rafmacalaba/datause-encoder-data huggingface dataset:
The definition of the items that were priced and the consumption group(s) that they map to is reported in appendix table 1 based on the data available from the 2010 survey. For most of the analysis we use this mapping, since the greater disaggregation afforded by the 2012 price survey cannot be used when working with the pooled data from 2010 and 2012. But using the more disaggregated items makes almost no difference to the spatial deflators since the additional items priced in 2012 have small budget shares and have regional price relativities that were similar to the relativities for the substitute item(s) that had been used in their stead in 2010.

Another modeling choice concerns use of imputed prices for item-market combinations with the target specification missing (13% of all cases). The imputations used regressions of the price of the target specification on prices of alternatives gathered in the survey, controlling for regional fixed effects and brand name fixed effects (for unbranded items, quasi-brands are formed by dividing into intervals based on unit prices). To show the effect of including imputed values (and other modeling choices discussed below) a bilateral Törnqvist index is reported in appendix table 2. This has the advantage of simplicity and also matches what the GSO and World Bank (2012) used in their poverty analysis. The regional deflators in columns (1) and (2) are almost the same with or without imputed values, so the imputed values are used for the remaining analysis.

One important price not observed was rents or the user cost of housing services. Instead, econometric analysis of the VHLSS housing module enabled a hedonic house value equation to be estimated. Regional and temporal variation in reported dwelling values (conditioning on over 60 variables) are used as a proxy for prices. Values are used because there is almost no rental activity recorded in the VHLSS, precluding use of either actual rents or hypothetical rents as measures of regional and temporal price relativities for housing. In the third column of appendix table 2, the price index that results from estimating the housing equation on pooled data for 2010 21
4 Note that, given a base year capital stock estimate, it is possible to estimate capital stocks beyond the base year by simply accumulating net investment in each period. Therefore, for constant _I G_ , we estimate the counterfactual series of produced capital for each country as: _G_ We calculate two versions of _K_<sup>_*_</sup> in what follows – one with _I_ = 0 (the standard Hartwick rule), and a second with _I G_ equal to a constant 5% of 1987 GDP. The choice of a particular level of genuine investment for the analysis is obviously arbitrary. We use 5% of 1987 GDP for the following reasons: (i) there is some logic to choosing the mid-point of our time series of data from 1970-2000, but 1987 is a slightly better choice, falling after the early 1980’s recession, after the collapse of oil prices in 1986, and before the early 1990’s recession; and (ii) a 5% genuine investment rate is roughly the average achieved by low income countries over time. Since the elasticity of output with respect to produced capital α is implicitly greater than 0.5 in the theoretical model of the preceding section, the choice of 5% of GDP ensures that the feasibility condition _G_ < α _F_ (0) of Proposition 3 is satisfied.

Resource depletion is estimated as the sum of total rents on the extraction of the following commodities: crude oil, natural gas, coal, bauxite, copper, gold, iron, lead, nickel, phosphate, silver and zinc. While the underlying theory suggests that scarcity rents are what should be invested under the Hartwick rule (i.e. price minus marginal extraction cost), the World Bank data do not include information on marginal extraction costs. This gives an upward bias to the hypothetical capital stock estimates under the genuine investment rules.

There is another clear divergence between our empirical methods and the theory of the preceding section. In the autarkic Dasgupta-Heal economy presented above, the choice of policy rule also determines the level and path of resource rents ( _FR_ ). By using historical rents in our calculations we are clearly diverging from the theory. However, in most instances we would expect resource exporters to be price takers, which favors using historical rents. If resource prices change exogenously, a further adjustment to saving to reflect future capital gains is required (see Vincent _et al._ 1997), but Hamilton and Bolt (2004) show that the adjustment is typically small if historical price trends are extrapolated.

