rafmacalaba/datause-encoder-data huggingface dataset:
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# Table 5: NEGATIVE AND AFFIRMATIVE DETERMINATIONS a/ IN SUBSIDY, DUMPING AND INJURY TESTS: NUMBERS AND PROPORTIONS

||Affirmative<br>number|Ne<br>number|gative<br>percentage|
|---|---|---|---|
|Subsidy and dumping tests||||
|Preliminary|403|50|11|
|Final|484|60|11|
|Injury tests||||
|Preliminary|444|129|23|
|Final|190|113|37|

- a/ Only cases that reached an affirmative or a negative determination are included, i.e., suspended, terminated, etc., cases are not included. Some cases that were superseded by VERs had previously reached final determinations.
In 1992 and 1993 the actual payroll tax revenue (total or ZUS social contributions) was higher than the counterfactual (total or ZUS contributions): the gain from higher tax rates again dominated the reduction of the tax base because of employment loss, more so in 1992 than in 1993. Wage claims were also more moderate in these two years than in _1991.5_ The contributions to the social security administration, ZUS which administers the Fundusz Ubezpiecien Socialnych (FUS), illustrate best the response of the tax revenue to wage and tax-rate changes. Tax rates changed in 1990 and in 1992. The revenue gain from each change was larger in the year of change than in subsequent years. Nevertheless, the contribution rate increase of 1992 was not entirely undone by the fall in employment in subsequent years. The wage level played a role. In 1991 wages rose too fast, while they rose only moderatly after 1992. As long as the workers were willing to abandon net wage growth for social contributions, a small revenue gain seemed possible. Figure 3 gives a decomposition of the contributions to ZUS. It shows actual revenue, counterfactual revenue and constant tax rate revenue. Comparing the evolution of the counterfactual with the 1989 level gives the wage effect at constant employment and tax rates: negative in 1990, positive thereafter. The difference between line 4 (CFUS) and 3 (TFUS) of the counterfactual is lost employment. The difference between actual revenue and the counterfactual can be called the "residual" tax rate effect: it is insufficient to compensate for employment loss in 1991, but is positive afterward. The difference between actual revenue in 1989 and the constant tax rate counterfactural can be called the "residual" wage effect, that is, it accounts for the fall in employment and ignores the

> _5_ The product of the constant employment coefficient by the constant tax rate coefficient gives the "apparent" elasticity of employment to the payroll tax (base 1989). It is 0.93 in 1990 1.02 in 1991, 0.97 in 1992 and 0.98 in 1993. The elasticity is higher than 1 only in 1991, the year in which wages rose most. But, wages and employment moved simultaneously for reasons other than changes in the tax wedge. The true tax elasticity could thus be obtained only from multivariate regression estimates.

12
**Figure 4: Trends in profitability of the SOEs in the mining and quarrying sector; 20052010H1**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>2005 2006 2007 2008 2009 2010H1<br>Gross operating profit Net operating profit Net profit<br><!-- End of picture text -->

**_<mark>Source:</mark>_** _<mark>Romanian Ministry of Public Finance (2011), “Fiscal Information and Balance Sheets”,</mark>_ _<u><mark>http://www.mfinante.ro/pjuridice.html?pagina=domenii, and authors’ calculations</mark></u>_

Figure 2 shows the trends in profitability of the SOEs in the energy and gas sector, which accounts for 31 percent of total SOE activity. The net profit of the SOEs in the energy and gas sector has been negative since 2007. The profitability of the SOEs in the transport and storage sector, accounting for 24 percent of total SOE activity, has deteriorated even more sharply (figure 3), with the sector suffering large losses in 2008, 2009 and the first half of 2010. The third largest SOE sector, mining and quarrying, has also incurred losses since 2007 (figure 4). The SOE sector also has incurred an increasing amount of arrears, mainly to suppliers, the social insurance budget and taxes. Total SOE arrears amounted to 4.8 percent of GDP in 2010 (Romania Fiscal Council, 2011:44).

