rafmacalaba/datause-encoder-data huggingface dataset:
generates 41 percent. At the opposite extreme, the historical rate of unsatisfactory projects in EAP is 18 percent and the model generates 17 percent. Only one region, MNA, stands out as having a large gap between the two: the historical rate of unsatisfactory projects is 31 percent while the model predicts 43 percent.<sup>8</sup> However, a crucial feature of the prediction model is that it allows us to identify specific projects in specific regions at greater risk of ultimately being unsatisfactory -- something that cannot be done by just looking at past rates of project success by region.

**Table 5:  Predictions for projects in the active portfolio by Region.**

||**ALL**|**AFR**|**EAP**|**ECA**|**LCR**|**MNA**|**SAR**|
|---|---|---|---|---|---|---|---|
|**Historical Project Performance**||||||||
|% Rated Unsatisfactory by IEG, 1995-2012:|27%|38%|18%|20%|22%|31%|27%|
|**Active Portfolio**||||||||
|# Projects:|1591|483|291|206|265|120|222|
|% Rated Unsatisfactory by ISR-DO:|13%|10%|14%|17%|14%|21%|10%|
|% Rated Unsatisfactory by Prediction Model:|27%|41%|17%|15%|20%|43%|22%|
|of which: also Rated Unsatisfactory by ISR-DO|5%|6%|3%|3%|5%|11%|3%|

Table 6 summarizes the predictions by project life. By construction, the model predicts a similar proportion of unsatisfactory projects around 27 percent across all stages of project life. This is because the inputs into the prediction model do not vary systematically over the life of the project. The key point however is that these predictions are substantially different from those provided by the ISR-DO ratings. For example, only 16 percent of projects having disbursed 25 percent or less are rated by TTLs as having unsatisfactory ISR-DO ratings, even though historical experience suggests that around 27 percent of them will ultimately be unsatisfactory. This disconnect is even starker for projects in the first quarter of their planned lifespan, where only 3 percent are flagged as unsatisfactory with the ISR-DO ratings.

> 8 It is of course possible to use region-specific thresholds to calibrate the fraction of projects identified as at risk of being unsatisfactory by the prediction model, in order to eliminate this potentially undesirable feature of the results.

16
initial months of the shock. WW2 had a particularly lasting impact, as market liquidity never fully recovered after 1945, with average bid-ask spreads remaining at a level far above those in the 1920s. Also, idiosyncratic crises affected aggregate market liquidity, for example the Ottoman and Egyptian defaults in the 1870s, the Russian default in 1998, or the Argentine defaults in the 1890s and early 2000s.

Second, total returns tend to be higher when bid-ask spreads are high, except for the first months of major shocks, when returns collapse while bid-ask spreads spike. On average, higher risk is associated with lower market liquidity. Appendix B6 confirms this using bond-level data, as average returns are significantly higher for less liquid bonds. The difference between the most liquid and least liquid bonds is large and statistically significant, especially in the historical sample, which is in line with Alquist (2010) and Chavaz and Flandreau (2017), who also find a substantial sovereign bond liquidity premium. An indepth analysis of sovereign bond market liquidity over the full 200 years is left for future research.

# **5. Investor performance around debt crises: Returns, defaults, and haircuts**

This section studies the link between bond returns, defaults, and haircuts, by combining the data on debt restructurings and creditor losses from Section 3 with those on bond prices and returns from Section 4.

Figure 11: Bond returns around sovereign defaults <!-- Start of picture text --> 175<br>Upper 75%<br>150<br>125 Average<br>cumulative<br>return<br>100<br>Lower 25%<br>75<br>50<br>-2 -1 t 1 2 3 4 5 6<br>Years around default<br>Start of holding period: 2 years before default<br>Cumulative total return index<br><!-- End of picture text --> _Note:_ This figure shows total cumulative returns on sovereign external bonds around default events, using real, monthly return data and taking into account losses due to missed coupons and haircuts. The total return series are normalized to one in year two (24 months) prior to the default. The bold black line shows the average across all defaulted bonds, while the dotted grey lines show upper and lower quartiles. We include all 92 default episodes for which we have sufficient pricing data before and after the default.

