rafmacalaba/datause-encoder-data huggingface dataset:
# empirical light on the helping-hand versus grabbing-hand debate.

To assess the impact of bank regulations and supervisory practices on corruption, we need to control for (a) exogenous determinants of government corruption and (b) the potential endogeneity of bank regulations and supervisory practices. We do this in two steps. First, we use existing theory and evidence to identify exogenous determinants of government corruption. We include these determinants as control variables in the corruption-regulation regressions but without controlling for endogeneity. Second, we also use instrumental variables to control for simultaneity.

In selecting exogenous determinants of government corruption, we use La Porta, Lopez-deSilanes, Shleifer, and Vishny's (1999, henceforth LLSV) comprehensive investigation of the quality of government. They note that some cultural theories of government quality predict that the Catholic and Muslim religions tend to produce comparatively centralized, powerful governments with correspondingly higher levels of corruption [Landes, 1998; Putnam, 1993; Weber, 1958]. LLSV (1999) present strong empirical support for these theories. Countries where a high percentage of the population is either Catholic or Muslim tend to have more corrupt governments than countries dominated by other religions and especially than those heavily populated by Protestants. Other theories focus on ethnic diversity [Alesina, Baqir, Easterly, 1999]. In ethnically diverse countries, there may be a tendency for governments dominated by one ethnic group to expropriate resources from other ethnic groups with adverse implications for governnent integrity [Easterly and Levine, 19971. Again, LLSV (1999) find a strong positive link between greater ethnic diversity and higher levels of corruption.<sup>13</sup> Thus, to assess the independent link between corruption and bank regulations and supervisory practices, we include measures of religious composition (the percentage of the country that is Catholic, Muslim, Protestant, or another denomination) and ethnic diversity. We also consider a wide array of other control variables to assess the robustness of the results as we discuss below.

> **13** Note, La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1999) find a strong connection between corruption and both ethnic diversity and religious composition only when they do not control for income per capita. The very strong link between corruption and low income per capita is consistent with the view that corruption hinders economic development [Mauro, 19951. As La Porta et al (1 999) argue, however, including per capita income in the corruption regression may spuriously eliminate the connection between corruption and both ethnic diversity and religious composition due to the impact of corruption on economic development, i.e., it is inappropriate to add an endogenous control variable. We do not include income per capita in the regressions for the same reason.

23
# Job Creation before and after the 2008 Crisis

**Firm and industry characteristics as well as the perceived business environment features are likely to account for some of the differences in the job creation rates across the EU11 region** . A structural analysis of the relationship between the business environment and job creation is warranted. A sample of 180,986 firms of different sizes, vintages, and sectors across the EU11 countries is used. The sample captures only surviving firms, making it impossible to examine exit dynamics. Hence, the results presented here are attributable solely to the sample of surviving firms in EU11. The data is made available by Bureau van Dijk and have been standardized for the analysis forthcoming in World Bank (2013). To unveil the firm and industry level characteristics as well as the business environment determinants of employment creation in the EU11, empirical firm growth equations are estimated for the annual average employment growth rate for surviving firms (Box 1). The main advantage of the regression framework presented in Box 1 is that it allows controlling for differences in firm-characteristics such as size when examining the impacts of industry and institutional characteristics on job creation. Given the large effect of the global financial crisis on countries, the period under investigation is divided into two: the “boom” years capturing the strong economic growth and job creation in the EU11 countries between 2002 and 2008; and a “bust” year representing 2009, the first year following the global financial crisis.

6
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 9_ need to be produced at each imputation step. For 20 food items, 8 iterations, and 5 imputations, the total number of models involved is 16,000, highlighting the need for fast model building.

# _C. Validation and optimization_

Since the food price data is incomplete, the true inflation of the food basket is never observed. This makes validation against true data particularly difficult. Cross-validation techniques are used to assess the predictive accuracy of the many individual price prediction models involved at the level of individual prices.

Since there are many food items (here _d ∈_ 1 _, ..., D_ ), each predicted by an ensemble of multiple models (here _m ∈_ 1 _, ..., M_ ), the robustness of the final estimate for the overall food basket imputation is summarized in a single score that summarizes the prediction accuracy of all models involved. First, a normalized MAE for the log price of food item _d_ is constructed as the ratio of the MAE of the model for that food item to the MAE obtained by a simple mean prediction. Since each MAE estimate represents an average point percent error rate due to the log nature of the price data, the individual MAE values are averaged geometrically.

where _MAE_<sup>�</sup> _m,d_ is a cross-validation estimate of MAE using the standard formula for MAE, _MAEd|μ_ ˆ is the MAE calculated using observed data and the sample estimate for the unconditional mean. Since the true data range is not observed, and averaging is known to improve ensemble performance, the quantity from equation 3 is likely a conservative estimate of true error.