The poor financial performance of the SOEs weakens fiscal sustainability through two channels. First, the accumulation of arrears to the state undermines tax revenue collections and the 13
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0.909 to the optimal 0.95. Table 16 displays the responses of selected individual firms." Marginal price changes induced by optimizing the tariff are small compared to Nigeria the smallest firms experience a decrease in their marginal tariffs of around 15 % and the largest users of about 11 %. Since unreliability is exogenous and fixed, both kinds of power are increased or decreased by the same percentage: the smallest firms increase their power use while the biggest ones decrease it. Total power use is decreased by 3.91%.

Next, as was done in the case of Nigeria, we resimulated the firms' responses and recalculated the optimal tariff udder the assumption that the degree of unreliability (X) was endogencus and that the elasticity of Xr to aggregate demand for PLN power was 1 %. As shown in Table 17, the optimal tariff has a , = 1.05 (a very slightly increasing block structure and very close to the actual tariff of 0.909) and total cost saving is merely 0.03%. Aggregate purchases from PLN decline by 10.71% and internal production of electricity by 5.93%. The aggregate cost of embedded production drops by 3.72% and total operating cost drops by 0.085%. Table 18 shows the responses of the selected firms. Smaller firms increase purchases from PLN and produce more power internally, but because reliability is improved, the percent increase in PLN power purchased is bigger as firms operate on lower rates of boosting. As firm size gets larger some firms begin to even decrease their own power production, and for the largest firms both power sources are used less but there is a bigger percentage cutback in the private power source. Of course, this pattern is induced by the big percentage decrease (as much as 45%) in the marginal PLN tariff offered to smaller firms and the big percentage increase (as much as 51 %) offered to larger firms. Although, the change in tariff from the actual tariff with = 0.909 to the optimal one with , = 1.05 appears rather slight, firms respond, as in the case As in the Nigeria tables, we selected the five smallest users of PLN, the five biggest users and every other tenth **firm after** the fifth smallest user.
**Table 9: Trade Complementarity Index of Major South Asian Countries**

|**Importing**<br>**Country**||<br>|**Exporting Count**<br>|**ry**<br>||
|---|---|---|---|---|---|
|||**Bangladesh**|**India**|**Pakistan**|**Sri Lanka**|
|India|1975|0.04|-|0.16|0.10|
||1980|0.04|-|0.23|0.70|
||1990|0.19|-|0.34|1.04|
||1995|0.18|-|0.50|0.17|
||1997|0.47|-|0.42|-|
||1998|1.59|-|0.37|0.76|
|Pakistan|1975|0.12|0.07|-|1.07|
||1980|0.98|0.07|-|6.89|
||1990|0.16|0.39|-|8.11|
||1995|0.23|0.83|-|4.91|
||1997|0.52|1.45|-|-|
||1998|2.51|1.55|-|8.17|
|Bangladesh|1975|-|0.43|4.48|0.10|
||1980|-|1.13|3.68|0.24|
||1990|-|1.2|3.23|0.36|
||1997|-|4.12|0.54|-|
||1998|-|3.21|8.25|1.05|
|Sri Lanka|1975|0.33|0.64|12.02|-|
||1980|0.12|1.24|2.63|-|
||1990|1.18|1.64|2.81|-|
||1995|0.39|1.98|-|-|
||1998|0.97|2.65|4.12|-|
|Nepal|1998||1.28||1.75|
|South Asia||1.3|MERCOSUR||28.6|
|European Com|munity|53.4|Canada-USA|FTA|64.3|
|NAFTA||56.3|LAFTA||22.3|
|Andean Pact||7.4||||

Notes: Data are presented column-wise (i.e. Figures show trade complementarily of country listed in column 1 with each country listed in row 1). Sources: Calculated from the WITS Database, World Bank. Each country index is derived by computing the complete schedule of exports at SITC 4-digit level . The complementarity Index for other regions EU, NAFTA, Mercosur, LAFTA, and Andean Pact is obtained from Yeats (1998).

