rafmacalaba/datause-encoder-data huggingface dataset:
|**T**|**able 9: Depth**|**of Petty Corr**|**uption and Fi**|**rm Performa**|**nce Interactio**|**n terms**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable: Financially<br>Constrained (dummy)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||**Annua**|**l sales**|**Labor Pro**|**ductivity**|**Sales gro**|**wth rate**|**Exp**|**orts**|
|Depth of Petty Corruption (Cell<br>average)*Annual Sales (2009<br>USD, logs)|0.250*<br>(0.139)|0.286**<br>(0.134)|||||||
|Depth of Petty Corruption (Cell|||0.255*|0.300**|||||
|<br>average)*Labor Productivity (logs,<br>2009 USD)|||(0.142)|(0.140)|||||
|Depth of Petty Corruption (Cell|||||0.026***|0.025***|||
|<br>average)*Sales growth rate (%,<br>annual)|||||(0.008)|(0.008)|||
|Depth of Petty Corruption (Cell|||||||2.071*|2.194*|
|<br>average)*Exports (proportion of|||||||(1.217)|(1.199)|
|sales)|||||||||
|Depth of Petty Corruption (Cell||-0.704||-0.710||-1.123**||-0.562|
|<br>average)*GDP per capita (logs)||(0.457)||(0.482)||(0.441)||(0.477)|
|Depth of Petty Corruption (Cell|-1.905|3.624|-1.275|4.321|0.865**|10.253***|0.959***|5.703|
|average)|(1.643)|(4.552)|(1.287)|(4.384)|(0.381)|(3.740)|(0.367)|(4.014)|
|Annual Sales (2009 USD, logs)|-0.186***|-0.198***|-0.345***|-0.347***|-0.171***|-0.176***|-0.123***|-0.125***|
||(0.044)|(0.044)|(0.072)|(0.072)|(0.035)|(0.036)|(0.037)|(0.038)|
|Age of Firm (logs)|0.047|0.048|0.071|0.072|0.005|0.001|0.045|0.046|
||(0.072)|(0.072)|(0.072)|(0.072)|(0.078)|(0.078)|(0.072)|(0.072)|
|Exports (proportion of sales)|-0.477|-0.455|-0.340|-0.320|-0.473|-0.426|-0.919**|-0.926**|
||(0.356)|(0.354)|(0.363)|(0.360)|(0.372)|(0.374)|(0.442)|(0.440)|
|Firm Has Female Owners Y:1 N:0|-0.190|-0.189|-0.178|-0.176|-0.201*|-0.207*|-0.192|-0.191|
||(0.118)|(0.117)|(0.119)|(0.118)|(0.118)|(0.118)|(0.118)|(0.118)|
|Foreign Ownership (proportion)|-0.032|-0.033|0.025|0.024|0.011|0.005|-0.036|-0.038|

52
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Table 6: Eect of a direct SLM training on contact farmers' knowledge of individual SLM techniques

|Knowledge<br>score|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.893|0.043*<br>[0.017]|347|0.135|
|Strip-tillage|0.512|0.089<br>[0.057]|347|0.095|
|Pit planting|0.798|0.050<br>[0.088]|347|0.075|
|Contour farming|0.520|0.127<br>[0.116]|347|0.098|
|Crop rotation|0.567|0.008<br>[0.046]|347|0.072|
|Row planting|0.310|-0.026<br>[0.113]|347|0.149|
|Improved fallowing|0.690|0.007<br>[0.040]|347|0.046|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

37
Croke et al. (2016)

- Croke (2014) finds that Ugandan children exposed to a deworming program originally studied in Alderman et al. (2006) have higher math test scores nearly a decade later, with effect sizes of over 0.2 standard deviation units.

- Ozier (2015) finds that infant children who lived in Kenyan communities where older school-age children were dewormed show large cognitive test score improvements ten years later, presumably due to reduced infection through beneficial spillover effects. The magnitude of the effect is 0.2 to 0.3 standard deviation units, which is equivalent to between 0.5 to 0.8 years of schooling.

- Using a difference-in-difference estimation methodology rather than a randomized design to study a deworming campaign in the U.S. South in the early 1900s, Bleakley (2007) finds that deworming led to increased school enrollment and attendance for children, and improved literacy and boosted income by 17% for adults who were treated as children.

