rafmacalaba/datause-encoder-data huggingface dataset:
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|---|---|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|MW: MiningGovernance and Growth Support Project|Javier Aguilar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Rio Grande do Sul SWAp|Tugba Gurcanlar|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|Maharashtra Agricultural Competitiveness Project|Manivannan Pathy|
|BD Private Sector Development|Michael Olavi Engman|
|BD Private Sector Development|Michael Olavi Engman|
|BD Private Sector Development|Michael Olavi Engman|
|BD Private Sector Development|Michael Olavi Engman|
|BD Private Sector Development|Michael Olavi Engman|
|BD Private Sector Development|Michael Olavi Engman|
|Region|State|Firms<br>(2007 )<br>(%)|Firms<br>(2010)<br>(%)|Unit<br>NO<br>(09-10)|Peiex’s <br>(2009)<br>(%)|Treated<br>(2010)<br>(%)|Exporting Firms<br>(07-08)<br>(%)|
|---|---|---|---|---|---|---|---|
|North|Rondˆonia|0.61|0.63|-|*<br>|*<br>|0.51|
|North|Acre|0.13|0.13|-|*|*|0.06|
|North|Amazonas|0.45|0.48|-|*|*|1.10|
|North|Roraima|0.05|0.06|-|*|*|0.12|
|North|Par ́a|1.04|1.04|-|*|*|1.66|
|North|Amap ́a|0.07|0.07|-|*|*|0.03|
|North|Tocantins|0.26|0.29|-|*|*|0.02|
|Northeast|Maranh ̃ao|0.50|0.54|-|*|*|0.16|
|Northeast|Piau ́ı|0.53|0.57|-|*|*|0.13|
|Northeast|Cear|2.52|2.80|1|6.06|6.74|1.24|
|Northeast|Rio Grande do Norte|0.80|0.88|-|*|*|0.26|
|Northeast|Para ́ıba|0.81|0.86|-|*|*|0.37|
|Northeast|Pernambuco|2.33|2.49|1|*|0.99|0.75|
|Northeast|Alagoas|0.36|0.39|1|0.75|0.82|0.15|
|Northeast|Sergipe|0.43|0.48|1|1.20|1.22|0.14|
|Northeast|Bahia|2.65|2.78|4|8.17|10.26|1.40|
|Southeast|Minas Gerais|12.48|12.58|6|24.69|23.62|6.86|
|Southeast|Esp ́ırito Santo|2.04|2.11|-|*|*|1.52|
|Southeast|Rio de Janeiro|4.88|4.95|-|*|*|3.89|
|Southeast|S ̃ao Paulo|29.53|29.06|-|*|*|46.66|
|South|Paran ́a|8.89|9.35|3|16.72|17.20|8.26|
|South|Santa Catarina|9.49|9.86|-|*|*|8.46|
|South|Rio Grande do Sul|11.16|11.33|8|35.35|33.46|14.01|
|Center West|Mato Grosso do Sul|0.75|0.81|-|*|*|0.32|
|Center West|Mato Grosso|1.38|1.55|-|*|*|0.82|
|Center West|Goi ́as|3.00|3.27|1|4.44|3.92|1.04|
|Center West|Distrito Federal|0.56|0.63|1|2.16|1.48|0.05|
|Total (%)||100.00|100.00|-|100.00|100.00|100.00|
|Total||232,159|247,187|27|2,410|3,518|24,161|

Table 5: Distribution of firms across states, by exporting and treatment status Note: (*) States with less than 10 treated firms.

In addition, table 6 shows that there is a weak and not statistically significant correlation (at 5% confidence level) between the share of exporting firms or the average changes in number of layers and the share of Peiex regional units at the state level prior to the program’s start. Nonetheless, there is a strong and significant correlation (at 1% confidence level) between the share of firms that received Peiex treatment and the share of Peiex regional units at the state level.

|Correlation with num|ber of NOs|||
|---|---|---|---|
|Peiex|Number Exporters|Change|Layer|
|2007-2010|2007<br>2008|2007|2008|
|corr<br>0.957|0.1786<br>0.1836|0.0451|0.1204|
|p-value<br>0.000|0.3727<br>0.3592|0.8232|0.5498|

Table 6: Correlations with number of Peiex’s regional units Note: The columns show the correlations between number of Peiex regional units (NOs) and (1) number of treated firms (Peiex), (2) number of exporters in 2007, (3) number of exporters in 2008, (4) average change in the number of layers at state level in 2007, (5) average change in the number of layers at state level in 2007.