� The focus next is on the quantity 1 _− NMAE_ , which is the share of the total absolute variation in the demeaned log price data explained by the imputation model. The _D_ values are averaged as follows.

where _Z_ is the Fischer Z-transformation and _Z_<sup>_−_1</sup> its inverse, and _w_ are the relative weights of the price component in the final price index. The final score from equation 4 roughly has the interpretation of the average _R_<sup>2</sup> of the food price index, using a robust calculation of out-of-sample errors (Andr ́ee, 2021).<sup>4</sup>

> 4The Fischer Z-transformation is used because 1 _−NMAE_ � approaches the R-squared when prediction
The impact evaluation involved variation not only in project selection procedures, but council election methods as well.<sup>6</sup> To ensure random and independent assignment of project selection procedures and council election methods villages were divided in quadruples using an optimal greedy matching algorithm (King et. al., 2007). The 50 villages in each district were first paired based on background characteristics and then matched in pairs of pairs to form quadruples.<sup>7</sup> Each village within the quadruple was then randomly assigned one of four combinations of project selection and council election procedures. Each village within the quadruple was then randomly assigned to one of four combinations of project selection and council election procedures. This assignment procedure ensures that each village in the sample had an equal probability of being assigned each of the project selection procedures.

### [TABLE 1 HERE]

The randomization resulted in a well-balanced set of villages. Table 1 presents a comparison between the two groups of villages with respect to a number of pre-intervention characteristics. The differences between the two groups never exceed 13 percent of the standard deviation.

## III.3. **Phasing of Intervention and Data Collection**

The baseline survey for the project was administered in September 2007, prior to the selection of projects or election of village councils. Village council elections took place between October 2007 and May 2008, with project selection occurring between November 2007 and August 2008.<sup>8</sup> Both council elections and project selections were monitored, providing additional data on the respective processes. A follow-up survey was administered between June and October 2009 following the start of project implementation.

# IV. **Data**

The outcomes of interest for the study are: (i) the degree of alignment between selection outcomes and elite and non-elite preferences; and (ii) villager satisfaction. The former measure is constructed from data on the _ex-ante_ preferences of different groups obtained prior to the start of project implementation and the latter measure is constructed from lists of proposed, selected, and

> 6 For more on the election methods results see Beath, Christia and Enikolopov 2012

> 7 These characteristics include village size (based on data collected by Afghanistan’s Central Statistics Office) and a set of geographic variables (distance to river, distance to major road, altitude, and average slope).

> 8 In all villages, there was at least a month between village council elections and project selection.

8
> general government expenditure data are reported in the **GFS.** We take this sample of 12 countries to ascertain whether the expenditure ratios used in our analysis are statistically different for general government from central government in these countries. Table **7** presents the sample

> statistics for the expenditure ratios. In comparing the statistics for the two different levels of government, a couple of interesting facts emerge: as defense is primarily the rL.soonsibility of the central government, the ratio of defense to total expenditure decreases for the general government; the share of education expenditure is larger for the general government indicating that state and local government allocate a higher budgetary share for education. The expenditure ratios presented in Table **7** also seem to indicate that state and local governments spend more

> money on capital but less on current expenditure. Based on - paired t test, we find that all

> expenditure ratios but transport and communication based on general government data are

> statistically different (significant at **99%** level) from the ratios based on central government data.<sup>**12**</sup>

> To test whether or not the relationship between composition of expenditure and economic growth is different when expenditure shares based on general government data are used, we run the same regression model based on each of the two data sets. The regression results are reported in Table **8.** While the signs and magnitudes of the coefficients are similar for both data sets, the coefficients are statistically insignificant. **A** paired t test, however, indicates that the difference between the coefficients is statistically insignificant. Hence, the coefficient estimates of the growth equations based on general government expenditure and central government

> expenditure are statistically the same.

> 1<sup>2</sup> For space considerations these results are not reported. The results are available from the authors.

