rafmacalaba/datause-encoder-data huggingface dataset:
framework and internationally agreed indicator on empowerment we adopted, we include them as part of the broader women’s empowerment index.

5. **Time spent working** : Total time spent working in the past 7 days in all occupations (agriculture, livestock, household chores and other paid and unpaid work), separately for the primary man and woman of the household.

# **Design Integrity**

## Baseline Balance

We test for baseline balance using the same empirical specification used for the outcome analysis, which compares baseline treatment and control households, controlling for randomization stratification (133 PRA dummy variables) to increase precision. Table A3 of the <u>Online Appendix</u> presents these comparisons. We find no statistically significant or economically meaningful differences across groups for all main outcome indicators at baseline. Across 23 variables that could influence our primary outcomes of interest, we observe statistically significant differences for land ownership and engagement in livestock activities at the 5% level. Treatment households are 2 percentage points less likely to engage in livestock production (control: 5%) and are significantly more likely to own land and/or dwellings based on a principal component score aggregating these assets. While the differential livestock activity at baseline may result in an underestimate of treatment effects, the significantly higher land ownership could overestimate effect sizes if this is an important capital input for generating value from the program. To assess whether these imbalances influence any of the main results, we include these variables as covariates in a robustness regression for main outcomes, presented in the Online Appendix. Results with and without these covariates do not differ in sign or significance.

## Compliance

We track monitoring data collected by CHA/MISFA on all TUP recipients throughout implementation and include an extensive compliance module in the follow-up survey of all treatment and control households. Table A4 of the <u>Online Appendix summarizes the</u> treatment compliance results, as reported in the survey. We find that the program was successful in delivering its components according to randomized assignment. At follow-up, 97% of treatment households report being aware of the TUP program, compared to 51% in the control group. Among the aware households, the majority indicate having received any of the program inputs in the treatment group (99.5%), compared to only 3% in the control group. Respondents reported receiving livestock assets in 96% of treatment and 1.5% of control households. All other intervention components had similar levels of compliance. Independent monitoring data from CHA/MISFA report similar levels of compliance, with 98% of treatment households represented on their official record of households receiving support, compared to 0.1% of control households. The data thus suggest high compliance 17
## **Box 7: Baoding City, Hebei – The rise and fall of a low-carbon city**

Promote a “clean energy city” or “low-carbon city” in Baoding, a municipality in Hebei Province – how did the initiative take shape and why has it begun to stagnate?

## **_2001-2010: The rise of a “low-carbon city” project_**

- Low-carbon initiatives were initially projects under the _High-Tech Administrative Committee_ , a small, low-key agency which pushed this agenda since 2001 despite strong opposition from the Baoding city government and other departments

- With the help of the high-tech administrative committee, clean energy technology firms started to flourish in the high-tech zone (e.g., adoption of various solar PV and LED projects within the zone)

- Only in 2006, after some early successes and national policy shifts, did the Baoding city government become interested and begin to legitimize these projects and diffuse the initiatives across the city

- <u>Main reason for early successes:</u> as a “sidelined local agency”, the _High-Tech Administrative Committee_ had strong organizational interest and motivation to ensure consistent progress of low-carbon projects (it _de facto_ owned the low carbon projects and could experiment around and operate “outside of local political rules”); also lobbied higher level political units

## **_Since 2010: Stagnation of “low-carbon city” project_**

- In 2010, transfer of low-carbon city project from the High-Tech Administrative Committee to the more powerful city government (i.e., DRC and other departments such as EPB, Bureau of Science and Technology)

- Since 2010, the city government has been passive about further carrying out the low-carbon city agenda in Baoding. For example, only few residential areas were renovated with solar PV panels and LED lamps since 2010 and some earlier renovated projects were in fact replaced again with traditional technologies.

- The city government allocated the specific tasks to build a low carbon city to lower levels of government, but do not check rigorously progress made

- Baoding city government also did not have the capacity and knowledge to ensure maintenance of solar PV and LED projects – instead the city blames the inherent instability and immaturity of the technologies for the non-functioning panels across the city.

- <u>Main reason for stagnation: low-carbon initiatives are but one of many projects under the city</u> government, limited personal motivation and organizational interest of city government officials; limited internal competence

_Source: Shin (2013)_

# _2) Conflicting priorities_

Implementing agencies also face multiple and sometimes conflicting goals within an organization, as summarized in _Table 2_ . For example, the local Development and Reform Commission (DRC) is in charge of multiple functions; its main interest lies in economic overseeing planning and investment management, while at the same time it is also responsible for overseeing energy efficiency and climate change issues.

