rafmacalaba/datause-encoder-data huggingface dataset:
The first survey was conducted a few weeks before the launch of the training activities. Then, the in-class training activities covered a six-week period. Following these in-class sessions, entrepreneurs in the enhanced training received visits from the program’s coach. The follow-up survey was conducted in July-August 2012 a year after the last in-class session and two years after the baseline survey was conducted and the whole intervention started.

Overall, participation and attendance remained high throughout the training. The monitoring and evaluation data collected by TGT show that 75 percent of the basic training program participants and 87 percent of the enhanced training program participants attended all of the five day-long sessions covering management skills. Attendance for the technical components of the program was slightly lower: 66 percent of basic training program participants and 67 percent of the enhanced program participants attended at least three technical sessions. However, these relatively lower participation rates may be explained by the fact that participants attended only the technical sessions that were relevant to their activities.

The survey attrition rate was 14 percent, meaning that 702 firms could be interviewed in the follow-up survey. There are no significant differences in attrition rates across the three groups. The firms that dropped out of our sample are not different at baseline from the firms that did not (see Annex table A.2 for details).

Following McKenzie (2012), we use an ANCOVA specification to increase the precision of our estimates. As assignment to treatment was randomized, we obtain unbiased estimates of the ITT effect by estimating the following equation: Where Y�� is the endline value of the outcome of interest; _BasicTreat_ is a dummy variable equal to 1 for individuals assigned to the basic training and zero otherwise; _EnhancedTreat_ is a dummy variable equal to 1 for individuals assigned to the enhanced training and zero otherwise; X� is matrix of the three time invariant covariates we used to stratify the randomization: the entrepreneur’s sector, her geographical district, and the type of gift she received; Y�� is the baseline value of the outcome of interest;. The parameter β� is an estimate of the average effect of the basic program. The parameter β� is an estimate of the average effect of the enhanced program. For binary outcomes, in addition to reporting the OLS estimates, we estimate a Probit model and report the marginal effects. The latter estimates can be interpreted as the difference in mean level of the outcome (for instance: difference in mean adoption of a specific business practice). We report robust standard errors throughout the study.

10
11 Institute of Statistics (SIS)<sup>12</sup> and contained observations for the variables described below at the three-digit International Standard Industry Classification (ISIC), distinguished by ownership, namely private versus public firms. The variables are:

-labor input defined as total number of production persons. The entry does not distinguish various types of workers, such as by sex, age etc. Also no information is available on the number of working days lost due to strikes or other factors.

-total labor cost which comprises wages and other payments made by the employer such as overtime payment, bonuses, employers contribution to retirements funds, etc.

-total value of intermediate inputs at current prices.

-total value of output at current prices.

-beginning-of-year and end-of-year value of inventories of final output and intermediate inputs.

-sectoral output price deflators.

The original data were subsequently used to derive the value of a number of additional variables necessary for computations. The latter included the stock of capital and industry specific input price deflators.

> 12.From 1973 to 1982, the data cover all manufacturing firms with 10 or more employees. From 1983 onwards, the data cover only firms with 25 employees or more. The change in coverage is not relevant for some industries with few large firms, but is important for others that comprise a large number of small, family concerns.
-11-

The sub-saharan countries form a set that is sufficiently homogenous (compared to other samples), for comparisons across countries. We can expect that the data will show a relation between the level of financial deepening and the "inflation environment" in the medium term. Hence, it is more relevant to look for a relation between averages of inflation rates and averages of M2 to GDP ratios. This data is represented in Figure la for almost all sub-saharan countries9.

The figure presents a striking pattern: countries can be divided in two non FA ad CF<sup>10</sup> groups, non CFA and CFA , respectively. All non CFA countries are found<sup>near</sup> a schedule that shows an inverse relation between inflation and financial deepening. The only exceptions are Rwanda, Burundi, and Malawi, which have low inflation and low financial deepening. This apparent anomaly is easily explained by differences of urbanization ratios. Whereas all the other countries have ubanization ratios between 17 and 30 percentl (except for Uganda, with 7 percent), the urbanization ratios for these three countries are 2%, 2% and 5%, respectively.

