rafmacalaba/datause-encoder-data huggingface dataset:
25 (the _guanxi)_ , and the combination of political centralization and administrative decentralization in the public sector.

# **I:2 Economic Consequences**

We do not really know which specific elements of China’s economic reforms during the last quarter of a century best explain the county’s successful growth performance.<sup>34</sup> We can only say that the actual _combination_ of elements in the reform package, schematically illustrated in Figure 1, has been highly conducive to GDP growth. An important component of the reforms has then simply been to remove various institutional obstacles for economic growth, and hence release initiatives that have boosted the accumulation of real and human capital, and stimulated import of foreign technology and organization.

It is tempting, and usual, to argue that China’s growth performance has also been enhanced by the _gradual_ and _experimental_ nature of the reform process. To a considerable extent, the process also relied on bottom-up initiatives, rather than topdown reforms, although new political signals from central political authorities have, of course, kept up the thrust of the process and influenced its course. However, the reforms do not seem to have been based on a blueprint of a specific “final stage”, although new intermediate goals have been spelled out consecutively in the Five-Year Plans.

Often mentioned examples of the gradualism are that agricultural reforms began as local initiatives before they became national policies, that the reforms in manufacturing were initiated only after the success of agricultural reforms, and that the national economy was only gradually opened to international trade and foreign investment.<sup>35</sup> As a consequence, while family farms were the most dynamic force in the Chinese economy during the first years of the reform process, the TVEs

> 34 According to official (revised) statistics, total GDP in China is today (2005) about US$ 2.3 trillion, and per capita GDP US$ 1.700 – both measured by the official exchange rate (after the upward revision of the national accounts announced in December 2005). It is often asserted to be three or four times higher in terms of PPP calculations. Such calculations are, however, quite uncertain, not least for China (Heston, 2003).

> 35 According to Tseng and Zebregs (2002), the tariff rate fell from well over 50 percent in the early 1980s to about 15 percent in 2002. Indeed, since a significant share of imports of goods subject to high tariffs is imported illegally, the authors argue that tariff revenues as a percentage of total imports are only 3 percent. Kanbur and Zhang (2005) report a fall in the tariff rate from about 15 to about 4 percent between 1978 and 2000.The expansion of the “special economic zones” was also gradual.
# **Figure 3.  Political instability**

<!-- Start of picture text --> Polit. Instab.<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>NPLAFG<br>B GD<br>PAK<br>BTN<br>6 7 8 9 10<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text -->

# **Figure 4.  Access to Land**

<!-- Start of picture text --> Access to Land<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>BGDAFG MDV<br>PAK<br>IND<br>NPL<br>LKABTN<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> 67
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Hendry, D. F. (1980). "Econometrics: Alchemy or Science?," _Economica,_ 47, 387-406

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- Knight, John (1996). "Labour Market Policies and Outcomes in Zimbabwe," Oxford: University of Oxford, Centre for the Study of African Economies, Processed: March.

- Kremers, J.J.M., N.R. Ericsson, and J. Dolado, (1992). "The Power of Co-integration Tests," _Oxford Bulletin of Economics and Statistics, 54, 325-48._

- Phillips, A. W. (1958). "The Relation between Unemployment and the Rate of Change of Money Wages in the United Kingdom, 1861-1957," _Economica, 25,_ 283-299.

- Sargan, J. D. (1980). "Some Tests of Dynamic Specification for a Single Equation," _Econometrica,_ 48, 879-97.

35
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# **4.4 Meta‐Regression**

Table 5 presents the results of the meta‐regressions of the correlation of study outcomes against study characteristics hypothesized to be associated with the studies’ effects (variables) for the entire meta‐ analysis data set. Overall, three study characteristics appeared to be significantly correlated with outcomes, namely, the type of WASH intervention, which showed a slight positive correlation, and reporting metric (risk difference) and implementing entity (government), which showed slight negative correlations.