Assuming that the decision of Peiex regional units’ location is exogenous to changes on firms’ organization based on hierarchy of knowledge, we used the number of Peiex regional units (NOs) in the state of firms’ headquarters as an instrument (A) for receiving Peiex assistance. Furthermore, the Peiex program targets firms with some potential to become an exporter. For this purpose, it uses as a reference the
owned affiliates (FDI) on all other firms in an industry, the magnitude of the negative effect becomes much smaller and loses statistical significance. This result, in conjunction with the fact that taken together joint ventures and FDI account for significant shares of total output in many industries, suggests that further research is required to determine the extent to which knowledge diffuses from firms with strong linkages to foreign firms to those that do not have such relationships. Of particular importance in this connection is to explore the extent of spillovers among joint venture firms and between foreign affiliates and firms with joint ventures. Insofar as joint venture firms invest more in technology capacity (as suggested by their training efforts), one expects these firms to be better able to absorb and benefit from know-how diffusion. The absence of such capacity may be a factor underlying the observed negative spillover effect. Longer times series and collection of data on variables that measure in-house technological effort by firms would help to identify the magnitude and determinants of technological spillovers.

21
where _Yijt_ is the outcome of interest for individual _i_ living in facility _j_ ’s catchment area in year _t_ ; _P4Pj_ is a dummy variable that takes value 1 if facility _j_ belongs to Phase I (i.e. started receiving P4P in 2006) and 0 otherwise; α _j_ is a facility fixed effect; γ _2008_ is a fixed effect for 2008; _I2008_ is a dummy variable that takes value 1 if the year of observation is 2008 and 0 otherwise; the _Xkijt_ are individual characteristics; and ε ijt is a zero mean error term. We compute robust standard errors clustered at the district level to correct for possible heteroskedasticity and correlation of the error terms within districts over time. We estimate each regression both with and without individual characteristics.

# **d. Statistical Inference**

A major limitation of our design is the limited number of districts. Since the unit of assignment to treatment and comparison was the district and not the facility, there may be intercluster correlation in the error terms. The asymptotic justification for inference with clusterrobust standard errors assumes that the number of clusters goes to infinity. Yet in our application there are too few clusters for this assumption to hold. Therefore we base our statistical inference on randomization inference hypothesis tests that use WILD bootstrapping Monte Carlo methods as recommended in Cameron et al (2008). These tests return a p-value for the hypothesis rather than a standard error. Hence we report the estimated coefficient and the p-value for the one-sided test of significance.

# **5. DATA**

# **a. Surveys**

We surveyed all 166 facilities plus a random sample of households in the catchment area of each facility. The surveys were conducted at baseline in 2006 prior to the implementation of P4P in treatment facilities and again approximately 23 months later, before the comparison facilities were incorporated in the program. The surveys were conducted independently from the operation of the P4P program. Payment was based on administrative records and reports from the facilities and never on the evaluation surveys.

14
- 17 -

# F1gure **4**

<!-- Start of picture text --> CURRENT ACCOUNT DEFICIT AS RATIO OF GDP<br>CAnnuat average, IncIuaiIng grants)<br>8.3<br>7<br>a'C<br>3<br>2<br>0<br>GO-a na Kenya W's Zlawl Tanzania LIurla<br>Igla 1981-6S196o<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.

more aid is available to countries that initiate economic reforms. Ultimately, one needs to look at an indicator such as the rate of growth of exports, in order to evaluate the success of the reforms in improving the external position.<sup>7</sup>

> 7 A recent survey by the IMF of 19 countries tht are underaking SAF/ESAF supported stabilization prograrms found that although the underlying extenal position in thee countries hu tended to strengthen, with the reserve position usally improving, the current account deficit of the balance of paymentA did not show a clear trend.
# **(i) India**