The measures for the Andean Pact were an exception among these groups. They were low enough to suggest that imports and exports between the member countries were largely similar and may have contributed to its failure (Yeats, 1998). The weighted average of the complementarity index for South Asia does not compare well even with the Andean Pact. The underlying “mismatch” between the import and export structures for most members in South Asia implies that South Asia will continue to rely on non-regional 39
# **2.4 Deficiencies in the legal, regulatory and judiciary framework**

At this time, three issues require attention to promote financial sector development: the ineffective judicial system, the lack of credit information available, and the weak insolvency regime.

Zambia has a relatively well developed legal and judicial system compared with other African countries, but there is scope for improvement in several areas. The legal process has resulted in delays in the timely disposal of cases involving the collection of bad loans. Collection efforts have been weak for defaulters in public financial institutions, as well as in failed banks. Failure to take strong measures against questionable banking practices that led to bank failures also contributed to serious moral hazard.

While the operation of the courts continues to attract criticism, the introduction of a Commercial List and the increased emphasis on mediation and arbitration as alternative dispute resolution mechanisms is welcome and has done much to refute complaints over delays in the handling of commercial disputes. Nevertheless, the financial community continues to attribute difficulties in collecting loans to legal procedures and the ease with which borrowers can block creditor efforts to collect delinquent loans.

Authorities have identified the following shortcomings of the legal and judicial system, which it is hoped will be addressed through the FSDP and with the support of the donor community:

- Delays in delivering judgments;

- Lack of reported judgments;

- Bureaucratic procedures leading to delays in the enactment of new laws;

- Fragmented laws relating to the financial sector;

- Lack of guidelines and directives under principal Acts;

- Lack of adequate legal framework for regulating foreign currency transactions;

- Lack of financial sector consumer protection laws;

- Inadequate skills for enforcement of the law relating financial crimes; and

- Tendency not to report financial crimes to the relevant law enforcement agencies.

The credit bureau being set up by the Bankers’ Association is a critical step to improve market information and to strengthen the credit culture **.** The current plan is to start the credit bureau with negative information on defaulters. That effort should be supported and expanded as soon as technically feasible to include positive information. This will help clients with a clean credit history enjoy the expanding services and lower costs. Over time, an attempt should be made to extend the credit bureau database to small- and medium-sized firms on whom there is little information available, and who need to use their good credit record to supplement their limited collateral.

In the past, the collection efforts against defaulting borrowers in some public financial institutions, as well as failed banks, have been ineffective **.** In Zambia, it is widely believed that the treatment of the management of failed banks during the late 1990s was 25
formation of the first COs in treatment villages. Households were surveyed again 3 years later at midline. In treatment villages, the midline survey occurred after approval of the VDP but before the disbursement of grant funds. This study focuses on impacts at midline between treatment and control villages, which isolates the impact of community mobilization.

# **3.1 Data**

The baseline and midline surveys were administered to a random sample of 40 households drawn from each of the treatment and control villages and included detailed modules on health facility utilization, health outcomes and household health behaviors.

All adult women in the household were separately surveyed for specific sub-modules related to maternal health, antenatal and postnatal care, and child birth and health outcomes. Respondents were asked about their most recent pregnancy in the past 3 years to cover relevant health care utilization for pregnancies occurring between the baseline and midline data collection. Finally, all women in the household were asked about their interaction with the LHW assigned to their village. As discussed above, the quality of service provided by the LHW should be responsive to changes in local accountability, given that the LHW is recruited from within the community that she serves.

# **3.2 Sample Characteristics**

Table 1 presents the mean of village level characteristics and checks for balance between treatment and control villages. Villages have about 279 households living in 7 to 8 settlements on average. Villages are relatively poor, with about 52% of households below the poverty line and landless households comprising about 67% of all households. Households have between 6 and 7 members on average. Most household heads (63%) do not have formal education.

