rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **Introduction**

In empirical studies, corruption and bribery have been found to have a negative effect on firm growth and performance. However, bribery can affect individual firms differently depending on their attributes, and the possible differential effect of bribery on firm growth has not been studied extensively. This paper identifies the differential effects of bribery by firm size based on sales as well as firm age. Understanding the significance of differential effects across firm characteristics is valuable from a policy perspective since it allows for specific recommendations on promoting private sector development.

We use firm-level data from the Latin America and Caribbean (LAC) region, covering 29 countries. There is large variation in sales growth for firms in LAC. Average annualized sales growth is modest at about 3 percent, and the standard deviation is 35 percent. Bribed firms in LAC have an average growth rate of 0.9 percent compared to 3.3 percent for firms that were not bribed.

Understanding constraints to firm growth is especially relevant in LAC since the region has experienced multiple financial crises in the early 2000s and again in 2008. While there has been great effort to promote development, efforts have been concentrated on increasing consumption rather than development of the private sector. Moreover, economic growth in LAC has been credited to changes in commodity prices rather than increases in productivity. Recognizing business climate factors constraining firms is valuable for future growth. Corruption in Latin America is regarded to be commonplace (Gaviria 2002). Many countries in the Latin America region rank as high corruption countries. Transparency International’s Corruption Perception Index ranges from 0 to 10, with low scores characterizing highly corrupt countries. As an example, in 2009, Argentina scored 2.9 compared to the U.S. with a score of 7.5.

Using data from 1999, Gaviria (2002) finds negative effects from corruption on sales growth for firms in Latin America. However, perception-based corruption data are used and the possibility of simultaneity is not addressed. Asideu & Freeman (2009) also use firm-level data from 1999 and find no significant effect from corruption on investment growth in the region. Compared to previous research on corruption in LAC, we offer new insights using recent data. We find objective measures of bribery have a negative effect on sales growth. In particular, 2
combined: 1990:3-1991:3, 1991:1-1992:1, 19 9 1:2 -199 2 :2 .5 Another panel was created spanning 1991:1-1992:1 that terminates in the more detailed Micro-Enterprises Survey (MES). This survey was constructed by identifying 11,000 owners of micro-enterprises, defined as firms of fewer than six individuals, from the 1990:4 NUES and reinterviewing them in 1991: 1 in more depth about issues of capital structures, costs, and employment patterns. It also specifically asks why they left their previous job and why they started the present business which offer a partial alternative to naive corrections for selectivity bias.<sup>6</sup> Thus, for a reduced sample, we have far more information on both earnings differentials, and motivations for moving. The panels in the combined NUES sample above were chosen to include and span either side of the MES panel.

Two popular definitions of informality are employed. The first focuses specifically on the issue of "protectedness" and comprises owners or workers in firms of fewer than 16 employees who ; The cohort beginning in the fourth quarter of 1990 was not used because it would incorporate the end of the year bonuses, the aguinaldo. This normally amounts to a 13th monthly payment but may vary by year and firm imparting an undetermined upward bias to the monthly wage reports. Since we are concerned with expected differentials in income, the wages of workers who reported nonnally receiving the aguinaldo were increased by 13/12.

6Heckman (1979) is insistent that the standard two stage methods of correctingfor selectivity bias depend on having confidence in the underlying model of how workers choose among sectors. A bad first stage selection specification may induce bias rather than correct for that existing. This is likely to be the case as the premise of this work is that we have little knowledge of the role each sector serves. Further, since a principle argument of this work is that, in the absence of knowledge of the value of unobservable components ofthe differential, we cannot use the differentials as evidence of segmentation, the issue becomes of somewhat less importance. The standard Heckman procedure was employed, however, for transitions in and out of formal salaried work using the variables employed in the logit analysis in section IV for the probit first stage. The model for Formal Salared to Self-Employment did not converge and yielded improbable results. For Self-Employmen **t** o Formal Salaried, Formal Salaried -Informal Salaried , Formal Salaried to Contract, Contract-Formal Salaried, it could not be rejected that the transformed correlation coefficient between the errors in the selection and wage equation was zero and hence the correction for selectivity bias was inappropriate. Only for the Informal Salaried to Formal Salaried regression was the transformed correlation coefficient significant. The correction changed the sign ofthe differential and greatly changed the magnitude. Again, however, in the absence of a model of what is being selected for, it is unclear how to interpret the restat.