The second group is formed by the CFA countries: they have experienced low inflation on average, (because of the institutional restraints on the creation of money), but their level of financial deepening is lower than in 9The missing countries (among the reporting members), are Ethiopia, Madagascar, Liberia, and some of the countries in the CFA zones that would be redundant with the data in the Figure (Burkina Faso, Chad, Niger) 1OCFA countries belong to one of the two currency zones with a fixed parity with the french franc (a constant nominal exchange rate<sup>since 1953),</sup> and specific constraints on monetary policy, (see Honohan [1990]). l<sup>1</sup> Word Development Report, 1988.
Food insecurity was captured by asking about changes relative to the start of the COVID-19 pandemic. Participants were asked “Since March 2020, did you ever cut the size of your meals or skip meals because there wasn’t enough money for food?” Answer options included not at all, very little, somewhat, or a great extent. To be classified as “experienced food insecurity” individuals had to have answered very little, somewhat, or to a great extent.

## **_Parental Educational Attainment_**

Maternal and paternal education levels were grouped into three categories: less than a primary education, completed primary, and completed secondary school. The questions were asked for both mothers and fathers regardless of who participated as the survey respondent.

## **_Enrollment in Early Childhood Education_**

ECE enrollment was determined using an open-ended item administered only if the index child for a given household was between 3 and 6 years of age. Responses included _katchi_ (the primary provision of ECE in primary schools), ECE, play group, preprimary (year 1, 2 or 3), nursery, kindergarten, and preparatory class (“prep”).

## **_Living in a Rural Area_**

Determination of living in a rural area was based on the respondent’s perception of whether they lived in an urban or rural area as a binary choice.

## **_Parental Beliefs about Appropriate Age for School Entry_**

In order to capture parents’ beliefs on when children should enter an organized learning environment outside the home, parents were asked a single open-ended question: “When do you think a child should start school?” Answers were captured by year of age and ranged from 2 to 10 years of age.

# Statistical Analyses<sup>6</sup>

First, we investigated the bivariate association between key variables: province, gender, each of our risk factors of interest (living in a rural area, psychosocial stimulation, maternal education, parental distress, food insecurity, and enrollment in ECE) and our outcomes of interest (child development for children under 36 months of age and school readiness for children 36-72 months of age), using unadjusted Ordinary Least Squares (OLS) linear regression analyses (See Tables 2 and 3). Second, we focused on individual risk factors and generated plots that showed the proportion of the population with each specific risk factor, and the overlap of the three most

> 6 All statistical analyses were performed in Stata v17 (Stata-Corp, College Station, Texas, USA).

12
# **Exhibit 2.2 – Profitability indicators** _<u>Medians and means for 2003-07</u>_

||**Real sales**<br>**growth(%)**|**Op. profit**<br>**margin(%)**|**ROA (%)**|**ROE (%)**|**Real asset**<br>**growth(%)**|
|---|---|---|---|---|---|
|**Peer group (WS)**||||||
|Median|6.0|9.3|5.2|7.7|0.9|
|Mean|9.1|12.3|4.4|5.3|6.5|
|**LMI countries, excl. Egypt (WS)**||||||
|Median|3.0|8.8|4.3|5.7|-4.2|
|Mean|5.6|12.6|3.9|4.3|0.2|
|**Top Tier, Egypt (WS)**||||||
|Median|9.8|18.0|11.3|22.1|0.9|
|Mean|14.1|20.3|13.4|23.9|6.1|

