rafmacalaba/datause-encoder-data huggingface dataset:
“major historic task”<sup>5</sup> The target and requirements of building new socialist villages can be summarized as developing production, raising living standards, (creating) a cultured rural environment, (ensuring) tidy and clean villages, and (instituting) democratic management.<sup>6</sup> This renewed emphasis on rural development is long overdue. The urban-rural income gap has grown over the past two decades and per capita urban incomes now stand at more than three times rural incomes, a ratio that is among the largest in the world. Moreover, under China’s decentralized fiscal system, the provision of public services has also grown increasingly biased in favor of urban, prosperous areas, and the rural areas are disadvantaged. Rural areas were also disadvantaged in the past, when, under the commune system, resources were extracted from agriculture to pay for industrialization. The rural sector was asked to be self-sufficient in the provision of basic services such as infrastructure, health and education. When budgetary revenues were centralized in 1994, the situation worsened for rural, grassroots governments. Since the mid-1990s, even as China grew richer and government revenues improved rapidly the rural sector remained largely self-financed. The result was that in many localities, rural governments were unable to finance mandated services in basic education, public health, water, sanitation, etc. Estimates of the Human Development Index at 0.81 in the urban sector and 0.67 in the rural sector (UNDP 2005) are undoubtedly a reflection of the poorer public services enjoyed by rural citizens.

The true extent to which rural services have lagged behind the growing needs of the rural populace is difficult to ascertain. The current statistical reporting system provides very limited information on service delivery at subnational levels. When such information is available, it is not disaggregated between the rural and urban sectors. Estimates are increasingly difficult to make from regional totals because of the commingling of urban units in what used to be more purely rural categories of counties and townships. The extent of the

> 5 Wen (2006).  Earlier, Premier Wen had already stated that “Solving the _sannong wenti_ remain the top priority work ( _zhongzhong zhizhong_ ) for the whole Party” and he stressed the guiding principle of “industry feeding (fanpu) agriculture,” and cities supporting rural villages. Wen Jiabao’s Explanations on the CCP Suggestions on the Eleventh Five Year Plan on National Economic and Social Development (October 8, 2005). See Xinhua News Agency, October 19, 2005.

> 6 In Chinese this is summed up in twenty characters: 生产发展,生活宽裕,乡风文明,村容整洁,管理民 主 。 A good summary of the history and policy trends and priorities for building a “new socialist countryside” are provided by Minister of Finance Jin Renqing (2006).

3
Minister Mahathir, and hurt firms connected to his principal political rival. Because the introduction of controls coincided with major government realignment, it is possible to identify the value of connections to the political winners. On this basis, the paper concludes that “The evidence suggests Malaysian capital controls provided a screen behind which favored firms could be supported.” Using traditional event study methodology applied to a natural experiment, Johnson and Mitton (2003) attain their objective of analyzing the value of political connections in Malaysia during the crisis in 1997.

RCTs are widely used in development economics (see, for example, Duflo, Glennerster, and Kremer, 2008). In development finance they have most been used to study small scale enterprises and issues such as financial literacy. For example, Cole, Sampson, and Zia (2010) test whether the low use of bank accounts in Indonesian villages can be attributed to a lack of financial literacy of villagers or to the costs associated with the use of financial intermediaries. To test this, they randomly assign a program of lessons on bank accounts to half of a sample of 564 households. Independently, they randomly offer payments, ranging from U.S. $3 to $14, for opening a bank account. They find that that the program of financial literacy they offered had a smaller effect on increasing demand for bank accounts, whereas direct payments for opening bank accounts greatly stimulate demand.

RCTs and natural experiments have high internal validity provided that the required randomization and exogeneity conditions, respectively, are satisfied. Both techniques are also relatively easy to understand and compelling.<sup>4</sup> However, in many instances to be useful as a guide for policy, the studies must be externally valid, so that its results can be generalized and applied out of sample. As pointed out by Cartwright (2007), attempts to generalize the results of a specific experiment out of sample require the researcher to make assumptions about the comparability of the experimental sample to the more general population. In practice, this often amounts to informal matching on observables between the sample and the population of interest. However, this generalization entails the loss of the principal advantage of RCTs, that in-sample they are not limited to matching on observables but also randomize over unobserved characteristics (Cartwright, 2007).