4
# **Annex 2. Summary statistics**

## **Table A.2.1. Summary statistics**

||**Missing**<br>**values**<br>**(%)**|**Unique**<br>**values**|**Min.**|**Max.**|**Mean**|**Median**|**Standard**<br>**deviation**|
|---|---|---|---|---|---|---|---|
|Absenteeism (incl. those with zero absence)|0.1|29|0.0|31.0|1.2|0.0|2.0|
|Absenteeism (excl. those with zero absence)|51.1|28|1.0|31.0|2.5|2.0|2.2|
|Average grade in four subjects|0.8|>500|25.5|97.8|78.4|78.5|5.1|
|Bahasa Indonesia|0.9|414|0.0|100.0|80.1|80.0|5.8|
|English|0.9|485|0.0|99.0|78.0|78.0|6.3|
|Math|0.9|493|0.0|100.0|77.2|77.2|6.6|
|Science|0.8|433|13.0|100.0|78.4|78.0|6.1|
|Average grade in four subjects (stz.)|0.8|>500|-5.1|4.6|0.0|-0.1|1.0|
|Bahasa Indonesia (stz.)|1.0|>500|-5.5|5.1|0.0|-0.1|1.0|
|English (stz.)|1.0|>500|-5.4|5.5|0.0|-0.2|1.0|
|Math (stz.)|1.2|>500|-5.6|6.0|0.0|-0.2|1.0|
|Science (stz.)|1.0|>500|-5.5|5.5|0.0|-0.1|1.0|
|Aspired edu. level is postsecondary|0.0|16|8.0|21.0|17.6|18.0|3.8|
|Expected edu. level is postsecondary|0.1|38|8.0|21.0|14.7|12.0|3.5|
|Aspired educational level (7 cat.)|0.0|16|0.0|1.0|0.8|1.0|0.4|
|Expected educational level (7 cat.)|0.1|38|0.0|1.0|0.5|0.0|0.5|
|Aspired education in years|0.0|16|1.0|7.0|5.5|6.0|1.8|
|Expected education in years|0.1|38|1.0|7.0|4.1|3.0|1.8|
|If there's a aspired-exp. difference for edu.|0.1|47|0.0|1.0|0.6|1.0|0.5|
|Difference in years of edu. between aspired|0.1|47|-13.0|13.0|2.9|1.0|3.8|
|Expect to get a degree in preferred subject|0.1|79|0.0|1.0|0.6|1.0|0.5|
|Self-esteem|0.0|17|1.0|5.0|3.5|3.5|0.6|
|Sense of belonging|0.5|25|1.0|5.0|4.2|4.3|0.5|
|Perseverance|0.1|17|1.0|5.0|3.6|3.8|0.6|
|Growth mindset|0.3|17|1.0|5.0|3.2|3.3|0.8|
|Effort beliefs|0.2|13|1.0|5.0|3.8|3.7|0.7|
|Learning orientation|0.1|17|1.0|5.0|3.8|4.0|0.7|
|Performance-avoidance orientation|0.2|13|1.0|5.0|3.4|3.3|0.9|
|Self-esteem (stz.)|0.0|17|-3.9|2.3|0.0|0.0|1.0|
|Sense of belonging (stz.)|0.5|25|-6.6|1.7|0.0|0.3|1.0|
|Perseverance (stz.)|0.1|17|-4.3|2.2|0.0|0.2|1.0|
|Growth mindset (stz.)|0.3|17|-2.8|2.2|0.0|0.0|1.0|
|Effort beliefs (stz.)|0.2|13|-3.9|1.8|0.0|-0.1|1.0|
|Learning orientation (stz.)|0.1|17|-4.1|1.6|0.0|0.2|1.0|
|Performance-avoidance orientation (stz.)|0.2|13|-2.7|1.8|0.0|-0.1|1.0|
|Age|0.1|>500|8.8|23.0|14.2|14.1|0.7|
|Being a girl (vs. a boy)|0.0|2|0.0|1.0|0.5|1.0|0.5|
|Living in urban area (vs. rural)|0.2|2|0.0|1.0|0.4|0.0|0.5|
|Living in Java (vs. in Sumatra)|0.0|2|0.0|1.0|0.6|1.0|0.5|

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that become relatively more expensive, then the direct effect of the price changes will exaggerate the welfare effects since it assumes no such substitution is possible. Estimating these substitution effects is therefore important, although substantially more involved. One direct approach is to econometrically estimate a demand system over the n consumption goods, using data from the household survey, and retrieve from this an ∂h estimate of the matrix of price derivatives of the compensated demand function, . ∂'p Doing so however requires data on goods prices at the household level. In the case of the Egyptian household survey, I have some information on unit values for individual consumption items. However, at the more aggregated level at which the exchange rate pass-through estimates are calculated, these unit values become very difficult to interpret.<sup>10</sup> In this paper I take a different and computationally much simpler approach that exploits the fact that I have two household surveys for Egypt, for 2000 and 2005. The basic idea is to use information on observed changes in expenditure shares over this period to back out estimates of the substitution effects. The key simplification of this approach is that it obviates the need to estimate an entire demand system, but instead requires only estimates of the expenditure elasticities for each consumption item. As long as prices faced by individual households are orthogonal to total expenditure, these elasticities can be estimated by simple regressions of expenditure shares on total expenditure alone.

