rafmacalaba/datause-encoder-data huggingface dataset:
- 59 -

Table 26: State-vise Distribution of IRDP Funds and their Measures of Success in 1987-88

|States|Distribution<br>of<br>IRDP Expenditure*|**%**Eligible**<br>Beneficiaries|**%**Investment<br> <br>Intact*I*|<br>% Intact and<br>No Credit Over|**%**Eligible and****<br>Crossed Poverty-line|
|---|---|---|---|---|---|
|Andhra Pradesh|<br>7.8|68|76|34|9|
|Assam|2.8|27|70|6|10|
|Bihar|14.4|76|85|18|3|
|Gujarat|3.5|78|88|43|4|
|Haryana|1.4|71|46|15|0|
|Jammu-Kashmir|<br>1.0|97|80|50|19|
|Karnataka|3.9|85|64|26|4|
|Kerala|2.8|89|74|19|5|
|Madhya Pradesh|<br>11.2|81|73|27|6|
|Maharashtra|7.1|83|69<br>.|30|10|
|Orissa|5.6|83|68|19|7|
|Punjab|1.4|30|77|57|18|
|Rajasthan|4.6|72|48|15|9|
|Tamil Nadu|6.8|83|63|28|3|
|Uttar Pradesh|<br>18.6|54|79|41|5|
|West Bengal|7.1|46|97|23|8|
|All India|100.0|70|73|29|7|

Source: National Concurrent Evaluation of IRDP, Round 2, 1987, Ministry of Rural Development, as quoted in Pulley (1989), and Rural Development Statistics, 1988.

- ** Proportion of beneficiaries with pre-IRDP household income < = 4800.

- Includes central and state expenditures, but excludes credit mobilised.

- *** Proportion of IRDP investments that remained fully operational after two years.

- **** Proportion of beneficiaries with pre-IRDP household income < = 4800 and post-IRDP income > = 6400 after two years in current pr..ce terms.
be stored for fewer days. The number of _injeras_ baked per cooking session is positively related to stove use frequency. Similarly, family size is consistently positive and significant. On the other hand, those who live in more remote locations (i.e. farther from roads) and who have more livestock use their stoves less than those closer to roads and who invest less in livestock.

An interesting result is that those who are found to be less risk averse in the field-based risk experiment use their Mirt stoves more than those who are more risk averse. This result may indicate that those with greater risk tolerance could have greater willingness to experiment. If successful, this experimentation could then lead to higher levels of usage. We do not suggest that this is necessarily the right explanation. As discussed in Yesuf and Bluffstone (2009) and Vieider et al. (2014) estimated risk aversion is a function of wealth, income and market failures. The negative coefficient estimate may therefore be picking up such omitted variables.

# **6. Conclusions**

This paper uses a randomized experimental design and real-time electronic stove use monitors to evaluate the frequency with which villagers use improved biomass-burning Mirt _injera_ cookstoves in rural Ethiopia. Understanding whether, how much and why improved cookstoves are used is important, because stove use is a critical determinant of total greenhouse gas and indoor air pollution reductions. Confirming use is, for example, a critical aspect of crediting improved cookstoves’ climate change benefits under REDD+.

We find that Ethiopian households in the study area use the Mirt stove in accord with its primary purpose to cook _injera_ , which is estimated to use more than half the fuelwood in the country. Reducing fuelwood use for _injera_ baking is therefore of keen importance. Households on average use the stove 2.4 times per week in the final round of monitoring, but there is significant spatial variation, with households living in SNNP Regional State on average using the stoves just over half as often as households in Amhara and Oromiya. We believe this result is related to SNNP having a less strong tradition of _injera_ baking than the other two regions. This result echoes the literature, which emphasizes the importance of local context and the difficulty – even with a relatively specialized cuisine and a technology designed for that type of cooking – of developing one-size-fits-all improved stoves. A homogeneous approach worked poorly in the past and we see that in 2014 local conditions continue to matter.

**26 |** P a g e
15 the first survey). But there were only marginal improvements in Ghana (1]992-98) and Zimbabwe (1991-96), and no change in Zambia during 1996-98. Macroeconomic destabilization is observed in two countries-C6te d'Ivoire during the 1980s, and Nigeria in the 1990s.

Setting these against the trends in poverty reduction (Figure 2) confirms that countries achieving improvements in their macroeconomic balances in Africa typically have not experienced (in the aggregate at least) increases in consumption poverty-rather the reverse.<sup>9</sup> Ten of the fifteen episodes of change for which we have data indicate both macroeconomic policy improvement and subsequent poverty reduction. In the two cases where macroeconomic balances substantially deteriorated, poverty is indicated to have increased. Only one of the fifteen observations (Zimbabwe during 1991-96) is in the 'wrong' quadrant in Figure 2 (improved macroeconomic policy and increased poverty).