Figure 11 shows a time series of total returns around all the sovereign bond default events in our sample for which we have sufficient bond price data, in the run-up and the aftermath of default. These are 92 cases of a total of 161 sovereign bond defaults in the sample. (This exercise does not include the many defaults on bank loans for which no prices exist; see Figure 1.) The total cumulative return series is 35
Policy Research Working Paper 8119

# **Abstract**

The resurgence of deliberative institutions in the developing world has prompted a renewed interest in the dynamics of citizen engagement. Using _text-as-data_ methods on an original corpus of village assembly transcripts from rural Tamil Nadu, India, this paper opens the “black box” of deliberation to examine the gendered and status-based patterns of influence. Drawing on normative theories of deliberation, this analysis identifies a set of clear empirical standards for “good” deliberation, based on an individual’s ability both to speak and be heard, and uses natural language processing methods to generate these measures. The study first shows that these assemblies are not mere “talking shop” for state officials to bluster and read banal announcements, but rather, provide opportunities for citizens to challenge their elected officials, demand transparency, and provide information about authentic local development needs. Second, the study finds that across multiple measures of deliberative influence, women are at a disadvantage relative to men; women are less likely to speak, set the agenda, and receive a relevant response from state officials. Finally, the paper shows that although quotas for women on village councils have little impact on the likelihood that they speak, they do improve the likelihood that female citizens are heard.

This paper is a product of the Poverty and Inequality 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 vrao@worldbank.org.

_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
urban disparities in both prices and living standards. For estimates based on the 2005 PPPs, adjustments to the PPPs for India and China were also motivated by a concern that the 2005 ICP price collection was heavily concentrated in urban areas and that the national PPPs therefore possibly had an urban bias (Ravallion and Chen, 2010; Chen and Ravallion, 2008; Ravallion, 2008).

For these three countries, national PPPs from the ICP are disaggregated into rural and urban PPPs that are constructed to reflect cost-of-living differences between rural and urban areas. In backing out separate rural and urban 2011 PPPs for these three countries, we follow steps that are conceptually equivalent to those used by Chen and Ravallion (2008, 2010). To unpack the national PPP into an urban and rural PPP, this method uses rural and urban poverty lines to measure differences in the cost of living, as well as information on the rural and urban shares of the ICP price collection.<sup>40</sup> Following notation from Lakner et al. (2015), the adjustments can be summarized as follows. Let � be the relative urban-rural difference in cost of living (expressed as the ratio of urban and rural poverty lines, ��/��) and �, the urban share of total price outlets in the ICP price collection for each country. The PPPs are then unpacked into urban and rural PPPs such that their ratio is equal to �, the urban-rural difference in the cost of living.

This of course only defines the ratio of the PPPs. To establish unique values for PPPU and PPPR, it is also necessary to know the magnitude of the difference between the urban and rural measures. To this end, the urban share of price outlets, �, is used as a weight to measure the difference between the urban and rural PPPs.

> 40 Because cost-of-basic-needs poverty lines are typically estimated to include an allowance for nonfood consumption, and this allowance frequently differs across urban and rural areas, differences in poverty lines are more accurately described as reflecting differences in the cost of living, rather than differences in prices: they potentially reflect differences in both prices and quantities. In contrast, the adjustments for LAC and ECA countries, discussed below, are more accurately referred to as spatial-price adjustments.

23
destruction of formal firms due to the rise in the minimum wage was accompanied by an increase in both inactivity and unemployment – rather than a rise in informal employment.

We also use the HLFS data to investigate whether the minimum-wage increase impacted the wages of workers who are informally employed using the HLFS data. In contrast to the EIS registry data, the HLFS data also collect information on the wages of informal workers. While informal workers are by nature not covered by official labor regulations including the minimum wage, prior literature suggests that minimum-wage increases for formal workers can have positive spillovers on workers who are informally employed.<sup>30</sup> The existence of such spillovers has important welfare implications because it implies that workers’ wages benefit from the minimum wage even for those whose employment is outside the formal sector. Columns (6) to (8) of Table 10 report the difference-in-difference estimates of the minimum-wage increase on wages using the HLFS data. We find a large, positive, and statistically significant effect of the minimum wage on the wages of formal workers (Column 7). We do find a positive effect of the minimum wage on the wages of informal workers, but the magnitude of the estimate is about three times smaller, and it is not statistically significant (Column 8). Thus, the spillover effects of the minimum wage on the informal sector appear to be limited; this finding contrasts with findings of some studies in other contexts, particularly for some Latin American countries.<sup>31</sup> However, this result is compatible with the lack of impact of the minimum-wage increase on informal employment that we have also found. This lack of a pronounced spillover effect could be explained by the high degree of segmentation between formal and informal employment in Turkey, compared to greater integration between the two types of employment in other developing economies.

# **6. Conclusion**

This paper examines the impacts of a large increase in the minimum wage on firms and workers in Turkey. We find that increase in the minimum wage that took place in 2016 substantially raised the destruction rate among formal firms. The results suggest that the minimum wage operates as a firm-selection mechanism by eliminating smaller, less-productive businesses from the formal

> 30 Such “lighthouse” effects have been evidenced, for example, in Colombia (Maloney and Mendez, 2004), Brazil (Lemos, 2009) and Argentina (Khamis, 2013).

