rafmacalaba/datause-encoder-data huggingface dataset:
provinces of China from _A Survey of Chinese SOEs: 1980-1989._ (See the data appendix for more details.) To take into account the potential differences in technology for firms in different industries, we decomposed the firmns into 12 industry-size categories. The four industries are chemical, light, machine, and material industries.<sup>9</sup> For each of these four industries three dilfferent technologies are allowed depending on size classification (large, medium, and small)."<sup>0</sup> The firms in the sample can be classified into two groups, those that eventually adopted PCs (the participation group), and those that did not (the non-participation group). Let _di_ represents these two groups: _di=I_ for the participation group, and _di=0,_ the other, where the subscript i denotes an individual firm. We assume the following Cobb-Douglas production function": non - participants: Y,,i = _8I + /8L In LJ,, + f3j ln k,' +_ **_/JRR;,_** + _r,_ + _u,,_

> participants: _Y-1 =_ A _+f_ lnL, +/ _lnk,_ **_+IJRRIf_** _+D,a, +D,,7,a 2 +rt +Ui' (1)_

where i indexes firn,j indexes industry-size category, and t indicates year. The variables are defined as follows:

- Yit: log(value added per worker) (see the data appendix for the construction of variables and the associated deflators.)

lnLi,: the number of employees, excluding those absent for more than half a year.

- 9We follow Li (1995), which uses the same data set. The data contains firms in 36 two-digit industries, but many industries contain too few observations.

> 0 Large, medium, andl small firms had, on average, 4083, 1486, and 615 employees respectively.

The use of a translog production function produced results similar empirical results for PC effects. To simply presentation, especially of elasticity of capital and labor, only the results using the CobbDouglas function are reported.

13
Policy Research Working Paper 6960

# **Abstract**

This paper proposes methods to incorporate firm heterogeneity in the standard input-output table–based approach to portray the domestic segment of global value chains in a country. The analysis uses Chinese firm census data for the manufacturing and service sectors, along with constrained optimization techniques. The conventional input-output table is split into sub-accounts, which are used to estimate direct and indirect domestic value added in exports of different types of firms. The analysis finds that in China, state-owned enterprises and small and medium domestic private enterprises have much higher shares of indirect exports and ratios of valueadded exports to gross exports compared with foreigninvested and large domestic private firms. Based on input-output tables for 2007 and 2010, the paper finds increasing value-added export ratios for all firm types, particularly for state-owned enterprises. It also finds that state-owned enterprises are consistently more upstream while small and medium domestic private enterprises are consistently more downstream within industries. These findings suggest that state-owned enterprises still play an important role in shaping China’s exports.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hwtang@jhu.edu and Zhi.Wang@usitc.gov.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
non-oil imports in our data are very similar to the corresponding total non-oil imports reported by COMTRADE (the average difference across country-years is 5.6%). COMTRADE data is accessed through WITS (World Bank, 2024b).

Measuring import market concentration requires defining a relevant market, or the set of products over which the firms in question have market power. While relevant market definition is the object of intense debate in antitrust litigation, a general principle is that it should include the set of goods that are close substitutes for the same set of consumers (Davis and Garc ́es, 2009). Benkard, Yurukoglu and Zhang (2021) note that economic census data, whereby firms are classified into industries, are collected at the point of production rather than consumption, and so may be less useful for analyzing the relation between market concentration and market power. In contrast, the Harmonized System (HS) product categories defined by the United Nations used in trade data classify goods with a similar end-use, and so are conceptually like the relevant product markets in antitrust analysis. Following Fernandes et al. (2016), we use a time-consistent consolidated classification that concords and harmonizes product codes across the HS revisions present in the raw data.<sup>5</sup> Our baseline measure of import market concentration is the Herfindahl-Hirschman Index (HHI) or where Mc,j,i,t is the import value of firm j in country c of product i in year t, J is the number of firms and Mc,i,t the total import value in the relevant import country, product market, and year. Values are measured including cost of freight and insurance (CIF) in United States dollars. Table S1 reports average and median HHI across HS 6-digit products by import country. We consider alternative measures of concentration: the share of the largest importing firm or the largest four importing firms, the number of importing firms, and the number of source countries, all defined by import country, product market and year. We also

> 5The consolidation incorporates HS revisions 1996 through 2017. A description of the methodology is available upon request.