The _association_ between the macro-policy stance and poverty reduction does not necessarily imply any causative or direct behavioral link.'° Rather this evidence serves to highlight the close interactions between macroeconomic policies and economic well-being at the household level. The changes in the macroeconomic accounts took place alongside other sectoral reforms-mostly of a 'structural' nature (trade liberalization, agricultural marketing reforms, privatization, and so on)-and changing institutional environments. Both the institutional environment and the sectoral reforms are certain to be important as well, as is illustrated by the fact that quite similar poverty reductions occurred among some of the countries despite quite different changes in their macro-economic indicators (see south-east quadrant in Figure 2).

> 9 Ali (1998) gets quite different results, with reforms being associated with increasing poverty. This is probably due to the different poverty data sets he uses (derived from IFAD data). Our concern here has been to use only data where careful attention has been paid to over time comparability. Without further information about Ali's data, it is difficult to establish the specific reasons for the differences in results. **'1** Both poverty changes and macro-policy scores might be favorably affected by a third factor, movements in the terms of trade, for example.
use rights were in many cases quite insecure. Changes in population or the community's need to obtain land for non-agncultural purposes such as infrastructure or local enterprises have often led to administrative reallocations of land within the village. A nationally representative survey suggests that about 80% of communities experienced at least one reallocation of land between 1983<sup>and1990</sup> (Nyberg and Rozelle, 2000). In 1996, the number of reallocations experienced by an average village since the inception of the HRS in the early 1980s was estimated at 1.7. Decisions on reallocations were made in a decentralized manner and arrangements for govemance at the local level seem to have had a significant impact on the final outcome (Tumer et al., 1998). Reports suggest that in some cases local cadres may have used their power over the land as a means of extraction rent from farmers (Huang 1999; Johnson 1995).

In 1999, the Chinese Govermment revisedthe 1986 Land Management Law in order to improve tenure security. This revision requires that all farmers receive written 30-year land use contracts and that the scope for readjustments is either circumscribed or completely eliminated. Evidence of mitial implementation shows, however, that much remains to be done to translate these legal provisions into effective tenure security. A recent survey in 17 provinces found that, although 60% of households are estimated to have held written land tenure contracts, only 13% of these contracts rule out further land readjustments, 25% explicitly allow for re-adjustments, and the remainder contain unclear or contradictory provisions (Schwarzwalder et al., 2001). As a consequence, only 12% of farmers were confident that there will be no more adjustments, implying that the impact of the legal provisions on farmers subjective tenure security has been limited. Was such a revision justified? Should it be implemented more quickly? These questions are not only relevant in terms of policy but have also been subject to considerable academic debate.

# **2.2 The Guizhou experiment**

To provide evidence that could help clarify these issues, and to guide the policy discussion in this area, the Chinese Government has encouraged provinces and counties to implement a number of land rights "experiments".' In 1987, Meitan county in Guizhou province was designated by the State Council as one of the initial 18 national experiment zones for rural reform. The "experiment" undertaken there consisted of the introduction of long-term land use rights, prohibition of redistribution (initially for 20 years for paddy land), and the policy of no land readjustment in response to changes in household size. In 1994, when the 15 year contract (starting in 1979) ended, the Guizhou Communist Party Committee issued a document requiring that, in the whole province, "the term of the contract be extended for another 30 years for arable land, and 60 years for non-arable land. The policy of 'no land increase for 4
assumption in turn implies that the next three months would be spring, the three months after that would be summer, and August, September and October would be fall (in the north). The choice of these particular seasonal definitions is motivated by the fact that they provided the best fit with the data and that they reflected the mid-point for key rainy seasons in the sample. We adjusted for the fact that seasons in the southern hemisphere occur at exactly opposite months of the year from northern hemisphere seasons.

Soil data were obtained from FAO (2003). The FAO data provide information about the major and minor soils in each location. Data concerning the hydrology were obtained from the University of Colorado (IWMI & University of Colorado 2003). Using a hydrological model for Africa, the hydrology team calculated flow and runoff for each district in the surveyed countries.