> 4 See Cox (1958) for a standard reference source.

15
**Figure 8 Average TTRI and Estimated “Ad-valorem Equivalents” of an Improvement in LPI and Doing Business Exports**

<!-- Start of picture text --> 25%<br>DB import cost ad-val equiv<br>20%<br>DB export cost ad-val equiv<br>15%<br>TTRI faced abroad (weighted av<br>by export shares)<br>10%<br>5%<br>0%<br>AngolaBurkina FasoCameroonChadCôte d'IvoireEthiopiaGabonGhanaKenyaMadagascarMalawi MozambiqueMali NamibiaNigeriaSenegalSouth AfricaSudan TogoZambiaZimbabwe<br><!-- End of picture text --> Consider the case of Ethiopia if its logistic environment were to improve so that the import costs measured by Doing Business were cut halfway to the level of Mauritius. The equivalent change in imports would be brought about by a reduction in Ethiopian tariffs of about 7.8 percent, assuming the composition of import volumes across partners does not change.<sup>23</sup> Similarly, if costs of exporting the standardized container in Ethiopia were cut halfway to the level of Mauritius, the change in exports would be equivalent to the one triggered by an average cut in the TTRI it faces of about 7.65 percent. This figure is substantial for Ethiopia as it faces an average TTRI of 1.85 percent.

It is also worth noting that such an exercise produces illustrative estimates. The standard caveats for gravity estimates hold, such as the appropriateness of the constant elasticity functional form, the dependence of the value of estimates on the choice of independent variables, and so on. However, constant elasticity gravity models are standard in the literature, and the estimated coefficients used to compute the “ad-valorem equivalents” seem stable across specifications and a specification leading to “conservative” estimates is retained.

> 23 For simplicity, we do not take into consideration estimates on the selection equation to compute the advalorem equivalent estimates. Indeed, the indirect effect of trade costs on trade volumes through trade propensity is negligible as the estimated coefficient of the inverse-Mills ratio is small in the outcome equation.

32
Table 1: Matrix of responses on the economic ladder versus decile of consumption (2015)

||Dec|ile of|Consum|ption||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|10|**Total**|
|**Responses**||||||||||||
|1|17|10|11|11|13|11|11|7|5|5|100|
||**5**|3|3|3|4|3|3|2|2|2|**3**|
|2|8|17|18|12|9|9|7|9|7|3|100|
||7|**14**|15|10|7|7|6|8|6|3|**8**|
|3|12|12|9|10|10|12|10|10|8|8|100|
||17|16|**12**|14|14|16|14|14|11|11|**14**|
|4|10|10|10|10|11|10|11|8|10|8|100|
||18|17|18|**18**|19|18|20|15|17|15|**17**|
|5|11|10|11|11|10|9|8|11|9|10|100|
||26|24|25|27|**25**|22|21|26|22|24|24|
|6|8|9|9|7|9|11|10|10|13|12|100|
||14|14|14|11|15|**17**|17|17|21|20|**16**|
|7|7|8|8|9|9|10|10|12|12|15|100|
||7|8|8|9|9|10|**11**|12|12|15|**10**|
|8|10|6|6|8|11|7|12|11|14|15|100|
||5|3|3|4|6|4|6|**5**|7|7|**5**|
|9|11|7|7|13|12|11|13|6|5|15|100|
||1|1|1|2|2|1|2|1|**1**|2|**1**|
|10|3|4|9|12|4|9|13|12|14|19|100|
||0|0|1|1|0|1|1|1|1|**1**|**1**|
|**Total**|10|10|10|10|10|10|10|10|10|10|100|
||100|100|100|100|100|100|100|100|100|100|100|

_Source:_ LiTS III with the decile of consumption measured at the country level. _Note:_ The table covers the year 2016. The same tables for the years 2006 and 2010 show similar patterns. The top number in each row shows how people who have placed themselves on that row-specific step of the ladder are distributed across the ten (objective) deciles calculated from consumption levels. For example, 17% of those that chose step 1 are also objectively in the first decile of consumption of their country, and the remaining 83% belong to other deciles. Considering the columns, the bottom number of each column corresponds to how people belonging to a column-specific consumption decile distribute themselves according to the responses they give to the ladder question. For example, only 5% of those in the first decile of consumption have given 1 as a response, and 95% feel richer than they actually are.