> 31 Evidence for positive effects of the minimum wage on wages in the informal sector has been found in Colombia (Maloney and Mendez, 2004), Brazil (Lemos, 2009) and Argentina (Khamis, 2013). By contrast, Ham (2018) reports negative effects of the minimum wage on the wages of informal workers in Honduras.

29
# **CONTES**

||ACKNWLEDGEMENT<br>.......................................|i|
|---|---|---|
|I.|INTRODUCTION<br>..........................................|1|
|II.|CONCEPTS AND CATEGORIES FOR CHNESE INDUSTRIAL STATISTICS .<br>.........|1|
|III.|CHINESE INDUSTRIAL PEILORMANCE SUMM-ARY OF RECENT OFFICIAL DATA.....|3|
|IV.|SYMPTOMS OF UPWAR BIAS IN CHINESE INDUSTRIAL STATISTICS ...<br>........|6|
||A. MISMATCH BETWEEN OUTPUT AND VALUE DATA ......<br>..................|6|
||B. Do RURAL INDUSTRIES ADHERE TO STANDARD AccOUNTING REGULATIONS ..|.<br>7|
||C.HAS THE RATE OF DOUBLE COUNTING INCREASED? ................|10|
||D. Do LOCAL AUTHORITIES FALSIFY INDUSTRIAL OUTPUT DATA? ...<br>......|12|
||E. Do INDUSTRIAL OUTPUT DATA ACCURATELY REFLECT RECENT INFLATION<br>EXPERIENCE ........|12|
||F. ARE REPORTED GAiNS IN ENERGY PRODUCTIVITY UNREALISTICALLY LARGE?|15|
|V.|CONCL.USIONS<br>**.............**|20|
||REFERENCES<br>..|43|
Vuolteenaho, 2004). In Serbia, the MMIR indicator is statistically significant and negative in the short run, which remains in alignment with the long-run relationship coefficient. Moreover, in the case of Serbia, the results imply statistical significance by the GDPPC indicator for the first time, although the estimating association coefficient is minor. This result draws comparisons to research claiming that in poorer markets with lower stock exchange capitalization and liquidity, the GDPPC indicator exhibits a greater correlation of the macroeconomic indicators to a systemic movement in stock prices (Morck, Yeung, & Yu, 2000). However, the respective statistical indicators for Serbia show that of the research countries in Southeast Europe it has the second weakest capitalization and liquidity, but a median GDPPC level.

# **5. Conclusions**

This research paper empirically demonstrates that there is a statistically significant relationship between macroeconomic indicators and stock prices. The results reaffirm that the capital markets in Southeast Europe are weak form information inefficient. The results of the aggregate group data panel PMG for the selected markets demonstrate the nonexistence of an estimated impact of the statistically significant short-run individual macroeconomic indicators on the relationship with SMI. The result is in line with older and recent research utilizing panel PMG in three Asian countries (Megaravalli & Sampagnaro, 2018).

The results of the empirical research test on the existence of statistically significant longrun cointegrating relationships show an associating negative impact from an increase in the MMIR and HICP macroeconomic indicators on the value of the SMI. Such results also are in line with previous research (Azar, 2010; Balduzzi, 1994; Campbell & Vuolteenaho, 2004). The existence of a significant long-run relationship association versus the nonexistence of a short-run significant association contradicts the expected performance of a decreasing long-run coefficient impact because of the implementation of control factors such as policy adjustments and a shift in investor sentiment.

Moreover, the nonexistence of significant short-run relationships raises questions about the prevalence of portfolio investors as the ones who are more likely to be sensitive to short-run deviations because of their strategy of having a more imminent response to change events by rebalancing the positions of their investments. The multivariate and cointegrating test results of 23 | P a g e
estimated average treatment effect is still well within the range of observed carbon values, because the maximum is 360 tons per hectare. NCF forest user groups that can identify their formation year also have more regeneration (seedlings per hectare), which indicates that trampling has been restricted. Compared with the mean, the effect is approximately 60% (p<0.10). These forest level findings reinforce the conclusion from the OLS forest level models that it is not CF status _per se_ that is good for forests, but collective action.

As shown in Tables 14 and 15 the forest level model results are very much confirmed at the plot level.<sup>16</sup> The panel-by-plot random effects models again suggest that collective action is an important factor in carbon sequestration, but that collective action may not need to be community forestry _per se_ . Plots that are part of forests where the formation year was identified by leaders are estimated to have over 30,000 kilograms more carbon per hectare than plots under presumed open access. This represents a marginal effect of approximately one-third of the mean.<sup>17</sup> CF status is again not estimated to be a statistically significant determinant of forest quality except when measured by seedlings per hectare for which CFs are estimated to have fewer seedlings per hectare (p<0.10).