# **4. Results**

Table 2 summarizes the crop choice categories in the African analysis. The most popular crop choice in the sample is maize. This crop is capable of growing across a range of climates because there are a number of varieties of maize grown in Africa. The second most popular crop portfolio is maize-groundnut. We hyphenate crop combinations throughout the paper. Cowpea-sorghum, sorghum and millet-groundnut are also popular choices. While growing maize alone is the most popular choice in Zambia and Ghana, farmers in Cameroon prefer to grow maize-groundnut. Drought tolerant crops were the revealed preference of farmers in Burkina Faso, Niger and Senegal (sorghum, cowpea, or cowpea-sorghum). In Egypt and South Africa, as expected, the popular choices are maize and wheat. South African farmers also selected a variety of other crops (which did not belong to any of the other revealed categories). Farmers in Zambia and Zimbabwe indicated a preference for maize and maize-groundnut.

In Table 3, we examine the determinants of crop choice. In order to have sufficient observations of each choice, we analyze farmers who chose one of the nine most popular choices. These choices are maize (1071 observations), maize-groundnut (811), cowpea-sorghum (666), sorghum (569), millet-groundnut (568), ‘other crops’ (556), maize-beans (399), cowpea (388), and maizemillet (331). The farms that made these choices account for almost three-fourths of all farms in 9
18 Figure 3: Indexes of real international prices and real producer prices of rice and wheat, developing countries’ unweighted average, 1972-76 (1972 = 100).

# (a) Rice

<!-- Start of picture text --> 400<br>International Price<br>350<br>Producer Price<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br><!-- End of picture text --> (b) Wheat <!-- Start of picture text --> 400<br>International Price<br>350<br>Producer Price<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br><!-- End of picture text --> Note: Countries included are: Argentina, Bangladesh, Brazil, Chile, Colombia, Cote d’Ivoire, Dominican Republic, Ecuador, Ghana, India, Indonesia, Kenya, Korea, Madagascar, Malaysia, Mozambique, Nigeria, Pakistan, Philippines, Senegal, Sri Lanka, Sudan, Taiwan, Tanzania, Thailand, Uganda, Zambia, and Zimbabwe.

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008).
Table 5: Backlash mechanisms: Gender attitudes

|Panel A: Women|(1)<br>No. gender<br>inequitable<br>attitudes|(2)<br>No. situations<br>thinks IPV<br>is justifed|(3)<br>No. situations<br>refusing sex<br>is justifed|
|---|---|---|---|
|Treated couple|-0.40***|-0.60***|-0.19***|
||(0.04)|(0.08)|(0.06)|
|Spillover couple|-0.10**|-0.23***|-0.14**|
||(0.04)|(0.08)|(0.06)|
|Observations|2,008|1,732|1,815|
|Baseline controls|Yes|Yes|Yes|
|Control mean|1.82|2.19|1.90|
|Treated vs Spillover p-value|0.00|0.00|0.46|
|Panel B: Men|(1)|(2)|(3)<br>(4)|
||||No. hypothetical|
||No. gender|No. situations|No. situations<br>situations|
||inequitable<br>attitudes|thinks IPV<br>is justifed|wife refusing sex<br>is justifed<br>would be<br>likely to|
||||become violent|
|Treated couple|-0.27***|-0.22***|-0.10*<br>-0.41**|
||(0.05)|(0.06)|(0.06)<br>(0.19)|
|Spillover couple|0.10|0.30***|-0.08<br>0.95***|
||(0.06)|(0.08)|(0.06)<br>(0.23)|
|Observations|1,989|1,640|1,746<br>1,990|
|Baseline controls|Yes|Yes|Yes<br>Yes|
|Control mean|1.61|0.83|2.03<br>3.46|
|Treated vs Spillover p-value|0.00|0.00|0.81<br>0.00|
|Panel C: Frictions||||
||(1)|(2)|(3)|
||Diference|husband - wife||
||Gender<br>ineuitable|IPV acceptabilit|y<br>Wife got more<br>progressive|
||q|attitudes|Husbandotmore|
||attitudes||g <br>conservative|
|Treated couple|0.19***|0.43***|-0.01|
||(0.06)|(0.09)|(0.01)|
|Spillover couple|0.15**|0.61***|0.05***|
||(0.06)|(0.09)|(0.02)|
|Observations|2,042|2,042|2,042|
|Baseline controls|Yes|Yes|No|
|Control mean|-0.49|-1.19|0.09|
|Treated vs Spillover p-value|0.57|0.13|0.00|

_Note_ : OLS = ordinary least squares. Strata fixed effects included. Standard errors are clustered at village level, shown in rounded brackets. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Results show OLS estimates of direct treatment and spillover effects, and differences between effects on trained and spillover couples, controlling for the baseline value of dependent variable (recoded to 0 if missing), as well as a dummy for whether the baseline response was missing. panel A reports the treatment effect on women’s answers; panel B reports on men’s answers. Column 1 uses the number of gender inequitable attitudes declared by the respondent (out of 4). Column 2 uses the number of reasons that justify IPV (out of 5). Column 3 uses the number of situations in which refusing sex is justified (out of 4). Column 4 uses the number of hypothetical situations in which a man would beat his wife (out of 10) or be intimidating (out of 5). Columns 1 and 2 of panel C use the difference between in gender inequitable attitudes and the number of situations IPV is justified between spouses. A positive value implies that the wife is more progressive than the husband. Column 3 uses a dichotomous variable equal to one if the wife became more progressive between baseline and endline while her husband became more conservative. Control mean is the average value of the dependent variable in the control group.