9
The second contribution of this paper is the descriptive analysis of a wide range of topics covered in this cross-city comparable data. This analysis has two main purposes. First, many general patterns found in our data match the existing knowledge about how the informal sector operates, thus providing validation of the database itself. Second, we have highlighted features of the informal sector and patterns in the data that warrant further research, which can be conducted using this data, potentially supplemented with other data sources. Importantly, the World Bank's Enterprise Analysis unit continues collecting similar data in other cities and countries, making such analysis possible with increasingly wider coverage.

An initial analysis of the data from the seven countries yields the following stylized observations. Informal businesses operate almost all year round and on a full-time basis but make meager earnings. Informal businesses are generally very small and young, started out of necessity rather than as an opportunity for growth, typically operating in the retail sector, often from the owner's household premises, and are frequently owned by women, who experience a sizeable productivity gap compared to businesses owned by men. Even within the small scale at which informal businesses operate, they considerably under-perform formally registered microenterprises, consistent with the existing literature (La Porta and Shleifer 2014), with the data suggesting an interesting and sizeable cross-country variation.

The informal sector appears to be poorly integrated into the formal financial sector (as expected due to their lack of registration and thus lack of access to formal financial products) as well as formal supply chains. <mark>The data on management practices suggests a considerable gap between informal businesses and the formally registered micro-enterprises, especially in terms of record-keeping.</mark> The aspects of management practices related to planning for the subsequent year appear among the most challenging for informal businesses, suggestive of uncertainties that they may face, preventing them from adopting longer-term horizons.

The data suggest that operating informally does not seem to be associated with high chances of harassment, bribery, or crime; neither does it seem to provide a flexible work schedule, suggesting that perhaps the costs and benefits of informality are not well understood in the existing literature. We find that most businesses choose to remain unregistered not because of a lack of information (as suggested by some policy makers) but rather due to the additional costs and insufficient perceived benefits associated with formalizing.

30
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quadratic form is assumed for _VALU_ , it is found that the probability of using rail transport increases with the value of shipment in a concave fashion. Under this specification, it is suggested that large-volume shippers may prefer to use rail transportation. The other results remain unchanged. Price matters. In addition, traditional firms seem to have a tendency to ship goods by rail. The coefficient of _AGE_ is positive and significant. New firms prefer to avoid rail transportation.

**<u>Table 9. Multinomial logit estimation with quadratic forms assumed</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|
|---|---|---|---|---|
|PRICE|-0.2888|(0.1531)|*<br>-0.2467|(0.1254) **|
|DIST|0.0012|(0.0004)|***<br>0.0011|(0.0004) ***|
|SPEED|-0.0670|(0.1077)|-0.0254|(0.0404)|
|SPEED*SPEED|0.0013|(0.0027)|||
|VALU|0.0008|(0.0017)|0.0391|(0.0111) ***|
|VALU*VALU|||-0.00007|(0.00002) ***|
|AGE|0.0248|(0.0155)|0.0312|(0.0142) **|
|EDU|-0.2682|(0.1956)|-0.3715|(0.1944) *|
|FORN|-0.0050|(0.0079)|-0.0018|(0.0074)|
|LOCL|-1.3924|(0.4456)|***<br>-1.6044|(0.4629) ***|
|NATN|2.4476|(1.0643)|**<br>2.7482|(0.8679) ***|
|EXPT|1.0846|(0.8135)|1.2087|(0.7606)|
|IMPT|1.2293|(0.5675)|**<br>1.2018|(0.6011) **|
|Constant|-4.8951|(1.2163)|***<br>-5.0455|(1.0208)***|
|Obs.|900||900||
|Pseudo R squared|0.2594||0.2995||
|Wald chi|61.89||77.47||

The dependent variable is one if rail is selected. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

To check robustness of the main results above, the alternative-specific conditional logit (McFadden’s choice) model is also performed. While all firm-specific characteristics are used as case-specific variables, two alternative-specific variables are created by using network analysis (as shown in **Figure 8** ). Given the road network condition data as well as average road user costs and rail tariffs, transport costs ( _TC_ ) and travel times ( _HR_ ) are estimated by minimizing the total transport costs from a firm to the shipping destination.
results to other concerns, including the omission of relevant regressors and alternative definitions of the dependent variable of interest (SAR).<sup>17</sup> Furthermore, because we have multiple waves within a country/year, our results might be influenced by the fact that each country changes over the sample period and in very different ways. We deal with this issue by first including country/wave fixed effects and then clustering the standard errors at the country/wave level, thus allowing for within country-wave intra-cluster correlations.<sup>18</sup> Thus, our results can be viewed as covariates of radicalization within the same country/wave group.

