rafmacalaba/datause-encoder-data huggingface dataset:
# **Contents**

|**1**<br>**Intr**|**oduction**|**2**|
|---|---|---|
|**2**<br>**Adv**|**erse events: Literature and stylized facts**|**5**|
|2.1|Natural disasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>6|
||2.1.1<br>Transmission channels of natural disaster shock to productivity . . . .|. . . .<br>6|
||2.1.2<br>Stylized facts for natural disasters<br>. . . . . . . . . . . . . . . . . . . .|. . . .<br>7|
|2.2|Wars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>9|
||2.2.1<br>Transmission channels of war shock to productivity. . . . . . . . . . .|. . . .<br>10|
||2.2.2<br>Stylized facts for wars . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>10|
|2.3|Financial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>11|
||2.3.1<br>Transmission channels of fnancial crisis shock to productivity . . . . .|. . . .<br>12|
||2.3.2<br>Stylized facts for fnancial crises<br>. . . . . . . . . . . . . . . . . . . . .|. . . .<br>13|
|2.4|Comparing across types of adverse events<br>. . . . . . . . . . . . . . . . . . . .|. . . .<br>13|
|**3**<br>**Em**|**pirical strategy**|**15**|
|3.1|Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>15|
|3.2|Methodology<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>16|
|**4**<br>**Res**|**ults**|**17**|
|4.1|Impacts of natural disasters . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>17|
|4.2|Impacts of wars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>20|
|4.3|Impacts of fnancial crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>21|
||4.3.1<br>Comparison across types of events<br>. . . . . . . . . . . . . . . . . . . .|. . . .<br>22|
||4.3.2<br>Severe adverse events<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>23|
||4.3.3<br>Global adverse events<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>24|
|**5**<br>**Wh**|**at policies can mitigate the efects of adverse events?**|**24**|
|**6**<br>**Con**|**clusions**|**27**|
|**A Dat**|**a, sources and defnitions**|**29**|
|**B Rob**|**ustness**|**32**|

1
# **Graph (14): Stability (One Step and N Down Chow Test) Test of - 3 Lags - Open Economy VAR:**

<!-- Start of picture text --> 2020 2020 2020 2020<br>2000 1% 2000 2000 2000<br>1% 1% 1%<br>1up INFL 1up Dexrtend Ndn rgdp Ndn DISC<br>1980 1980 1980 1980<br>1 0 1 0 1.0 0.5 1 0<br>2020 2020 2020 2020 2020<br>2000 2000 2000 1% 2000 1% 2000<br>1% 1% 1%<br>1up rgdp 1up DISC Ndn rm2 Ndn Interestrt Ndn CHOWs<br>1980 1980 1980 1980 1980<br>1 0 1 0 1 0 1 0 1.0 0.5<br>2020 2020 2020 2020 2020<br>2000 1% 2000 1% 2000 2000 1% 2000<br>1%<br>1%<br>1up rm2 1up Interestrt 1up CHOWs Ndn INFL Ndn Dexrtend<br>1980 1980 1980 1980 1980<br>1 0 1 0 1.0 0.5 1 0 1.0 0.5<br><!-- End of picture text --> 76
- Lucas, R., 1988. On the mechanics of economic development. Journal of Monetary Economics 22 (1), 342.

- Mazvimavi, K., Myathi, P., Murendo, C., 2011. Conservation agriculture practices and challenges in zimbabwe. In: 5th World Congress of Conservation Agriculture Incroporating 3rd Farming Systems Design Conference, September 2011 Brisbane, Australia.

- Munshi, K., 2004. Social learning in a heterogeneous population: technology diusion in the indian green revolution. Journal of Development Economics 73 (1), 185213.

- Pamuk, H., Bulte, E., Adekunle, A. A., 2014. Do decentralized innovation systems promote agricultural technology adoption? experimental evidence from africa. Food Policy 44, 227236.

