rafmacalaba/datause-encoder-data huggingface dataset:
**Calibration.** Calibration is the process by which household and individual‐level information is used to model labor market behavior and outcomes and to predict the likelihood of receiving remittances.<sup>2</sup> This is done in three steps. First, we model labor force status for all working age individuals (15‐64) as a function of household and individual characteristics, where labor force status can be out of the labor force, unemployed, and employed in agriculture, manufacturing, other industries or services. Although ideally we would like to work with a more detailed menu of options, the number of labor force states that can be considered is dictated by the level of disaggregation available for the macro projections. We then use a multinomial logit to estimate the parameters of the model, as well as the individual‐level probability of remaining in a particular state and/or changing to a different one, as given by (1). The approach is similar to that used in Ferreira et al (2009). We estimate the model separately for high and low‐skill individuals to allow for structural differences in the labor market behavior of the two groups.<sup>3</sup>

Second, we model labor earnings for all employed individuals ages 15 to 64 as a function of individual and job characteristics and use a standard Mincerian OLS regression to estimate the parameters of the model, as given by (2) (similar to Ferreira et al. 2008). We use a fairly broad definition of labor earnings for the purpose of the exercise that includes wages and salaries, but also income from self‐employment. This is particularly important in the case of agriculture and for economies with large informal sectors, such as Philippines, since wage and salaried workers constitute a limited fraction of those employed in these sectors. It may lead however to a loss in precision and/or predictive power given that the structural relationship between individual and job characteristics and earnings could be different for salaried and non‐salaried workers. To allow for maximum flexibility and (indirectly) account for some of these differences we estimate the model separately for agriculture, manufacturing, other industries and services and for low and high‐skill workers.<sup>4</sup> _where x =gender, age, education, region, land ownership, and indicators for export industry, salaried and public employment._ The results of the estimation of equations (1) and (2) and a full description of all variables can be found in Annex 1.

> 2 We estimate a reduced form of the household income‐generation model which is based on Bourguignon and Ferreira (2005) and Alatas and Bourguignon (2005)

> 3 For Philippines, low and high‐skilled refer to individuals with 0 – 9 and >10 years of education, respectively.

> 4 Notice that, although we could estimate separate models for salaried and non‐salaried workers based on the information from the household survey, we would not be able to use these models for the purpose of predicting future earnings since we do not have earnings and employment information disaggregated by salaried/non‐ salaried workers from macro data.

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9

# Ficure ls MODEL STRUCTURE

<!-- Start of picture text --> Production and Its Allocation Domestic Supply<br>Groom Output - CET\<br>Export Supply<br>Leontief<br>Value Added \Tntermediate<br>I I<br>CES Leontief<br>Composite / \\Composite<br>Capital Labor Intermediate ... Intermediate<br>I I<br>CES CE<br>Domestic Imported .... Domestic Imported<br>Intermediate Intermediate Intermediate Intermediate<br>2. Substitution in Final Demand<br>Linear Expenditure System (LES)<br>LES<br>Consumption Consumption<br>i i<br>jE \ LES<br>Domestic Imported Domestic Ipr ted<br><!-- End of picture text -->

# 2. Substitution in Final Demand
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# _Table 7: Price Differences Between Kinshasa and Brazzaville – Panel Regression Estimates_

||only locally<br>produced<br>and<br>consumed<br>goods|only DRC-<br>produced<br>goods|||all g|oods|||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|_Brazzaville_|0.13<br>_0.11_|0.20***<br>_0.05_|0.09<br>_0.06_|0.09*<br>_0.05_|0.09<br>_0.06_|0.05<br>_0.07_|0.16**<br>_0.08_|0.22***<br>_0.05_|
|_Overseas_good_||||0.34***<br>_0.01_|0.34***<br>_0.01_|0.33***<br>_0.01_|0.41***<br>_0.07_|0.46***<br>_0.06_|
|_RC_good_|||||0.01<br>_0.10_|0.01<br>_0.10_||-0.09<br>_0.08_|
|_Brazzaville *_|||||||-0.12|-0.17*|
|_Overseas_good_|||||||_0.11_|_0.09_|
|_Brazzaville * RC_good_||||||||(not<br>identified)<sup>†</sup>|
|_Wholesale_price_|-0.22***<br>_0.05_|-0.15***<br>_0.05_|-0.16***<br>_0.03_|-0.16***<br>_0.03_|-0.16***<br>_0.03_|-0.12***<br>_0.02_|-0.17***<br>_0.03_|-0.16***<br>_0.03_|
|Fixed effects for goods|**yes**|**yes**|**yes**|**yes**|**yes**|**yes**|**yes**|**yes**|
|Random effects for goods|no|no|no|no|no|no|no|no|
|Fixed effects for markets|no|no|no|no|no|**yes**|no|no|
|Within-R<sup>2</sup>|0.06|0.13|0.04|0.06|0.06|0.08|0.07|0.08|
|_N_|356|283|970|970|970|970|970|970|