38
private sector workers earned 5750 dongs per hour (see table 2). Our measure of earnings is the hourly compensation declared by the workers and includes the wages and benefits received from employers in cash and in kind.<sup>5</sup> The ability to account for workers’ benefits is crucial for the purpose of this study. Without benefits, public employees’ compensation would be much lower than the compensation of private sector employees (see table 2). Interestingly, wages, rather than benefits, drive the increase in the publicprivate pay gap. Finally, the last row of table 2 shows that the number of hours worked increased slightly more in the private sector, contributing to lower growth in hourly earnings compared with the public sector.<sup>6</sup>

|TABLE2. P|ublic and Privat<br>1|e Sector Earnin<br>993|gs in 1993 an|d 2006(Dongs,i<br>1998|n Nominal T<br>Ch|erms)<br>ange|
|---|---|---|---|---|---|---|
||Public|Private|Public|Private|Public|Private|
|Hourlyearnings|1,460|1,321|9,321|5,747|538%|335%|
|Hourlybenefits|315|81|1,203|414|282%|414%|
|Hours worked|1850|1621|2047|1892|11%|17%|

_Source:_ Author’s calculations based on VLSS 1993 and VHLSS 2006 data.

The distribution of earnings for public and private employees and their changes over time is displayed in figure 1. In 1993, the public and private sectors were difficult to distinguish whereas, in 2006, the public sector wage distribution clearly dominated the private sector distribution. Another striking change is the shape of the two distributions: the dispersion of the earnings distribution decreased between 1993 and 2006, strongly in the private sector and mildly in the public sector. The decrease in within-sector inequality dominated, so the overall earnings inequality was, in fact, falling. The main fact that we attempt to explain in this paper is the widening of the public-private pay gap combined with an increase in the relative dispersion of earnings in the public sector as compared 9
Table 5 shows the estimated regression results. We only observe statistically significant differences between the second-stage estimates obtained from affinity through the UNGA voting and the other two instrumental variables for one of the five energy mix outcomes (electricity production from oil sources). There are either no statistically significant differences across estimated second-stage coefficients, and the three chosen instruments, or the differences are only marginally significant for all other energy mix outcomes. These results give us greater confidence that our second-stage estimates reflect the causal outcome of renewable energy policy reforms.

Table 5: Specification Regression Test for IV Exogeneity

||(1)<br>FCC|(2)<br>EFF|(3)<br>EOS|(4)<br>REC|(5)<br>REO|
|---|---|---|---|---|---|
|IV: EU Agreements|0.27|0.78**|1.85|-0.43|-0.22|
||(0.16)|(0.34)|(1.44)|(0.30)|(0.24)|
|IV: Affinity through bilateral trade|0.79|6.34*|4.23***|-0.15|-1.27*|
||(0.67)|(3.27)|(0.72)|(0.42)|(0.67)|
|Policy: Planning for renewable energy expansion|-1.11*|-2.95***|-2.5*|1.8**|3.06**|
||(0.57)|(0.93)|(1.48)|(0.68)|(1.33)|
|Policy: Incentives and regulatory support for RE|-0.33|-1.02|-1.02|0.21|1.16|
||(0.69)|(1.06)|(1.56)|(0.44)|(0.94)|
|Policy: Attributes of financial and regulatory<br>incentives|-0.47|-0.9*|-0.95|0.73|2.21*|
||(0.33)|(0.52)|(0.87)|(0.73)|(1.13)|
|Policy: Network connection and use|-0.14|-0.39|1.38|0.74**|2.19**|
||(0.33)|(0.39)|(1.00)|(0.35)|(0.89)|
|Policy: Counterparty risk|0.15|0.47|0.73|-0.26|0.51|
||(0.36)|(0.96)|(1.86)|(0.44)|(0.84)|
|Policy: Carbon pricing and monitoring|||-0.45|0.81***|1.68**|
||||(0.84)|(0.29)|(0.80)|
|Index: Composite|-0.22**|-0.1|-0.98**|0.67**|.99**|
||(0.08)|(0.30)|(0.46)|(0.32)|(0.38)|
|Index: Summation|-0.05|0.14|-0.28|-0.09|0.02|
||(0.07)|(0.23)|(0.45)|(0.12)|(0.14)|
|Constant|-0.62*|-1.65***|-3.61***|0.24|-0.2|
||(0.33)|(0.50)|(0.84)|(0.36)|(0.88)|
|Observations|707|717|726|947|923|
|R-squared|0.071|0.146|0.075|0.01|0.055|
|CountryFixed Effects|YES|YES|YES|YES|YES|