7
> distribution of _C_ 's . For example, in our context, if more public buildings survive in a village, those are items where maintenance is required via volunteer labor, relative to a village receiving new facilities that have yet to age. If the village head’s energy is diverted to the aid process (akin to Knack and Rahman 2007), that could reduce the

> efficiency (  ) with which volunteer labor is used. On the supply side, there should be differences in typical costs on volunteering in a village, _b_ , apart from within village differences. Higher stocks of social capital could reduce perceived costs of volunteering or working with others in the village towards common goals. Akin to Svennson (2000) and Labonne and Chase (2008), more aid delivered in a village, although coming from outside aid agencies, could increase market opportunities in the local transport and food service sector, raising costs of volunteering, or divert villagers’ energies in negotiating over the division of aid. Higher costs, _b_ , for the village cause both curves in figure 1 to shift up, meaning that the minimum _<u>C</u> j_ is increased (fewer projects under either regime). While the impact on<sup>_<u>s</u>_</sup> _~~j~~_<sup>is ambiguous, total volunteerism declines—fewer projects with</sup> less volunteerism for each.

# 3. THE DETERMINANTS OF VOLUNTEERISM ACROSS VILLAGES

The key outcome in this study is volunteer days called in the last month, where the village head calls volunteer days up to twice a week during the month. In the village level surveys in 2007 and 2009, we ask whether volunteer days were called in the last month and, if so, how many. In 2007, we also asked how many days typically were called pretsunami in a month. Table 3 gives some basic numbers. The number of villages regularly calling volunteer days declines from a pre-tsunami level of 97% to 72% in 2007, with 15
but proxy means testing likely offers biased estimates since it offers the estimate for only one of these terms (i.e., β̂<sup>′</sup> xij).<sup>11</sup> Perhaps the largest advantages of proxy means testing is that for a smaller population group, it is rather simple to implement (relatively speaking, compared with other imputation methods). Consequently, it can offer a quick and inexpensive estimate of poverty in the absence of household consumption data. This, however, may come at the expense of estimate precision.

# **IV. Poverty Estimates for Tracking Trends at the National Level** _Motivation_

While proxy means testing targets a subset of the poor population that are to benefit from some social transfer programs, tracking trends in poverty rates at the national level requires poverty estimates for the whole population. The motivation for poverty imputation in this case is related to discussions regarding national poverty trends. For example, the Sustainable Development Goals (SDGs) require frequent monitoring of (national and global) poverty trends that would perhaps require much more frequent collection of household consumption data than most existing household consumption surveys allow (see our earlier discussion with Figure 1).<sup>12</sup> In particular, most developing countries, say India, do not collect household consumption data annually. As such, poverty estimates for these countries would have to be obtained using alternative methods such as poverty imputation methods.

In this case, similar to proxy means testing, we also need two sources of household survey data: one survey that collects both household consumption data and the variables (xij, that are used

> 11  Proxy means tests offer unbiased estimates of poverty only in the special case that the poverty line is set exactly at the mean consumption level (see Dang _et al._ (2017a) for a more detailed technical discussion). See also Diamond _et al._ (2016) for a careful comparison of the poverty score card—a variant of proxy means testing—and other (regressionbased) poverty imputation methods.

> 12 The first goal of the SDGs is to eliminate extreme poverty and reduce national poverty levels at least by half by 2030. For details see <u>https://sustainabledevelopment.un.org/sdg1.</u>

13
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Levels|||First differenc|es|
|Tariff|0.007|0.002|0.003|0.000|0.001|0.001|
||(0.001)***|(0.001)*|(0.001)***|(0.001)|(0.001)|(0.001)|
|Tariff*Conservative dummy||0.012<br>(0.001)***|||-0.001<br>(0.002)||
|Tariff*liberal dummy|||0.01<br>(0.001)***|||-0.001<br>(0.002)|
|Country-year fixed effects|Yes|Yes|Yes|No|No|No|
|Product fixed effects|Yes|Yes|Yes|No|No|No|
|Country fixed effects|No|No|No|Yes|Yes|Yes|
|Agricultural products|Excluded|Excluded|Excluded|Excluded|Excluded|Excluded|
|Observations|121963|121963|121963|94658|94658|94658|
|Adjusted R-squared|0.01|0.01|0.01|0.0002|0.0002|0.0002|

All regressions include a constant. Robust standard errors are listed in parentheses. **** significant at 10%; ** significant at 5%; *** significant at 1%.