- Purcell, D., Anderson, J. R., 1997. Agricultural extension and research: Achievements and problems in national systems. World Bank Publications.

- Tan, J.-P., Lane, J., Lassibille, G., 1999. Student outcomes in philippine elementary schools: An evaluation of four experiments. The World Bank Economic Review 13 (3), 493508.

- Thierfelder, C., Matemba-Mutasa, R., Rusinamhodzi, L., 2015. Yield response of maize (zea mays l.) to conservation agriculture cropping system in southern africa. Soil & Tillage Research 146, 230242.

- Thompson, P., 2010. Learning by doing. Handbook of the Economics of Innovation 1, 429476.

- TIA, 2008. National agricultural household survey. Michigan State University and The Ministry of Agriculture of Mozambique (MINAG).

- URL http://fsg.afre.msu.edu/Mozambique/survey/index.htm.

- Waddington, H., White, H., Anderson, J., 2014. Farmer eld schools: from agricultural extension to adult education. Systematic Review Summary 1.

# Figures and Tables

27
However, results from correlation analysis on time-series data show that apart from the Asian economies, the colTelations are not significant. Hyper-inflation in the Latin American economies may have reduced the power of correlation analysis because of the distortions it introduced into the variables we constructed. Also the time series available are for a relatively short period and stock price volatility may have distorted the findings.

The results of cross-sectional and time-series analysis can be reconciled in that there might be bounds of variation of MCY. Although over time MCY fluctuated widely and there was poor correlation with the indicators of financial depth, these variations were bounded to a certain extent so that the means of MCY in that period of time still reflected the relative size of the market. Thus cross-sectionally, we can still observe high correlations between MCY and other indicators of financial depth. That is to say, in the economies which had better developed financial system, although the stock market fluctuated widely thus making the correlations over time poor, the means of MCY in these economies were still higher because the stock markets were larger and better developed.

A threshold hypothesis of stock market development proves to be difficult to test due to several factors. It is thus difficult to tell at one particular time whether one economy has fulfilled the necessary conditions for the development of its stock market. All we can say is that when an economy is well equipped with financial infrastructure it may have a better chance of attracting new capital into its equity market.

This paper has established some stylized facts concerning the relationship between the development of the stock market and the process of financial deepening. It would be helpful for future research in identifying the conditions for the development of the stock market if a wider range of data was considered. Firstly, detailed case studies would be useful for identifying country-specific factors which may affect the course of development of the stock market. For example, examination of the general financial conditions should be supplemented by considering the role of industrial structure, foreign investment and stock market regulatory policies. Secondly, indicators of the development of the stock market such as 13
adopted in a number of national and global applications (Lin _et al._ , 2018; Koks _et al._ , 2019) (Lin et al. 2018, Koks et al. 2019).

# **3. Results**

# **3.1. Flood Exposure**

We draw two main conclusions from this analysis. First, there will be more assets and users that face increasing flood hazards in both the near future by 2035 and far future by 2055 (Figures 1‐ 4). However, this number varies depending on the individual model and return periods examined. For instance, the highest from Model BCC‐CSM1‐1 projects the number of assets exposed to net increase flooding hazards is 472 and a potential 17.5 million users could be affected for RCP 4.5 at return period 30 by 2035. The lowest for the same set of assumptions projects 29 and a potential of 7.8 million affected users. In particular, we find that the number of assets and their users facing net increasing flooding hazards are almost always higher for a lower return period than a higher return period, implying that the focus on extreme events is often overlooked and we should be paying more attention to flood risks at lower magnitudes.

9
population are from China City Statistical Yearbook.<sup>14</sup> All time-varying variables are adjusted to the 1990 constant price with the province-specific GDP deflators from the China Statistical Yearbook.

Local party secretaries, with the greatest _de jure_ and _de facto_ power at all government levels in China (McGregor, 2010), have the ultimate decision power on the privatization of local SOEs. These officials are thus our focus. For the complete list of the 294 prefectural cities in China, we could collect information on 883 party secretaries in 282 cities from 1998 to 2009.