The estimates for India underpinning the 2020 global inequality and poverty estimates are based on Roy and van der Weide (2022).<sup>1</sup> Depending on the data sources used, there exists a wide range of estimates of growth shocks in 2020 for India. This, combined with the large population, means that the global estimates of inequality and poverty are sensitive to the choice of estimate used for India. Below, we outline the derivation of the 2020 COVID-19 welfare distribution for India used in the current study and report alternate estimates based on other growth rates reported for India. The last official household survey released for India dates back to 2011/12. Roy and van der Weide (2022) use various methods to create a comparable time trend using the Consumer Pyramid Household Survey (CPHS) which is a private survey collected in more recent years. Their _approach 1_ uses survey-to-survey imputation techniques to estimate consumption in the CPHS using a set of (non-monetary) household characteristics that are common to both the CPHS and the 2011/12 National Sample Survey (NSS). This approach is similar to poverty mapping methods (Elbers et al., 2003) and does not exploit the consumption data that the CPHS collects. In contrast, _approach 2_ converts the observed CPHS consumption into NSS-type consumption in an attempt to account for the differences between the two aggregates (e.g. questionnaire design, sampling design).

The World Bank’s Poverty and Inequality Platform (PIP) uses the CPHS data adjusted using approach 2 for years 2015/16, 2016/17, 2017/18, 2018/19, and 2019/20, which we follow in the present study.<sup>2</sup> However, estimates based on approach 2 are not yet available for 2020/2021. Estimates here are based on approach 1 using the surveys conducted face to face (see Annex 1G of World Bank 2022 for a discussion on the construction of estimates for 2020/21).

Our preferred estimate of the growth rate in consumption for India for 2020 is based on a comparison of the CPHS distributions derived using approach 1 from Roy and van der Weide (2022) for 2019/20 and 2020/21, or more specifically the growth rate from 2019/20 to 2020. To be used for this study, the 2020/21 estimate needs to be converted from fiscal year (FY) to calendar year (CY) estimate. We calculate the C 20201 (calendar year 2020 based on approach 1) distribution of consumption using the standard method used to interpolate poverty estimates in PIP. The method forward and backward extrapolates the two estimates to 2020 using growth rates from national accounts, and calculates a weighted average of the two, amounting to the following:

> 1 The estimates for 2020/2021 are available from the authors and will be included in the next iteration of their working paper. Also see Annex 1G of World Bank (2022) for a detailed discussion of the 2020/21 estimates.

> 2 Each round follows the Indian fiscal year, which starts in April of a given year and ends in March of the following calendar year.

38
The course of real earnings of formal sector wage earners, as we have seen, mirrors the downward trend in the real wage of minimum wage earners, both of whom are to be found mostly in the urban areas. This is balanced by the increase in the real income of traders, many of whom are also found in the urban economy. The basic reasons for the increase in real earnings of traders over this period are two-fold: first, the deterioration of infrastructure meant that the cost of marketing went up; and secondly, the shortage of marketed goods-both imported and domestically producedmeant that important sources of "rent" opened up for traders. Within the rural sector, cocoa farmers and the food producers in the South suffered relative to the farmers in the North. Because of the mixed nature of the impact of the economic decline on different

**Figure 4.1 Trends in real return (1970-85) (1970=100)**

**Trends in Real Earnings 1970-85**

<!-- Start of picture text --> 180<br>160 ---- -- -- --------- -- - -<br>140 - - - - _.- -<br>LX120- /------------ -- ----------------- -<br>E 8 e --- ----------- -- --- - --- -<br>2 0 - - - - - - - - - - - - - - - - - -<br>I o<br>t~~~~~~~~~~ .Traders 0..<br>Su ce Ta0a0ba F igur T 03 t n T 0<br>'fearsl<br>wMnirrum age earners wa earsi ooer r f i<br>I Food f arnrers 2 - Cocoa f amers I--<br>grops,it s iffcul tobeconidet -. . Traders aou th cors of rualura .__. incomedfeec<br><!-- End of picture text --> _Source:_ Tabatabai Figure 3. The data are presented in Tabatabai, Table 9.

groups, it is difficult to be confident about the course of rural-urban income difference over this period. But in so far as food producers are the most important part of the rural economy, and wage earners constitute a sizable section of the urban labor force, it is likely that real income per head, on the average, probably had declined in the urban areas relative to the rural, and indeed could have been lower in the former at the end of the period. Official Census data are consistent with this conclusion. They show that between the two Censuses of 1970 and 1984, urbanization had slowed down considerably, and the share of the three largest cities in total population had remained largely the same. (Tabatabai, table 7, p.719).