When comparing estimates of the stock of produced capital for different countries, it is worth noting that the Perpetual Inventory Model underestimates the capital stock for countries with very old infrastructure, as in most European countries. The value of roads, bridges and buildings constructed many decades and even centuries ago is not captured by the PIM. Pritchett (2000)
# **References**

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22
**Table 5. Annual expenditure needs for infrastructure in the MENA region**

|**Sector**|**OIC**|**OEC**|**GCC**|**TOTAL**|
|---|---|---|---|---|
|||**Percent of GD**|**P**||
|**Transport**|**2.2**|**5.1**|**2.0**|**3.0**|
|Paved Roads|1.4|2.7|0.6|1.4|
|Unpaved Roads|0.6|2.1|1.3|1.4|
|Rails|0.1|0.1|0.0|0.1|
|Ports|0.1|0.1|0.1|0.1|
|**ICT**|**0.8**|**1.1**|**0.2**|**0.6**|
|Telephone mainlines|0.2|0.3|0.0|0.2|
|Mobile lines|0.6|0.7|0.2|0.4|
|**Electricity**|**2.5**|**4.2**|**2.5**|**3.0**|
|Electricity generation|2.1|3.7|2.4|2.7|
|ElectricityAccess|0.4|0.5|0.1|0.3|
|**Water and Sanitation**|**0.5**|**0.6**|**0.1**|**0.3**|
|Water|0.2|0.2|0.0|0.13|
|Sanitation|0.3|0.3|0.1|0.19|
|**Total needs**|**6.0**|**10.9**|**4.8**|**6.9**|
|**Total financing**|**4.3**|**12.2**|**8.3**|**8.8**|
||**Am**|**ount(in 2005 US**|**$ million)**||
|**Transport**|**8,575**|**22,492**|**14,453**|**45,519**|
|Paved Roads|5,448|12,088|3,956|21,492|
|Unpaved Roads|2,246|9,497|9,461|21,204|
|Rails|428|639|75|1,143|
|Ports|452|268|960|1,680|
|**ICT**|**3,021**|**4,707**|**1,559**|**9,287**|
|Telephone mainlines|696|1,464|259|2,419|
|Mobile lines|2,325|3,243|1,300|6,868|
|**Electricity**|**9,894**|**18,607**|**17,602**|**46,103**|
|Electricity generation|8,214|16,467|17,139|41,820|
|ElectricityAccess|1,680|2,140|463|4,283|
|**Water and Sanitation**|**1,764**|**2,497**|**647**|**4,908**|
|Water|745|1,040|190|1,975|
|Sanitation|1,019|1,458|457|2,934|
|**TOTAL**|**23,254**|**48,303**|**34,261**|**105,818**|
|_Share by country group_|_22%_|_46%_|_32%_|_100%_|
|**Investment**|**10,261**|**20,739**|**15,786**|**46,786**|
|**Maintenance**|**12,992**|**27,564**|**18,475**|**59,032**|

_Source_ : Authors’ calculations. Note: Total financing is estimated based on IMF/IFS investment data and World Bank Private Participation Infrastructure database.

7
Figure 1 shows a simple time-series plot of respondents’ average evaluations of their present living conditions (subjective well-being) for each day of the survey. Figure 2 shows a plot of the development in an index of food prices from July 2004 to September 2006, with the value of September 2006 indexed at 100 (Central Statistics Office 2008). The vertical lines indicate the timing of the devaluation. As is clearly visible in Figure 1, upon the devaluation of the Pula, there is an immediate and substantial drop in respondents’ average evaluations of living conditions in the magnitude of 0.16 on a scale from 0 to 1. Compared to individuals surveyed prior to the devaluation, the subjective well-being of people surveyed after the devaluation was much lower. The immediacy of this drop in well-being is important too, as prices are unlikely to have adjusted very much already on the first day after the devaluation. While there were media reports of upward re-pricing by retailers and a consequent “shock of skyrocketing prices”<sup>8</sup> shortly after the devaluation, the price level of consumables did not fully adjust to its new equilibrium within the short period where the survey data were collected. As shown in Figure 2, food prices developed as expected in the months following the devaluation. While the food price index was relatively stable in the year preceding the devaluation, it increased dramatically in the year after the devaluation. This suggests that people’s reaction to the devaluation—the drop in their evaluations of subjective well-being shown in Figure 1—is in large part driven by (qualitatively correct) expectations about the effects of the devaluation. Indeed, while the price effect of the devaluation materialized over months, there are good reasons to believe that people in Botswana knew what to expect, because 16 months earlier—in early February 2004—the Bank of Botswana also implemented a 7.5 percent devaluation of the Pula. While this did not make the May 2005 devaluation any less of a shock to people in Botswana, the prior experience with the consequences of a sizeable currency devaluation means that people may have rationally updated their expectations concerning the effects of the

