rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Multinomial logit regression results**

|||Reason for closure<br>(Base outcome: Survived at<br>|time t)<br>|
|---|---|---|---|
||(1)|(2)|(3)|
||Makinga loss|Illness and familycare|Better opportunities|
|Years since baseline|1.1824***|1.3050***|1.2972***|
||(0.0514)|(0.0838)|(0.1104)|
|Age of firm (years)|0.8967***|0.9249***|0.9661|
||(0.0180)|(0.0195)|(0.0442)|
|Age of firm squared|1.0016***|1.0012***|0.9997|
||(0.0004)|(0.0004)|(0.0014)|
|Firm in first year of life|1.3569*|1.4249*|2.0584***|
||(0.2228)|(0.2610)|(0.5764)|
|Number of employees|0.9674|0.7591**|0.9270|
||(0.0529)|(0.1044)|(0.1973)|
|Log business profits|0.8272***|0.8608**|0.9343|
||(0.0459)|(0.0580)|(0.0803)|
|Sector (Omitted category = Retail)||||
|Sector=Manufacturing|0.6488**|0.6803|0.4160**|
||(0.1320)|(0.1999)|(0.1441)|
|Sector=Services|0.7583**|0.8832|1.0270|
||(0.1055)|(0.1441)|(0.2254)|
|Sector=Other|0.4181|0.6776|0.4866|
||(0.2410)|(0.4621)|(0.4990)|
|Age of owner|0.9714|0.9467|1.2123|
||(0.0389)|(0.0337)|(0.1486)|
|Age of owner squared|1.0002|1.0008*|0.9971*|
||(0.0005)|(0.0005)|(0.0017)|
|Owner is female|1.3811**|2.4835***|0.8244|
||(0.2107)|(0.6069)|(0.2129)|
|Owner has tertiary education|1.4118|0.9010|2.1314**|
||(0.3170)|(0.2553)|(0.6714)|
|Constant|0.1851|0.0010***|10799000***|
||(0.3269)|(0.0018)|(25950000)|
|Observations|16,308|16,308|16,308|

Note: Robust standard errors in parentheses, clustered at the firm level. The specification includes survey fixed effects and controls for missing values in firm age, number of employees, log business profits, sector=other, age of owner, and owner has tertiary education. Estimation is over time horizons of five years or less. *** p<0.01, ** p<0.05, * p<0.1.

38
9 of the economy. rhe real exchange rate index<sup>(RERA, RERB',</sup> illustrates government's short- and medium-term efforts to maintain<sup>competitiveness</sup> in international trade.

The environment within which structural adjustment policies<sup>are made and</sup> economic performance is attained is characterized by country-specific realizetions of external variables. The real international interest rate (RR) is defined ex post by subtracting the US inflation rate from the<sup>country's</sup> average nominal rate on interiiational borrowing, Tnternational debt is total debt, including private, public and publicly guaranteed, deflated to billions of 1980 US dllars. It is stated in Rer caRita form, and is divided<sup>into a</sup> longer-term component (LTDPC) and a short-term component (STDPC).5 The terms of trade (TOTA, TOTB) is the ratf.o of average expor to average import prices. The share of tot:al output produced in the agricultural sector (YASHR) is a proxy inverse'.y ralated to the country's secular stage of economic develo ent. It is likely that there are other cotrmon international influences on economic performance in these countries as well, and<sup>to measure</sup> the impact of these I introduce a series of year-specific dummy variables (D7 for 1977 through D5 for 1985) as explanatory variables. These will register, for example, the average annual impact of the restrictions on international credit that as Sachs (1989b) documents were imposed in the<sup>post-1982 period.6</sup> These data were adjusted to address two conceptual problems. Fir¢F.<sup>the</sup> terms of trade and real exchange rate variables must be adjusted<sup>to provide</sup> 5 The lcng-term/short-term distinction is made as in World Bank (1987). Long-term refers to debt with original maturity of one year or more.