# 6.3 **The Rural-Urban Differential After the Recovery**

39
**Table 2: Argentina, 2006. Intermediate cost structure (%)**

|**Sectors**|**Soy**|**Maize**|**Sugar**<br>**cane**|**Soy oil**|**Other**<br>**vegetable**<br>**oils**|**Sugar**|**Gasoli-**<br>**ne &**<br>**Diesel**|**Bio-**<br>**diesel**|**Bio-**<br>**ethanol**|
|---|---|---|---|---|---|---|---|---|---|
|Soy||||69.47||||||
|Maize|||||2.78||||30.77|
|Sugar cane||||||35.89|||26.67|
|Rest of agricultura|25.14|30.13|50.02|0.69|73.41|12.17||||
|Petroleum and Mining||||0.01|0.02|0.28|76.85|||
|Soy oil||||||||82.02||
|Other vegetable oils||||0.05|0.05|||||
|Sugar||||||1.89||||
|Rest of Food, Beverages and Tobacco||||0.07|0.08|0.56|0.13|||
|Textiles, Leather, Wood, Paper & Edit||||0.51|0.78|3.28|0.31|||
|Gasoline|0.55|0.49||0.01|0.01|0.10|0.43|||
|Diesel|10.48|9.35||0.11|0.12|1.84|2.80|||
|Biodiesel|0.04||||||0.01|||
|Bioethanol|||||||0.00|||
|Refineries: other products|||||||1.76|||
|Other manufacturing industries|59.05|50.64|27.74|0.47|0.54|3.92|1.59|17.54|11.94|
|Transport|3.84|8.36|22.25|2.17|10.20|20.81|7.39||11.16|
|Trade, construction & Services|0.90|1.03||26.45|12.01|19.27|8.74|0.44|19.47|
|Totals|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|

Source: Own elaboration Information on the government accounts was obtained from the Ministry of the Economy (Oficina Nacional de Presupuesto)<sup>6</sup> . Income and expenditures of the public sector are consolidated results for the federal administration, the provinces and the municipalities. Considering expenditures, government consumption represents around 14% of GDP followed by household transfers (10% of GDP). The information on national and local taxes was provided by the “Administración Federal de Ingresos Públicos” and Provincial ministries, respectively.

The SAM also accounts for the positive result of the trade balance and the current account observed in 2006. The information on the balance of payments was obtained from the “Banco Central de la República Argentina”.

A summary of the SAM of the Argentine economy of 2006 is shown in Table 3. This simplified SAM has three activity sectors, two factors (with capital representing an aggregate of land and physical and financial capital), taxes, public and private investment and the rest of the world (ROW).

> 6 www.mecon.gov.ar/onp

8
**Table 2.** Structural change: The role of imported capital

||**OL**<br>|**S**<br>|**2SLS(seco**<br>|**nd stage)**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||||**Panel A**<br>||
|Total imported capital (log)|0.115***<br>|0.071***<br>|0.058*<br>|0.053<br>|
||(0.034)|(0.025)|(0.035)|(0.034)|
|constant|-0.524|-1.029**|-0.866|-0.809|
||(0.482)|(0.436)|(0.549)|(0.548)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.30|0.69|0.69|0.69|
||||**Panel B**||
|Imported capital from North (log)|0.097**|0.034|0.0541|0.052|
||(0.039)|(0.030)|(0.036)|(0.036)|
|constant|-0.286|-0.587|-0.8155|-0.790|
||(0.544)|(0.492)|(0.578)|(0.585)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.30|0.69|0.69|0.69|
||||**Panel C**||
|Imported capital from South (log)|0.078***|0.073***|0.075**|0.071**|
||(0.028)|(0.020)|(0.032)|(0.031)|
|constant|0.122|-0.920***|<br>-0.934**|-0.895*|
||(0.349)|(0.349)|(0.471)|(0.472)|
|Controls|NO|YES|YES|YES|
|Region FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|
|# Observations|1,012|889|848|848|
|R-squared|0.29|0.69|0.69|0.69|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.10 **_Note_** : The controls include initial industry structure, rule of law, human capital, inflation rate, mobile broadband, transport infrastructure, total natural resource rent (% GDP), and bank credit to private sector (% GDP).