Figures 1a and 1b illustrate the age pattern and the age-of-promotion pattern of the citylevel party secretaries in our sample of 2,631 city-year observations. The age of city-level party secretaries exhibits a hump-shaped distribution from 39 to 61, peaking around the average and the median age of 52. Most local party secretaries, with only a few exceptions, leave their positions in their late 50’s. The incidence of promotion falls dramatically after the age of 55 and drop to zero once a politician reaches the age of 58. Overall, these observations corroborate our earlier discussion of the role of local politicians’ age in their chance of promotion.

Panels B and C of Table 1 describe the summary statistics of all main and control variables.<sup>15</sup> About 88% of the city-level party secretaries have higher education. A small number of local party secretaries have experience working in the central government (4.1%), while most of them have experience working in provincial governments (55.1%). The SOEs in our sample generally displayed poor performance: the average leverage ratio is as high as 72%, whereas the return to asset (ROA) is low, with the averages being -0.3%, making slight losses.

# **4. Effects of Politicians’ Age on Privatization**

We provide evidence that privatization decisions are closely related to the age of city party secretaries. We first employ an ordinary least square (OLS) regression of the privatization dummy on a series of age dummies and control variables as follows:<sup>16</sup> r r r i i 0 A i i (1) ss ss ssi ii ii ii ii 14 Four municipalities, Beijing, Shanghai, Tianjin and Chongqing are under the direct administration of the central PPr rr = ββ + ∑ββ rr + δδXX + αα + φφ + γγ + εε

> 14 Four municipalities, Beijing, Shanghai, Tianjin and Chongqing are under the direct administration of the central government and have the same rank as provinces. Thus, our sample does not include these four municipalities. 15 We report summary statistics for two samples, one used for the determinants of privatization, which cut the post-privatization observations for a firm, and another used for the effects of privatization, which keep all postprivatization observations.

16 The results based on probit analysis are of course very similar and not reported. `12`
**2** THE BACKDROP

# **2.1 Orissa and the study districts**

On the basis of its physical features and agro-climatic conditions, Orissa can be divided roughly into four zones (see Map):

1. the northern plateau covering the districts<sup>2</sup> of Mayurbhanj, Keonjhar, Sundargarh and part of Dhenkanal district, constitutes 23 percent of the state's total geographical area,

2. the central river basin encompassing Bolangir, Sambalpur and Dhenkanal districts, also covers 23 percent of its landmass, 3. the eastern Ghat region includes the erstwhile Kalahandi, Phulbani, and Ganjam and Koraput districts and is spread over 36 percent, and 4. the coastal plains of Balasore, Cuttack, Puri and a part of Ganjam make up the remaining 18 percent.

The coastal plain region is the most agriculturally advanced in the state as a result of high soil fertility and more widespread availability of irrigation.

Orissa is the second poorest state in India (after Bihar). Some 87 percent of the total population of 32 million (1991 census) live in rural areas, and 50 percent of the rural population (head count index) live below the poverty line (World Bank 1998). While employment in Orissa's rural non-farm sector grew at a rate of 2.8 percent a year over 1981-91 (Samal, 1997), the great majority of the rural population continue to depend upon agriculture and allied sectors.

Figure 2 shows the distribution of household operational land holdings by size class. It indicates the change in land distribution brought about by post-Independence land reforms (section 2.3), combined with demographic and socio-economic change. Over the period 1953-54 to 1961-62, the number of households not operating any land increased substantially, following widespread evictions of tenant farmers after the abolition of large landlord estates (sections 2.3 and 4.4). Estates abolition and the enforcement of an upper ceiling on land holding size appear to have been reasonably effective in reducing large (greater than 6 ha) operational holdings from 5 percent to I per cent of the total between 1953-54 and 1982. Over the same period, the greatest gains were in marginal (0.4 - I ha) holdings, which increased from 17 percent to 24 percent of all land holdings. These are small, but not the smallest land holdings. The share of households operating no land declined over the 1960s, as the number of households acquiring sub-marginal (less than 0.4 ha) and marginal holdings increased, and by 1982 remained at around 25 percent of all households, which is comparable with the all-India average (Meams 1998).