_Source: Worldscope; World Bank staff analysis Notes: Retained earnings to Equity from Coface also include Reserves. Whole peer group: Colombia, Chile, Hungary, Indonesia, Israel, Jordan, Mexico, Morocco, Pakistan, Philippines, and Turkey. LMI countries: Colombia, Indonesia, Jordan, Morocco, and Philippines._ The industry level analysis in Exhibit 2.3 echoes this finding and shows that across all sectors, all profitability measures have been enjoying healthy growth rates over the period 2003-07, relative to the peer group. Exhibit 2.3 also shows that although all sectors have much higher net profitability, it is especially high in the Travel and Leisure sector (+19.5 pp), although the margins have been shrinking fast with a CAGR of -11.9 percent. The consistently high ROA points to sustainable profitability which is not due to lower asset growth (Exhibit 2.1). The extremely high ROE further points to durable profitability, which for all sectors has been growing at a positive rate. Again, the Travel and Leisure sector exhibits superior profitability growth (CAGR 44.1 percent; Exhibit 2.3).

The above findings could just represent high variation within industries and over the years. In addition, they do not take into account that countries have different industry compositions. Therefore we perform median regression analysis that explicitly accounts for these factors and corrects for differences in countries’ industries. In addition, this technique withstands noisy data, leading to more reliable results (see Box 1).

Exhibit 2.4 shows the median Egyptian firm had superior performance across the board compared to the median non-Egyptian firm in the LMI group, even after correcting for differences in industry composition between countries. For example, the median Egyptian firm enjoyed a 6.8 percent higher sales growth compared to the median LMI-firm. Moreover, Egypt’s outperformance for all indicators is statistically significant. Differences are slightly lower compared to the peer group and although sales growth is higher in Egypt it is no longer statistically significant from the peer group. However, the difference in the profitability indicators is still statistically significant.

6
WPS 3261

# **DETECTING ILLEGAL TRADE PRACTICES BY ANALYZING DISCREPANCIES IN FOREST PRODUCTS TRADE STATISTICS**

**AN APPLICATION TO EUROPE, WITH A FOCUS ON ROMANIA**

## **Jeffrey R. Vincent**<sup>1</sup>

World Bank Policy Research Working Paper 3261, April 2004 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Professor, Graduate School of International Relations & Pacific Studies, University of California at San Diego. E-mail address: jvincent@ucsd.edu.  This paper was prepared in conjunction with on-going studies of governance in the forestry sector of Romania, supported by the Environmetnally and Socially Sustainable Development Unit for Europe and Central Asia (ECSSD), managed by Peter Dewees, Lead Environment Specialist.

1
To estimate the model in (1) we draw on the literature on partial linear models (as reviewed in Yatchew, 1998).<sup>7</sup> All observations are ordered in terms of their consumption values. Differences are taken between the data for successive ranked observations, giving the regression: where Δ is the difference between the values for the _i_ ’th observation and that for _i_ -1 when<sup>_x_</sup> _ij_ ranked in ascending order of _y_ . Under our assumption about the function φ , the first term on the RHS vanishes as the sample size goes to infinity ( plim[ φ _j_ ( _yij_ ) −φ _j_ ( _yi_ − ,1 _j_ )] = 0 ). So the following parametric regression can be estimated by least squares: To estimate the functions φ _j_ (.) , we can then estimate the non-parametric regression: Finally, we estimate the constant terms in the usual way by forcing the estimate of (1) through ˆ ˆ ˆ the mean points, so α _j_ = _S j_ −φ _jj_ − _x j_ π _j_ where φ<sup>ˆ</sup> _jj_ is the sample predicted value of φ _j_ (<sup>_y_</sup> _ij_ ) .

Higher-order differencing allows efficiency gains in this method (Yatchew, 1998). We re-write (8) as:

> where ∑ _d k_ = 0 (which allows us to drop the non-parametric effect from equation 6) and the 2

> normalization condition ∑ _d k_ = 1 (which assures that the transformed residuals have variance

2 σν ). Hall et al., (1990) provide the optimal weights up to _m_ =10.

> 7 We have used the PLREG program for STATA by Lokshin (2006).

