rafmacalaba/datause-encoder-data huggingface dataset:
and 75% of dibao recipients were not poor. The exclusion error is between 92 and 95%, indicating that the overwhelming majority of the poor did not benefit from the dibao program.

**VII. Correlates of dibao participation and propensity score analysis of dibao targeting**

The conventional analysis of dibao targeting implicitly assumes that selection of program beneficiaries is based on current year household incomes as collected by the NBS and reported in the CHIP data. As discussed by Chen, Ravallion and Wang (2006), such assumptions may not be correct. Local officials who implement the dibao program do not have access to the NBS income data, and even if they did, the NBS data contain some unknown degree of measurement error.<sup>9</sup> In practice, local officials are likely to select beneficiaries based on some observable correlates of income. China’s national rural dibao policies in fact endorse such practices, and local regulations explicitly mention alternative criteria for identifying recipients.

In view of these considerations, Chen, Ravallion and Wang (2006) propose an alternative approach, propensity score targeting analysis, based on the idea that local officials select beneficiaries with reference to a latent income variable that is correlated with ex ante income as measured in the survey data as well as with other observed characteristics plus an error term. Targeting analysis can then be carried out based on latent household incomes (Ravallion 2008).

The first step of propensity score targeting analysis is to estimate a probit regression with dibao participation as the dependent variable and with ex ante income and other relevant observed household characteristics as the independent variables. These other characteristics are chosen to reflect local implementation practices and include variables such as household demographic composition, health of household members, and human and physical capital or assets. Second, the results of the probit model are used to predict a conditional probability of program assignment (the propensity score). The estimated coefficients from the probit regression correspond to the implicit weights assigned by program administrators when deciding

> 9 The income data were collected using a diary method.  Although the diary method reduces recall error, measurement error could arise due to difficulties keeping track of the complex and diverse income sources in rural China, which include farming, nonagricultural self‐employment, formal wage employment, and informal or casual jobs, and which generate incomes both in cash and in kind.  Error could also arise due differences in the ability and willingness of respondents to record accurate data in the diaries.

18
# **Appendix C. Calibration**

# _Appendix C.1. Other data sets used for the calibration_

We use the housing sales registry of the City of Cape Town. This data set includes records of housing transactions, including sales year, area, type, price and location of properties. We only consider transactions that took place in 2011 and aggregate the sales information at the Sub-Place level. We calculate a median price (denoted _Ps_ ) per square meter of land for Sub-Places with more than 20 transactions recorded. We also use a data set of average formal dwelling sizes provided by the Municipality of Cape Town, at the Transport Zone level. We aggregate the values at the Sub-Place level (denoted _qs_ ).

# _Appendix C.2. Estimation of construction function parameters_

We estimate the relation between the estimated stock of formal private housing and housing prices in cross-section to identify the parameters of the construction function _{_ κ _, a}_ . Combining (6) and the equilibrium condition (iv), we have: 4 where ∑ _Ni_<sup>_FP_</sup> ( **x** ) = _N_<sup>_FP_</sup> ( **x** ) is the number of household living in formal dwellings. We regress _i_ =1 the log of the previous equation at the sub-place level, denoted _s_ : where _Ns_<sup>_FP_</sup> is the number of households in formal housing at the Sub-Place level, _Ps_ is the median price per unit of land, _Ls_<sup>_FP_</sup> is the amount of available land for formal housing and _qS_ is the dwelling size. From equation (C.1), we expect coefficients γ3 to be close to 1 and γ4 to be close to _−_ 1. Note that equation (C.1) theoretically only applies to formal private housing. However, because housing in low-income neighborhoods is a mix of formal private, formal subsidized housing and informal housing, we restrict our sample for the estimation, by excluding the Sub-Places in the bottom quintile of property prices _Ps_ and for which more than 5% of dwellings are reported to live in informal housing<sup>43</sup> We also exclude rural sub-places (i.e., those that are large, with a small share than can be urbanized). We find: log � _Ns_<sup>_FP_</sup> � = _−_ 3 _._ 51 (0 _._ 97) + 0 _._ 25 (0 _._ 07) log ( _Ps_ ) _−_ 0 _._ 98 (0 _._ 07) log ( _qs_ ) + 0 _._ 92 (0 _._ 08) log � _Ls_<sup>_FP_</sup> �, with standard errors in parenthesis. We find that coefficient γ3 is close to 1 and coefficient γ4 is close to _−_ 1. Coefficient κ and _a_ in the construction function are retrieved from γ1 and γ2, with _a_ = 1 _−_ γ2 and

> 43Our data set for dwelling sizes only provides the average dwelling size at the Sub-Place level, aggregating formal and informal housing.