To implement this idea, I first need to relate observed changes over time in quantities demanded to the substitution effects of interest. Taking a first-order approximation to changes in the observed ordinary demand function I have: where x(p,e) is the ordinary demand function; Δx(p,e) are the changes in quantities demanded between 2000 and 2005; and Δp* is the vector of overall price changes 10 Friedman and Levinsohn (2002) implement an approach originally due to Deaton (1988, 1990) who shows how to estimate demand systems when only unit value data are available.

12
|Figure 4.<br>(Percent of<br>|Japan<br>GDP un|: Bas<br>less i<br>|eline <br>ndicat<br>|Scena<br>ed oth<br>|rio<br>erwis<br>|e)|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Actual<br>||Med<br>|um-Téen<br>|m projecti<br>|on<br>||E<br>|tended <br>|Projecti<br>|on<br>|
|2024|m22|2023|202d|2025|20Oh|RSF|86208R|2020|«FD|ROOT|
|Public debt<br>2554|3615|2SR2|266.9|257.8|2502|2815|280|DT|OBR|BAO|
|Change in public debt<br>AS|59|31|A9|143|17|a2|25|OF|15|18|
|Contibutian<br>of identified flows:|70|20|08|235|26|a2|45|17|24|26|
|Primary deficit<br>58|75|62|28|2F|29|34|42|G4|435|46|
|Noninterestrevenues<br>66|41|ME|M4|M4|M4|B44|M4|M4|MA|A|
|<br><br>Noninisres!expenditures<br>412|426|409|SRO|374|473|375|WT|oO|<br>oa7S)|<br>AO|
|<br><br>Automatic debt dynamics<br>43|AS|A417|£0|27|24|30|28|PF|21|«AB|
|Real inlerest rateand relative inflation<br>a4|O08|83|64|22|22|20|AF|AF|9a|Ao|
|Real growth rate<br>$4|DF|Sd|2B|ofS|oh|AO.|td|od|oe|oe|
|Other identified flows<br>oo|if|G4|43|42|42|44|a4||i|D|
|Contingent liabilities<br>oo|oo|oF|oO|ofF|OO|oO|oo|oo|oo|oo|
|Other transactions<br>oo|i|434|4&3|G2|42|44|4|100|610|080|
|Contribution ofresidual<br>$6|ft|Ad|of|oO|oO|WO|to||t|d|
|Gross financing reeds<br>640|621|S72|549|529|S29|S44|65.9|S80|S49|545|
|ofwhich:deblservice<br>634|S557|S20|S21|812|S10|5299|S90|SSB|S29|SIT|
|<br><br>Locelcurrency<br>634|S57|S20|S521|512|S10|529|S50|SOB|S29|SIT|
|<br><br>Foreigncurrency<br>oo|oo|oo|oO|oo|oO|oO|oo|oo|oo|oo|
|Memeo:|||||||||||
|Real GDPgrowth (percent)<br>24|i146|1|430|COO|||o4|O4|O38|OB|
|Inflation(GOP deflator: percent)<br>2|oS|38|28|13|18|134|1|913|«#18|0«18|
|Nominal GOP grow#h (percent)<br>19|19|62|4&7?|19|18|17|17|647)|168|O18|
|Effectiveinlerestrate(percent)<br>O68|O68|OF|OF|O8|Of|OS|os|oF|oF|10|
|<br><br>Contribu|tion to|Chang|e in Pu|blic De|bt||||||
|»|(pe|rcentofG|OP)||ao|||eeP|rimarydef|icit|
|25<br>———|——Pic|iect|on|||l||mamR|sIntere|st|
|20<br>||||||||<br>wa<br>|<br>lesandre<br>|lative|
|1s|||||40||15|mee <br>gr|<br>eb<br>owth||
|10|||||20|#||mmmmEx|ch.rate||
|5<br>0<br>=<br>=<br>:*i<br>=<br>“V<br><br>|7<br>SES|EE|SS|E==|°<br><br>.,<br>|=<br>-1i||de<br>Ma i<br>|<br>preciatio<br>he fiowe<br>|n<br>s|
|“10<br>||||||a0)|||Re|sidual||
|a<br><br><br><br><br>|||||||||||
|22<br>20d<br>2016<br>2018<br>3020<br>2097|204<br>|2098|2028|2000|“a0||;<br>|—|—in|pubic|
|<br><br><br><br><br><br>Stabilizing level in 2031.||||||Cumula|<br>tive||||
demand-pull policies stimulated non-incremental (radical) change in the California wind power industry in the 1975-1991 period also finds no evidence that demand-side policies alone encouraged more radical frontier-type technical change (Nemet, 2009).