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- 27 -

# **Annex I.  Energy efficiency projects in building sector**

Energy efficiency in buildings has a great potential to mitigate greenhouse gas emissions. Buildings are estimated to consume about 40 percent of the world’s final energy (IEA 2008; World Bank 2010; Worldwatch Institute 2009). In general, energy efficiency projects are cost-effective and pay for themselves (Figure 4). Importantly, they are a win-win solution for both asset owners and service providers. While asset owners can reduce their energy spending, ESCOs make profit on the sales of energy services.

The evidence is clearly supportive of significant potential of energy savings in buildings. For new buildings in Europe, the cumulative energy savings from building codes is about 60 percent more than for those built before the first oil shock in the 1970s (WEC 2008; World Bank 2010). In Brazil, the National Electrical Energy Conservation Program aims to retrofit government buildings and save 140GWh of energy per year (UNEP 2010). In the developing world, managing peak energy demand for buildings―namely, air conditioning―will be an important challenge, since a significant amount of energy is used for air conditioning, and this demand seems to continue increasing.<sup>25</sup> Notably, however, energy efficiency projects are not necessarily low-hanging fruits. There are many institutional barriers, particularly in the public sector. Singh et al. (2010) point out some major barriers: (i) lack of awareness and information about benefits and costs, (ii) lack of technical capacity for energy audits, project design and implementation, and operation and maintenance, (iii) limited incentives for governments to improve energy efficiency, reduce budget, and take risks of new technology, (iv) lack of public accountability for energy savings under proper reporting and monitoring systems, (v) restrictive procurement,

> 25 The electricity demand for air conditioning is very price-inelastic. Reiss and White (2005) show that the price elasticity for households with central or air conditioning is three times higher (in absolute terms) than that for those who have no air conditioning. As the result, the impact of air conditioning can be substantial, as in the United States during the late 1980s, when it was estimated to have reached about one-third of the total electricity peak demand (Andrew, 1989). In Hong Kong SAR, China, heating, ventilation and air-conditioning are found to be the single largest electricity end-user, accounting for 30 to 60 percent of the total electrical demand during the hot summer months of July and August (Lam et al., 2003).
This allows us to calculate the aggregate indicator of interest as **W**<sup> ̃</sup> (<sup>(</sup> _r_<sup>_k_</sup> )<sup>)</sup> _,V_<sup>=</sup><sup>**W**(</sup><sup>_{y_ ̃</sup> _i,_<sup>(</sup><sup>_k_</sup> (<sup>)</sup> _r_ )<sup>_}i∈V_)</sup> in _r_ -th simulation. The point estimate and its associated standard error are estimated as the mean and standard deviation of **W**<sup> ̃</sup> (<sup>(</sup> _r_<sup>_k_</sup> )<sup>)takenoverthesimulation.Forexample,thepoint</sup> estimate of the prevalence of stunting for commune _κ_ is as follows:

# **4 Data**

Our basic building blocks comprise a survey dataset, a census dataset and a dataset of geographic variables. The survey dataset we used is CDHS 2000, which was designed to collect health and demographic information for the Cambodian population with a particular focus on women of childbearing age and young children. The sample covered 12,236 households in 17 strata across the country. Data collection took place over a six-month period between February and July in 2000. In addition to detailed information about each household, its members, and housing characteristics, one-quarter of these households were systematically selected to participate in the anthropometric data collection. All children under 60 months of age in the sub-sampled households were weighed and measured. After excluding children for which information on height or weight is missing or implausible, 3,596 observations were used for this analysis (for further details, see National Institute of Statistics et al. (2001)). We first derived the height-for-age and weight-for-age Z-score measures, which were then converted to the height and weight for 24 month-old females with the same Z-score measures.

The second source of data is the Cambodian National Population Census, the first population census to be conducted in Cambodia since 1962. The census covered virtually all persons staying in Cambodia at the reference time of midnight of March 3, 1998.<sup>8</sup> The census data 8Due to military operations, about 0.5 percent of the population was not covered.

13