32
|**Table A2: OLS -comparison I**|**V production**|**functions**||
|---|---|---|---|
|Dependent variable:Log ofann|ual revenue|||
|**Columns compared:**|**1**<br>|**2 and 4**<br>|**3 and 5**<br>|
||(1)|(2)|(3)|
|**_Factors_**<br>||||
|Capital-per-worker|0.055<br>|0.053<br>|0.047<br>|
||(0.04)<br>|(0.04)<br>|(0.04)<br>|
|Inputs-per-worker|0.586***|0.589***|0.605***|
||(0.05)|(0.05)|(0.07)|
|**_The Impact of the program_**||||
|Program-Dummie||-0.159|-0.027|
|||(0.17)|(0.92)|
|Program-capital-per-worker|||0.022<br>|
||||(0.09)|
|Program-Inputs-per-Worker|||-0.036<br>(0.09)|
|**_Activities_**||||
|PCB|0.096|0.170|0.170|
||(0.22)|(0.23)|(0.22)|
|HCB|-0.050|-0.046|-0.045|
||(0.19)|(0.20)|(0.18)|
|Gravel|0.674|0.724|0.679|
||(0.45)|(0.46)|(0.45)|
|Wood|0.023|0.046|0.056|
||(0.19)|(0.19)|(0.19)|
|Wall|-0.063|-0.029|-0.050|
||(0.33)|(0.33)|(0.32)|
|Structural|0.154|0.146|0.121|
||(1.18)|(1.18)|(1.12)|
|Electrical|-0.294|-0.287|-0.286|
||(0.44)|(0.44)|(0.42)|
|Sanitary|0.424|0.436|0.457|
||(0.36)|(0.37)|(0.35)|
|Finishing|0.015|-0.005|0.001|
||(0.40)|(0.40)|(0.38)|
|Production-inputs|0.315|0.247|0.232|
||(0.26)|(0.27)|(0.27)|
|Other|0.360|0.328|0.341|
||(0.30)|(0.30)|(0.29)|
|Constant|3.754***|3.805***|3.709***|
||(0.52)|(0.53)|(0.62)|
|N|148|148|148|
|R2|0.6|0.603|0.603|
|Adjusted R2|0.561|0.561|0.555|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-24-
Asia from 2000 to 2003, the AICD Database applied the same methodology for Sub-Saharan Africa and collected data from 2004 to 2009.

### **(b) Have energy subsidy reforms succeeded in restraining energy consumption?**

To provide an answer to this question the study uses both indicators of energy prices and related consumption. In the case of gasoline and diesel, road sector consumption of fuels is reported, as the transport sector is one of the most intensive users of such fuels. In the case of electricity power consumption is provided:

- **Fossil Fuel Prices** (US$ per liter): Gasoline, diesel and kerosene retail tariff (US$ per liter).

- **Electricity Prices** (US$ cent per kWh) – Residential Average Electricity Tariff level (US$ cent per kWh)

- **Road Fuel Consumption** (kt of oil equivalent per capita): Gasoline and Diesel Fuel Consumption (kt of oil equivalent per capita) by the road sector

- **Power Consumption** (kWh per capita) - Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants. (kWh per capita)

### **(c) Have energy subsidy reforms managed to avoid adverse social impacts?**

To address this final question, evidence from household surveys on the distributional impact of subsidies by income quintile (or deciles, if available) as well as the materiality of energy subsidies, reporting for each fuel how much income is spent by each quintile of the population is used in the case studies.

- **Expenditure of different energy sources** (what proportion of income poor households spend on each fuel), by quintiles or deciles where available divided by rural/urban areas.

- **Welfare impact of removing energy subsidies, by fuels and/or by quintiles or deciles** (what proportion of poor households as a whole receive the subsidy).