- 2 The districts mentioned in this section refer to the 13 'undivided' districts. As described in Box 1, there are now 30 districts in the state (see Map).

7
20 otherwise be the case. In this section we draw attention to the concerns of the regulator that it does not wish to be duped by the concessionaire at each regulatory review.

Ideally, the regulator would wish the bidding process to reveal each bidders expectations of the future in order that they can be benchmarked against outcomes. If the outcomes are the same as the expectation, then good information provided at the time of the bid will prove that this has been the case, and will enable the regulator to resist any demands by the business to relax the regulatory regime.

If, on the other hand, the outcomes are different to expectations, then the information provided will enable the regulator to act reasonably to change the regulatory parameters within the framework of regulation laid down. The regulator should therefore request from the bidders all the information that any sensible business would itself make use of before comrnitting itself to a 25 year investment.

Consequently, the government should request from the bidders, for each year of the concession:

1. volume forecasts by customer type

2. number of customer connections by customer type 3. the cost of connection 4. disaggregated operating cost information 5. disaggregated investment information 6. key financial ratios 7. sensitivity analyses Since the costs will be commensurate with the regulatory targets and with the bidders own expectations of volumes and connections, relevant information should also be forthcoming on cost estimates under altemative volume scenarios. The cost information should be as disaggregated as possible. The Ofwat regulatory accounting guidelines (RAGs) provide a benchmark of good regulatory practice.<sup>7</sup> These 5 RAGs deal with:

1. Accounting for current costs

2. Classification of infrastructure expenditure 3. Contents of regulatory accounts 4. Analysis of operating costs and assets 5. Transfer pricing between businesses RAG 4 is particularly useful as a potential model for other regulators. It distinguishes between 5 business activities and 3 service activities. The service activities are: water resources

> 7 They can easily **be** requested from OFWAT and are worth going through, considering that they are probably more of an upper limit on what a regulator can ask for rather than strict model or benchmark.
Policy ReseaRch WoRking PaPeR 4819

# **Abstract**

This paper uses firm-level data from the Business Environment and Enterprise Performance Surveys to study the process of convergence of transition countries with developed market economies. The study focuses on competition and market structure, finance and the structure of lending to firms, and how firms respond to the economic environment by restructuring. The authors find substantial evidence of convergence in a number of dimensions. The pattern of growth at the country, sector, and firm levels shows rapid growth of the new private sector and of the micro and small-firm sectors, with the size distribution of firms moving toward the pattern observed in the surveys of developed market economies. In finance, increasing reliance on retained earnings in transition countries reflects a maturation of the sector as new firms come to rely less on informal and family sources of finance. The authors find evidence of an inverse-U pattern, with the peak of restructuring activity taking place in 2002, the middle of the period analyzed. Throughout, the regional patterns suggest greater convergence in the transition countries that joined the European Union in 2004 than in the other, lower-income transition economies.

This paper—a product of the Office of the Regional Vice-President, Europe and Central Asia Region—is part of the Region's program of flagship studies on where the transition economies of Eastern Europe and the former Soviet Union stand in their goal of convergence to institutions characteristic of developed market economies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pmitra@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
Let denote the detrended yield for insurance unit in year . These are tabulated in columns [2] to [8] of Table 4;

- , since there are nine insurance units in the district;

- , since all products have seven years of yield data to be used;

- , is given in column [9] of Table 4;

- kg/ha (see the bottom of column [9] of Table 4);

- (see the bottom of column [9] of Table 4);

- the sample variance of yields for each insurance unit is

- given in column [10] of Table 4;

- (see the bottom of column [10] of Table 4);

- ;

-

- .