**Figure 9. Motivation of firms introducing environmental innovations, 2006-08**

**(** **_Firms citing factors as motivations, percentage of innovative firms)_**

<!-- Start of picture text --> HUN<br>CZE<br>SVK<br>ISR<br>PRT Current or expected<br>IRL market demand<br>POL<br>EST<br>Existing<br>FRA environmental<br>ITA regulations or taxes<br>on pollution<br>DEU<br>Fiscal incentives<br>BEL<br>FIN<br>NLD<br>LUX<br>SWE<br>0% 10% 20% 30% 40% 50%<br><!-- End of picture text --> Source: OECD, based on Eurostat (CIS-2008) and national data sources, June 2011. Note: The Community Innovation Survey (CIS-2008) was the first to introduce a voluntary module on environmental innovation. An ̳environmental innovation‘ is defined as an innovation that leads to more environmental benefits than alternatives. The environmental benefits can be the primary objective or the result of other innovation objectives. ̳Innovative firms‘ refers to firms with any of the four types of innovation (product, <u>process, organizational and marketing).</u> However, for technologies that are more mature, a greater emphasis on demand-side policies may be more effective in spurring firms to introduce more incremental innovations. Based on firm responses in 16 countries as part of the 2008 EU Community Innovation Surveys, Figure 9 shows that existing or future environmental regulations, followed by market demand from customers, are identified by firms in most countries as the main driver of introducing environmental innovations. The availability of direct public support in the form of fiscal incentives is the least important reported motivation in all countries. Figure 10 highlights the reported benefits of environmental innovations (from the same survey). Interestingly, in most countries surveyed (15 of 19 for this question), a larger percentage of innovative firms perceive the environmental benefits of innovation to be on the cost side (in terms of reduced energy use per unit of output) than on the revenue side (in terms of customers being willing to pay more for new products that reduce energy usage by end-users). This suggests that most of these innovations are likely to be incremental process rather than more radical product innovations.

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# **2.4** **<u>Definition of regions</u>**

We have grouped countries into eight regions. The first group consists of “mature economies”, which are the EU-27 countries (members in 2008) plus the high-income countries in the world.<sup>45</sup> We treat India and China as regions in their own right. The remaining groups are defined as residuals according to the geographic regions used in the WDI.

# **2.5** **<u>Pareto imputation and scaling to national accounts consumption</u>**

We allocate the excess of national accounts consumption over household surveys in two steps. First, we adjust the country-mean to equal the maximum of the survey mean and national accounts consumption.<sup>46</sup> Second, we re-compute the decile shares for all deciles except the top using the original average decile incomes and the adjusted mean (the share in total income of those deciles therefore decreases). Third, we compute the new top decile share as the difference between 100% and the sum of the revised shares of the bottom 9 deciles. We use the revised top 10% and top 20% shares in the Pareto imputation.

We obtain household final consumption expenditure<sup>47</sup> (in 2005 $PPP) from the World Development Indicators for the survey years.<sup>48</sup> It is important to note that the sample changes in this part of the analysis because of two reasons. First, due to the unavailability of macro data, we lose some country-year observations. Second, because of a lack of disaggregated macro data, we

> 44 This is likely to understate within-country inequality in these countries, because we expect the rural areas to be poorer compared with the urban areas. The share of the rural population in several of them (Argentina, Uruguay) is minimal though.

> 45 To be precise, the mature economies include EU-27, Australia, Bermuda, Canada, Hong Kong, Iceland, Israel, Japan, Korea, New Zealand, Norway, Singapore, Switzerland, Taiwan and USA.

46 In the majority of cases, national accounts consumption exceeds the income recorded in the survey, so the revised mean equals the national accounts consumption. When the survey mean is greater than per capita national accounts consumption, we keep the survey mean.

