rafmacalaba/datause-encoder-data huggingface dataset:
_Table 2 Transition matrix (3x3) 2014–2016_ Upper bound mobility estimates

||Poor 2016|Vulnerable 2016|Secure 2016|Total|
|---|---|---|---|---|
|Poor 2014|0.024|0.018|0.078|0.119|
|Vulnerable 2014|0.016|0.014|0.072|0.102|
|Secure 2014|0.066|0.065|0.648|0.779|
|Total|0.106|0.097|0.798||

# Lower bound mobility estimates

||Poor 2016|Vulnerable 2016|Secure 2016|Total|
|---|---|---|---|---|
|Poor 2014|0.105|0.030|0.000|0.135|
|Vulnerable 2014|0.001|0.065|0.045|0.111|
|Secure 2014|0.000|0.001|0.752|0.754|
|Total|0.106|0.097|0.798||

Note: Estimated based on the synthetic panel, using a poverty line of MYR 527 and a vulnerability line of MYR 701. N = 56917. Cells give the fraction of the population that is in each transition categories.

We investigate upward and downward mobility by aggregating some of the nine categories. Upward mobility is defined as moving up one category or more, downward mobility as moving down one category or more. In addition, we will also present the share of those secure in both rounds, as this limits the scope for upward mobility. Figure 4 displays the development of the population shares of these groups over time the 2004–2016 period.<sup>27</sup> As we have seen, the secure group has grown steadily, especially since 2009. Persistent security increased from bounds of 32 - 47 percent at the start to a range of 65–75 percent at the end of the study period. In addition, we can observe that downward mobility is likely to have decreased over our timeframe, from between 1 – 25 percent of the population in 2004–2007 to the range of 0 - 15 percent in 2014–2016. Upward mobility seems to have peaked in the 2009–2012 and 2012–2014 intervals with about 20 percent of the population being upward mobile in each of these. In the most recent period, however, we cannot be sure that upward mobility is still larger than downward mobility. Obviously, the larger the share of the population that is secure, the less scope for upward mobility there is.

> 27 Note that the upper bound for the secure group goes with the lower bounds of the upward and downward mobile (minimum mobility scenario), and the other way around (maximum mobility scenario). The poor-poor and vulnerable-vulnerable transitions are omitted from this figure, so the totals for each scenario do not sum to one.

21
**non-compliance.** The study examines the extent and distribution of tax evasion among different income groups, regions, and sectors of the economy. The goal is to identify the factors contributing to tax evasion and develop measures to reduce them **.** To investigate tax evasion in Romania, we match tax administrative data with the survey data (EU-SILC) to assess Romania's tax (non)compliance. We impute tax income to EU-SILC and compare the value of imputed tax income (reported income) and survey income (true income).

**According to our results, the tax compliance at the distribution median is approximately 94%; it decreases to 83% at the 25**<sup>**th**</sup> **percentile and increases to 96% at the 10**<sup>**th**</sup> **percentile.** The average tax compliance (after censoring of top income) is 94%. The average underreporting of income is 6%. This result is close to the estimate for Hungary (9%– 13%) by Bendek & Lelkes (2011), an estimate by Kiełczewska et al. (2021) for Poland (6%), and the estimate for Estonia (12%) by Paulus (2015). Tax compliance varies across sectors of the economy. It is particularly low in transport, construction, food, and accommodation. Women are more tax-compliant than men. Tax compliance is highest among the youngest and oldest employees and lowest in prime working age. Tax compliance varies across country regions; it is lowest in the North and highest in the East and West.

**The share of minimum wage earners in the tax data is inflated due to the underreporting of income.** According to tax data, 27.5% of employees are minimum wage earners. According to the raw EU-SILC data, this share is 13.5%. Based on the imputation of tax income into the EU-SILC data, our results show that the share of minimum wage earners is significantly higher, 22.3%.

