rafmacalaba/datause-encoder-data huggingface dataset:
of income inequality such as the Gini coefficient, when derived from official statistics, must be based on province-level or percentile data. The CHIP surveys relate to the years 1988, 1995, 2002, and 2007, they use a subsample of the NBS surveys, and they ask many more questions. There is an edited volume on each of the CHIP surveys (Griffin and Zhao 1993; Riskin et al. 2001; Gustafsson et al. 2007a; Li et al. 2013). The two sources use different definitions of income; the CHIP definition is more comprehensive.

Because of the sharp administrative and economic divide between urban and rural China and the need for different survey questionnaires, measures of inequality are generally reported for urban and rural areas separately as well as with a weighted national measure. The NBS surveys are based on urban or rural residence registration ( _hukou_ ), so they exclude most rural-urban migrants (who normally retain rural _hukou_ ) from the urban sample. By 2002, the number of rural-urban migrants exceeded 100 million. The 2002 and 2007 CHIP surveys added a separate sample of rural _hukou_ households in urban China.

China’s poverty and inequality decreased dramatically in 1978–1985 during the years of rural reform, when farming was decollectivized, household production was restored, and farm incomes responded. It is possible to obtain a fairly consistent set of Gini coefficients from the 1988–2007 CHIP surveys. In 1988, the urban Gini (0.24) was very low by international standards, the rural Gini (0.33) reflected regional income disparities, and the national Gini (0.38) was higher than both the urban and 4
revenues collected within their borders, while poorer, transfer-dependent regions have been supportive of more centralized finances. Many of the heavily ethnic regions have supported more autonomy from Moscow, while those regions with heavy Russian populations identified more with the Russian state. The dominant political color of the regions has also played a role; those regions with a high level of communist party support have been traditionally more antagonist toward Moscow (Martinez-Vazquez, 2002). Because of these differences, Alm et al. (2006) stress that attitudes toward paying taxes may differ across these regions during the transition years. Subsequently, their data sets for 1995 and 1999 allow us to analyze the different levels of tax morale in 11 different territories of the Russian Federation. Their results indicate a decay in tax morale in the first four years of the transition from 1991 to 1995, and a small recovery in 1999 (see _Figure A2_ in the Appendix). Interestingly, Hanousek and Palda (2008) observe a similar pattern exploring tax evasion using survey data from 2000, 2002, 2004, and 2006 from the Czech Republic to measure its development for the years 1995 to 2006. Their results indicate that the number and percentage of evaders increased until the early millennium and started to decrease calling such an inverse-U shape ―an evasional Kuznets curve‖ (p. 3) stressing also that this might be an indication that hysteresis ―may not be a feature of evasion in a transition economy‖ (p. 3).

These results from Russia are consistent with the relevance of social norms in tax compliance. The widespread perception of tax evasion along with the economic convulsions revealed inadequate social institutions, and led to an initial crowding out of the intrinsic motivation to pay taxes from 1991 to 1995. These results also suggest restoration of higher trust levels in the state in 1999, after progress in the transition to a market economy had been made - a transition that positively influenced individual attitudes toward paying taxes. The analysis of disaggregated data for Russian regions also shows significant regional differences in tax morale, reflecting self-interest and the degree of trust different regions have toward Moscow‘s institutions and policies. Looking at the distribution of tax morale scores in each of the three different years, the results indicate large differences between the percentage of individuals with a high tax morale (a score of 3) and those with the lowest scores across these years (see _Figure A3_ ). The deterioration in tax morale in Russia from 1991 to 1995 was strongly affected by a 39
# References

- [1] Acemoglu, D., & Angrist, J. (2000). How large are human- ~~c~~ apital externalities? Ev ~~-~~ idence from compulsory schooling laws. in B. S. Bernanke and K. Rogoff (editors). NBER Macroeconomics Annual, 15, 9 ~~-~~ 59.

- [2} Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40(1), 7 ~~-~~ 72.

- [3] Amuedo- ~~D~~ orantes, C., & De la Rica, S. (2007). Labour market assimilation of recent immigrants in Spain. British Journal of Industrial Relations, 45(2), 257284

- [4] Angrist, J. (1995). The economic returns to schooling in the West Bank and Gaza Strip. American Economic Review, vol. 85(5), pp. 1065 ~~-8~~ 7.