6
capturing the conditions of the plot are important because they influence the extent to which crops take up nutrients from the soil. The household-level variables include wealth, education, household size and dependency ratio and age of household head. Household wealth is measured in terms of ownership of agricultural tools and durable goods. It has also been well established that crop yield is lower on female-managed plots than on male-managed plots and has inverse relationship with plot size; as such, we also control for plot size and the gender of the plot manager in out model (Carletto et al., 2011; UN and World Bank, 2018; Kilic et al; Darko and Purnamasari, 2019). The study also controls for temperature and precipitation, the two most important weather variables affecting crop production in Malawi.

Although the complementarity between soil nutrients in terms of their effect on crop yield are well documented, we only focus on nitrogen. We do so for several reasons. First, the fertilizers used in maize production in Malawi are predominantly _chitowe_ (NPK 23:21:0 +4S) and the urea fertilizers, both of which are nitrogenous fertilizers. This is probably the case because nitrogen is a limiting nutrient in many soils in Malawi (and SSA as a whole) and its estimated net loss from soils is higher than any other nutrient (World Bank, 1998). Second, while all applied nitrogen is used by plants in the same growing season, only about 20% of applied phosphorus (the second most important nutrient in the fertilizers used in maize production in Malawi), is used by plants in the first growing season of application (Griffith, n.d.; Sheahan, 2013). Third, the nitrogen variable in our model strongly correlates with the other nutrient variables, especially the phosphorus variable. Hence including other nutrient variables in the model will result in multicollinearity, a condition that would result in large variances and covariances of the estimates thereby rendering the estimates inconsistent. Fourth, including other nutrients and their interaction terms with the nitrogen variable in the model will complicate the computation of the spatial variation of the nitrogen response which is of particular interest in this study. The reasons notwithstanding, we do 8
This trade liberalization episode provides an excellent setting<sup>tostudythe relationship</sup> between wages and trade for several reasons. Table I<sup>reports the averagetariff across20</sup> manufacturing industries from 1987 to 1998, the period of our<sup>study.7Theaveragetariff</sup> declined from 60 percent in 1987 to 15.4 percent in 1998. Second,<sup>thereforms changed the</sup> structure of protection across industries, as different industries experienced<sup>differentratesof</sup> tariff changes. Figure I plots tariffs in 1986 and 1998 in various<sup>industries.It showsthat</sup> declines in tariffs differed across industries, and that<sup>the dispersion of tariffs was significantly</sup> reduced. The changing structure of protection is also reflected<sup>in relativelylow year-to-year</sup> correlations of industry tariffs from 1987 to 1998. For<sup>example,the correlationcoefficient</sup> between tariffs in 1987, a year preceding the trade reforms,<sup>and tariffsin1989is .81.The</sup> correlation between tariffs in 1987 and 1995, the<sup>year afterthelarge reforms werecompleted,</sup> drops to .6. The intertemporal correlation of Brazilian tariffs<sup>is similar to the correlationof</sup> Colombian tariffs during the 1984-1998, but<sup>is</sup> significantly<sup>lower than theintertemporal</sup> correlation in the U.S. tariffs, where the correlation between<sup>post-KennedyGA.TT Round Tariffs</sup> (1972) and post Tokyo GATT round tariffs (1988) is .98. This<sup>vast variationin Braziliantariffs</sup> across industries in a given time period and across time provides<sup>an excellent setting tostudy the</sup> relationship between trade and wages.