**Underreporting of tax income is an important challenge for public policy in Romania.** The discrepancy between tax data and survey data should be further investigated. Tax evasion weakens the fiscal capacity of the country. Under-reporting of income in the bottom half of the income distribution may result in an incorrect allocation of means-tested benefits and limit the efficiency of the social policy. The estimates of the share of minimum wage earners based on the tax data should be treated cautiously. Our results suggest that approximately one-quarter of the minimum wage earners receive envelope wages and earn more than the minimum wage.

32
||Shakantu<br>(2009)|||||
|---|---|---|---|---|---|
|4|Waissbein<br>et al. (2013)|9 major risk<br>categories and<br>20 underlying<br>barriers|1. Power market risk<br>2. Permit risk<br>3. Social acceptance risk<br>4. Resource and technology risk<br>5. Grid/transmission risk<br>6. Counterparty risk<br>7. Financial sector risk<br>8. Political risk<br>9. Currency macro-economic risk|Rating<br>(1-5)|35<sup>7</sup>|
|5|Gatzert and<br>Kosub,<br>(2016)<sup>8</sup>|7 risk categories<br>and 13<br>underlying<br>barriers|1. Strategic/business risk<br>2. Transport/construction/completion<br>3. Operation/maintenance<br>4. Liability/legal risks<br>5. Market/sales risk<br>6. Counterparty risk<br>7. Political, policy, regulatory risk|NA|NA|

In terms of the operationalization of the framework, some of the frameworks have been developed without directly using the structure of the framework to survey investors (denoted as NA in Table 2). For those frameworks that have been directly applied, all existing studies use rating methodologies, which allows the surveyed participants to rate each item from 1 to x (where x commonly is the highest possible ranking that can be given). Ranking survey methodologies stand in contrast to ranking methodologies, which presuppose an implicit order of importance between categories, which can be elicited. The average number of participants ranges from 28 – 48.

Section 3 combines relevant parts of the frameworks discussed above and develops a framework to evaluate different risk categories, while also containing non-overlapping categories that allow for a robust quantification of the framework.

> 7 The report by Waissbein et al. (2013) states that for the assessment of the risk environment 14 informal interviews and 21 structured interviews were conducted for four case studies used in the report to illustrate the framework.

> 8 Specific focus on risks in off-shore wind.

9
the average LCR over the period 1975 and subsequent, with equal weighting of all years. An alternative to equal weighting would be liability weighted LCR.

Regarding excess loss adjustment, RMA has developed a procedure in order to reduce the impact of a single year on the average loss cost of each county. Under this procedure, the adjusted average LCR for any single year is capped at the 80<sup>th</sup> percentile LCR of all years. Excess indemnity is then the amount of indemnity that is excluded from the capped LCR. However, this 80/20 rule may be too evasive for some crops/states to capture the impact of catastrophes and, therefore, a threshold varying by state and by crop should be considered.

The final predictive value of the loss experience of a specific county is based on the LCR of this county, and the simple circle LCR that is the average of surroundings counties’ LCRs weighted by each county’s total adjusted liability. Weighting together different LCR estimates is part of the credibility process (see Box 4). The MPCI procedure uses the credibility formula _Z_ = min(,1 _P_ 271) × 60% , where _P_ is the number of insurance policies indemnified. A county with 271 or more policies indemnified will receive the maximum credibility of 60%. The remaining is assigned to the simple circle LCR. The county unloaded rate is thus defined as the weighted sum of the county’s LCR and the simple circle LCR. It should be noticed that the number of claims, while being a common measure of credibility for property insurance ratemaking, has no clear justification. In addition, this number may need to vary, by state or region, to reflect the expected claim frequency.

## **Box 4.  Credibility**

Credibility is a measure of the predictive value of the loss experience. The credibility process is the weighting together of different estimates to come up with a combined estimate. The general credibility formula is I=Z x I1 + (100% - Z) x I2 where I is the item that is estimated (e.g., county LCR) I1 is an indicator of the item experience (e.g., county average LCR) Z is the credibility attached to I1 I2 is an alternative indicator (e.g., simple circle LCR).