The number of publication views is also slightly, but significantly, correlated to outcomes, which is intuitive and may be indicative of studies seeking to build upon the evidence of their predecessors. Moreover, the direction of the relationship is negative, which could suggest well‐known studies are eventually refuted over time, indicative of good practice and conducive to the well‐being of the sector as a whole. Overall, very few variables were significantly correlated and standard errors were robust. Thus, the regression model was correctly specified, and 57 to 65 percent of the variation in correlations can be explained by the selected study characteristics.

**Table 5. Correlation of Outcomes against Study Characteristics for the Entire Meta‐Analysis Data Set**

|Variables|Model (1)<br>Study Correlation<br>(pooled effects)|Model (2)<br>Study Correlation<br>(groupdummies)|Model (3)<br>Study Correlation<br>(groupdummies)|
|---|---|---|---|
|Region fixed effects|No|No|Yes|
|Number of views|‐0.0113***|‐0.0113***|‐0.0110**|
||(0.00413)|(0.00360)|(0.00444)|
|Journal, dummy|‐0.00501|‐0.00501|‐0.00823|
||(0.00908)|(0.00792)|(0.0109)|
|Report/chapter, dummy|‐0.0213|‐0.0213*|‐0.0194|
||(0.0133)|(0.0116)|(0.0178)|
|Single intervention, dummy|‐0.00726|‐0.00726|‐0.00589|
||(0.0116)|(0.0101)|(0.0118)|
|Randomized assignment, dummy|‐0.00142|‐0.00142|0.00184|
||(0.00804)|(0.00701)|(0.00994)|
|Child health, growth outcomes group|‐0.00409|‐0.00409|‐0.00317|
||(0.0117)|(0.0102)|(0.0146)|
|Risk difference (RD) effect, dummy|‐0.0389***<br>(0.0140)|‐0.0389***<br>(0.0122)|‐0.0433***<br>(0.0109)|
|Subtheme groups, categorical|0.0319***|0.0319***|0.0337***|
||(0.00715)|(0.00623)|(0.00548)|
|Years of implementation, cont.|‐1.153|‐1.153|‐0.220|
||(1.494)|(1.301)|(1.630)|
|Government implemented, dummy|‐0.0219**|‐0.0219***|‐0.0259**|
||(0.00870)|(0.00758)|(0.0120)|
|Sample size, continuous|‐0.00227|‐0.00227|‐0.00167|
||(0.00389)|(0.00339)|(0.00370)|
|Urban interventions, categorical|‐0.0141|‐0.0141*|‐0.0175|
||(0.00925)|(0.00806)|(0.0110)|
|Constant|8.906|8.906|9.821|
||(11.36)|(9.895)|(12.39)|
|Observations|136|135|104|
|R‐squared|0.621|0.573|0.650|

_Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

14
# **Table 2: Distance-to-Default Regional Time Series**

This table shows the weighted-average distance-to-default measure for 9 different regions computed each year. We calculate weighted-average distance-to-default by market capitalization each week for each region. We then compute arithmetic averages across 52 weeks in a given year.

|**regions**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Africa**|3.49|1.92|3.42|4.17|3.83|4.81|4.87|5.02|4.72|4.27|4.04|2.62|4.39|
|**Central Asia & Eastern Europe**|2.29|2.07|2.80|3.12|3.77|4.50|4.20|4.34|3.74|4.08|3.64|1.65|2.73|
|**East Asia and Pacific**|4.44|3.64|4.65|4.98|4.75|6.07|6.23|7.65|7.49|6.11|4.36|3.13|4.59|
|**Japan**|4.37|3.68|4.17|5.60|5.39|5.39|4.70|6.51|5.35|6.31|5.16|4.81|7.30|
|**Latin America & Caribbean**|3.23|2.93|3.77|4.29|4.78|4.42|5.39|5.18|4.19|4.32|3.32|2.11|4.11|
|**Middle East & North Africa**|6.97|5.89|6.78|8.19|8.97|6.77|7.12|5.17|4.20|4.67|4.62|2.88|4.98|
|**North America**|4.87|3.89|3.46|3.89|5.19|5.65|8.05|8.79|8.92|8.47|4.47|1.68|3.88|
|**South Asia**|4.75|3.65|3.56|4.85|5.64|6.39|4.32|4.20|4.33|3.90|3.07|2.35|3.36|
|**Western Europe **|4.86|4.21|5.45|5.18|5.39|5.88|8.41|10.18|8.87|8.33|7.07|2.97|5.26|