- [5] Arslan, C., Dumont, J. ~~-~~ C., Kone, Z., Moullan, Y., Ozden, C., Parsons, C., & Xenogiani, T. (2015). A new profile of migrants in the aftermath of the recent eco ~~-~~ nomic crisis. OECD Social, Employment and Migration Working Papers, (160).

- [6] Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent tech ~~-~~ nological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279 ~~-~~ 1333.

- [7] Autor, D. H., Katz, L. F., & Kearney, M. S. (2008). Trends in US wage inequality: Revising the revisionists. Review of Economics and Statistics, 90(2), 300 ~~-3~~ 23.

- [8] Battisti, M., Felbermayr, G., Peri, G., & Poutvaara, P. (2018). Immigration, Search and Redistribution: A Quantitative Assessment of Native Welfare. Journal of the European Economic Association, 16(4), 1137 ~~-~~ 1188.

- [9] Blake, D., & Mayhew, L. (2006). On the sustainability of the UK state pension system in the light of population ageing and declining fertility. Economic Journal, 116(512), F286 ~~-F~~ 305.

- [10] Bleakley, H., & Chin, A. (2004). Language Skills and Earnings: Evidence from Child ~~-~~ hood Immigrants. Review of Economics and Statistics, 86(2), 481 ~~-4~~ 96.

- [11] Bloom, D. E., Canning, D., & Fink, G. (2010). Implications of population ageing for economic growth. Oxford Review of Economic Policy, 26(4), 583 ~~-6~~ 12.

- [12] Bound, J., & Johnson, G. (1992). Changes in the structure of wages in the 1980’s: an evaluation of alternative explanations. American Economic Review, 82(3), 371.

30
managerial, and business environment measures. Within the WBES, the Green Economy module is rolled out by the World Bank Group, jointly with the EBRD-EIB.

_Generic and Energy-centric Management Scores_ . General management scores are calculated using 11 questions from the management practice module. These questions map to eight questions from the U.S. Management and Organizational Practices Survey (U.S. Census Bureau, 2015). They cover four categories of practices: target-setting, monitoring, incentives, and operations (see Table A1). Firms’ responses to questions on management practices are aggregated into a single management score following Bloom et al. (2019a) in two steps. _First_ , the responses to each of the management practice questions are normalized on a 0-1 scale: the response associated with the most structure is normalized to one, and the one associated with the least structured is normalized to zero. _Second_ , overall management score is calculated as the unweighted average of the individual question scores as in Bloom et al. (2012c) in computing the average based on the z-scores of individual questions. We use these scores to compute descriptive statistics. About 2% of the firms with non-missing entries report a zero average score, while the average for all firms is 0.48 (standard deviation of 0.21). For regressions, we take the z-scores of individual questions, and compute the unweighted average. We then once again take the z-score to obtain a composite management score. General management score also separates the overall management index into four subindices to separately assess the importance of monitoring, incentives, targets and operations practices.

Energy management scores use questions from the Green Economy Module of the WBES that relate to monitoring, target setting, and relative emphasis on energy efficiency. Questions and scoring scales are listed in Table 1. All scores are normalized to a 0-1 scale. Six out of nine questions in the Green Economy Module require binary responses. More structured practices are those that are more specific, formal, frequent, or comprehensive and hence are assigned a higher score. Overall scores are computed as an unweighted average. Given that energy-centric practices are likely specific to firm energy requirements, it is not surprising 9
The climate resilience analysis was updated with new hydraulic design to calculate reduction of loss of accessibility during flood events. Four main scenarios are modeled: pre-project (non-flooded and flooded) and post-project (non-flooded and flooded).

To prioritize the interventions, the individual contribution of each road in terms of gains on accessibility and reduction of climate vulnerability (loss of accessibility) was calculated. The accessibility model was run for both non-flooded and flooded scenarios for each road under evaluation individually and assuming all other roads are not intervened. The pilot application considered 133 road segments, so in total, the accessibility analysis was computed for 134 (Baseline + 133 roads) × 3 points of interest × 2 (baseline and flooding) = 804 travel time scenarios.

# **3. Results**

The model was used in the evaluation of more than 1,600 km of new investment in three flood-prone provinces in Cambodia: **Kampong Cham, Kratie, and Tboung Khmum** . The total population across these three provinces is over 2 million, Kampong Cham being the most populated province with the largest number of high schools and hospitals, Kratie being the least populated province, and Tboung Khmum being the province with the largest value of agricultural production among the three provinces.