The above shifts in Brazil's trading environment are mirrored<sup>in the increasein the</sup> import penetration (defined as imports/(output+net<sup>imnports))and export exposure (definedas</sup> Tariff data was obtained from Muendler (2002) at http://socra.tes.berkeley.edu/-muendler/<sup>and are basedon Kume,</sup> et. al. (2000). The original data provide the tariff levels for<sup>53 sectorsat the nivel80 industrialclassification.We</sup> have aggregated the data to nivel 50 and made some<sup>additionaladjustmentsso that thetariff information</sup> corresponds to the level of industry aggregation in the labor<sup>forcedata.The reportedtariffs are simple averagesof</sup> more disaggregated data. When constructing our tariff series,<sup>we havealso experimented withusing nivel80import</sup> penetration as weights, which yielded similar aggregate means.<sup>The correlationbetweenthe two series was .98.We</sup> thus use the tariffs constructed as simple averages throughout<sup>the paper.</sup> 8
We examine the following questions:

- Which firm characteristics, e.g. size, age, industry, and legal status are associated with bribe payments and underreporting of revenues to tax authorities?

- Is corruption a tax on innovation? Are there particular innovative activities such as introducing new products and introducing new technology, associated with greater bribe payments to government officials? Do innovative firms that bribe receive special advantages in dealing with bureaucracy and regulation?

- Do firms that pay more bribes also evade more taxes? Do firm characteristics explain whether a firm is on balance a victim or perpetrator across countries?

- What is the role of the financial system in limiting the extent of underreporting of income? How do banks compare with informal financing channels in curbing illegal behavior?

To answer these questions, we use a rich multi-country data set, the World Bank Enterprise Surveys, sampling over 25,000 firms (80% of which are small and medium enterprises) in 57 countries (50 low and middle income countries and 7 high-income countries). The surveys provide information on firms’ innovation projects, bribe payments, tax evasion, their perception of the government, and their sources of financing. Our data is unique in three aspects. First, the data allows us to examine firm behavior in small and medium enterprises in developing countries, which haven’t been the focus of earlier studies though such firms account for the overwhelming majority of firms in developing countries. Second, the survey tracks specific activities that result in new-to-firm innovation. New-to-firm innovation consists of improvements such as new product introductions or use of new technologies, which is of more relevance for our sample of developing countries where firms are less likely to develop globally new technologies (e.g. Segerstrom, 1991; Grossman and Helpman, 1991; Acemoglu, Aghion, and Zilibotti, 2006; Dutz, 2007). This perspective on innovation also fits in with the claim by Murphy, Shleifer, and Vishny (1993) that “..public rent-seeking attacks innovation, since innovators need government-supplied goods such as permits, licenses, import quotas, and so on, much more so than established producers.” Third, for the very first time, we have consistently 3
18 Poland. In Madagascar, the average deposit account balance is nine times GDP per capita, while it is only 4 percent of GDP per capita in Iran.

The authors also show that these aggregate indicators are not only interesting measures in their own right, but can be used to predict the proportion of households using bank accounts, when there are no household surveys available. Regressing share of households with deposit accounts obtained from household surveys on their aggregate indicators of deposit accounts and branch penetration, they show that predicted share of households with deposit accounts from this regression provides a reasonably accurate estimate of the actual share of households with deposit accounts obtained from household surveys. Hence using aggregate indicators, it is also possible to obtain out-of-sample estimates of the proportion of households using a bank account, although the fit is likely to be poorer. Honohan (2006) combines this data from commercial banks with data from savings banks and other socially oriented institutions that target low-income clients such as microfinance institutions, postal savings banks, credit unions etc. to estimate a headline indicator, which provides a share of households with use of financial accounts in most of the world.<sup>1</sup> Figure 4 illustrates that use of finance is indeed very limited around the developing world: in most countries less than half the population has an account with a financial institution and in many of the poorest, less than one in five households do. Those excluded often also include the non-poor, such as middle class and small and medium enterprises.

Comparing this indicator of use of finance with the commonly used financial depth indicator shows a positive but imperfect correlation (Figure 5). This shows that access really is a distinct dimension: financial systems can become deep without delivering access to all. Take Colombia and Lithuania as examples. Both countries have similar levels of private credit to GDP at around 20

> 1 Information on microfinance institutions, postal savings banks, credit unions and state-owned agricultural and development banks comes from Christen, Rosenberg and Jayadeva (2004), and on savings banks from Peachey and Roe (2006).