27
Table 10: Perceived Legitimacy of Village Head

||Mean Control|Effect of VH<br>Training|Effect of VH<br>+ CL Training|CL Effect|N|
|---|---|---|---|---|---|
|Legitimacy of VH (Mean effects index)|0.00|-0.070|0.048|0.118*|2154|
||(1.00)|(0.061)|(0.056)|(0.067)||
|How much trust VH to take right action (1-4)|3.547<br>(0.728)|-0.047<br>(0.042)|0.037<br>(0.043)|0.085*<br>(0.049)|2130|
|Rating of relationship with VH (0-10)|8.153|-0.288**|-0.085|0.202|1975|
||(2.127)|(0.138)|(0.122)|(0.152)||
|How much think people in the village infuenced|3.186|0.015|0.050|0.034|2114|
|by the VH’s opinions (1-4)|(0.817)|(0.046)|(0.044)|(0.051)||
|Whether people say they would take a livestock|0.726|0.005|0.020|0.015|2150|
|dispute to the VH’s court if they had one (0/1)|(0.446)|(0.029)|(0.025)|(0.032)||

Table displays standard errors in parentheses. *, ** and *** indicate significance at the 90, 95 and 99 percent confidence levels respectively.

However, they beg the question of how civil society leaders had this impact. We turn to this in the next section.

# **7 Mechanisms**

The inclusion of civil society leaders in the training sessions for village heads could have resulted in reforms to village governance through a variety of different mechanisms. In this section, we consider the evidence in support of three mechanisms, first outlined in figure 1. In particular, we examine whether civil society leaders caused reform through (1) competition between the two sets of leaders, (2) capacity upgrading or (3) increased monitoring. We probe the plausibility of each of these mechanisms using both quantitative and qualitative evidence.

## **7.1 Quantitative evidence**

First, we use our survey data to test observable implications of each mechanism, drawing on evidence from our survey of village heads, households and especially civil society leaders. It is important to note that 29
# **Figure 10. Average markup differences relative to non-exporters by export status**

<!-- Start of picture text --> 50<br>45 41.6<br>40<br>35<br>30 25.8<br>25<br>20<br>15 12 11.9<br>10<br>5<br>0<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set.

_Note:_ Numbers reflect estimated coefficients (multiplied by 100) for R&D dummy category—relative to non-R&D performer firms, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**Figure 11. Average markup differences relative to non-R&D performers by R&D status**

<!-- Start of picture text --> 50 46.0<br>45<br>40<br>34.0<br>35<br>30 27.3<br>25<br>20 16.0<br>15<br>10<br>5<br>0<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for export dummy category—relative to non-exporters, the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**Performing R&D activities is positively correlated with markup differences, while location, proxied by county, also matters to explain markup performance.** Results displayed in figure 11 show that R&D performers—identified by those firms whose R&D expenses surpass more than 1 percent of their total revenue—tend to earn higher markups than non-R&D performers. The markup premium for R&D performers is always positive, suggesting that this group of firms is able to recoup fixed costs associated with R&D activities. The magnitude of the premium varies from 16 percent to 46 percent, depending how data are clustered across large sectors (manufacturing, services, and mining). In addition, results displayed in figure 12 show that firms in the same product market (at NACE 4-digit level) and with similar characteristics (in terms of ownership, age, size, and export and R&D profiles) charge different markups depending on the county in which they operate. Except for Dolj, firms in all other counties demonstrate lower average markups than Bucharest; the negative markup premium in relation to Bucharest varies from −15.4 percent (Vrancea) to −3.6 percent (SatuMare).