> 16 Only panel data models are shown.  Pooled OLS results are similar and are available by request.

> 17 In the plot level random effects model we find that CanIDFUGyear reduces crown cover (p<0.05).

**31 |** P a g e
# Table **8.** Continued.

||199|2 Value of Expo|rts ($million)|||Share|of Exports C|overed by N|TBs||
|---|---|---|---|---|---|---|---|---|---|---|
|Exporter|All OECD|EFTA|EU|Japan|USA|All OECD|EFTA|EU|Japan|USA|
|Sierra Leone|354|3|256|3|65|0.1|7.6|0.1|0.0|0.0|
|Somalia|19|1|14|-|2|9.0|0.0|9.6|0.0|0.0|
|South Africa|11,132|525|4,892|1,781|1,878|13.0|5.2|14.9|21.1|0.0|
|Sudan|149|5|74|29|11|12.4|0.1|17.4|0.0|0.0|
|Tanzania|266|8|158|33|12|3.8|1.2|5.4|0.1|0.0|
|Togo|121|8|65|1|7|_0.5_|0.7|0.5|2.2|0.0|
|Uganda|162|4|120|4|14|0.0|0.7|0.0|0.0|0.0|
|<br>Zaire|1,277|17|801|83|259|0.0|0.4|0.0|0.0|0.0|
|Zambia|630|8|279|249|71|0.7|12.1|0.7|0.0|0.0|
|Zimbabwe|875|54|418|120|114|20.3|6.5|28.5|4.4|_8.5_|
|TOTAL SUB-SAHARAN AFRICA|44,791|1,556|21,592|2,945|12.675|13.1|3.4|19.7|13.2|1.6|
|Memo Item|||||||||||
|SSA excluding S. Africa|33,659|1,031|16,700|1,164|10,798|13.1|2.5|21.2|3.2|1.9|
|Developed Countries|1,986,779|186,789|908,622|110,548|317,996|10.2|6.6|8.8|_16.5_|15.4|
|Developing Countries|705,634|29,551|204,374|120,426|233,595|16.6|15.1|24.9|4.2|15.4|

Note: See the notes to Table 7 for country definitions and a listing of measures that have been included in the computation of the NTB coverage ratios.

Source: Trade statistics from UN COMTRADE records. NTB information from the World Bank-UNCTAD SMART Database.
# **1. Introduction**

The Indian manufacturing sector is undergoing several significant structural transformations. Since the policy deregulations and economic liberalization process began in the early 1990s, substantial shifts in the spatial placement of Indian’s manufacturing sector have occurred. This spatial sorting is occurring both across Indian districts and also within them (e.g., Fernandes and Sharma 2012, Ghani et al. 2012a,b). Understanding these structural shifts and how they accrue over different parts of the manufacturing sector (e.g., the organized and unorganized sectors) is an essential input for urban planning and economic policy, as they govern the distribution of economic activity and industrial development of the country. Improved spatial sorting is also needed in India for stronger productivity growth and better allocation of scarce resources.

This study considers how connectivity across districts influences the levels and spatial organization of manufacturing within districts. Specifically, we consider the development and improvements of a major highway in system in India—the Golden Quadrilateral (GQ) project. The GQ project sought to improve the connection of four major cities in India: Delhi, Mumbai, Chennai, and Kolkata. The GQ system comprises 5,846 km (3,633 mi) of road connecting many of the major industrial, agricultural, and cultural centers of India. It is the fifth-longest highway in the world. The massive project began in 2001, was two-thirds complete by 2005, and mostly finished in 2007. We compare how GQ influenced the manufacturing operations and entrepreneurship rates in the urban and rural portions of the districts through which it passed.

In an earlier study, Ghani et al. (2012a) consider the aggregate impact of the GQ upgrades on the activity of India’s organized manufacturing sector using enterprise data from the Annual Survey of Industries. This study finds significant increases in manufacturing entry rates and productivity in districts that were very close to these upgrades (0-10 km), compared to districts farther away (10-50 km). We continue with this approach, taking upon the additional task of separating activity in districts by the urban versus rural locations of plants. We find that the entry effects for the organized sector are present in both types of areas, with somewhat stronger outcomes in rural settings. The differences are most substantial when considering where new plants with major output levels are locating. By contrast, the productivity gains for the organized sector are very similar in both urban and rural locations. These findings suggest that in 2