_Notes_ : heteroskedasticity consistent standard errors in italics; */**/***: significant at the 90/95/99% level;<sup>†</sup> : effect not identified in fixedeffects model but identifiable in random-effects model
<!-- Start of picture text --> Retail-Advertisement Retail-Customer Service<br>5 Search Engin Big data or AI to generate personalized advertiseme e Marketing 5 nts Chatbots<br>Social media (YouTube)Search Engine Marketing Online requests (website, digital platform, app)<br>4 Social media (YouTube)Search Engin e Marketing 4 Online requests (website, digital platform, app)<br>Social media (YouTube) Social Media (e.g., Facebook)Online requests (website, digital platform, app)<br>3 Email or mobile phone 3 Social Media (e.g . , Facebook)<br>Radio, Billboards, TVEmail or mobile phone Social Media (e.g., Facebook)<br>2 Radio, Billboards, TV 2 Phone call or help desk<br>Radio, Billboards, TV Phone call or help desk<br>1 Paper-based communication (in-store signage, l eaflets) Functionality 1 At the store Functionality<br>Integration Integration<br>Automation Automation<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Expert rankings Expert rankings<br>Retail-Inventory Retail-Merchandising<br>5 Automated Storage and Retrieval Systems (AS/RS) 5 Product trend analytics (Big Data, Machine Learning)<br>Automated inventory control (CAO, VMI, RFID) Retail Merchandising Systems or Digital Merchandising<br>4 Warehouse ManagemenAutomated t Systeminven tory control (CAO, VMI, RFID) 4 Retail Merchand i sing Systems or Digital Merchandising<br>Warehouse Management System Category Management tooRetai Merchandising Systems or Digital Merchandising l s<br>3 3 Category Management tools<br>Computer databases with manual updates Category Management tools<br>2 Compute r databases with manual updates 2<br>Computer databases with manual updates<br>1 Handwri t ten record keeping Functionality 1 Manually selecting products Functionality<br>Integration Integration<br>Automation Automation<br>0 0<br>0 1 2 3 4 5 0 1 2 3 4 5<br>Expert rankings Expert rankings<br>Retail-Pricing<br>5 Pricing - Personalized pricing (predictive analytics)<br>Pricing - Dynamic pricing systems (software)<br>4 Pricing - Dynam ic pricing systems (software)<br>3 Au t omated promotional (season ality or predictable events)<br>Automated markup(eg-excel)Automated promotional (seasonality or predictable events)<br>2 Automated markup(eg-excel)<br>1 Manual cost Functionality<br>Integration<br>Automation<br>0<br>0 1 2 3 4 5<br>Expert rankings<br>ChatGPT rankings ChatGPT rankings<br>ChatGPT rankings ChatGPT rankings<br>ChatGPT rankings<br><!-- End of picture text --> Figure A.16: Comparison between expert’s versus ChatGPT’s in Retail Note: The figure compares the experts’ ranking of technology sophistication (horizontal axis) with ChatGPT’s rankings across the dimensions of functionality, integration" "and automation (vertical axis).