25
Figure 5 reports the total number of restrictive measures registered between 2003 and 2022, where it is evident that there was a large increase starting in 2020. Figure 6 disentangles the measures into three types: export barriers, import barriers and industrial policies. Clearly, the latter ones are the predominant force leading the total number of measures reported in Figure 5.

The data suggest that the most prevalent restrictive measures take the form of subsidies, followed by trade related investment measures (see Figure 7a). In terms of liberalizing measures, the most frequent ones appear to be linked to the elimination of non-automatic licensing or quotas (see Figure 7b).

To capture the significance of the new trade restrictions and industrial policy measures, we also investigate the information at the most granular level offered by the Global Trade Alert database. We find that these measures include diverse, finely targeted, and technocratic forms of public support to production and exports, as well as financial guarantees. Unsurprisingly, the measures are mostly implemented by rich countries and some of the larger developing countries. For example, we see a significant increase in the use of export bans and subsidies in ICT and technology.

Table 1 reports two examples that allow understanding the virtues and potential limits of the GTA. Example #1 is an intervention by the US government to support General Motors and Chrysler through a capital injection. The intervention was announced on March 30, 2009, and is recorded as affecting only specific firms. The HS6 products affected by this interventions represent different types of cars (of different cylinder capacities), which, as is well known, are the main final products produced by General Motors and Chrysler. Example #2 reports instead the establishment of a China-backed state fund to promote the domestic integrated circuit industry. The fund was announced in 2014 for a duration of 10 years. The form of the intervention was state loans available to all firms. The HS6 products reported as potentially affected by this intervention include products at different stages of the semiconductor value chains, from the “Machines and apparatus for the manufacture of boules or wafers” (HS code 858610) to the “Processors And Controllers, Electronic Integrated Circuits” (HS code 854231). Examples #1 and #2 illustrate the virtues of a comprehensive cross-country database of rich information about many different aspects of different types of policies. On the other hand, the association of these measures with the potentially affected products is inevitably imperfect. This might introduce significant measurement error in an important explanatory variable of interest.<sup>4</sup>

> 4We consider for our exercise all the measures reported by the GTA for which a set of potentially products is reported. The alternative would be to consider only the interventions directed to specific firms, with the benefit of isolating better discriminatory measures, but the cost would be excluding important industrial policy episodes such as the establishment of the “Big Fund” in China in 2013.

6
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21
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Tanzania. Second, since the Tanzanian development budget records all donor-funded expenditure regardless of whether it is in fact recurrent in nature, the level of development expenditure is not an appropriate proxy for public investment. Thus, in a second step, we adjust the fiscal data based on rough estimates indicating that about one third of donor-funded expenditures recorded in the development budget are in fact recurrent, and the breakdown between public consumption and public investment in the national accounts is adjusted accordingly.

# 111.2.2 Exports

In Tanzania, the balance of payments does not capture the large volume of unofficial exports. Since 1984, Tanzanian citizens have been allowed to import a large variety of commodities if they used their 'own funds", i.e., if no foreign exchange was requested from the Bank of Tanzania. At the same time, it was illegal to hold foreign exchange, as all receipts had to be immediately surrendered. The myth was therefore upheld that Own Funds imports were financed from private transfers from abroad, and a counterpart to these imports was recorded as such in the balance of payments. Evidence strongly suggests, however, that these imports are financed by exports otherwise not recorded, and we have therefore used the data on private transfers as a proxy for unofficial exports. In recent years, we estimate that almost half of exports have remained uarecorded, accounting for about 13 percent of GDP.

By distorting the trade balance, the exclusion of these unrecorded exports introduces an important flaw in the national accounts framework. Within the familiar output-absorption relationship in equation (1):