22
<!-- Start of picture text --> Figure 1<br>US Imports of tra and basa from Vietnam<br>Monthly Quantities (tons)<br>Jan 02 Jul 02 Jan 03 Jul 03 Jan 04 Jul 04<br>date<br>600<br>400<br>200<br>0<br><!-- End of picture text --> Source: USITC. The two vertical lines correspond to the dates of the DoC announcement of AD tariffs (left) and of the ratification of the decision by the USITC (right).

at the 10-digit level of the Harmonized System.<sup>9</sup> The graph shows a striking drop in the imported quantities of tra and basa immediately following the DoC announcement in January 2003 (left vertical line). Average monthly imports dropped from nearly 380 monthly tons in 2002, to around 180 in the first semester of 2003, a more than 50 percent decline. After the ratification of the USITC in July 2003 (right vertical line), imports plummeted to a monthly average of 56 tons in the second semester of 2003, an 85 percent drop since 2002. These changes in import are consistent with the literature: Staiger and Wolak (1994) document similar drops in U.S. imports during the investigation phase in several anti-dumping cases and Prusa (2001) estimate overall drops of about 50 percent in U.S. AD-subject imports.

9 See the USITC Interactive Tariff and Trade DataWeb, version 2.8.0. at `http://dataweb.usitc.gov` .

8
containing balance sheet information. We also create a database of entry restrictions ordained in the Investment Incentives code.

The resulting dataset enables us to document the association between the severity of entry regulation and the performance of politically connected firms, which public choice theory predicts to be positively correlated (Stigler, 1971, Peltzman, 1976, McChesney, 1987, De Soto, 1990, Shleifer and Vishny, 1993, 1994). By virtue of spanning the universe of registered firms, it allows us to analyze how the prevalence of political connections and the returns to them vary across the distribution of output and profits. Moreover, it enables us to assess how competition evolved as the Ben Ali family expanded its business empire, and whether potential adverse impacts on competition associated with this expansion were more pronounced in sectors subject to entry regulation.

Tunisia provides a pertinent case study to assess the link between regulation and state-business relationships. Like many other developing countries, Tunisia has a development strategy predicated on extensive state intervention. The Ben Ali family’s involvement in the economy was well known, and Tunisia’s investment promotion agency advertised his close interactions with the business community as enhancing public welfare. In part because Tunisia registered stable positive growth rates hovering around 4–5% per annum, Ben Ali also had a fairly favorable external image. The World Economic Forum repeatedly ranked Tunisia as the most competitive economy in Africa and the IMF as well as the World Bank heralded Tunisia as a role model for other developing countries. Yet, the Tunisian model had serious flaws; unemployment and corruption were high over the period studied, and contributed to Ben Ali’s downfall. Last but not least, Tunisia has a high-quality firm census, and authorities willing to grant access to data on both firm performance and political connections.

The first step in the analysis is showing the importance of Ben Ali firms in the Tunisian economy and the link with entry regulations. While only 0.2% of all private firms reporting positive output and employment were connected, they accounted for 5% of private sector output and appropriated 16% of all net private sector profits.<sup>2</sup> These contributions are to a large extent driven by

> 2 Since we identify only firms with direct links to the Ben Ali family, as opposed to all firms with cultivated connections, this number is probably best interpreted as a lower bound on the importance of political connections. These estimates are not out of line with previous studies of the economic significance of connected firms. For example, in his study of firms with connections to the Suharto regime, Fisman (2001) observes that the 25 business groups he identifies account for approximately a third of Indonesian GDP. Similarly, Ferguson and Voth (2008) argue that firms with ties to the Nazi

3
71

**Table 28  Funding Ratios**<sup>67</sup>

|||Panel A: PF<br>|without apub<br>|lic warranty<br>|||
|---|---|---|---|---|---|---|
||PF w|th DC|Underf|unding|% of tot|al assets|
|Liquidity<br>|||(m C<br>|HF)<br>|of PF w<br>|ith DC<br>|
|Ratio(%)|2003|2002|2003|2002|2003|2002|
|95 – 99.9|221|272|666|1,175|2.80%|3.30%|
|90– 94.9|108|227|3,145|3,524|11.20%|9.90%|
|< 90|57|185|5,222|8,757|21.00%|20.60%|
|Total|386|684|9,033|13456|11.80%|11.80%|
|||Panel B: P<br>|F with apubli<br>|c warranty<br>|||
||PF wi|th DC|underf|unding|% of tot|al assets|
|Liquidity<br>|||(m C<br>|HF)<br>|of PF w<br>|ith DC<br>|
|Ratio(%)|2003|2002|2003|2002|2003|2002|
|95 – 99.9|10|4|263|167|3.20%|11.30%|
|90– 94.9|6|8|301|489|9.20%|7.30%|
|< 90|29|26|15,779|28,597|38.40%|44.40%|
|Total|45|38|16,343|29253|31.00%|40.30%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.10 sqq, T 10 and T11 2: Bundesamt für Sozialversicherung (2003), p.12 sqq, T11 and T12