Credibility Z ranges from 100% (full credibility) to 0% (no credibility). For an item with credibility less than 100%, an appropriate item to receive the remaining wight must be identified.

Sources: Authors, from Josephson et al. (2000).

# _Base rate loading_

The county unloaded rate is an estimate of the expected capped LCR for the forthcoming year. It is then adjusted by several factors.

First, a disaster reserve factor, equal to 13.64% of the unloaded rate, is added. This reserve factor is intended to meet the Congressional requirements to maintain “reasonable reserves”. RMA ha defined a reasonable reserve as an amount sufficient to achieve financial adequacy over a 10 year period at a 85% confidence level.

23
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In recent years some life insurance companies have decided to exit the annuities segment of the life insurance market, discouraged by the very intensive degree of competition, the thin intermediation spreads and the relatively low returns on equity. These factors have resulted in some increase in concentration ratios. However, the insurance sector in Chile remains much more competitive than the pension fund sector, whether measured by the number of participants or concentration ratios, as shown in Figures 1 and 2.

**Figure 1**

<!-- Start of picture text --> Number of Life Insurance Companies, Annuity<br>Providers, and AFPs, 1988-2005<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1988 1990 1992 1994 1996 1998 2000 2002 2004<br>Life Insurance Companies Annuity Providers AFPs<br><!-- End of picture text --> <!-- Start of picture text --> 1996<br><!-- End of picture text -->

**Figure 2**

<!-- Start of picture text --> Market Concentration Ratios in Pensions and Annuities<br>Herfindahl and Share of Three Largest Firms, 1988-05<br>2500 100%<br>90%<br>2000 80%<br>70%<br>1500 60%<br>50%<br>1000 40%<br>30%<br>500 20%<br>10%<br>0 0%<br>1988 1990 1992 1994 1996 1998 2000 2002 2004<br>AFP Herfindahl Annuity Herfindahl<br>AFP 3 Larges t Annuity 3 Larges t<br>Herfindahl Ratio Share of 3 Largest<br><!-- End of picture text -->
Figure 2: Effect of a 1 Percentage Point Increase in the Income Share Held by 3<sup>rd</sup> Quintile on Log GDP per capita (ASEAN countries, 2010) <!-- Start of picture text --> KHM LAO VNM PHL IDN THA MYS SGP BRN<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> Note: The figure is based on column (2) of Table 2.

14
comparison LGAs, drawing on three sources of information:

1. Propensity score matching exercise. The matching process involved a "5-Nearest Neighbors" matching exercise based on population size in 2011, total revenues in 2011, revenue growth between 2007 and 2011, area in sq km in 2012, and population density. The results of this exercise are reported below in Table A1 and Figure A1, which report the coefficients of the probit regression used to estimate the propensity scores and the distribution of the estimated propensity score, respectively. As shown in Figure A1, common support (i.e., finding untreated LGAs with values of the propensity score similar to that of treated LGAs) is very thin for higher values of the propensity score and some treated LGAs are off support (i.e., the estimation cannot identify a comparison LGA that is close enough, in terms of values of propensity score, to the treated LGAs).

2. Survey of treatment LGAs. A short survey was sent to representatives of local offices of PO-RALG in all 18 treatment LGAs. The survey asked representatives to identify the most similar LGA in their own region/zone (compared to their own), the most similar LGA outside of their region, and then to rank the 3 most similar LGAs within own region/zone.

3. Suggestions of potential comparison LGAs from the PO-RALG central team based on their knowledge Based on the information above, we prepared 2 lists for each program LGA:

1. Survey list: The three most similar LGAs as reported in the survey: (a) most similar within region, (b) most similar by rank beyond the LGA listed in (a), and (c) the most similar LGA outside of the region. If a similar LGA outside of the region was not provided, we replaced it with the next most similar within the region. If any of these listed LGAs was actually in the program, then we moved down the list to the next most similar not in the program. Similarly, if any of these LGAs were participating in the Tanzania Strategic Cities Program (another major World Bank project), then we moved down the list to the next most similar LGA not in the program.