- Finally, Baird et al. (2016) estimate that a decade after treatment, males who participated in mass deworming in Kenya worked 17% more hours per week and had higher living standards, missing approximately one fewer meal per week. Females were approximately one-quarter more likely to have passed the primaryschool leaving exam and attended secondary school. The estimated value of benefits, in terms of the net present value of future earnings net of increased schooling costs, exceeds the cost by more than one hundred fold.

These results suggest that the expected benefits of deworming would greatly exceed its costs even if one took a conservative approach, assuming a very low probability of effects 10
estimates we discussed earlier (Table 4).

In light of these estimates and the event studies presented earlier, we believe that the most plausible interpretation of our findings is that increased competition from Venezuelan migrants adversely affected the quality of employment and earnings of young, low-educated Ecuadoran workers.

# **7 Conclusions**

Using data from Ecuador’s labor force survey (ENEMDU) merged with new data on the geographic distribution of Venezuelan migrants across Ecuador, this paper has analyzed the location choices of Venezuelans within Ecuador and the labor market consequences of these choices.

Our analysis has shown that Venezuelans’ location choices have been fundamentally driven by the size of regional economies, measured by the wage bill at the canton level. We have also grouped Ecuador’s cantons as a function of the size of the Venezuelan population living in the canton relative to its population size, and produced differencein-difference estimates of the labor market effects of Venezuelan inflows. The estimates clearly indicate that young, low-educated Ecuadoran workers have been adversely affected. Specifically, they have experienced reductions in the quality of their employment, increases in informality, and declining earnings, relative to workers with similar characteristics living in cantons with very low or non-existing inflows of Venezuelans.<sup>25</sup> Our findings are in line with recent studies analyzing the labor market effects of Syrian refugee inflows into Turkey that also find that the newly arrived migrant workers have found employment mainly in informal jobs, placing the burden of the adjustment disproportionately on the more vulnerable workers in the main host regions. These effects are also highly localized in a few regions, suggesting the use of targeted interventions to alleviate the economic burden falling on the vulnerable groups of workers in those regions.

Last, it is important to highlight that these adjustment costs can lead to an increase in economic prosperity, both for the migrants and the hosting regions. The inflow of migrant labor is likely to spur investment and the creation of new businesses, generating economic opportunities that can benefit both migrant and domestic workers. The Ecuadoran government could adopt policies to speed up this process and help Venezue-

> 25It is also worth noting that our identification strategy accounts for the general economic slowdown affecting Ecuador since 2014.

19
**Table 11. Distributional effects of inflation inequality**

||**Initial**|**Final**||<br>**Base in ini**|<br>**tialperiod**|<br>**Base in fi**|<br>**nalperiod**|
|---|---|---|---|---|---|---|---|
|**Period Inequality measure**|**inequality**|**inequality**|**% Change**|**Price**<br>**Change**|**Quantity**<br>**Change**|**Price**<br>**Change**|**Quantity**<br>**Change**|
|||**Br**|**azil**|||||
|**1988 - 1996** GINI Coefficient|**0.54**|**0.55**|**1.60**|**2.17**|**-0.58**|**1.45**|**0.14**|
|Mean Log Deviation|0.55|0.57|4.56|4.34|0.21|3.24|1.32|
|Theil Index|0.57|0.59|3.44|6.56|-3.12|3.41|0.03|
|||**Colo**|**mbia**|||||
|**1997 - 2003** GINI Coefficient|**0.53**|**0.50**|**-5.49**|**1.92**|**-7.41**|**0.95**|**-6.43**|
|Mean Log Deviation|0.51|0.45|-11.87|4.02|-15.89|1.76|-13.63|
|Theil Index|0.53|0.47|-11.76|5.16|-16.92|2.12|-13.89|
|||**Me**|**xico**|||||
|**1984 - 1989** GINI Coefficient|**0.50**|**0.50**|**-0.20**|**2.77**|**-2.97**|**2.02**|**-2.21**|
|Mean Log Deviation|0.48|0.47|-1.96|5.07|-7.03|3.89|-5.85|
|Theil Index|0.50|0.50|-0.03|8.91|-8.94|5.19|-5.23|
|**1989 - 1994** GINI Coefficient|**0.50**|**0.49**|**-1.85**|**1.38**|**-3.23**|**1.50**|**-3.35**|
|Mean Log Deviation|0.47|0.44|-5.11|2.30|-7.41|2.85|-7.96|
|Theil Index|0.50|0.45|-10.27|3.66|-13.93|3.34|-13.62|
|**1994 - 1996** GINI Coefficient|**0.49**|**0.46**|**-6.88**|**-1.30**|**-5.57**|**-1.08**|**-5.79**|
|Mean Log Deviation|0.44|0.38|-13.79|-2.44|-11.35|-1.96|-11.83|
|Theil Index|0.45|0.39|-13.48|-3.14|-10.33|-2.34|-11.14|
|**1996 - 2002** GINI Coefficient|**0.46**|**0.49**|**6.32**|**1.42**|**4.90**|**1.02**|**5.30**|
|Mean Log Deviation|0.38|0.43|12.51|2.88|9.63|2.22|10.29|
|Theil Index|0.39|0.45|14.06|3.42|10.64|2.53|11.53|
|||**P**|**eru**|||||
|**1995 - 1999** GINI Coefficient|**0.46**|**0.50**|**9.91**|**1.28**|**8.63**|**0.79**|**9.13**|
|Mean Log Deviation|0.38|0.51|34.43|2.56|31.88|1.80|32.63|
|Theil Index|0.38|0.46|21.54|2.98|18.55|2.05|19.48|
|**1999 - 2001** GINI Coefficient|**0.50**|**0.49**|**-2.72**|**1.05**|**-3.78**|**0.40**|**-3.13**|
|Mean Log Deviation|0.51|0.49|-4.06|1.78|-5.84|0.75|-4.80|
|Theil Index|0.46|0.42|-7.32|2.71|-10.03|1.00|-8.32|
|**2001 - 2003** GINI Coefficient|**0.49**|**0.48**|**-1.21**|**0.47**|**-1.67**|**0.40**|**-1.60**|
|Mean Log Deviation|0.49|0.47|-3.97|0.82|-4.79|0.69|-4.66|
|Theil Index|0.42|0.41|-2.05|1.19|-3.24|0.98|-3.03|