# **5.3 Misclassification of imported products**

Finally, we turn to misclassification of products as another potential channel of tariff evasion. We follow Fisman and Wei (2004) and include in our basic specification an additional regressor–the average tariff on similar products which are defined as all other 6-digit products belonging to the same 4-digit HS category. The average is weighted by the share of each product in German exports within each 4-digit HS category.<sup>12</sup> This additional regressor enters the estimated equation by itself as well as in interaction with the differentiated product dummy. If misclassification takes place, we expect to see a negative coefficient on the tariff on similar products, which would signify that holding the own tariff rate constant, a lower tariff on similar products creates more opportunities for misreporting. If such misclassification is easier for differentiated products, we would expect the coefficient on the interaction term to bear a negative sign.

|**Table 14: Results with tariffs on **|**similar product**|**s.**|||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||Levels|||First differen|ces|
|Tariff|0.006|0.000|0.000|0.004|-0.002|-0.002|
||(0.001)***|<br>(0.002)|(0.002)|(0.004)|(0.006)|(0.006)|
|Tariff on similar products|0.003|0.001|0.002|-0.001|-0.001|0.001|
||(0.002)|(0.002)|(0.002)|(0.004)|(0.006)|(0.005)|

12 The summary statistics for each importing country are presented in Appendix I Table A3. Note that using an unweighted average would lead to similar conclusions.

22
The mother’s education variable is highly significant but its impact on reducing the probability a child is malnourished is relatively small—only about 1.5 percent. This is probably a reflection of the very low level of female education in Ethiopia, particularly in rural areas. In contrast, the father’s level of education is not significant in any of the equations. It is possible that the indirect effect of his education is already picked up by other variables, such as household wealth.

We now come to the results on the variables regarding access to water and sanitation, the primary focus of this paper.<sup>16</sup> We first discuss the results of the specification which includes only measures of a household’s own environmental conditions. While the coefficients for households’ own access to water and sanitation in both the underweight and stunting equation are negative, as expected, these coefficients are small and not significant. The results for the model including community environmental conditions for the whole sample and for the rural sample are discussed next. The coefficients on the proportion of households with access to these services are highly significant in the underweight equations. The large coefficient on these variables suggests a significant spillover effect of other household’s access to these services. These results, however, do not hold for stunting. Note also that the coefficients on the proportion of households with access to water and sanitation in the stunting equations are generally smaller. Trying to measure the impact of these variables on the proportion of children stunted can be a bit problematic, given that stunting is a reflection of chronic malnutrition. We only observe these variables at the time of the survey and therefore cannot account for their past impact on stunting.

We also find a significant external impact on stunting from female’s education, as in the Alderman, Hentschel and Sabates (2002) study. However, this impact is small and does not hold for the probability of a child being underweight. Again, the weakness of the female education variable in this study is probably a reflection of the very low levels of education in Ethiopia. An alternative way to capture the role of knowledge would be to construct an indicator variable of “good care” practices relating to appropriate child feeding, preventive use of health facilities, knowledge of treatment options for common childhood illnesses. Ruel and others (1999) find that in some instances, these factors can mitigate the negative impacts

> 16 The generalized Hausman test does not reject the consistency of the estimated model which includes the household’s own access to water and sanitation.

21
Table 2: Poverty and Family Composition

||_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Reublic_|_Russia_|
|---|---|---|---|---|---|---|
|_FamilyComposition_<br>||<br>|**Headcount**<br>|**(P0,**in**pe**<br>|_p_<br>**rcent)**||
|One Male Adult, No Children|33.1|24.2|15.6|32.5|40.0|52.5|
|One Female Adult, No Children|45.0|27.8|13.5|37.0|51.8|47.8|
|One Adult, One or More Children|23.4|32.1|28.2|43.5|39.7|_45.0_|
|Two Adults, No Children|27.4|17.9|12.2|28.2|40.1|37.4|
|Two Adults, One Child|15.2|20.1|16.1|30.5|42.4|37.0|
|Two Adults, Two Children|19.4|19.9|24.7|29.6|39.9|38.7|
|Two Adults, Three or More Children|61.3|38.1|43.3|28.5|49.1|64.2|
|Three or More Adults, No Children|22.7|13.9|16.6|24.4|37.0|30.2|
|Three or More Adults. One Child|20.1|17.7|20.2|27.8|35.6|35.8|
|ThreeorMoreAdults.TwoChildren|35.8|29.5|36.2|31.6|43.3|51.6|
|Three or More Adults, Three or More|55.9|71.1|46.2|57.6|43.6|60.4|
|Children|||||||
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||Poverty G<br>|ap (in pe<br>|rcent) 11<br>||||
|One Male Adult, No Children|26.0|17.9|22.4|34.3|39.5|42.0|
|One Female Adult, No Children|28.9|18.6|17.3|27.5|47.4|44.2|
|One Adult, One or More Children|25.0|20.8|19.7|24.3|26.8|35.9|
|Two Adults, No Children|20.8|13.8|14.5|20.3|31.8|33.5|
|Two Adults, One Child|14.3|15.1|13.6|18.8|27.7|26.9|
|Two Adults, Two Children|18.1|13.1|12.8|17.4|26.7|27.0|
|Two Adults, Three or More Children|22.0|13.5|13.6|16.8|27.0|28.3|
|Three or More Adults, No Children|16.2|12.6|13.2|17.3|26.2|27.6|
|Three or More Adults, One Child|18.6|12.7|12.5|16.2|21.8|26.1|
|Three or More Adults, Two Children|19.3|12.8|12.8|16.6|23.0|25.0|
|Three or More Adults, Three or More|24.5|13.8|11.7|17.1|22.8|26.6|
|Children|||||||
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes:_ 1/ The poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