20
-4-

# Table 1: HOUSING SHORTAGES IN SELECTED R.C.R. COUNTRIES (Ratios of Households to Dwelling Units)

|Country|1960|Year<br>1970|1980|
|---|---|---|---|
|North America and WeFtern Europe:||||
|Austria|1.04<br>|0.96<br>|0.92<br>|
|Belgium<br>|U.97<br>|0.97<br>|0.94<br>|
|Canada|0.99|0.99<br>|0.99<br>|
|Denmark<br>|1.08<br>|1.00<br>|0.99<br>|
|Fed. Republic of Germany|1.28<br>|1.09<br>|1.01<br>|
|Finland|1.11<br>|1.02<br>|1.00<br>|
|France|0.93<br>|0.85<br>|0.87<br>|
|Greece<br>|0.97<br>|0.86<br>|0.79<br>|
|Iceland|1.12|1.09<br>|0.95<br>|
|Ireland<br>|1.09<br>|1.08<br>|1.04<br>|
|Italy|1.00<br>|0.95<br>|1.01<br>|
|Luxembourg<br>|1.03<br>|0.98<br>|0.93<br>|
|Netherlands|1.09|1.01|0.99<br>|
|Norway<br>|0.92<br>|0.85<br>|0.84<br>|
|Portugal|0.99|0.84|0.73|
|Spain<br>|1.00<br>|0.98<br>|0.99<br>|
|Sweden|1.00|0.98|0.90|
|Switzerland<br>|1.04<br>|0.98<br>|0.90<br>|
|United Kingdom|1.02|1.00<br>|0.96<br>|
|United States|0.93|0.95|0.95|
|Median|1.03|0.98|0.95|
|Eastern Europe:||||
|Bulgaria<br>|1.10<br>|1.14<br>|1.08<br>|
|Czechoslovakia|1.16|1.08|1.01|
|German Democratic Republic|l.25<br>|1.09<br>|1.02<br>|
|Hungary|1.16|1.10|1.08|
|Poland|1.25|1.16<br>|1.18<br>|
|Romania|1.16|1.15|1.12|
|U.S.S.R<br>|1.29<br>|1.09<br>|1.05<br>|
|Yugoslavia|1.14|1.07|1.02|
|Median|1.16|1.10|1.07|

Source: Calculated from Tables 1.6 and .II.1, United Nations Economic Commission for Europe (E.C.E), (1986).
SDS value-added prices along with prices for other products in the chemical industry.<sup>7</sup> The latter series is included as a benchmark because these are products that use related technologies, but most are not consumer goods, and thus are not subject to the Walmex effect. To provide an additional benchmark, an aggregate value-added price index for the manufacturing sector is also included. While one can easily read too much into this graph, it does confirm that inflation rates came down relatively rapidly in the SDS and cosmetics sector as the structural transformation of the retail sector took shape in the late 1990s and early 2000s. (The spike in 1995 reflects the Tequila Crisis and the associated devaluation ⎯ refer to the appendix for details.)

**Figure 1: Annual inflation rates and value-added prices**

<!-- Start of picture text --> 60.0<br>50.0<br>40.0 SDS & C<br>30.0 Chemicals<br>20.0 Manufac<br>10.0<br>0.0 1993 1994 1995 1996 1997 1998 1999 2000 2001 20022003 2004<br><!-- End of picture text --> Source: INEGI (2005) Some interviewees mentioned that one of the difficulties they faced was their inability to pass increases in material prices on to consumers, and they traced this

> 7 Note that cosmetics are included with SDS products in the available series—since these are also consumer goods sold by Walmex, this should not change the basic message.

10
<!-- Start of picture text --> Average FDI Intensity (FDI inflows as percent of nominal GRP)<br>9%<br>8%<br>7%<br>6%<br>5%<br>4%<br>3%<br><!-- End of picture text --> <!-- Start of picture text --> XS SS Sf SC KF wh SF KF S<br>a s x? <? > Ria x we &<br>Ca= & Pad of i <a E &<br>> s < o &<br>s “2 ©<br><2 Rd<br>Ps<br>TM 2000-2008 TM 2009-2011<br><!-- End of picture text -->