2. Propensity score list: The 2 LGAs with the closest propensity scores.

Finally, we selected the comparison LGA for each program LGA with the following procedure (with allowed repetition of non-program LGAs among comparators) in three steps:

1. If the top comparison LGA in both lists was the same, then that one was chosen.

2. If Step 1 did not hold, then if there was any overlap for the top 2 comparison LGAs across the 2 lists, then we chose the LGA highest on the survey list for which there is overlap between the survey list and the propensity score list.

53
- 18 -

# How Broad-Based Was The Ownership of **These** Programs?

The earlier Bank studies of adjustment lending have concluded that the chances of success are higher if there is ownership of the program by the country. These findings also corroborate Kahler'sLw earlier work whic.h showed that prior commitment and policy action (taken before external support is offered) are a good predictor of successful implementation. Successful influence is more likely if governments- commit themselves to an adjustment program and only then are supported financially by the IF's.

The meaning and definition of ownership have, however, remained fuzzy, despite the Bank's OED's attempt to elaborate on the definition of ownership and its application to the Bank's own lending experience for adjustment in Africaf'0 There are several other practical problems. First, what is the appropriate time path for strengthening the ownership? If the reforms produce fairly early pay-offs, does this help strengthen the ownership or slow the pace? Neither theory nor evidence provide any guidance to answer these questions- Second, the stability of the Government itself is equally critical. If the governments keep changing rapidly it is difficult to establish ownership. In Africa, the pattern has been frequent changes in the govemments that has retarded the movement towards ownership. A third problem is that the economic generosity of the donors, in so far as it results in a soft budget constraint or allows the governments to postpone taking tough decisions, may also slow down the progress towards ownership. Finally, at the intellectual level, there are still remnants of ideological resistance to "marketfriendly" paradigm of development which underpins the adjustment policies. The neomarxian, "dependency" school of thought, anti-colonialists and others, who genuinely believe that the "markets" are anti-social, create inequities, hurt the poor and are repressive, still command considerable influence particularly among the non-economists. There are well-meaning African scholars who fear that integration of Africa in the world _16/_ Kahler, M. op.cit.

_17/_ World Banl, _Adjusmewnt in Sub-Saharan A!fica,_ OED Report No. 12155, July 1993.
_average._ The qualifier “on average” is important because Thailand, for example, had quite a good investment climate and grew well, but it is relatively small, dwarfed in size by Indonesia, Bangladesh, and Pakistan.

In the area of human capital, it is important to note that China’s advantage has historical roots. Already in 1870, 21% of adults in China were literate – since nearly all of these would have been males, this means that about 40% of adult males were literate. In South Asia the literacy rate in 1870 was 3% of the adult population, about the same as in Africa. Latin America had a literacy rate of 15% in 1870 (Morrisson and Murtin, 2005). In 1990, even though China was poorer than India or its other neighbors, it had a more educated population. Average years of schooling of the adult population in China was 5.2 years in 1990, compared to 3.7 in India or 3.5 for the rest of developing Asia (Barro and Lee, 2000). The superior human capital of China can be seen as well in infant mortality data, which are a good summary indicator of health status. In 1990 China’s infant mortality rate was 38 per 1000, far below India’s 80 or the rest of developing Asia’s 69 (Table 1). Despite its good human capital, in 1990 China had about the same per capita income as Bangladesh, India, Pakistan, and Vietnam, and was substantially poorer than Indonesia, Philippines, and Thailand. It is hard to get reliable data on wages, but the available data suggest that China had wages somewhat lower than those in Bangladesh, India, and Pakistan, and far behind those in the more advanced Asian developing countries (Table 2).