Fourth, calculate the weighted average de-trended yield at the Threshold Yield Collective level, where the weight is the same portfolio weight used in the de-trending step. For cotton in Gujarat, the de-trending step used area sown in 2007 as the portfolio weight. This gives a weighted average de-trended yield of 2,465 kg/ha for cotton in district GJ8 of Gujarat (column [4] of Table 6). Note that this differs from the unweighted average of 2,391 calculated in Table 4.

Fifth, calculate the smoothed probable yield for each insurance unit as the weighted average of the insurance unit and Threshold Yield Collective de-trended yields, where the Empirical Bayes Credibility Factor is used as the weight. For cotton in Gujarat the smoothed probable yields are given in column [6] of Table 6.

Table 6. Cotton in district GJ8 of Gujarat: probable yield calculations

|**Insurance**<br>**Unit**|**Area**<br>**Sown**<br>**2007 (ha)**|**Average**<br>**yield for IU**<br>**(kg/ha)**|**Weighted**<br>**average yield for**<br>**district (kg/ha)**|**Probable Yield**<br>**Credibility**<br>**Factor,**|**Smoothed**<br>**Probable Yield**<br>**(kg/ha)**<br> <br>|
|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|
|GJ8_1|29,000|3,069|2,465|53%|2,787|
|GJ8_2|20,300|2,775|2,465|53%|2,631|
|GJ8_3|21,894|1,256|2,465|53%|1,820|
|GJ8_4|26,440|1,975|2,465|53%|2,204|
|GJ8_5|38,000|2,979|2,465|53%|2,739|
|GJ8_6|16,500|2,931|2,465|53%|2,714|
|GJ8_7|10,000|2,049|2,465|53%|2,243|
|GJ8_8|18,000|2,064|2,465|53%|2,251|
|GJ8_9|4,810|2,421|2,465|53%|2,441|

13
formal fees, legal gifts and illegal payments which render the measurement of informal payments extremely difficult. Several authors tried to go around this problem by considering as informal, payments which are made for officially free services (see Chakraborty et al. 2002, Chawla et al. 1998). Some of these payments may sometimes be misclassified as informal especially in countries where gift-giving is an entrenched cultural practice and patients voluntarily present the health workers with a gift.<sup>2</sup> This paper eschews this difficulty by collecting the information on informal payments directly from the health workers who are perfectly able to screen the legal and voluntary from the informal and requested or forced.

The pervasiveness of informal payments in developing and transition economies has attracted a lot of attention because of their potentially detrimental effects on the health sector. Recent studies have shown that informal payments reduce access to health for the poor, worsen health outcomes and reinforce inequities (Killingsworth et al. 1999, Ensor 2004, Falkingham 2004, Gotsadze et al. 2005). Others argue that informal payments act as a barrier to reform (Killingsworth et al. 1999, Lewis 2000, Thompson and Witter 2000), especially when they are entrenched and constitute an important source of income for powerful people in the sector such as doctors (Chawla et al. 1998). Yet, surprisingly others find that informal payments increase performance measured by the time health staff work in the facility (McPake et al. 1999). This is not necessarily a sign of performance and could well be the opportunity cost of staying out of the facility in terms of foregone income from informal payments. Given the importance of informal payments, it is crucial to understand their determinants. The literature has been heavily focused on the users’ side to answer who pays informal payments. This paper comes as a complement by looking at the issue from the perspective of the providers themselves controlling for a wide array of health workers’ characteristics and the environment in which they operate. The main question this paper intends to address is “do women _take or accept_ informal payments less often than men?” A second form of corruption which is of interest in this paper is moonlighting. Akerlof and Yellen (1990) show theoretically that workers reduce their effort when they perceive to be

> 2 Recent household surveys make an extra effort to try and distinguish between formal fees, gifts and informal payments. They also try to draw the difference between voluntary and requested payments (e.g. Albania LSMS 2005).

4