23
On the supply side, our base projection suggests that food production would increase by 112 percent, slightly faster than the CE consumption increase of 102 percent. The impact of convergence on the supply side is much more muted than on the demand side, suggesting that convergence—if it continues to occur— will contribute to upward pressure on world food prices. Using the key elasticity values from Hertel _et al_ . (2016), we see the pattern of deviations from uniform growth in the SSP2 scenario pushing up food prices by nearly 8 percentage points. Such increases are relative to a baseline which, like Baldos and Hertel (2016), involves falling real food prices, so meeting this demand appears to be manageable if agricultural productivity growth continues in line with historical patterns.

Finally, while our minimalist approach turns out to be useful in highlighting the interplay of key drivers of food demand, supply and prices, our model is subject to a number of limitations. In particular, our price baseline is based on an assumption that agricultural productivity will continue to rise steadily in line with past growth. Negative productivity shocks coming from, for instance, climate change, reductions in agricultural research investment and environmental degradation, would negatively affect food production capacities. On the other hand, good policies and actions at different levels, perhaps those envisioned in the SDGs such as responsible consumption and production, climate action and sustainable management of land and water, may counter-balance likely negative impacts on production.

25
# **Figure 2: General information**

## **2a. Year of establishment By type**

<!-- Start of picture text --> Commercial Development<br>Before 1990 1990-2007<br>After 2007<br>15<br>10<br>5<br>0<br><!-- End of picture text -->

## **2b. Structural characteristics By type**

<!-- Start of picture text --> Commercial Development<br>Financial group Domestic subsidiaries<br>Fx branches/subsidiaries Listed<br>2c. Legal status and ownership<br>By type<br>Commercial Development<br>Created by Law or Act Always state owned<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **2c. Legal status and ownership By type**

## **By granular regions**

<!-- Start of picture text --> Benchmark Europe EAST ECA WEST ECA<br>Before 1990 1990-2007<br>After 2007<br>15<br>10<br>5<br>0<br><!-- End of picture text -->

## **By granular regions**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Financial group Domestic subsidiaries<br>Fx branches/subsidiaries Listed<br>By granular regions<br>Benchmark Europe WEST ECA EAST ECA<br>Created by Law or Act Always state owned<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br>100%<br>80%<br>Percent Yes 60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

## **By granular regions**

13
10 Our sample comprises 24 emerging market economies: nine are inflation targeters and 15 are non-targeters. The period analyzed extends from the first quarter of 2000 to the second quarter of 2015, following periods of turmoil in the late 1990s for emerging markets, including the Asian crisis of 1997-98, the Russian debt crisis of 1998 and the Brazilian abandon of its pegged exchange rate regime in early 1999. The main source for our data is the IMF International Financial Statistics; only the interest rates were taken from individual country sources. Interest rates are the benchmark policy rates (or, alternatively, the money market, discount or short-term T-bill rates if the benchmark is not available), inflation is measured by consumer price index (CPI) changes in all countries, and real gross domestic product (RGDP) gap is RGDP growth compared to trend generated by HodrikPrescott (HP) filter. Exchange rates are the bilateral currency of each emerging market against the U.S. dollar (USD). We also collected real exchange rates for currencies of emerging markets against a basket of currencies but there were less data availability in this case to operate consistently across countries. For this reason, we will focus below on the estimates of each currency against the USD.

Table 1 lists the countries and their respective averages of inflation, interest rates, GDP growth and nominal exchange rates against the U.S. dollar. In terms of inflation, two digit rates are reported only across non-IT countries (Dominican Republic, Ecuador and Nigeria) and the highest average inflation among ITers is observed in Brazil with 6.62%. Two digits average interest rates are observed in five non-IT countries (Argentina, Costa Rica, Dominican Republic, Nigeria and Pakistan) but only in one inflation targeter (Brazil). The four fastest growing economies in the sample are part of the group of non-targeters: China (9.87%), Nigeria (8.05%) and Panama (7.27%). Across the inflation targeters the two with the highest average GDP growth rates are Peru (5.81%) and the Philippines (5.1%). As for exchange rate changes, three countries in the sample of non-targeters are dollarized economies: Panama (since 1904), Ecuador (2000) and El Salvador (2001). Because of this they
turn, the **MG** estimates are fairly imprecise, with the exception of the return coefficient in the specification without time effects, which is significant at the **10** percent level but incorrectly signed.

The average speed of adjustment is similar to that found in the low capital controls sample if time effects are excluded, and marginally higher when they are included. In the former case, one country (Mauritius) displays unstable dynamics, and two (Mauritius and Brazil) do so in the latter case.