- **Benefit incidence of energy subsidies** (how well the subsidy targets benefits to poor households as opposed to other households), **by quintiles or deciles (and fuels where available).**

- **Beneficiary incidence of energy subsidies** (what proportion of poor households as a whole receive the subsidy), **by quintiles or deciles (and fuels where available)**

# **_iv. Summary of the Results_**

## **(a) Have energy subsidy reforms succeeded in reducing the associated fiscal burden?**

### _1. Evidence across Groups_

Some interesting patterns emerge by grouping countries in our taxonomy, distinguishing countries according to their energy dependency and the level of income. As expected, net energy importers (Group A and Group B) have reduced more significantly the burden of energy subsidies in the budget, from 1% of GDP in 2004 to 0.5% of GDP in 2010 for group A and from 0.7% to 0.3% for Group B (see Fig. 1). On the other hand, for net energy exporters (Group C and D), the burden of energy subsidy in the budget increased only slightly from 2.8% of GDP in 2004 to almost 3% of 4
the countries in Table 1 for which the U.S. is the number one migrant destination in the UN data, we see that at the individual country level real GDP per capita growth also appears to be a reasonable first-order approximation for most countries. Thus we think any biases in proxying migrant income growth with real GDP growth are likely to be small on average, and typically be such that our estimates are upper bounds.

# _3.1.4. What if remittance shares change?_

Unfortunately there are few data sets which measure both the income and remittances of immigrants, making it difficult to assess empirically how the share remitted may have changed over time. One exception is the Mexican Migration Project, which asks returnee migrants about the incomes they earned abroad and how much they remitted. Amuedo-Dorantes et al. (2005) use these data to show monthly remittances per migrant made by Mexican immigrants actually fell between the 1990s and 2000s, which given rising incomes, means the share of income remitted actually fell. They attribute this to rising living standards in Mexico reducing the need for remittances, and to migrants staying abroad for longer. Although this is just one country, and there are concerns about representativeness, it does not suggest widespread increases in the share of income being sent to the home country during the 1990 to 2010 period.

If the share of income which migrants remit changes, then equation (5) becomes: Where gθ is growth in the share of income remitted by migrants. Table 3 examines the robustness of our accounting in Table 2 to a range of potential changes in this share. We see that a 10 percent change in this share would result in a 22 percent change in total remittance growth over the 1990 to 2010 period, which is small relative to the 557.8 percent measured growth. Even if the remittance share increased by 25 percent, which would be a very large increase, the combination of migrant growth, income growth, and this change in the share remitted will still only account for less than one third of measured remittance growth.

16
growth rate of 2.00% to 2.14%). This substantial boost holds in the joint analysis in Column 3, and similar elasticities are evident without the state fixed effects, too.

Column 4 further shows that the results are robust to including district-level covariates taken from the 2001 Census discussed earlier. These covariates tend to have weak multivariate explanatory power, and the inclusion of these controls does not impact the diversity index’s connection to growth. Columns 5 and 6 show similar results when we do not weight observations or when we exclude districts with less than one million people in population. The latter check is important given that the exclusion of small districts guards against cases where our data start to become too thin for measuring the index values, and it is noteworthy that the standard errors remain consistent despite the reduction in sample size. Column 7 shows similar results when we include a control for the expected growth based upon the industrial distribution for the district in 2000 and the national rate of growth by industry across the period.

Column 8 replaces the ten-point measure with two indicator variables for districts being in the 50<sup>th</sup> -75<sup>th</sup> percentile range or in the 76<sup>th</sup> -99<sup>th</sup> percentile range. These estimations measure coefficients relative to the bottom half of the distribution. The results indicate that the most substantial differences for growth come from districts being in the top half of initial diversity. We also find quantitatively similar results when considering raw index values.

We will next move to a variety of growth decompositions, but before doing so it is valuable to compare these patterns against our earlier results. Ghani, Kerr and Tewari (2013) identify a different relationship—that manufacturing specialization in the 1989-1994 period was important for explaining the employment growth in manufacturing from 1989 to 2010. Our current work emphasizes instead local diversity. We have investigated how to reconcile these features, and we are able to mostly close the gap. The biggest contributor to the different outcomes is simply moving to a 2000-2010 analysis. There were exceptionally strong adjustments in India’s manufacturing base and its spatial allocation during the 1990s, reflective of the null correlation in the index values over decadal frequencies discussed earlier. By measuring our present study from 2000 onwards, we pick India up at very different point in its economic emergence. Unfortunately, we will never be able to construct similar services indices for 1989 to compare. A second and smaller factor is removal of manufacturing growth during the 1990s for India that was responsible for a big portion of the employment growth relationship. A 19
**Table 3: Effect of the Interventions on Behavior Change**