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# **Tables**

Table 1: Impact of migration on <u>poverty</u> at Village level

||A|ll|Ru|ral|Ur|ban|
|---|---|---|---|---|---|---|
||p0|Log (PC-<br>cons)|p0|Log (PC-<br>cons)|p0|Log (PC-<br>cons)|
||(1)|(2)|(3)|(4)|(5)|(6)|
||**M**|**igration rat**|**es to Gulf-**|**Malaysia**|||
|to Gulf-Malaysia|-0.659***|1.071***|-0.574***|0.594***|-1.387***|5.142**|
||(0.097)|(0.332)|(0.087)|(0.123)|(0.506)|(2.554)|
|Observations|1922|1922|1808|1808|114|114|
|Adj R-squared|0.814|0.883|0.788|0.941|0.801|0.708|
|||**Migratio**|**n rates to I**|**ndia**|||
|to India|0.203|-0.125|0.187|-0.142|0.379|0.072|
||(0.147)|(0.303)|(0.147)|(0.198)|(0.759)|(3.069)|
|Observations|1922|1922|1808|1808|114|114|
|Adj R-squared|0.790|0.874|0.765|0.938|0.724|0.606|

Source: Author’s estimates using the Census 2001 and 2011 data, and small area poverty estimates for _illakas_ . Note: This table shows the impact of _illaka_ level migration rates on _illaka_ level estimates of poverty and per-capital consumption. Reported coefficients are the estimates of _β_ from Equation (2). The column headings indicate the outcome variables and sample restrictions. Migration rates at the _illaka_ level is the dependent variables. The top panel shows the impact of migration to Gulf-Malaysia and the bottom panel shows the impact of migration to India. Standard errors, clustered at the _illaka_ level, are reported in parentheses.<sup>_∗∗∗_</sup> : _p <_ 0 _._ 01;<sup>_∗∗_</sup> : _p <_ 0 _._ 05;<sup>_∗_</sup> : _p <_ 0 _._ 1 Table 2: Impact of migration on <u>poverty</u> at Household level

||A|ll|Ru|ral|U|rban|
|---|---|---|---|---|---|---|
||p0|Log (PC-<br>cons)|p0|Log (PC-<br>cons)|p0|Log (PC-<br>cons)|
||(1)|(2)|(3)|(4)|(5)|(6)|
||**Househo**|**ld has a mi**|**grant in Gu**|**lf-Malaysia**|||
|in Gulf-Malaysia|-0.777***|1.534***|-0.649***|0.809***|-1.527*|5.286|
||(0.188)|(0.433)|(0.182)|(0.276)|(0.803)|(3.237)|
|First stage F-stat|32.0|32.0|34.7|34.7|4.4|4.4|
|Observations|5988|5988|3900|3900|2088|2088|
||**Hou**|**sehold has **|**a migrant i**|**n India**|||
|in India|0.049|-0.260|0.049|-0.208|0.031|-0.975|
||(0.179)|(0.213)|(0.187)|(0.222)|(0.324)|(0.760)|
|First-Stage F-stat|34.2|34.2|30.2|30.2|5.9|5.9|
|Observations|3004|3004|2186|2186|818|818|

Source: Author’s estimates using the NLSS-III data. Note: This table shows the impact of household migration status on poverty and consumption. Reported coefficients are the estimates of _β_ from Equation (1). The column headings indicate the outcome variables and sample restrictions. Dependent variable is an indicator of whether the household has a current migrant in Gulf-Malaysia (top panel) or India (bottom panel), and is instrumented with the village level migration rates in 2001. Standard errors, clustered at the ward level, are reported in parentheses.<sup>_∗∗∗_</sup> : _p <_ 0 _._ 01;<sup>_∗∗_</sup> : _p <_ 0 _._ 05;<sup>_∗_</sup> : _p <_ 0 _._ 1 22
cause for concern for two reasons. First, the SC population in two unreserved GPs increased by more than 600 percent from 1991–2005, growing from 23 to 192 and 64 to 465. There are also four other unreserved GPs whose SC populations grew by more than 150 percent. Excluding these huge growers causes the statistical significance to disappear. Second, there is anecdotal evidence that with the advent of SC reservations and other SC affirmative action-type programs, more people are claiming SC status or are no longer denying their SC status (Anand and Sharma 2011). This phenomenon might explain the observed difference in the SC population.

We further refine our DID estimation to include covariates, so our estimates will still be accurate even if there are different trends in the SC population or other variables. The DID estimate in this case is given by γ�3 of the following regression: Yit = α + γ1RESERVEDi + γ2AFTERt + γ3(RESERVEDi ∗AFTERt) + φX+ εit (3) where X contains covariates such as the GP’s female and SC population ratios in 1991 (i.e., the values on which the reservations are based).<sup>19</sup>

# **IV. The Analysis**

Of the 144 villages in our survey, 47 have GPs with presidential seats reserved for women,<sup>20</sup> which perfectly represents India’s requirement that one-third of GP president seats be reserved for women. Only one GP failed to comply with the reservation policy by seating a male president, leaving 46 women-led GPs in our analysis. The women presidents are 42 years of age on average (ranging from 27 to 65), and 61 percent cite farming as their primary occupation.