6 There is also a strong common element to movements in the terms of trade and the real interest rate across countries. Introduction of the year-specific dummy variables will thus lower the significance of TOTB and RR variables<sup>in</sup> least-squares regression but will not bias the estimation of fixed-effect terms.
# ESTIMATES OF NRW

|||||Ra|tio|(billio|Volume<br>ns of m*/|year)|
|---|---|---|---|---|---|---|---|---|
||Supplied<br>population<br>(millions,<br>2002)|System<br>input<br>=<br>l/eapita/ <br>day|Level<br>of NRW<br> (% of<br> system<br>input)|Physical <br>losses<br>(%)|Com-<br> mercial<br>losses<br>(95)|Physical<br>losses|Com-<br>mercial<br>losses|Total<br>NRW|
|Developed<br>countries|744.8|300|15|80|20|9.8|2.4|12.2|
|Eurasia<br>(C15)|178.0|500|30|70|30|6.8|2.9|a7|
|Developing<br>countries|837.2°|250°|35|60|40|16.1|10.6|26.7|
|||||TOTAL||32.7|15.9|48.6|

Sources: WHO and authors’ estimates.

| = liters: m? = cubic meters

a. Based on a total population having aecess to safe water supply of 1,902.7 million people, with 44 percent of these receiving water through individual household connections.

b. This figure reflects a wide ciscrepancy among developing countries, from 100 |/capita/day for same utilities in the peorest countries or these experiencing severe water shortages te mere than 400 lvcapita‘day in many megacities of Latin America and East Asia. The figure used in this calculation is a conservative average.
# **Can Improved Biomass Cookstoves Contribute to REDD+ in Low-Income Countries? Evidence from a Controlled Cooking Test Trial with Randomized Behavioral Treatments**

Abebe D. Beyene<sup>A</sup> Randall Bluffstone<sup>B</sup> Sahan Dissanayake<sup>C</sup> Zenebe Gebreegziabher<sup>A, D</sup> (Corresponding author) Peter Martinsson<sup>E</sup> Alemu Mekonnen<sup>A, F</sup> Michael Toman<sup>G</sup> 30 May 2015 Keywords: improved biomass cookstoves controlled cooking test, randomized treatment; REDD+; Ethiopia.

_JEL_ classification: _C93, D12, O13, Q41, Q56_ Authorship is shared and authors are listed in alphabetical order.<sup>A</sup> Environmental Economics Policy Forum for Ethiopia (EEPFE)/ Ethiopian Development Research Institute (EDRI) Addis Ababa, Ethiopia;<sup>B</sup> Department of Economics Portland State University USA;<sup>C</sup> Colby College, USA;<sup>D</sup> ( _Corresponding author_ ) Department of Economics Mekelle University P.O.Box 451 Mekelle, Tigrai Ethiopia zenebeg2002@yahoo.com;<sup>E</sup> University of Gothenburg, Sweden;<sup>F</sup> Department of Economics Addis Ababa University;<sup>G</sup> Development Research Group, World Bank.

**Acknowledgements** : The authors gratefully acknowledge financial support for this work from the World Bank Knowledge for Change Program.
**Figure 3. ICT use at work and at home**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.8 1<br>0.6 0.8<br>LKA<br>PER<br>TUR MEX 0.6<br>0.4 LAO<br>RUS ECU<br>GRC 0.4 GHA<br>LTU ITA KAZ<br>0.2 CHL<br>POL<br>HUN ESP 0.2 SLV<br>CZE FRA SVK<br>DEU 0 COL BOL<br>KEN<br>-0.6 -0.4 BELEST-0.2 AUT0 IRLJPN 0.2 0.4 0.6 0.8 1 -1 -0.8 -0.6 -0.4 -0.2 0 0 0.2 0.4 0.6 0.8 1 1.2<br>SWE CANSVN KOR-0.2 ISR GEO VNM<br>NLDFIN KOS ARM UKR -0.2<br>NOR GBR USA<br>DNK NZL -0.4 -0.4 PHL<br>SGP SRB<br>MKD<br>-0.6 -0.6 Low ICT at home<br>Low ICT at home<br>Low ICT at work Low ICT at work<br><!-- End of picture text --> Note: The vertical axis measures the Low ICT use index (a higher value means lower ICT use at work), while the horizontal axis measures the Low ICT at home index (a higher value means poorer internet access at home). The variable to measure internet connectivity at home is not available for El Salvador, thereby we use a different approach for this country. We consider that households have internet access at home if they have a computer and fixed telephone access.

When combining the four indexes, we find substantial cross-country variation in the amenability of jobs to working from home. As seen in Figure 4, the most vulnerable countries in the PIAAC sample are Turkey and those from the LAC region. In the STEP sample, countries from the ECA region have jobs more amenable to working from home, while the opposite is true for Sri Lanka, Lao PDR, El Salvador and Ghana. In contrast to Dingel & Neiman (2020a), we find that the United States ranks lower than most OECD countries in terms of its jobs’ amenability to working from home. Our findings are consistent with Hardy et al. (2018), who use the PIAAC surveys and find that the United States has more jobs that are more manually intensive than most other countries.