> 8 “Consumers Shocked at Effect of Pula Devaluation,” _Mmegi_ , May 31, 2005.

15
# **Figure 2: Components of Social Spending, as a share of GDP**

<!-- Start of picture text --> 30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>LIC LMC UMC HIC<br>Direct transfers Education Health Contributory pensions<br>Uganda (2016) Burkina Faso (2014) Togo (2015) India (2011) Indonesia (2012) * El Salvador (2017) Tajikistan (2015) Nicaragua (2009) Mongolia (2016) * Guatemala (2014) Dominican Republic (2013) Peru (2011) China (2014) * China (2018) *** Panama (2016) Mexico (2014) Armenia (2011) Colombia (2014) South Africa (2015) Romania (2016) Costa Rica (2010) Turkey (2014) Russia (2010) Brazil (2009) Belarus (2015) Argentina (2017) Chile (2013) United States (2016) Spain (2017) Poland (2014) Croatia (2014)<br><!-- End of picture text --> _Notes_ :

- Indicates a regional comparator of China.

- *** China (2018): the data used in the present analysis. _Source_ : Own analysis and CEQ Data Center (available at www.commitmenttoequity.org)

As in the case of contributions, the pension/benefit amounts in China differ widely between the employee’s and resident’s social insurance programs. For the pension programs, the number of contributors and beneficiaries in the resident’s program (365 million and 159 million respectively) was relatively similar to the number of enrollees in the employee’s program (301 million and 118 million respectively) but the contribution and pension amounts were orders of magnitudes lower for the resident’s program (see Appendix A). In the case of the medical insurance program, the number of people covered under the resident’s program (1,028 million) was more than three times larger than the number of people covered under the employee’s program (317 million), in part because the residents’ health insurance includes children (not included in the employees’ system), and yet the contribution and benefit amounts were still significantly lower for the residents’ program. These numbers indicate that the average contribution and pension or benefit amounts are much lower for those enrolled in the residents’ programs relative to what is provided through the employee system.