> 19 Because we perform a fixed-effects regression, time-invariant covariates (such as 1991 population ratios) are interacted with a time dummy (i.e., AFTER).  A more intuitive way to think about this is as a cross-sectional regression of the form _yDIFFi = α + δ1RESERVEDi +_ **_ηX_** _+ εi_ , where _yDIFF_ is the change in the amount of public good _y_ from 1991 to 2005 and δ<sup>�</sup> 1 is the DID estimate.

> 20 In a previous version of this paper, we cited different reservation statistics.  This was the result of a data entry error, which has been checked and rectified in this paper.

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# **III. Chongqing Survey**

## **Water Pricing in China**

The China Water Law, China Pricing Law, and China Administrative Regulation on Urban Water Supply Pricing (1998) govern domestic water pricing in China. The water tariff is volumetric, which may contain a water price, wastewater treatment fee, and water resource fee.

According to the Pricing Law, the water price consists of water supply costs, fees, taxes and profits, and net profit, which is not expected to exceed 8 to 12 percent of net capital investment. Currently, most cities in China are applying a two-part pricing mechanism, which includes a water price and a wastewater treatment fee. Big cities—such as Beijing, Shanghai, and Guangzhou—apply increasing block pricing to generate more incentives to save water in the near future (HWCC 2006).

Water tariffs in China vary from city to city. In major cities, water prices range from 1 to 3 yuan per ton. Appendix I lists the water prices and wastewater treatment fees in the major cities at the end of 2006. As part of the water tariff, a wastewater treatment fee is collected together with the water price, but paid to water treatment centers or sewage centers.

The 1996 Water Pollution Prevention and Control Law require all users of urban sewage systems to pay for the wastewater treatment service. Currently, the wastewater treatment fee varies from 0.25 to 1 yuan per ton. As defined by the 2006 Ordinance on Water Permits and Water Resource Fee Management, the water resource fee represents the value of water resources as the raw material of water supply service and is paid to the water resources management department, pricing department, and finance department of the government. The local water resource department determines the water price based on the water resources available and society’s affordability. Since the allocation of water resources is very unbalanced within China, the water resource fee has a big variance, ranging from 0.02 yuan per ton in Changsha to 1.10 yuan per ton in Beijing (Rednet 2006; WB 2007).

Despite the pricing mechanism described in the Price Regulations, household water supplies have been heavily subsidized in China. In reality, however, vulnerable groups actually benefit from the water subsidy the least, and are even harmed by such a policy. On the one hand, low-income families tend to use less water compared to high-income families. As a result, low-income families—the group targeted to benefit from subsidy policies—benefit less than the high-income group. On the other hand, heavy government subsidies distort the cost-effectiveness of the market, create incentives for excessive water consumption, and cause externality problems; as a result, they harm the water supply industry. With fewer alternatives to obtain water, the vulnerable groups suffer more due to a poor water supply system. Therefore, more price reforms are needed to increase water tariffs and reduce subsidies (WB 2007).
decomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange et al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the common sample of 37 countries in Sub-Saharan African during the period 1996-2014 is approximately 2.4 percent.

Figure 5 reports the comparison of the growth decomposition across the different groups within the region when we exclude and include natural capital. The first column of each group shows the contribution of capital, and the second measures a composite value for capital, that includes both physical and natural capital. The second column for each group in figure 5, depicts the growth decomposition that includes accounting for natural capital in the physical capital share, named “composite capital”. Both columns in each group include human capital, and TFP. The first column for Sub-Saharan Africa (traditional decomposition) shows that physical capital explains 36 percent of the observed growth in output per worker (0.88 percent per year) whereas that of TFP accounts for 39 percent (0.95 percent per year). The second column, on the other hand, shows that the composite stock of capital, which includes both physical and natural capital, contributes with 1.6 percent per year (about 65 percent of the observed growth per worker) while TFP grows at an annual rate of 0.25 percent (thus, explaining only 10 percent of growth per worker).