## 3.1. Baseline Results

Table 3 and Table 4 show the results of the baseline accessibility and climate disruption analysis for the population across the three provinces (2.2 million people). Each row is the population and percentage of total population that can reach a certain destination in 0–30 minutes, 30–60 minutes, and more than 60 minutes. Value of agricultural production is not measured in population but rather in the value of agricultural production that can reach the regional market.

In terms of accessibility to education, constraints are high for high schools with only 55 percent of the population<sup>14</sup> able to reach high schools in 30 minutes, making it suitable for an intervention (Table 3 and Figure 3). Accessibility constraints are high for referral and emergency medical facilitates with only 32 percent of the population able to reach them in 30 minutes and about 37 percent at more than 60 minutes, therefore making it suitable for an intervention (Figure 4). Finally, in terms of accessibility to markets, about 28 percent of the value of agricultural production can reach a market within 60 minutes (Table 3).

> 14 The access to high-schools considers the whole population disregarding school-age. This assumption is made due to lack of undelaying georeferenced age population data.

8
7

**increased comp.ti ion within the TVP sector (see the** discussion of markets below). This is confirmed by aggregate **data,** which show profits riwing more slowly than output, assets, and wage payments.

Employment in rural nonagricultural activities rose s'- rply in this period, from 22 million in 1978 to 77 million by 1986 (including seasonal and part-time workers). Huch of this increase was due to service activities, however, and the industrial TVP sector posted more modest absolute increases. In the more advanced localities, in particular, rural industrialization has accompanied declines in the agricultural labor force. For example, in Wuxi County during 1978-85 the industrial labor force grew by 19 percent a year while the agricultural force declined by 13 percent a year. Average wages paid by TVPs, which were initially well below state-firm levels, rose rapidly but then moderated to slightly below the rate of increase in the state sector. In 197887, the average real TVP wage rose by almost 8 percent a year.

As a result of rapid TVP growth, the ownership structure of China's industry as a whole has changed markedly (see table 3). The share of the TVP sector has increased sharply, from only 3 percent in 1971 to over 23 percent by 1987, while the share of state enterprises has declined considernbly. All types of TVPs have seen an increase in their share, but i.owth has been most marked for firms owned by private individuals and private groups of individuals. There has been a corresponding shift in the composition of rural gross output. Whereas in 1978 crop cultivation represented 53 percent and industry only 19 percent of output, by 1987 the share of crop cultivation had fallen to 30 percent while that of industry had risen to 35 percent. Smaller gains were posted by other farming activities and by construction, transport, and commerce.
- 41 -

**Table 9 (Con't). Consolidated Budget Base Infrastructure Regressions**

|Variables|Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|GASTIL|-12.42|-11.49|-11.35|-12.94|-12.83-|
||(-3.56)|**(-3.44)**|(-3.36)|(-3.92)|(-3.98)|
|GASTILSQ|1.40|1.35|1.33-|1.50-|1.47-|
||(3.22)|(3.25)|(3.19)|(3.68)|(3.70)|
|PCOM|7.87E-2|1.52E-1|1.92E-1|-1.78E-1|-5.11E-1|
||(0.08)|(0.16)|(0.20)|(-0.19) (-0.55)||
|NFFCAP1K|-|3.17E-2<br>(3.51)|3.04E-2<br>(3.14)|2.99E-2-<br>(3.39)|3.05E-27<br>(3.36)|
|NOFGDP1|-|-|1.85E-1<br>(0.41)|-||
|NCBGDPSQ|-|-|-|3.OOE-1-<br>(2.75)|-|
|DFIGDP|-|-|-|-|4.489<br>(-2.55)|
|DFIGDPSQ|**-**|**-**|**-**|-|1.52-<br>(3.43)|
|**R**<sup>**2**</sup><br>|0.7308|0.7571|0.7574|0.7723|0.7815|
|Adjusted R<sup>2</sup>|0.6934|0.7208|0.7188|0.7360|0.7444|
|F-Value<br>N|19.515<br>132|20.897<br>132|19.601<br>132|21.287<br>132|21.083<br>132|

Significant at the .01 level Significant at the .05 level Significant at the .10 level

# _3. Urbanization_

Urbanization reduces per capita consolidated budget infrastructure expenditures, _ceteris paribus,_ as hypothesized and was found for central budget expenditures. As can be seen from Tables 9 and 10 and from Appendix C, the estimated coefficient for URBAN4 is significantly negative at the 1 percent level in all base runs and sensitivity analyses except the estimates replacing PCOM with HDRPOOR and WBPOOR where the significance levels are somewhat lower. The estimated elasticity at the sample mean is -1.15, marginally greater
where no respondent receives information. The decision to omit the no information condition from our research design was based on results from a pilot, in which surveyors found it difficult to re-elicit beliefs when no new information had been provided to the respondent. Since this treatment condition is not necessary to estimate the effects of information, we chose to exclude it from our experimental design altogether.