7
|Table B5. Characteristics of Informals<br>from Informal Sector Surveys conducted by the author|_<25_hours per<br>Taxicabs|w<br>45 hours<br>Taxicabs|Entire<br>pSampl<br>Taxicabs|Entire<br>e<br>Kiosks|Entire<br>Sample<br>Markets|<br>Entire<br> <br>Sample<br>Street Services &<br> <br>Vendors|0<br>_G)_<br>CA|
|---|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on<br>the question)|99|101|308|245|137|<br>80|0<br>0|
|Gender||||||||
|Male|90.9|95.1|91.9|36.6|52.6|<br>48.8|m<br>**0**|
|Female|9.1|_5.0_|8.1|63.4|47.5|<br>51.3|0|
|Age of Person Interviewed|||||||**I**|
|Mean|37.0|36.8|37.1|38.7|37.3|<br>36.5||
|Married|||||||>|
|Yes|84.9|83.0|82.1|82.7|70.8|<br>60.0|R-|
|Education|||||||RI|
|Primary|2.3|1.1|2.5|1.6|13.9|<br>10.0||
|Secondary|13.8|34.1|27.1|34.4|55.5|<br>35.0|X|
|Vocational|46.0|46.2|44.4|37.3|24.8|<br>42.5|OQ|
|Higher|37.9|18.7|26.0|26.6|5.8|<br>12.5|**0**|
|Another job or source of income||||||||
|Yes|72.5|16.8|43.1|32.2|22.6|<br>18.8||
|Type of other job|||||||9|
|State|31.9|6.3|26.4|41.7|44.4|<br>40.0||
|Non-State Formal Sector|43.5|43.8|42.4|35.4|22.2|<br>20.0||
|Other Informal Sector|24.6|50.0|31.2|22.9|33.3|<br>40.0|co|
|Other job consists of...||||||||
|Active Work|na|na|na|37.3|19.4|<br>33.3|**g**|
|Pension|na|na|na|44.0|71.0|<br>66.7||
|Leave|na|na|na|18.7|9.7|<br>0.0||
|Monthly income from other job||||||||
|Mean|39.6|61.6|42.9|19.7|12.0|<br>17.8||
|Hours per week at other job||||||||
|Mean|38.4|30.2|39.1|26.9|13.1|<br>34.6||
Total real private household consumption falls under all scenarios, although the declines are much smaller in Scenarios 3 and 4 where the government income from the subsidy is redistributed. Real saving also falls under all scenarios, albeit by a very small amount. Overall, changes in real spending are driven by the extent to which real government expenditure increases, with the largest increase in real spending in Scenario 1 (0.55%) and smallest in Scenario 3 (0.22%). Real spending in Scenario 4 rises by 0.27%, less than in Scenario 1, due to the larger increases in prices in Scenario 4.

# **4.4.2 Trade**

Petroleum product imports constitute 14.6 percent of Nigerian import value (GTAP 8 database). The four scenarios reduce it by 15.6, 9.1, 13.6 and 8.9 percent, respectively. The greatest reductions are in Scenarios 1 and 3 because the former removes the subsidy on petroleum product imports entirely and the latter subsidizes domestic petroleum product production and reduces the subsidies on imports by 50 percent. This, among other changes, results in total imports increasing by 0.3 and 0.1 percent under scenario 1 and 2, respectively. Meanwhile, imports decrease by 1.0 and 0.4 percent under Scenarios 3 and 4, respectively (Figure 7).