To summarize, the estimation results lend considerable support to the model when applied to countries with low capital controls and/or high and upper-middle income. Under the PMG specification, the long-run parameters carry the expected signs, are well determined (especially when time effects are included in the empirical specification), and the pooling restrictions are not rejected **by** Hausman tests. Moreover, the PMG estimates appear robust to outlying country-specific coefficients, which in contrast pose serious problems for the **MG** estimates.

The results for countries with high capital controls and, especially, lower-income countries, are less encouraging. For the former countries, this might be viewed as evidence that capital controls achieve some degree of success **-** they dampen the effects **of** risk and return factors on portfolio decisions. For the lower income countries, the likely reason is the limited role that optimal diversification decisions play in the observed evolution of net foreign assets, which may **be** dominated instead **by** other considerations such as the willingness of donor governments to extend, and forgive, concessional lending.

# **4.2 Actual vs. long-run equilibrium foreign asset ratios**

As an important byproduct of the model's estimation, it is possible to obtain estimates of the long-run equilibrium NFA/wealth ratios conditional on the observed values of the explanatory variables. This permits an assessment of the extent to which actual ratios diverge from their long-run counterparts, and hence of the sustainability of current net foreign asset positions.

We perform this exercise below for a selected group of industrial and developing countries. Specifically, we use the long-run PMG estimates to construct the long-run equilibrium NFA/Wealth ratios. For this, we take the estimates from the high and upper-middle income

**23**
WPS3811

# **Nonfarm Activity and Rural Income Inequality: A Case Study of Two Provinces in China**

Nong Zhu (INRS-UCS, University of Québec)<sup>°</sup> Xubei Luo (DECVP, World Bank)<sup>°°</sup>

**_Abstract:_** Non-farm activity plays an increasingly important role in rural household income. Based on data from the Living Standards Measurement Study in the provinces of Hebei and Liaoning, we study the distribution of non-farm income in rural China. First, we assume non-farm income as an exogenous transfer to total income to decompose the Gini index; second, we assume non-farm income as a potential substitute for farm income to take household choices into account and simulate household income. The results show that non-farm activity reduces rural income inequality by raising the income of poor households to a larger extent than that of rich households. Improving rural infrastructure and implementing universal basic education are critical to build up the capacity of households (in particular poor households) to participate in non-farm activity. Strengthening the linkages between farm activity and non-farm activity is essential to optimize the contribution of non-farm activity to pro-poor rural economic development.

## **JEL Classification:** D63, O15, Q12

**Key words:** Non-farm income, Inequality, China

## World Bank Policy Research Working Paper 3811, January 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ° Email: Nong_Zhu@inrs-ucs.uquebec.ca.

> °° Email: Xluo@worldbank.org.
# _Comparison of Results from Secretary of Education Targets and Targets Based on Quantile Regressions_

Figure 3 graphs the same relationship – this time replacing the target set by the Secretary of Education with the target generated from quantile regressions (our recommended approach). The target for each school is generated from the following procedure. First, a quantile regression at the 67<sup>th</sup> percentile was run that regressed the change in Portuguese proficiency between 2007 and 2006 on a regional dummy for Grande Norte, the total number of students in the school in 2006 and the initial level of Portuguese proficiency in 2006. Then, the estimated coefficients from that estimation were multiplied by the 2007 values of the right hand side variables in the quantile regression (i.e. the constant term and the regional dummy and the 2007 values of the number of students and Portuguese proficiency). For each school, this yields the change in Portuguese proficiency that they would have if they were able to achieve the performance equivalent to those in the 67<sup>th</sup> percentile of schools with their given characteristics. Finally, this estimated change at the 67<sup>th</sup> percentile of performance for the particular characteristics of the school was added to the base 2007 value of Portuguese proficiency for each school to generate a target level for 2008.

**Figure 3.   Variation in Actual 2008 Performance and 2008 Quantile Regression Target in Portuguese Proficiency,  5**<sup>**th**</sup> **Grade Schools - Minas Gerais, Brazil**

<!-- Start of picture text --> 100 150 200 250 300<br>School Average Proficiency Score in Portuguese of 5th Grade Students, 2007<br>100<br>50<br>0<br>-50<br>Difference 2008 Actual - QR Target<br>-100<br><!-- End of picture text --> 9
**Figure 4. Deprivations in Basic Needs, 2015, Percent**

<!-- Start of picture text --> 56%<br>60%<br>50%<br>40%<br>30%<br>20%<br>8%<br>10% 2% 2%<br>0%<br>Extreme Poverty Life in Dignity Humanitarian Aid Remittance Dependent<br><!-- End of picture text -->

**Figure 5. Deprivations in Basic Needs by Location, 2015, Percent**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Yerevan city Other urban areas Rural areas<br>Aid<br>Extreme Poverty Life in Dignity Humanitarian Remittance Dependent<br><!-- End of picture text --> _Source:_ ILCS data.