11
33

**Figure 14  Market Returns on Risky and Risk Free Assets**

<!-- Start of picture text --> 0.5<br>0.3<br>0.1<br>-0.1 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04<br>-0.3<br>SPI MSCI CH Bond Index Performence (Pictet) MIR BVG<br><!-- End of picture text --> Data-Source: SNB, MSCI, SWX and Pictet Figure 15 depicts the accumulated returns of these indexes, as well as of a “simulated portfolio” and an “average empirical portfolio” since 1987. The simulated portfolio comprises 30 percent Swiss shares (represented by the MSCI Index), 20 percent foreign shares (MSCI World), and 50 percent bonds, thus containing the maximal fraction of shares permitted by the BVG law in the mandatory part. The average empirical portfolio consists of the average reported fraction of shares held in an average portfolio of a pension fund. The remaining fraction is invested in bonds. The yields on portfolios of pension funds outperform the MIR yields on average. The performance ratios, which are presented in Table 9, represent the return to a portfolio relative to an investment strategy with a fixed yield of 4 percent and an investment period from 1987 to 2004. In this setting, the empirical portfolio exceeded by 42 percent the return of an investment strategy with a fixed yield of 4 percent. By investing the maximum fraction in shares at all times, an excess return of 84 percent could have been achieved.

However, it is important to note that the MIR does not necessarily represent the effectively credited return on old-age balances. Nonetheless, most pension funds used this rate to calculate the returns on the old–age capital. Excess returns in the past had been used to accumulate reserves, to finance early retirement programs and inflation indexing, and to take contribution holidays.
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 15_ possible temporal overlap between a higher number of food items may again be exploited to estimate (“stitch together”) a complete basket price over a long time period, albeit with a higher share of missing data.

When compared to the average FCS country considered by Andr ́ee (2021), the PNG data stands out in the sense that it has more food items but fewer markets. The data availability is below the average of FCS countries considered by the application presented by Andr ́ee (2021).

# _A. Validation results_

Table 3 summarizes the cross-validation results along with basic aggregate statistics calculated from the imputed Food Price Index. Compared to results obtained in other FCS countries, the annualized inflation rates, maximum drawndown, and annualized volatility are similar to what has been observed for staple food prices in comparable countries. The annualized volatility is slightly higher, but not by a standard deviation. higher. The _R_<sup> ̃2</sup> here is more than a standard deviation below the FCS median. Nevertheless, it is within the range of FCS results (the results presented for Chad reached 0.69, and 0.75 for Central African Republic). The results therefore show that the new approach reaches comparable results even with lower data coverage and a much higher number of food items.

Table 3—Summary of raw food price data in Papua New Guinea.

||Annualized|Annualized|Max.|Avg. monthly|Avg. annual|CV-|
|---|---|---|---|---|---|---|
||Infation|Volatility|Drawdown|market corr.|market corr.|score|
|2009-2022|6.21%|15.33%|-21.28%|0.18|0.33|0.71|
|2014-2022|4.9%|11.83%|-19.08%|0.26|0.57|0.76|
|2017-2022|6.5%|12.4%|-18.35%|0.24|0.35|0.77|
|FCS median|4.92%|11.83%|-24.17%|||0.87|
|FCS St.Dev|13.02|5.10|10.36|||0.072|

_Note:_ The first column reports the study period, the second column reports the data coverage across the full study period, the third column reports the data coverage in the last 12 months, the last columns reports the number of markets modeled out of the total number of markets included in the data snapshot. _Source:_ The statistics have been prepared by the author for this paper based on end-of-July (2022) food price data from IFPRI. The FCS median and St.Dev have been calculated from table A4 in Andr ́ee (2021), who present identical estimates for 25 FCS countries (excluding PNG).

The average food price inflation rates are in a reasonable range. For instance, the annualized official CPI price inflation rate for the full 2009-2022(Q2) period was 5.33%. Note that a direct comparison with food price inflation would be interesting, but would have to account for the differences in basket specification.

Considering the high price volatility, two to almost three times annualized inflation, it is also reasonable to assume that any direct measurement of prices would face difficulties with precision. For instance, consider 12 _._ 4%<sup>1</sup><sup>_/_12</sup> = 1 _._ 23% as a typical error in monthly price measurement of a single item that stems from natural volatility. That level of error is almost 20% of a 6.21% inflation rate (the
**Table 4.3b: Non-response bias in Ethiopia HFPS by Urban-Rural domain**