Another potential problem is the item nonresponse bias, which is frequent in household surveys in developing countries. Item nonresponses are characterized by survey noncompliance in which some of the participating individuals refuse to answer some questions. If the process driving these nonresponses is not random, then the parameter estimates from the system (1) and (2) may be biased (see, among others, Van Praag et al., 1983; Korinek et al., 2006). We deal with this problem in three different ways. First, we check whether there is a systematic association between the distribution of item nonresponses on our LHS variable of interest (SAR) and our covariates. Our estimates would be contaminated if this association is significant. Second, we follow the empirical literature on this issue (see, among others, Lillard et al., 1986; Little and Rubin, 1987) and estimate the system (1) and (2) using an extended sample. The extended sample is obtained by imputing the nonresponses using the within country-wave sample median of each variable in the system.

> 17 Due to missing observations and therefore reduced sample sizes, we include these additional controls only in the sensitivity checks. We believe that the resulting potential sampling bias due to limited sample size may be a more serious concern than the omitted variable bias.

> 18 However, this does not allow for correlations across clusters.

19
8 Table 2 below presents a number of summary statistics for these<sup>distributions -in addition to the</sup> mean, which was provided in Table 1 above. The four inequality indices, which<sup>will be used</sup> throughout this paper, are the Gini coefficient and three<sup>members of the Generalized Entropy</sup> Class of inequality indices, E(e). This class of measures satisfies a number<sup>of desirable</sup> properties, such as the strong Pigou-Dalton transfer principle,<sup>scale invariance, population</sup> replication invariance and decomposability. See Cowell (1995) for<sup>a discussion. Specifically, we</sup> have chosen E(O), also known as the mean log deviation<sup>or the Theil -L index; E(l), more</sup> famously known as the Theil - T index, and E(2), which is half<sup>of the square of the coefficient of</sup> variation. These provide a useful range of sensitivities to different<sup>parts of the distribution. E(O)</sup> is more sensitive to the bottom of the distribution, while<sup>E(2) is more sensitive to higher</sup> incomes. E(1) is roughly neutral, whereas the Gini places greater weight<sup>around the mean.</sup> We also present three poverty indices from the Foster-Greer-Thorbecke<sup>(FGT) additively</sup> decomposable class P(a). P(O), also known as the headcount index, measures<sup>poverty incidence.</sup> P(l) is the normalized poverty deficit; and P(2) is an average of<sup>squared normalized deficits, thus</sup> placing greater weight on incomes furthest from the poverty line.<sup>We calculate each of these with</sup> respect to two poverty lines, representing R$1 and R$2 per day, at 1996<sup>prices.</sup> Each of these poverty and inequality indices is presented both for<sup>the (individual) distribution of</sup> total household incomes per capita, and for an equivalized distribution using<sup>the Buhmann et. al.</sup> (1988) parametric class of equivalence scales, (with 0 = 0.5). This provides<sup>a rough test that the</sup> trends we describe are robust to different assumptions about<sup>the degree of economies of scale in</sup> consumption within households. Whereas a per capita distribution does not<sup>allow for any such</sup> economies of scale, taking the square root of family size allows for them to a rather<sup>generous</sup> degree. As usual, per capita incomes generate an upper bound for inequality<sup>measures, whereas</sup> allowing for some extent of local public goods within households raises the income<sup>of</sup> (predominantly poor) very large households, and lowers inequality. In the case of<sup>the poverty</sup> measures, in order to concentrate on the household re-ranking effect, and to abstract<sup>from the</sup>