<!-- Start of picture text --> Scenario 1<br><!-- End of picture text --> <!-- Start of picture text --> Scenario 3<br><!-- End of picture text --> <!-- Start of picture text --> Price Quantity Value Price Quantity Value<br>Imports indices Exports indices<br>0.3 0.3 0.3<br>0.2<br>0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1<br>0.0 0.0<br>0.0 0.0 0.0<br>‐0.1<br>‐0.4 ‐0.4<br>‐0.7<br>‐0.8<br>‐1.0 ‐1.0<br><!-- End of picture text -->

**Figure 7: Change in Trade Indices (%).**

The increase in the real imports under Scenarios 1 and 2 are mainly driven by increases in the quantities imported of other manufactures, which represent about 40.0 percent of imports and increase by 2.1 and 1.2 percent respectively under the two scenarios (not shown). Imports of transport-communication increase by 12.5 and 6.4 percent, other services increases by 13.1 and 7.3 percent and transport equipment by 11.1 and 6.1 percent, respectively. The increases caused 19
Policy Research Working Paper 7388

# **Abstract**

As part of a national experiment in 2008, Chengdu prefecture implemented ambitious property rights reforms, including complete registration of all land together with measures to ease transferability and eliminate migration restrictions. A triple difference approach using the Statistics Bureau’s regular household panel suggests that the reforms increased consumption and income, especially for less wealthy and less educated households, with estimated benefits well above the cost of implementation. Local labor supply increased, with the young shifting toward agriculture and the old toward off-farm employment. Agricultural yields, intensity of input use, and diversity of output also increased. Improving property rights in peri-urban China appears to have increased investment and diversification.

This paper is a product of the Agriculture and Rural Development 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 kdeininger@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
**Evidence of phasing out electricity subsidies is also strong.** Fig. 13a shows the domestic retail price for electricity from 2002 to 2010. Countries are ranked according to the level of tariff (in US$ cent per kWh) in 2010. The annualized rate of growth of tariffs (the blue bubbles) is measured on the right hand axis.  For our sample of countries the average electricity tariffs increased by 50% from US$ 6 cent in 2002 to US$ 9 cent per kWh in 2010. Net importer countries, including Chile, Dominican Republic and Turkey display the highest electricity tariffs. Net energy exporters, including Egypt, Yemen and Iran but also Argentina charge the lowest electricity tariffs. Only Yemen and Argentina recorded a decrease in tariffs. Among the countries that recorded the most dramatic increase in gasoline and diesel tariffs are both net energy exporters, such as Azerbaijan, Indonesia, Iran and Egypt, and net energy importers, such as Chile, Turkey, Moldova, and Dominican Republic.

**In our sample of countries, power consumption per capita increased from 1000 kWh in 1998 to above 1500 kWh in 2008 despite price increases.** Energy intensity in 2008, however, became less than half of the 1998 level, declining from 0.9 kWh to 0.4 kWh (in unit of GDP). These results are in line with the majority of the estimates found in the literature that indicate that income elasticity is higher than price elasticity for electricity. Also, in general, demand for electricity is more responsive to changes in price and income in the long run than in the short run (see Box 2).

**Box 2**

**Evidence from estimation of electricity price and income elasticity**

The results of Dahl (2011b), based on a global survey of electricity demand including more than 450 studies for about 60 countries for the period from 1951 to the present, indicate that the mean price elasticity in the short run is -0.21 and -0.48 in the long run, and that the mean income elasticity is 0.32 (in the short run) and 1.08 (in the long run). The earlier results by Espey and Espey (2004) based on a meta-analysis of residential electricity demand elasticities from 36 studies (mostly from developed countries) found the mean price elasticity equal to -0.35 and -0.85 respectively in the short and long run and the mean income elasticity equal to 0.28 and 0.97 respectively in the short and long run.

Estimates of income elasticity for countries with lower income than the US are higher. As income increases in developing countries, electrification, ownership of appliances, and demand for electricity increase at a fast rate. However, as the access to electricity reaches 100% and ownership of appliances approaches saturation, increases in income have a smaller effect on demand for electricity.