27
increasingly rely on hard information from financial statements rather than soft data gathered over time through relationship lending (Haas et al., 2007). Following Graham (2000), we use _return on assets_ and _current ratio_ as measures of cash flows and liquidity, respectively, which have a crucial effect on firm borrowing since firms with poor cash flows and liquidity are in higher need of debt. At the same time, firms with higher profitability can use retained profits thus relying less on external finance. Table 1 presents the summary statistics for the aforementioned independent variables used in the estimations. In order to capture firm-specific costs of financial distress as well as managerial entrenchment, we introduce _firm-specific effects_ (MacKie-Mason, 1990, Jensen, 1986). Moreover, with firm fixed-effects we effectively account for product market and industry considerations, which could influence firm capital structure due to industry concentration (Chevalier, 1995; Phillips, 1995), dependence on intangible or specialized assets (Rajan and Zingales, 1995; Titman and Wessels, 1988), location, and volatility of cash flows (Graham, 2000). Finally, year dummies are included in order to control for different economic cycles or other systemic changes.

# **IV. ANALYSIS OF RESULTS**

Table 2 reports the estimation results. The effective tax rate ( _ETR_ ) is positive and significant, both as a standalone and as an interaction term with the firm size, thus indicating that the tax reform in Croatia had a significant effect on firms’ capital structure. Moreover, it is found that the effect of corporate tax reform is declining with firm size. Separate estimations for four size quartiles of firms (based on total assets) show that the effect is stronger and highly significant for firms in the two smallest categories, but becomes weaker and insignificant for the two larger categories. Larger firms seem to be less sensitive to the tax reform because they enjoy more tax exemptions, deductions and allowances (Kesner-Skreb et al., 2001), as well as regressive compliance costs due to corporate taxation (Blazic, 2004). The declining effect of taxation reform in larger firms is also confirmed by the positive and significant coefficient for the interaction between _ETR_ and firm size. Also, as expected, the positive and significant effect from the interaction between _ETR_ and accounting performance illustrates that after the tax reform the more profitable firms were particularly keen to reduce their debt levels.

The control variables also offer interesting findings. In line with theoretical predictions, firm performance, as measured by return on assets, has a negative effect on long-term debt. This 8
# **1. Introduction**

Public procurement of services, works and supplies is estimated to account for 15-20% of GDP in developing countries, and up to 50% or more of total government expenditure (World Bank, 2015). Efficient and effective procurement is vital to core government functions, including public service delivery and provision of infrastructure. Weaknesses in procurement systems can lead to large-scale waste of public funds, reduced quality of services, corruption, and loss of trust in government.

Where procurement processes are vulnerable to fraud and other forms of corruption, public expenditure allocation may be distorted, in favor of highly-differentiated products where corruption is more difficult to detect, due to the absence of comparable reference prices (Mauro, 1998; Evenett and Hoekman, 2005a). If tendering processes are rigged, “competition cannot play its role of driving the prices down and the quality up” (World Bank, 2016).

Maximizing “value for money” and minimizing corruption in procurement are thought to be facilitated in general by competitive tendering procedures that limit discretion on the part of procurement officials (Ware et al., 2007: 297). More transparent systems that make procurement information widely accessible, and complaints mechanisms and auditing procedures that establish accountability for procurement decisions, are believed to encourage participation by more firms and deter “kickbacks” and other forms of fraud and corruption (Ware et al., 2007: 296-297).

However, there is relatively little systematic evidence on the links between procurement systems and outcomes. “The paucity of quantitative, cross-country analyses of national procurement practices is striking” (Evenett and Hoekman (2005b: 11). This paper adds to the evidence base, using data on almost 34,000 firms from the World Bank’s Enterprise Surveys, in 88 countries that also have data on procurement systems from PEFA (Public Expenditure and Financial Accountability) assessments.<sup>1</sup> We find that in countries with more transparent procurement systems, where exceptions to open competition in tendering must be explicitly justified, firms are more likely to participate in public procurement markets. Moreover, firms report paying fewer and smaller kickbacks to officials in countries with more transparent procurement systems, with effective and independent complaints

> 1 Descriptions of these data sources can be found at <u>http://www.enterprisesurveys.org/data</u> and https://pefa.org/.

2