|Feeder Roads Project||
|Feeder Roads Project||
|Feeder Roads Project||
|HighwayProject(04)||
|HighwayProject(04)||
|HighwayProject(04)||
|RailwayProject(04)||
|RailwayProject(04)<br>||
|Livestock Development Project(02)<br>||
|Livestock Development Project(02)||
|Livestock Development Project(02)||
|Livestock Development Project(02)||
|HEVECAM Rubber Project(02)||
|Water SupplyProject(02)||
|Water SupplyProject(02)||
|Northern Province Rural Development Project(01)||
|Northern Province Rural Development Project(01)||
|Northern Province Rural Development Project(01)||
|Technical Assistance Project(02)||
|Technical Assistance Project(02)||
|Technical Assistance Project(02)||
|Technical Assistance Project(02)||
|Technical Assistance Project(02)||
|Oil Palm and Rubber Consolidation Project||
|Oil Palm and Rubber Consolidation Project||
|Oil Palm and Rubber Consolidation Project||
|Oil Palm and Rubber Consolidation Project||
|ForestryProject||
|ForestryProject||
|ForestryProject||
|ForestryProject||
|ForestryProject||
|ForestryProject||
|Post and Telecommunications Project - Technical Assistance||
|Post and Telecommunications Project - Technical Assistance||
**Table 3. Mean ECD Outcomes by Round and INDH**

||**Control**|**2008**<br>**Treatmen**|**t**<br>**Total**|**Control**|**2011**<br>**Treatment**|**Total**|**Control**|**2013**<br>**Treatment**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Prenatal**|54.72|54.44|54.57|64.93|65.77|65.40|65.22|63.55|64.31|
|**Delivery**|52.52|47.78|50.00|77.99**|70.83**|74.01|74.28|68.67|71.22|
|**Height-for-age**|˗1.76|˗1.85|˗1.81|˗1.94|˗2.15|˗2.05|˗2.55**|˗1.86**|˗2.20|
|**Weight-for-age**|˗0.15|˗0.34|˗0.25|0.87|0.82|0.84|0.52|0.64|0.58|
|**Weight-for-height**|1.26|1.05|1.15|2.22|2.73|2.51|2.79|2.80|2.80|
|**Preschool**|10.67|7.69|9.09|4.18|3.10|3.58|5.88|4.08|4.87|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01.

16
- 42 -

Figure 19. Tanzania's price incentives and agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>120<br>100<br>80<br>40-<br>20<br>o 0 1 I I 1 I I I I I I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-<br>producer price export volume<br><!-- End of picture text --> Figure 20. Togo's price incentives an, agricultural exports <!-- Start of picture text --> Index 1970 = 100<br>120-<br>100<br>80<br>60<br>40<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>- -<br>producer price export volume<br><!-- End of picture text -->