Note: The table reports the change in different inequality indices between the initial and final period for each spell under analysis. The table also reports the changes in inequality (in %) due to prices and quantity changes computed using as base the initial and final periods.

27
|Decision number|||(13.89)|||(13.89)|(19.52)|
|---|---|---|---|---|---|---|---|
|Treatment X||||55.5||54.7|54.7|
|Risk preference||||(130.3)||(129.2)|(129.2)|
|Treatment X|||||2.048*|2.046*|-0.252|
|Religious preference|||||(1.20)|(1.20)|(1.53)|
|Religious preference X|||||||-0.014|
|Decision number|||||||(0.09)|
|Treatment X|||||||0.354**|
|Religious preference X De|cision number||||||(0.1)|
|Constant|2955.3***|3221.7***|3320.8***|3252.2***|3053.3***|3182.7***|3172.5***|
||(92.84)|(1112.20)|(1113.10)|(1118.60)|(1106.90)|(1114.30)|(1116.20)|
|Within R2|0.329|0.329|0.331|0.329|0.329|0.331|0.336|
|Between R2|0.026|0.153|0.153|0.155|0.175|0.177|0.177|
|Overall R2|0.250|0.283|0.285|0.284|0.289|0.291|0.294|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. * 10%, ** 5%, *** 1% significance level.
* Coppedge and Reinicke (1990) constructed five series on upolyarchy"<sup>22</sup> in 170 countries for the year 1985. We use "media pluralism" and 'freedom to organize' as indicators of two fundamental civil liberties.

While each of these indices of civil liberties are subjective and debatable, their cross correlations are reasonably high, which creates some confidence that they measure the same thing and do so reasonably well (although we note that, since Coppedge and Reinicke used the Freedom House and Humana studies in their own ranking procedure, part of the high correlation between ' "The set of institutional arrangements that permits public opposition and establishes the right to participate in politics" (Coppedge and Reinicke 1990). The other series are 'fair elections", "extension of suffrage", and "freedom of expression". We do not report the results on these series because they either have less variance and/or capture politica freedoms. Consistent with reports reported below on political indicators, they are in no case statistically significant across specifications.