||**Province lev**|**el intervention**||**District leve**|**l intervention**||
|---|---|---|---|---|---|---|
||**Mass med**|**ia treatment**|**Community tr**|**eatment sample**|**School co**|**mponent sample**|
||Control mean|Effect of treatment|Control mean|Effect of treatment|Control mean|Effect of treatment|
|**Handwashing facilities**|||||||
|HW Facilities stocked with water and soap|0.653|-0.019|0.653|0.032|0.67|0.062|
|||(0.057)||(0.048)||(0.043)|
|Water and soap anywhere in the house|0.787|-0.025|0.787|0.0267|0.768|0.065**|
|||(0.040)||(0.037)||(0.032)|
|||||||[8.4%]|
|**HW hand cleanliness**|||||||
|Hands Cleanliness Index|7.527|-0.178|7.527|0.278*|7.403|0.210|
|||(0.166)||(0.161)||(0.151)|
|||||[3.6%]|||
|**Self-Reported handwashing behavior**|||||||
|HW with soap and water previous to eat|0.415|-0.156***<br>(0.040)<br>[-37.5%]|0.415|0.0252<br>(0.046)|0.402|0.095**<br>(0.044)<br>[23.6%]|
|HW with soap and water before food preparation|0.653|-0.007<br>(0.037)|0.653|0.027<br>(0.036)|0.694|-0.042<br>(0.039)|
|HW with soap and water after fecal contact|0.654|-0.084**<br>(0.040)<br>[-12.8%]|0.654|0.0254<br>(0.042)|0.656|0.019<br>(0.043)|
|HW with soap and water before feeding child|0.206|0.037|0.206|0.004|0.194|0.054*|
|||(0.031)||(0.034)||(0.030)|
|||||||[27.8%]|
|**Structured observations**|||||||
|Observed HW with soap and water previous to|||||0.189|0.115**|
|eat|-|-|-|-||(0.049)<br>[60.8%]|
|Observed HW with soap and water before food<br>preparation|-|-|-|-|0.099|0.068*<br>(0.038)<br>[68.6%]|
|Observed HW with soap and water after fecal<br>contact|-|-|-|-|0.342|-0.022<br>(0.056)|
|Observed HW with soap and water before feeding|||||0.076|0.04|
|baby|-|-|-|-|||
|Clustered standard errors in parentheses; *** p<0.01 ** p<0.|05 * p<0.1|||||(0.060)|

**Table 4: Effect of the Interventions on Environmental Contamination**

||**Province level intervention**<br>|**District level**<br>|**intervention**<br>||
|---|---|---|---|---|
||**Mass media treatment**|**Community treatment sample**|**School co**|**mponent sample**|
||Control mean<br>Effect of treatment|Control mean<br>Effect of treatment|Control mean|Effect of treatment|
|**Water analysis**|||||
|Prevalence of E.Coli (Percentage)|-<br>-|-<br>-|0.607|-0.041<br>(0.072)|
|Prevalence of total coliforms (Percentage)|-<br>-|-<br>-|0.793|-0.044<br>(0.062)|

Clustered standard errors in parentheses; *** p<0.01 ** p<0.05 * p<0.1 37
TABLE A2: Principal component analysis: ECD (cognitive) outcomes

||(1)<br>Variance|(2)<br>Proportion|(3)<br>Factor loadings|
|---|---|---|---|
|**Denver subcomponents (used for children 1-3 years old)**|1.390|0.3476||
|Personal - Social|||0.385|
|Language|||0.651|
|Fine Motor|||0.675|
|Gross Motor|||0.601|
|**All tests together (used for children 3-7 years old)**|2.268|0.324||
|<br>Denver Personal - Social|||0.100|
|Denver Language|||0.744|
|<br>Denver Fine Motor|||0.595|
|Denver Gross Motor|||0.486|
|Vocabulary (TVIP)|||0.754|
|<br>Memory|||0.735|
|Self-control|||0.100|

_Note:_ Column 1 reports the eigenvalue of the first principal component. Column 2 reports the proportion of variance accounted for by the first principal component. Column 3 reports the principal component factor loadings, which show the correlation between the variables and the first principal component.

# TABLE A3: Principal component analysis: socio-emotional outcomes

||(1)<br>Variance|(2)<br>Proportion|(3)<br>Factor loadings|
|---|---|---|---|
|**Behavioral (used for children 1-3 years old )**|1.320|0.440||
|Control|||0.504|
|Affect|||0.761|
|Ability|||0.697|
|**Strengths and Diffculties (used for children 3-7 years old)**<br>Emotional|1.718|0.245|0.687|
|Conduct|||0.783|
|Hyperactivity|||0.124|
|Peer|||0.733|
|Pro-social|||-0.075|
|Control|||0.262|
|Laugh|||-0.059|

_Note:_ Column 1 reports the eigenvalue of the first principal component. Column 2 reports the proportion of variance accounted for by the first principal component. Column 3 reports the principal component factor loadings, which show the correlation between the variables and the first principal component.