15
# **_Does Market Size Make a Difference?_**

An _a priori_ view that markets will consolidate and company numbers will reduce may be mitigated by the view that the market has the potential, instead, to grow in size. As the size of the market increases, then it can accommodate more market participants whilst still providing the access to economies of scale that they are seeking. In such an environment, organic growth albeit with lower than otherwise acceptable market share values, might be a more effective strategy than seeking growth through acquisition and merger.

That said, observing that higher index levels are not found in larger markets and that smaller markets tend to be more concentrated, it would seem more speculative to advance the view that market size is a result of the competitive settings.

Figure 7 suggests that lower index values can be supported in larger markets measured in terms of absolute size. The regression lines fitted (refer Table 4) also support the relevance of absolute market size as a factor that contributes to interpreting observed concentration measure values and speculating on their future direction.

**<u>Figure 7: Variation in Herfindahl Index and Total Premium</u>**

<!-- Start of picture text --> Life Non Life<br>7100 7100<br>6100 6100<br>5100 5100<br>4100 4100<br>3100 3100<br>2100 2100<br>1100 1100<br>100 100<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br>ln(Premium) ln(Premium)<br>Source: Staff Analysis<br>Herfindahl Herfindahl<br><!-- End of picture text -->

**Table 4: Statistical Summary of Results of Regression Lines Comparing Premium to Herfindahl**

**<u>Indices</u>**

|Case|Constant||Slope||_r_<sup>_2_</sup>|
|---|---|---|---|---|---|
|||Value|Upper Confidence<br>Interval|Lower Confidence<br>Interval||
|Life|3894.763|(262.545)|(237.274)|(287.816)|20.20%|
|Non Life|2795.412|(187.774)|(165.958)|(209.591)|11.17%|

Note: Confidence intervals shown are calculated on the basis of a two sided 95% probability. Source: Staff Analysis 13
in Bangladesh. A recent estimate of the price elasticity of palm oil for USA at the wholesale level is -1.24 (Kojima et al. (2014)). Since a large part of demand for palm oil comes from the poor households in rural Bangladesh, we would expect that price elasticity would be larger in magnitude. The available estimate at the household level for India is -1.62 (Srinivasan (2005)). If we assume that the price elasticity of Palm Oil at the wholesale level in Bangladesh is approximately -2.00, then a 5-6 percent increase (using estimates from Tables 3 and 4) in wholesale prices would require a 10-12 percent reduction in quantity, implying that the wholesale traders were able to find alternative sources of credit for the remaining 20-22 percent of the quantity on suppliers credit under the DOTs. To get a sense of the quantity adjustments by the wholesalers after the reform, we asked retrospective questions to the 50 wholesalers in Dhaka surveyed in February 2016 noted above. The estimates from the survey show that, among the wholesalers who used to take oil from DOTs on credit before the reform, quantity of oil fell by 45 percent during the first six months after the reform. This would imply that these wholesalers were not able to arrange credit from alternative sources for about half of the quantity (about 15 percent of the total quantity), implying a price elasticity close to -3.00, with a 5-6 percent increase in the price. The evidence taken together thus suggests that the breakdown of the long-term credit arrangements in 2011 resulted in approximately 15 percent reduction in the quantity of oil at the wholesale level, and about 5-6 percent increase in wholesale price in the post reform period (especially the first 9 months) because of the supply disruption.

# **(8) Concluding Comments**

There is a widely held view that the presence of marketing intermediaries in commodity markets ends up raising consumer prices; the effects of their market power in the supply chain exceed the value of the cost-reducing distribution services they provide. The Bangladesh 2011 policy experiment provides a rare opportunity to test this view, as it attempted to eliminate an entire layer of such intermediaries. Using high frequency data for the prices of imported crude palm oil besides wholesale and retail prices of refined oil relative to wheat, which is also imported in bulk and incurs similar storage and transport costs, we showed that the reform increased the average margin between the crude import and the wholesale price. In particular, the data firmly rejects the predictions of the standard double marginalization model which formalizes 34