- 13 -
others, using our data on posterior probabilities and selecting their own 's from the copious data ii available from the WBES or other sources. ZZ

# **III.4 Selecting a Specific LCA Model and Evaluating Its Properties**

**III.4 Selecting a Specific LCA Model and Evaluating Its Properties** The implementation of step 1 of the 3-step process entails estimation of and the by cc maximizing the likelihood at (3). LCA is typical of latent-variable models in that implementation ff(∙| ∙) ππ requires making many detailed specification decisions. In most practical applications of LCA, the choice of how many classes ( _C_ ) to estimate is primary, with a relatively small number of options considered. However, relaxing local independence enormously increases the number of possible options when specifying the DGP. In this subsection, we provide an overview of the procedures we used to choose our favored model, relegating most details to Appendix C.<sup>53</sup>

Settling on the details of model specification moves through three stages. First, statistical measures of model-fit are used as criteria to choose a very small set of satisfactory models. Second, the results from those models are evaluated using more subjective criteria. For example, parsimony is important to avoid over-fitting and to facilitate meaningful interpretation, which usually means the use of a simpler model (with fewer parameters). Third, the researcher examines class homogeneity and separability. Homogeneity is the notion that the members of a specific class exhibit similar characteristics or, equivalently, that there are certain configurations of responses typifying each class. Separability captures whether each class looks quite different from all other classes, or, equivalently, that there are certain configurations of responses that distinguish each class from the others. In all three stages, statistical measures are used as model-selection criteria.

To begin the model selection process, three preliminary decisions were necessary. First, we chose to analyze the responses for suppliers and customers separately. We presume that firms

> 53 Detailed background information can be found in Collins and Lanza (2010), Masyn (2013), and Vermunt and Magidson (2016).

28
# Table 3: **Who are America’s Stars? Correcting for intangible capital (Continued...)**

Panel B: Generalized Quantile Estimation of Model (5) in Panel A

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ROIC<sup>TOT</sup>|ROIC<sup>TOT</sup>|ROIC<sup>TOT</sup>|ROIC<sup>TOT</sup>|
|Quantile|25|50|75|90|
|Markups|18.079***|17.665***|25.242***|38.081***|
||(0.894)|(1.103)|(1.570)|(1.920)|
|N|83842|83842|83842|83842|
|Fixed Efects||Industr|y, Year||
30 countries) must be reconsidered. It relies primarily on the “neo-Keynesian model”, which postulates three relations: (i) an aggregate demand relation, where production is determined by demand, which itself depends on anticipations made by agents regarding future production and future interest rates; s(ii) a relation based on the Philips curve, where inflation depends on production as well as on anticipation of future inflation levels; and (iii), a relation of monetary policy, which reflects in the model the idea that monetary policy can be used to influence prevailing real interest rates.

The availability of new powerful software and computers now makes it possible to carry out complex and simultaneous calculations. Macroeconomics is no longer concerned solely or primarily with the resolution of differential equations systems. New methods such as dynamic stochastic programming, which simultaneously integrate some of the lessons of microeconomics (consumer and employee utility maximization, value maximization by companies, rational anticipations, detailed specification of imperfections, etc.) represent a breakthrough in economics.

Macroeconomists working on African economies have shown that the basic neoKeynesian model, which has been expanded to take into account many imperfections, especially those found in credit or employment markets could be strengthened even further (Collier and Mayer 1989). But some fundamental issues remain: first, the amount of detailed and disaggregated information necessary for the use of these new dynamic stochastic general equilibrium models (DSGE) seldom exists in many poor countries. Also, the meaning of the structural parameters often generated by DSGE models has become so doubtful (Canova and Sala, 2006) that “this may be a case in which technology has run ahead of our ability to use it, or at least to use it best.” (Blanchard, 2008).

In addition, the micro-macro linkages are still ignored in DSGE models, as well as the heterogeneity of households and firms, which are too quickly the rapid amalgamated in randomly created global categories. Again, the study of African economies sheds light on the need to explicitly address issues of aggregation. In the African context, the very idea of household (an economic concept that applies to all persons living under the same roof, regardless of whether they are linked by family ties) poses conceptual challenges to statisticians and demographers (van de Walle, 2006). The same is true for firms, a term so general and so broad that it could be quite misleading in economic modeling exercises.

The solution to such difficulties is not for macroeconomists to content themselves with structural parameters generated from industrialized economies but rather, to complement macroeconomic analyses with microeconomic, country studies, and lessons from thematic monographs from other disciplines. Far from weakening the identity of macroeconomics and diluting it, it would enrich it and strengthen the credibility of the entire discipline of economics.

# **5. CONCLUSION**
Several papers have analyzed the microeconomic effects of income transfers targeted to vulnerable populations and, especially, conditional cash transfer (CCT) studies have become increasingly popular: Gaardner, Glassman and Todd (2010) unpack the causal chain between CCTs and health; Leroy, Ruel and Verhofstadt (2009) focus on CCTs and child nutrition; and Maluccio, Murphy and Regalia (2010) research the relations between the success of CCTs and initial supply conditions.