**Figure 5. Contribution of TFP to output per worker growth in SSA, 1996-2014** _(Annual average percentage change, population-weighted)_

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Traditional Natural Traditional Natural Traditional Natural Traditional Natural<br>Decomposition Resources Decomposition Resources Decomposition Resources Decomposition Resources<br>Sub-Saharan Africa (SSA) SSA Oil Rich Countries SSA Metal Rich Countries SSA Non-Resource Rich<br>Countries<br>Capital (Composite) Human Capital TFP Output<br><!-- End of picture text --> _Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._ The estimation of the contribution of TFP to growth per worker is reduced sharply with the inclusion of natural capital among countries that are abundant in oil and rich in minerals and metals (Figure 5). For instance, TFP grows at _page 10_
5.1 Alternative Estimates of Sectoral Pollution Intensities The impact on industrial sector rankings of different intensity measures is best illustrated by their rank correlation coefficients. As described in section 3.2.4, a range of intensity measures can be calculated for each industrial sector. Table 5.1 presents the rank correlation coefficients across these measures for toxic air pollution intensity.

Table 5.1: RankIntensityCorrelationCoefficientsMeasures: Toxic AirBetweenPollutionDifferInt **en** sityt With Respect to Total Value of Output | | upper noma | too | 9 |b [ tnter-guartite mean | 0.79 | aco | o. 72 | er ee ee ee The toxic air correlations are quite high, as are the corresponding correlations for toxic land pollution (not shown). For water and non-toxic air pollution, however, the results are not so clear. The water pollution intensity measures are not very robust for a few sectors because of the presence of large outliers in the EPA database. The rankings differ considerably across intensity measures, with correlation coefficients typically around 0.5. The presence of extreme outliers suggests reliance on LB or IQ estimates. For water pollution LB estimates may be optimum for most uses, because

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# 5.2.2 Panel Speci...cations

The cross-sectional results clearly point to an important role of trade in the development of a country’s ...nancial system. We would like to go beyond the cross-section, however, and exploit the time series dimension of our data. To this end we estimate the full panel version of equation (12) on a sample of non-overlapping ...ve-year averages of all the variables from 1970-74 to 1995-99. In order to identify our e¤ect from the time variation in the variable of interest, all our speci...cations include a full set of country and time ...xed e¤ects. Furthermore, we cluster the standard errors at country level throughout, to address the problem of time series correlation in our variables (see Bertrand, Du‡o, and Mullainathan, 2004). This is the most conservative clustering available to us with this dataset.

Table 4 presents the results. Columns 1 through 4 report the OLS exercise. We ...rst demonstrate, in Column 1, that overall trade openness does not a¤ect ...nancial development when we control for country and time ...xed e¤ects. Column 2 reports a speci...cation in which only EFNXct is included in the regression aside from the battery of ...xed e¤ects. The coe¢ cient of interest is signi...cant at 1% level with the t-statistic of 3.1. Including trade openness, as in Column 3, hardly changes the coe¢ cient. However, when we control for per capita income, the coe¢ cient is reduced by about one third, similarly to the cross-sectional regressions. Nonetheless, it remains signi...cant at the 1% level. Note that the use of ...xed e¤ects results in the adjusted R-squared of between 0.87 and 0.9, indeed the R-squared of the regression with no independent variables aside from the ...xed e¤ects is 0.86. Thus, while the cross-sectional variation across countries accounts for the overwhelming majority of the variation in ...nancial development, we can still detect the e¤ect of the time variation in the external ...nance need of exports quite clearly in our regressions.

Columns 5 through 7 report the results of the 2SLS exercise. Once again, we instrument for \ EFNXct with predicted EFNX ct, the main di¤erence being that now both the actual and the predicted values of the external ...nance need of exports vary over time. One possible di¢ culty we face is that all of the gravity regressors in equation (13) aside from population do not vary over \ time. Thus, to the extent that EFNX ct changes from period to period, it will do so primarily due to changes in the estimated coe¢ cients on the gravity regressors for the various sectors over time. As we discuss above, our gravity coe¢ cients do change over time, giving us variation in predicted \ EFNX ct. Furthermore, to sweep out the country component, we always include the full set of country ...xed e¤ects in the ...rst stage regressions, and the standard errors we report are clustered at the country level as well.

The 2SLS results support what we found with OLS. The top panel reports the second stage coe¢ cients. These are generally about one third larger than the OLS coe¢ cients, and signi...cant 16