# _Source:_ ILCS data.

Indicators related to the dimension on housing describe living conditions in Armenia (Figure 6). There is a substantial divide between urban and rural areas linked to agglomeration effects in the provision of public services and to differences in their cost (Figure 7). Unsatisfactory services not only affect individual wellbeing, but also reduce willingness to pay for goods and services provided by the government.

**Figure 6. Housing-Related, 2015, Percent**

**Figure 7. Housing-Related Deprivations by Location, 2015, Percent**

<!-- Start of picture text --> 50% 45% 90%<br>45% 40% 40% 80%<br>40% 70%<br>32%<br>35% 60%<br>27%<br>30% 50%<br>25% 20% 22% 40%<br>20% 16% 30%<br>15% 11% 20%<br>10% 10%<br>5% 0%<br>0%<br>Yerevan city Other urban areas Rural areas<br>Source: ILCS data. Source: ILCS data.<br>Overcrowding Healthy heating<br>conditions Adequate housing Hot running water<br>Overcrowding Healthy heating garbage disposal<br>conditions Adequate housing Hot running water Satisfactionwith housing Centralized water system Centralized sanitation and Access to transportation<br>garbage disposal<br>Quality of paid public services<br>Satisfaction with housing Centralized water system Centralized sanitation and Quality of paid public services Access to transportation<br><!-- End of picture text --> The indicator on healthy heating is especially important in Armenia; rising electricity and gas tariffs have led an increased share of the poor, especially the rural poor, to turn to wood to heat their homes (WB 2014). However, a solely monetary measure, such as consumption poverty, does not take into account the extent to which some households will switch to wood, which is often linked to worsening health conditions and 14
<!-- Start of picture text --> 45 ——___<br>ga<br>£35<br>&@wa 30<br>Ba<br>iA 20<br>5 15<br>&5gicf 100 mill f<br><l 13 Bh >»<br>Average tenure(In years)<br>92-2001 = 2002-2011<br>4<br><!-- End of picture text -->
# **6 Conclusion**

In this paper, we studied the possibility of using Internet search keyword data to nowcast price changes in Central America. We gathered price data for Costa Rica, El Salvador, and Honduras. We also identified several search keywords and downloaded data for them from Google Trends over a period of weeks. We tried several aggregation, smoothing, and linear index construction methods for this Internet search data and were partially successful in improving nowcasts for Costa Rica and for El Salvador, countries for which the search data were of higher quality.

As part of the exercise, we were able to identify several important points for practictioners who wish to forecast using high-dimensional Internet search keyword time series. First, variable selection is of upmost importance. Many, if not most, of the successful forecasting studies that use Internet search keyword data are based on some theory of consumer behavior. This may be the idea that consumers use the Internet to do research before the purchase of a consumer durable as in Carri`ere-Swallow and Labb ́e [2013] or searches for jobs and unemployment and welfare as in Choi and Varian [2009]. In the absence of a strong model of consumer behavior, one should incorporate some kind of variable selection mechanism. Naively including a large number of search keyword terms into a model for a search index with the hope that the coefficients on unimportant terms will be small leads to very poor results. However, by employing some variable selection methods from the statistical learning literature we were able to substantially improve all of our forecasts and beat both the ARMA models and benchmarks in several instances.

The second takeaway is the importance of order identification in ARIMA modeling. This is perhaps not a surprise for any forecaster, but the successful results here using automatic techniques are encouraging. If a forecaster were to focus on fewer series and apply the Box-Jenkins methodology rather than relying on automatic model selection procedures it might be possible to outperform the benchmark models further.

Finally, this study suggests several avenues for further research. We might consider more estimators such as the TS-LARS, which is LARS estimator explicitly written with time-series data in mind [Gelper and Croux, 2008]. It allows selection of distributed lags and ranking of predictors. Ranking of predictors will be of particular interest for those who use an exercise such as the one in this paper to generate ideas about consumer behavior and the search for keywords and categories that help forecast price changes. One might also explore using dynamic linear models or a structural model where the Internet search information stands in explicitly for some aspect of the theoretical model. There are also a number of different smoothing techniques and variable selection methods 29