This paper, related to research in this field, studies one of the microeconomic conditions through which income transfers targeted to vulnerable people can reduce labor supply and spark a dependency syndrome.

The argument is based on a distributional effect of pro-vulnerable transfers. Assuming two groups defined in terms of income level, the beneficiaries of the program (vulnerable) and the nonbeneficiaries (non-vulnerable): we study why a pro-vulnerable transfer to the beneficiary group (i.e., foreign aid, national poverty programs, CCT) may provoke a labor supply reduction among the non-beneficiaries. Assuming that the program is targeted to all vulnerable individuals, start from a traditional neoclassical labor-leisure model, in which the non-beneficiary group reacts to the introduction of the aid program, through a “relative reservation constrain” that may become binding. In this model for a given income transfer provided to vulnerable people, marginally richer individuals (non-targeted by the program) may react by dropping working hours and become involved in the program. For this reason, we introduce an additional “relative reservation” constraint, stating that the equilibrium utility level of the non-vulnerable group is higher or equal to that of the treated group.

Therefore, we interpret “aid dependency” as a social strategic interaction, rather than as emerging from an individual maximization problem. Because the two groups are specified in accordance with their income rather than utility, marginally richer individuals (non-beneficiaries) may obtain substantial disutility by working for long hours. Therefore, as pro-vulnerable transfers increase the beneficiary group’s income, it also increases the “relative reservation” utility level of the nonbeneficiary, and this may affect their utility maximization and lower the non-beneficiary labor supply, sparking dependency.

Several researches tend to be rather skeptical of the claim that an income boost (produced by income transfers or foreign aid) can produce an increase in the demand for leisure, so-called “dependency syndrome”. In this paper, we do not reject this claim, but argue that marginally richer individuals (non-beneficiary of aid programs) may introduce strategic behaviors (i.e., reduce working hours) in order to qualify as vulnerable and join the program. For this reason the distributional dimension may help disentangling this phenomenon and enrich the individual maximization problem.

One of the central papers in this field by Kanbur, Keen and Tuomala (1994) introduced “labour supply considerations into the targeting of poverty alleviation programs in developing countries”, 2
# **2. Survey design and definitions of wage rigidities**

# **_2.1. Survey design_**

The analysis in the current paper is based on a survey of firms conducted between the second half of 2007 and the first quarter of 2008 in 15 European Union countries: Austria, Belgium, the Czech Republic, Estonia, France, Greece, Hungary, Ireland, Italy, the Netherlands, Lithuania, Poland, Portugal, Slovenia and Spain.<sup>3</sup> The survey was carried out by the National Central Banks and all countries used as the basis for the survey a harmonised questionnaire developed in the context of the Eurosystem Wage Dynamics Network, a research network analysing wage and labour cost dynamics. The harmonised questionnaire contained a core set of questions referring to the firms’ wage-setting strategies, which was included in all countries’ questionnaires. The harmonised questionnaire was further adapted by some countries to account for specific country characteristics and differences in institutional framework. As a result, some countries opted for shorter versions of this questionnaire, while others extended it in several dimensions.

Appendix 1 gives an overview of the main characteristics of the national surveys. The sample frame in each country was based on firms with at least 5 employees. The sectors covered are manufacturing, energy, construction, market services, non-market services, trade and financial intermediation; there are, however, some differences in the sectoral coverage of individual countries. The sample covers around 15,300 firms representing around 47.5 million employees. A description of the distribution of the sample by country, sector and size is provided in Appendix 2. In order to make the results representative of the total population the cross-country statistics presented in the following sections use employment-adjusted weights. For each firm/observation these weights indicate the number of employees each observation represents in the population. They can be calculated as the population employment divided by the number of firms (in each stratum) in the realised sample.<sup>4</sup> Appendix 3 gives a detailed description of the construction of the employment-based weights.<sup>5</sup>

> 3 The survey was conducted either by traditional mail, phone and face-to-face interviews or through the internet. Germany also conducted the survey, but with a different questionnaire (Radowski and Bonin, 2009). Hence, it is not included in the sample.

> 4 For most of the cases the stratification is based on sector and firm size; some countries also used region as an additional stratum.

> 5 The employment-adjusted weights account for the unequal probabilities of receiving and responding to the questionnaire across strata as well as for the average firm size (measured on the basis of number of employees) in the population in each stratum.

7