47 It is defined as “the market value of all goods and services [...] purchased by households” (http://data.worldbank.org/indicator/NE.CON.PRVT.PP.KD). It includes durable products, imputed rent for owneroccupiers, payments to obtain permits and licenses, and expenditures of non-profit institutions serving households.

> 48 We filled any gaps in the WDI with data from the IMF’s International Financial Statistics (IFS) or the country’s statistical offices. We complement the series in constant 2005 $PPP with information from the WDI and the IFS in current and constant local currencies as well as current USD. We convert the current USD using market exchange rates. In the rest of the conversion we follow the same approach as with the micro data using consumption PPPs and CPI.

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_Programa Joven in Argentina_ : Aedo and Nunez (2004) study the effectiveness of another training program introduced in Argentina called _Programa Joven_ that was targeted at lowincome individuals aged less than 35. The program targeted young people from poor households with low education levels, little or no working experience, who were either unemployed or inactive. It provided an average of 200 hours of training, a monetary subsidy for females with young children, transportation expenses, medical checkups, books, material and work clothing. The duration of training varied from 14-20 weeks and was divided into a technical knowledge phase, in which participants were taught occupational skills, and an internship phase in which participants completed an eight-week internship at a firm. The impact evaluation study is based on two analysis samples: (i) a sample of 139,732 so-called “ _Acreditados_ ” who qualified and registered to take training and who were at different points in their training at the time of answering the survey (some had not started, some were in the technical knowledge phase, some in the internship phase and some had finished or dropped out), and (ii) a subsample of 3,340 program beneficiaries and matched comparisons, drawn from the _Acreditados_ sample, who were surveyed at the time of registration and then again one year later.

The evaluation study examines whether the program increased the labor income of trainees and their probability of employment, using a cross-sectional propensity score matching methodology to control for preprogram differences between program participants and nonparticipants.<sup>9</sup> The propensity score model depends on current labor force status of the individual, a poverty measure, sociodemographics, education, marital status, and geographic region, and is estimating separately for four groups: young males, adult males, young females and adult females. A potential drawback of the propensity score model is that there are no historical data available on earnings or employment history at the time of program registration. Recent labor force history is a good predictor of participation in training programs and, lacking such data, the predictive power of the propensity score models is not high.

The impact estimates show statistically significant effects of the program on earnings of adult women (age 21-35) and young males (age less than 21) but not for young females or adult males. Statistically significant effects on employment are found only on adult women, in the range of 9- 12 percentage points. Estimates obtained using alternative propensity score models and nearest

> 9 An individual was defined as a program beneficiary if he/she has completed the technical knowledge phase, else was designated a nonbeneficiary.

17
<!-- Start of picture text --> Other Europe & Usa<br>North America if<br>é 5) Northeast Asia<br>me i<br>Eastern Europe . }<br>a"!<br>e<br>Poland ‘Hong<br>Hungary reSSir Kong<br>%i<br>/smsF<br>e<br>rof]<br>. Turkey 2 eo a<br>on r eet<br>Romani fr 1; ager a Southeast<br>‘China Le - 47 Asia<br>ee ell =e" Indonesia<br>4 ey =<br>Bulgaria ae er - e: fA,F he<br>4<br>a” 2 : int q<br>soos Bangbderh i * Malaysia<br>se ~aee : J. 7 Thailand<br>Sd Lanka India ; .<br>a:t Ae * % Vietnam<br>.<br>ré‘%<br>Pakistan ; Tunisia a<br>a %,<br>f s<br>South Asia i Moron “The rings indicate the share of<br>F, i dollarstotal Europeanby partner imports country: im U.S.<br>! 1. 10% +<br>cacahes 3.2. 6.0%4.0% -- 9.5%,6.8%,<br>4. 2.0% - 3.9%,<br>§. 1.0% - 1.5%<br>Total valoe of extra-regional<br>European clothing imports<br>wee $45.5 billion in 1996 anc<br>$103.4 billion in S008.<br>' Apoael imports are for the EU-15 counties only. The calculations inciwes the value of intra-El15 trade, but the char<br>eucludes the names of tee individual EU-15 counties. EU-15: Austria, Belgium, Denmars, Finland, France, Germany,<br>Greece, Ireland, Italy, Lusembourg, Meihedands, Portugal, Spain, Sweden and the United Kingdom.<br>“The 2006 position corresponds to the ring where the country’s name is located: the 1996 position, if different, is<br>indicated by a small circle. The arrows represent the magnitude and direction of change over tire.<br>Source: UNCombadeSITC 84, Rey 3, imports ta EUS<br><!-- End of picture text -->