# **4.3.4 Elicitation of Posterior Beliefs**

The second round of belief elicitation takes place immediately after respondents are provided with information about inflation, the exchange rate, or both. To ensure that the responses are comparable to the elicitation of prior beliefs, the second round of belief elicitation uses the exact same wording as the first. The goal of this second round of belief elicitation is to understand whether individuals incorporate the information provided to them through the information treatments into their expectations.

# **4.3.5 Elicitation of Consumption Plans**

While the main goal of our experiment is to estimate the effect of information on _actual consumption_ , as measured objectively in administrative data, we also asked a series of questions on respondents’ _consumption plans_ . These responses were collected after the elicitation of posterior beliefs and serve two purposes. First, they allow us to test whether our information treatments affect intended behavior. With that goal in mind, we measure expected future spending in the main consumption categories highlighted by the theoretical framework (durable goods, tradable goods, and credit card debt), as well as other categories that act as useful proxies or benchmarks. Second, these questions allow us to confirm that survey responses have predictive content by testing whether predicted consumption correlates with actual future consumption.

The first of these questions elicit respondents’ expected change in total credit card expenditures (which corresponds to the total expenditures we observe in administrative data). Specifically, respondents were asked: “Do you expect your credit card spending to go up, stay the same, or go down during the next 3 months?” We code this and other similar questions on a simple three-step scale. the variable takes the value -1 if the individual responded “go down,” 0 if the individual responded “stay the same,” and +1 if the individual responded “go up.” Another pair of questions uses similar language to elicit total spending, not limited to spending on credit cards, and spending on groceries for comparison.

The next set of questions on expected spending asks about spending on durable goods and uses wording that closely follows the Michigan Survey of Consumers. We first ask respondents: “Do you think now is a good time, a bad time, or neither a good nor a bad time to buy household items, such as furniture or a refrigerator?” We code responses using the same _{−_ 1 _,_ 0 _,_ +1 _}_ scale 13
**<u>Table 6: Incidence of entry and exit, overall and by year</u>**

||||**Reg**|**ion**||
|---|---|---|---|---|---|
||**Total**|**West**|**East**|**North**|**South**|
|Total area (million ha)|19.056|4.456|4.268|5.569|4.763|
|Share of area init. under farms > 3000 ha|0.358|0.087|0.623|0.332|0.608|
|Share of area init. under farms > 5000 ha|0.134|0.019|0.256|0.096|0.271|
|Initial productivity all farms|-0.109|-0.148|-0.086|-0.098|-0.080|
|Initial productivity farms > 3000 ha|-0.065|-0.025|-0.094|-0.065|-0.073|
|Initial productivity farms > 5000 ha|-0.053|0.037|-0.080|-0.013|-0.078|
|Rayon fixed effect|-0.030|-0.238|0.095|0.041|0.107|
|Share of permanent exit|0.541|0.637|0.504|0.526|0.433|
|Share of permanent entry|0.382|0.276|0.434|0.412|0.470|
|Share of total exit|0.689|0.808|0.614|0.697|0.548|
|Share of total entry|0.538|0.452|0.550|0.596|0.593|
|**Share of area exiting by year**||||||
|2002|0.150|0.156|0.162|0.149|0.130|
|2003|0.114|0.107|0.119|0.126|0.105|
|2004|0.084|0.089|0.082|0.076|0.087|
|2005|0.064|0.094|0.050|0.052|0.042|
|2006|0.049|0.068|0.047|0.045|0.024|
|2007|0.028|0.045|0.019|0.024|0.012|
|2008|0.019|0.027|0.007|0.022|0.010|
|2009|0.016|0.022|0.007|0.015|0.015|
|2010|0.020|0.031|0.012|0.018|0.010|
|**Share of area entering by year**||||||
|<br>2002|0.025|0.011|0.031|0.032|0.034|
|2003|0.027|0.014|0.040|0.029|0.033|
|2004|0.039|0.017|0.042|0.056|0.048|
|2005|0.030|0.020|0.024|0.033|0.047|
|2006|0.039|0.037|0.033|0.032|0.057|
|2007|0.084|0.046|0.122|0.086|0.109|
|2008|0.075|0.077|0.094|0.069|0.064|
|2009|0.036|0.031|0.026|0.042|0.044|
|2010|0.029|0.024|0.023|0.032|0.038|

23
-8-

negative effects on the rest of the OECD area, while lower US demand and, especially, higher US interest rates have similar, or possibly more dramatic, effects on the developing world.