51
In all SP studies, the main challenge is to persuade the respondent to seriously consider a proposal, typically to be delivered at some point of time in the future. We found respondents to be willing to look ahead and earnestly consider the commodity—improved water supply. This is presumably because water supply is a serious issue that commands people’s time and attention and because the scenario we presented was credible. The credibility of the scenario could stem from the fact that many households seemed to have heard about plans to expand `the` water supply network in the area. The local population’s willingness to understand and consider our proposed scenarios was a valuable signal regarding the content of the survey instrument and the feasibility of the conjoint approach.

# **_Survey administration_**

The data come from a 2003 survey of 1800 households in the Greater Negombo and the coastal strip from Kalutara to Galle in Southwest Sri Lanka. This survey was implemented to gauge current water and sanitation conditions, estimate household demand, and assess access and affordability of poor households before reform measures were implemented. A 3-stage stratified random sampling approach was used to select our sample (see Pattanayak et al. 2004 for more details on how we selected our sample). Our survey instrument was developed through a series of focus groups (in Kandy, Galle, and Kalutara); several purposive discussions with households, the National Water Supply and Drainage Board officials, and the Water Sector Reform Unit staff; and 120 pre-tests in Negombo, Kalutara and Peradeniya. The final 69-page survey contained 7 modules, with a split sample design on the contingent valuation and conjoint questions to gauge household demand. The seven modules included: location and policy priorities; water sources; water treatment, storage and hygiene; sanitation and sewerage; contingent valuation; family roster and health; and a socio-economic profile.

Enumerators were selected from a pool of recent Peradeniya graduates and trained using a mix of lectures, role-plays and field trials in the final survey area. Each survey was conducted as an in-person interview that lasted approximately 50 minutes. The survey process was completed in 2 months.

11
A country starts to impose the reference lockdown when the infected population reaches 2.6%. This rate is based on the COVID-19 prevalence at the time of the first lockdown in the Italian municipality of Vo, the site of the first COVID-19-related death detected in Italy (Lavezzo et al., 2020). In our calibration, the countries start to impose the reference lockdown policy between week 9 and 13, with an average start date at week 11.

The length of the reference lockdown policy is based on Flaxman et al. (2020) that estimates the impacts of non-pharmaceutical interventions in 11 European countries during the first months of the pandemic.<sup>15</sup> We drop the four countries that only imposed mild or no lockdown policies (Denmark, Norway, Sweden, and Switzerland) and work with the remaining seven countries.<sup>16</sup> We compute the lockdown length for each country based on the difference between the reported lockdown date and the end of the sample period in Flaxman et al. (2020). The lockdown policies range between 43 to 54 days, with an average of 7 weeks, which we use as the length of the reference lockdown. The strength of the lockdown was inferred from the GDP decline in the first two quarters of the year 2020. As explained later in this section, we calibrate _θ_ = 1, which implies that a lockdown policy of _μ_ ̄ reduces aggregate ̄ labor supply and GDP by _μ_ . Therefore, an _x_ -day lockdown reduces the two-quarter GDP by _xμ/_ ̄ 180, from which one can infer _μ_ ̄, conditional on the length of the lockdown calibrated ̄ above and the observed decline in GDP. For example, the inferred _μ_ in Germany with a 6 _._ 68 percent decline in GDP and a 43-day lockdown is 0.0668*180/43 = 0.2796. We repeat the calculation for all seven countries and find that the strength of the lockdown policy to be between 28% and 46%. The average across the seven countries is 38%, which we use as _μ_ ̄ for the reference policy.

Given the country-specific _μ_ ̄ and lockdown length in these 7 countries, we can then compute _ξ_ , the elasticity of community transmission with respect to lockdown, for each of these countries. To do this, we simulate the model and target the post-lockdown _R_ 0 of 0.66 as reported in Flaxman et al. (2020) for the sample countries. The corresponding _R_ 0 at period _t_

> 15Out of the five non-pharmaceutical interventions studied in Flaxman et al. (2020), we focus on “lockdowns” to quantify the reference lockdown. Note that the “lockdown” in our model should be interpreted broadly to include the other four forms of intervention policies in Flaxman et al. (2020), such as social distancing, self-isolation, school closure, and restricting public events.

> 16The four countries with mild or no lockdown policies lead to corner solutions in the calibration of _ξ_ , as explained below. The remaining 7 countries are Austria, Belgium, Britain, France, Germany, Italy, and Spain.

23