The Chinese refer to their reform program as “ _Gai ge kai fang,”_ which translates as “change the system, open the door.”<sup>1</sup> The whole reform program is often referred to in brief as the “open door policy.” This highlights that a key component of Chinese reform has been trade liberalization and opening up to direct foreign investment, but not opening the capital account more generally to portfolio flows. By 1990 China’s economy was far more open than those of the other low-wage countries in Asia: China’s average import tariff was 40%, well below those of Bangladesh (94%), India (82%), or Pakistan (65%). Thailand (40%) had the same average tariff rate in 1990; the Philippines (28%) and Indonesia (21%) were more open still, but with significantly higher wages they were not competing directly with China (Table 2). After joining the WTO China’s average tariffs have dropped below 10%, and to around 5% for manufactured imports.<sup>2</sup> It initially 4
- 16 -

# V. EMPIRICAL ESTIMATES OF BRAZIL'S INFLUENCE **ON** THE WORLD PRICE

The world sugar market model of Wong, et al (1989) was used to estimate Brazil's potential impact on the world market price. Sustained increases in Brazilian sugar production of O.S million tons, 2 million tons and 6 million tons were simulated and compared with a baseline run in which producLion was assumed to increase in a fashion similar to what has occurred in the past. To examine the sensitivity of Brazil's influence on price at different phases of the world price cycle, sustained increases were simulated from two different start dates.

Although sustained output increases provide a basis on which to estimate Brazil's influence on the market, in reality if producers were exposed to world prices they would have incentives to respond to and anticipate price changes. Accordingly, production would vary to some extent with fluctuations in the world price. The potential for Brazilian producers to respond to the world price in an unregulated environment cannot be easily determined. However, to provide some representation of the potential effects of producer flexibility, a procedure similar to that used by Wong, et al was used. I/

> 1/ The procedure used in this study varies from that used by Wong et al in the way the price expectation variable is specified. In Wong et al the deterministic world price is used as the expected price, and the assumed forecasting error is the difference between the deterministic and stochastic world price. The forecasting error is therefore explained by random shocks which producers cannot anticipate. This technique is computationally complex because both deterministic and stochastic versions of the model must be solved simultaneously. The computer time taken to run the model more than doubles compared to when just one version of the
driven," and improve the quality of governance by increasing the proximity of decision-making processes to citizens and thus enhance transparency and accountability. This has led countries around the world to give increasing powers to local governments(Bardhan and Mookherjee 2006). Several scholars have expressed concern that in unequal societies this would subject village decisions to the risk of elite-capture (Bardhan and Mookherjee 2000, Bardhan 2002), but there is not much evidence about how these processes actually work<sup>2</sup> .

Much of what we know about the empirics of deliberative processes is from deliberative polls which are a set of methods developed by the political scientist James Fishkin and his colleagues where groups of randomly chosen individuals are gathered in groups to conduct discussions on particular subjects (http://cdd.stanford.edu/). The method has generated a wealth of information on deliberation, but it has the limitation that the deliberative processes studied are not a part of a regular and routine system of government but the result of an academic intervention within a constrained setting. Studies of deliberative systems of government are very rare and largely qualitative. Jane Mainsbridge’s(1983) seminal ethnography of town meetings in Vermont provides rich insights into how deliberation works as a system of government and comes closest to an analysis of the kind we conduct in this paper. Her work outlines the complexity of the deliberative process but largely supports the idea that common interests facilitate deliberation, particularly in settings where citizens prefer to avoid adversarial discussions<sup>3</sup> . On the other hand, James Madison in the Federalist Papers (Federalist No. 10, 1787) famously cautioned that "a

> 2 There is some evidence analyzing the match between the preferences of individuals and the outcomes of commmunity-based decisions, a process known in that literature as "preferencetargetting" (Mansuri and Rao 2004). Chattopadhaya and Du‡o2004b examine the role of political reservations for women on the match between women’s preferences and the decisions of gram panchayats, Rao and Ibanez2005 and Labonne and Chase2007 study the match between preferences of households and the outcomes of commity-based decision making showing some elite dominance.

> 3 Also see the Fung and Wright2003 edited volume that has several case-studies of deliberative decision making.

4