|||**Et**|**hiopia HFPS -**|**Urban**|||**Et**|**hiopia HFPS -**|**Rural**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Sample**|**Con**|**tacted**|**Inte**|**rviewed**|**Sample**|**Cont**|**acted**|**Inte**|**rviewed**|
||||_Bias due to_||_Bias due to_|||_Bias due_||_Bias due to_|
||_Mean_|_Mean_|_non-_<br>_contact†_<br>_(2) -(1)_|_Mean_|_non-_<br>_interview†_<br>_(4) -(2)_|_Mean_|_Mean_|_to non-_<br>_contact†_<br>_(7) -(6)_|_Mean_|_non-_<br>_interview†_<br>_(9) -(7)_|
|**Characteristic**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Sample size(number of households)**|_3,410_|_2,340_||_2,270_||_1,962_|_1,013_||_977_||
|Average household size|3.651|3.696|0.045|3.695|-0.001|5.067|5.131|0.063|5.142|0.011|
|Dependencyratio|0.635|0.618|-0.017|0.615|-0.003|1.113|1.085|-0.028|1.083|-0.002|
|Female head(%)|0.339|0.320|-0.019|0.324|0.005|0.208|0.178|-0.029**|0.174|-0.005|
|Age of head|38.584|38.422|-0.162|38.359|-0.063|44.332|43.337|-0.995|43.387|0.050|
|**_Religion of head_**|||||||||||
|Orthodox|0.579|0.604|0.024|0.607|0.004|0.418|0.451|0.033|0.458|0.007|
|Muslim|0.201|0.178|-0.023|0.178|-0.001|0.341|0.344|0.002|0.347|0.004|
|Christian|0.214|0.214|0.000|0.210|-0.003|0.220|0.190|-0.029|0.180|-0.011|
|**_Marital status of head_**|||||||||||
|Married|0.621|0.646|0.024|0.641|-0.004|0.824|0.826|0.002|0.828|0.002|
|Formerlymarried|0.214|0.207|-0.007|0.209|0.002|0.148|0.139|-0.009*|0.137|-0.001|
|Never married|0.164|0.148|-0.017|0.150|0.002|0.028|0.035|0.007|0.035|-0.001|
|**_Education level of head_**|||||||||||
|Head Literate|0.775|0.809|0.034*|0.813|0.003|0.454|0.492|0.038***|0.495|0.003|
|None|0.263|0.228|-0.035*|0.225|-0.003|0.592|0.545|-0.047***|0.542|-0.003|
|Primary|0.301|0.302|0.002|0.306|0.004|0.317|0.340|0.023|0.340|0.000|
|Secondary|0.226|0.241|0.015|0.243|0.002|0.060|0.073|0.013|0.074|0.001|
|Tertiary|0.210|0.228|0.018|0.226|-0.003|0.031|0.042|0.011*|0.043|0.001|
|**_Working status of head_**|||||||||||
|Any|0.594|0.606|0.012|0.605|-0.001|0.816|0.825|0.009|0.827|0.002|
|Wage|0.310|0.326|0.016|0.325|-0.001|0.063|0.078|0.016|0.076|-0.002|
|Agriculture|0.115|0.108|-0.007|0.105|-0.003|0.756|0.747|-0.009|0.749|0.002|
|Nonfarm enterprise|0.220|0.220|0.000|0.220|0.001|0.077|0.083|0.006|0.083|0.000|

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

25
# Integration of Venezuelan Refugees and Migrants in Brazil<sup>∗</sup>

Mrittika Shamsuddin<sup>1</sup> , Pablo Ariel Acosta<sup>1</sup> , Rovane Battaglin Schwengber<sup>1</sup> , Jedediah Fix<sup>2</sup> and Nikolas Pirani<sup>2</sup> 1World Bank 2UNHCR

**Keywords: integration in Brazil, Venezuelan refugees and migrants, forced displacement JEL Codes: J15, J31, J61, F22, F15**