What the foregoing suggests, is that, barring a timely and coordinated, but highly unlikely, policy response by the major countries, growth in the world economy will, at best, be only modest over the next few years. Given the range of uncertainties (including political ones, - especially for the United States), putting forward precise forec:. may not be a very rewarding exercise. But some rough idea of the range of possibilities can be gleaned from a number of scenarios for the coming five years put forward by the World Bank. Starting from a "reference" scenario, which basically extrapolates the policy intentions and exhange rate levels of early 1987 (and is thought to be untenable), the World Bank goes on to illustrate the effects of a number of different policy and exchange rate assumptions on crucial variables between 1987 and 1992.

Three of these scenarios are presented in Table 2 - the "reference" scenario, in which exchange rates are frozen at their end-1986 level and the US Federal budget deficit is forecast to decline to $ 100 bn. by 1992, and two variants which explore the effects of a more restrictive fiscal policy in the United States and a 1- per cent additional depreciation of the dollar (the latter already achieved by end-1987). It will be seen that these more realistic variants p, erate outcomes that can hardly be considered very favourable. Growth in the OECD area, which had slowed down from a 2.7 per cent annual rate in the years 1973-79 to one of 2.3 per cent per annum in the years 1979-87, slows down even further to roughly 2 per cent on average. It is true that the US General Government records a budget surpluu by the end of the period (implying rough bal&nce for .ie
probability _π_ ( _q_ ) is also higher than the farmer's customary right enforcement probability _q_ ( _π_ ( _q_ ) _> q_ ) because the sale is generally documented and brokers use their higher social status and larger social network to prevent conicts from materializing and threatening the certication of the land. With probability _π_ ( _q_ ) , the broker's ownership of the plot is not contested, allowing her to pay the certication cost _c_ and obtain a statutory property right from the land administration. This property right is fully secure (there is no more risk of eviction) and transferable. In this case, the broker formally re-sells the land for a value _pS_ ( _x_ ) to urban workers and transfers the statutory property rights to them. With probability 1 _− π_ ( _q_ ) , however, the broker does not succeed in certifying the land plot and thus does not incur any certication cost but resells the plot as a non-statutory residential plot at a price _pNS_ ( _x, q_ ) .

Benchmark case. Before proceeding with the rest of the analysis, it is interesting to briey discuss the benchmark case of free and secure property rights. The uncertainty in land property rights can be eliminated through the setting up and enforcement of a registration system (land registry or cadaster) that extinguishes competing claims and unequivocally assigns statutory property rights on each land plot. Furthermore, if the registry is costless and free to access, we can impose _q_ = _π_ ( _q_ ) = _θ_ ( _q_ ) = 1 and _c_ = 0 . This implies that all land plots have statutory rights and brokers are not needed. A farmer gets a utility _a_ ( _x_ ) from her farm production and _pS_ ( _x_ ) + _u_ from selling the land and leaving the city (or becoming an urban worker). A land transaction between a farmer and an urban worker takes place if and only if To t reality, we assume that urban residences lie close to the CBD, which requires that the LHS falls more rapidly than the RHS. That is, Under the above assumption, commuting costs should be higher than the costs of transporting farming goods to the CBD (corrected for farm size and farm productivity). The above condition then determines the following unique frontier between the urban workers' residential area and the urban farming area: In our benchmark case, the city includes urban workers' residences on the interval [0 _,_ � _x_ ] and urban farms on ( _x, x_ � _a_ ) . There is no mix of land uses and rights. Intuitively, � the residential frontier _x_ expands with higher urban wages, lower commuting cost and higher unit transportation cost of farming goods.

# 3 Informed brokers

In this section, we study the city structure when brokers are perfectly informed about customary enforcement risks. We rst determine the price that brokers pay for land 8