**Table 29  Funding Ratios** **<u>of Employer–Accounts in “Collective–Institutions”</u>**

|Liquidity<br>Ratio(%)|Accounts in “Collective-<br>Institutions” with DC<br>2003<br>2002|# of polic<br>2003|y holders<br>2002|
|---|---|---|---|
|95 – 99.9|7051<br>5278|75,388|75,229|
|90– 94.9|409<br>4199|12,322|75,949|
|< 90|372|12,552||
|Total|7832<br>9477|100,262|151,178|
||underfunding|% of tot|al assets|
|Liquidity|(m CHF)|of PF w|ith DC|
|Ratio(%)|2003<br>2002|2003|2002|
|95 – 99.9|96<br>127|2.07%|2.56%|
|90– 94.9<br>< 90|69<br>513<br>113|15.16%<br>30.71%|11.86%|
|Total|278<br>640|5.09%|6.89%|

Source: 1: Bundesamt für Sozialversicherung (2004a), p.17 sqq, T21 and T22 2: Bundesamt für Sozialversicherung (2003), p.19 sqq, T22 and T23

# **_5.4.3  Causes for the Underfunding_**

Table 30 summarizes the main reasons that were responsible for the funding deficits. Note that theses reasons have been self-reported by the pension funds. Losses on securities were the main trigger for nearly 60 percent of the pension funds. In 2003 and for pension funds except “collective–institutions” (Panel A), an insufficient actuarial funding caused a funding shortage in 22 percent of the cases. In 2002, insufficient returns on investments were also often the reason for such a situation.

> 67 without “Collective-Institutions”
<!-- Start of picture text --> 1-person firm manufacturing employment in India<br>iN<br>oO<br>a<br>s<br>=<br>eo<br>E<br>s—s<br>E<br>+<br>1990 1995 2000 2005 2010<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Urban 1-person firm manufacturing employment in India<br>‘©<br>at<br>-<br>€<br>=<br>ise)<br>2<br>s<br>a<br>Gig<br>1990 1995 2000 2005 2010<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 1-person firm manufacturing employment in India<br>©<br>a<br>ge<br>E<br>i<br>=<br>sea<br>E<br>N<br>1990 1995 2000 2005 2010<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> Urban 1-person firm manufacturing employment in India<br><3)<br>2<br>oN<br>ro<br>Ea<br>=<<br><<br>Ew<br>so<br>Qa<br>Gi<br>©<br>1990 1995 2000 2005 2010<br>Year<br><!-- End of picture text -->
19 I am aware that the above TFP result is an arithmetic trick. I could have simply done the t-test using the regression results in table I that the human capital coefficient is equal to the assumed values of aH<sup>_32_</sup> _._ But the TFP arithmetic is an effective way of making the point that failing to reject the hypothesis that educational capital increases growth is interesting because it addresses two of the issues raised above, prior beliefs on the coefficient and low power.

First, we know from non-regression based estimates what the educational capital coefficient ought to be, and it is not zero. If we make seemingly reasonable assumptions about schooling returns based on available microeconomic evidence we know that educational capital accounts for a large share of output. Hence its growth accounting share ought to be far from zero. But it is not<sup>33</sup> .

Second, the TFP formulation makes it clear that the failure to reject a zero impact of educational growth is not due to low statistical power. Many times a failure to reject that a coefficient is zero simply reflects very imprecise estimates. But not here. The results in table I are a high powered failure to reject. While the data fail to reject that the educational capital coefficient is zero, the data provide enough precision to reject the hypothesis the educational capital estimate is not in a large range of values. This rejection range includes all of the values that non-regression estirnates suggest. Matter of fact, the estimates and their standard errors reject pretty much all plausible values for the human capital share (as the N-S-D data reject zero from below and the B-L data can reject any share above .06).

**F:) Can** thi.g really he right?

Since I am arguing for what many find an implausible proposition, that increased education did not pay off in economic growth, I want to go beyond what regression coefricients say and show its intuitive 32 A t-test from the specification in table I (B-L data) that includes initial income that the estimated humnan capital share is equal to .167 gives a value of 4.16, a convincing rejection.

33 Weale (1994) makes similar calculations about the likely extemality impact of education on TFP using the coefficients from enrolmnt rates. As shown below regressions using enrollment rates are invalid, but even using those esarates Weale found _hat_ the evidence was only consistent with micro returns to education in the S to 8 percent range, which is significantly lower than found for most LDCs.