> ∗ **Funding Disclaimer:** This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. We further thank FCDO for additional funding support through its Knowledge for Change (KCP) program. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|---|---|
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|Scalingupof Assam PWD Computerization Project|Ashok Kumar|
|STRENGTHENING OF CAPACITY OF BIHAR&#8217;S ROAD CONST|RU Ashok Kumar|
|SPWA - Scalingupthe impacts ofgoodpractices in linking povert|yaYemdaogo Emmanuel Nikiema|
|BEIA-Promotion of Social Biofuels in Benin|Franklin Koffi S.W. Gbedey|
|BEIA-Promotion of Social Biofuels in Benin|Franklin Koffi S.W. Gbedey|
|BEIA-Promotion of Social Biofuels in Benin|Franklin Koffi S.W. Gbedey|
|Household Budget Survey2010|Umar Serajuddin|
|Household Budget Survey2010|Umar Serajuddin|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|Gas and Oil CapacityBuildingProject|David John Santley|
|GH - Natural Resources and Environmental Governance DPO II|Flavio Chaves|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|KE ElectricityModernization Project|Laurencia Karimi Njagi|
|2nd YOUTH DEVELOPMENT PROJECT|Tamir Ibragimoff|
|2nd YOUTH DEVELOPMENT PROJECT|Tamir Ibragimoff|
|2nd YOUTH DEVELOPMENT PROJECT|Tamir Ibragimoff|
|2nd YOUTH DEVELOPMENT PROJECT|Tamir Ibragimoff|
|BEIA- Promotion of Improved Biomass Vesto Stoves in Gambia|Juliet Pumpuni|
|BEIA- Promotion of Improved Biomass Vesto Stoves in Gambia|Juliet Pumpuni|
|BEIA- Promotion of Improved Biomass Vesto Stoves in Gambia|Juliet Pumpuni|
|GH:Sustainable Rural Water & Sanit Serv|Yitbarek Tessema Mammo|
|GH:Sustainable Rural Water & Sanit Serv|Yitbarek Tessema Mammo|
39 illiteracy. Alternatively, there may be a lack of profitable investment projects in the economy that deserve financing based on their expected return. This problem can actually not be illustrated in our figure as it focuses on loan applicants. A second type of access problem can arise from regulatory distortions or insufficient contestability that cause lenders not to fully exploit all the outreach opportunities and thus settle at a point below the access possibilities frontier with a higher marginal interest rate (Figure 10, Point II, B). A third and very different access problem is associated with “excess access,” that is, an equilibrium above the access possibilities frontier with loans being granted to a larger share of loan applicants than is prudently warranted or SMEs achieve too high a leverage, given the lending interest rate and the institutional framework (Figure 10, Point III, C). A final access problem consists of too low a prudent access possibilities frontier, caused by deficiencies in an economy’s institutional framework compared with that of countries with similar levels of economic development. An improvement along these lines would lead to an expansion of the frontier from S* to S*’’ in Figure 11. Similarly, lower opportunity costs of funding (imc), e.g. due to better macroeconomic conditions, will increase the universe of potential loan applicants receiving finance (Figure 11, Point II, B).

# **6.2. Matching the concept with data**

As in the case of aggregate indicators, the frontier concept can be matched with empirical data. First, using the benchmarking model described above, one can predict the share of (small) firms with access to a credit line or loan by a formal financial institution and compare them to the actual share, as gauged by the Enterprise Surveys, undertaken by the World Bank/IFC. Figure 12 graphs the gap between the predicted and the actual share of small enterprises that use a credit line or loan from a formal financial institution. There is a large variation, ranging from Serbia, where the predicted is 44 percentage points above the 39
for the young, medication for the ill, care for the old-aged and housing for the homeless."<sup>60</sup> This was earlier voiced at the 17th Party Congress in October 2007, which reaffirmed the importance of upgrading public services in building a Harmonious Society in China. The Report of the Congress emphasized that on the basis of economic development, more attention must be paid to guaranteeing and improving people's livelihood and promoting social reform, expanding public services, improving social management, and promoting social equity and justice. Since the most glaring shortfalls in these services are virtually all in the rural areas, redressing them will require strengthening rural public finance to provide the resources to support these laudable goals.

The reforms implemented during this decade have already succeeded in achieving some key objectives, chiefly reducing farmers’ burdens, ensuring that teachers’ salaries are paid, reducing out-of-pocket costs for schooling, extending the coverage of cooperative medical insurance, channeling more resources to rural infrastructure, etc. Other reforms, such as extending the TSS to sub-provincial levels, pilot reforms in provinces directly managing county finances, counties managing township finances, treasury reform, the “three rewards and one subsidy” program, and the consolidation of jurisdictions (in particular townships and villages) have also made progress in the intended direction, but will take more time to produce clear effects. For example, completing the TSS reform applies only to incremental revenues, and merging townships and villages will also require time to implement and to yield their intended results.

However, while each of the reforms has achieved specific successes, their overall effect has been more mixed. This is partly due to the contradictions existing among the reforms – for example, while several of the reform measures are intended to enhance the fiscal resources of county and township governments (as well as village authorities), the RTFR has eliminated a good part of their revenue bases. The number and pace of introduction of recent reforms is proving overwhelming at the grassroots levels, especially since the reforms have also brought dramatic shifts in authorities and resources among the three levels of rural government. As a result, not all are implemented as intended, and the net effects of these changes on the

> 60 Xinhua in English, downloaded 2/24/08.

58