China’s social protection system also includes a social assistance component. Some of the main social assistance programs include the urban and rural Minimum Living Standard Scheme ( _Dibao_ ), assistance for the extreme poor in both urban and rural areas ( _Tekun_ ), temporary and natural disaster relief programs, as well as medical, education, housing and employment assistance programs (see Appendix Table 2 for summary statistics of these programs). The flagship cash transfer program _Dibao_ provides cash transfers and employment opportunities to households whose income and living conditions are below local Dibao thresholds. The program is managed at the local level, and the value of benefits varies at the prefectural level. In 2018, 45 million people lived in a household that received Dibao, with average annual benefits of 5,712 Yuan RMB for urban recipients and 3,003 Yuan RMB for rural 10
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||_H1: There is a correlation_<br>_between environmental_<br>_commitment and_<br>_liberalisation_|wholesale reform<br>-reform model (pool,<br>wholesale wheeling,<br>competitive bidding)<br>-timing (reform in 1990, 0.5<br>points deducted for each<br>year of delay)<br>retail reform<br>-reform model (regulated<br>TPA, negotiated TPA)<br>-timing (reform in 1990, 0.5<br>points deducted for each<br>year of delay)|-energy independence index<br>(computed from total primary<br>energy supply of each different<br>fuel, total exports of each fuel,<br>indigenous production of each<br>fuel, share of each fuel in total<br>electricity production)<br>(SE)<br>_Result: for the restricted model_<br>_the coefficient is significant at_<br>_the 1% level_<br>-environmental index<br>(constructed from information on<br>Kyoto target increase, renewable<br>energy R&D expenditure/ GNP,<br>implementation of energy/<br>carbon tax, energy related<br>carbon-dioxide emissions,<br>renewable energy capacity<br>excluding hydro, renewable<br>energy capacity including hydro)<br>(SE)<br>_Result: insignificant_||Zealand, Sweden, Finland,<br>UK, USA.<br>Domestic primary fuel<br>production and domestic<br>primary fuel used, hard coal<br>production, fuel imports and<br>exports, fuel shares in total<br>electricity production<br>(required for all the countries<br>in the sample for 1995 – for<br>the calculation of the energy<br>independence indices);<br>prices discriminated by types<br>of customer; qualitative data<br>on the timing and model of<br>the reform (both at wholesale<br>and retail levels, required to<br>construct the liberalisation<br>index); qualitative and<br>quantitative data on national<br>commitment to protect the<br>environment (such as Kyoto<br>protocol targets, renewable<br>energy R&D expenditure/<br>GNP, etc.).<br>Data: mostly IEA for<br>quantitative data, but various<br>sources for qualitative data.<br>Periods: 01 - mostly works<br>with 1995 data.<br>Number of observations: 13<br>countries.|
|**Ruffin (2003)**|_H0: Judicial independence,_<br>_economic ideology, and_<br>_distributional conflict_<br>_influences competition._|- competition score<br>(composite variable) (RM)|-judicial independence<br>(composite indicators/indices)<br>(CL).<br>_Results: mostly positively, but not_<br>_significantly correlated with_<br>_competition._|-real per capita income<br>(CL)<br>-average percentage of<br>foreign investment in<br>country total investment|Up to 75 countries for<br>different model<br>specifications. Data: mostly<br>for 1990s from a diverse set<br>of sources.|

51
(Wooldridge, 2010). The results are in columns (5) to (8) in Table 7. The first and the second stage results of 2SLS are presented in panels B and A, respectively. In columns (5) and (6), the dependent variable is a dummy variable indicating fraud victimization. In columns (7) and (8), the dependent variable is the total amount of economic losses subject to fraud schemes. The full sample containing all urban households is used in columns (5) and (7), while the restricted sample consisting of approached urban households is used in columns (6) and (8). In all specifications in Table 7, county fixed effects are included and standard errors are clustered at the city level.

Based on the selection model (columns (1) to (4) of panel B in Table 7), the _Ecommerce coverage_ at the community level is a good predictor of fraud approaching. The coverage of community _E-commerce_ is significantly and positively associated with the probability of being approached by fraudsters. More importantly, after considering the potential selection bias, the relationship between credit constraints and fraud victimization still holds. The probability of becoming fraud victims and the total amount of economic losses are both significantly higher for households with credit constraints, and the coefficients are similar to the baseline regression results in Table 5.

When dealing with the endogeneity of credit constraints indicator and sample selection bias jointly (columns (5) to (8) in Table 7), our baseline regression results remain robust. In comparison with Table 5, the coefficients of credit constraints and other explanatory variables are largely the same. Credit-constrained households are more likely to become fraud victims with a larger amount of subsequent economic losses. In all specifications, the F-test statistic for the weak identification test is well above 10, and the Hansen’s J statistic and its P-value suggest that we cannot reject the over-identifying restrictions.

# **7. Alternative Interpretation: Information Acquisition and Financial Literacy**

A concern about our previous findings is that fraud victimization is not affected by credit constraints but by anti-fraud information acquisition, which is correlated with credit constraints. With widespread fraud schemes in China, many local governments and law enforcement institutions have initiated anti-fraud campaigns. Since victims often transfer money to fraudsters via banks, many banks take the responsibility of anti-fraud education. Indeed, smart-phone users often receive reminders from their bank branches on the need 21
garment and footwear industries in foreign direct investment sectors, which employ more formal workers.

The VHLSS collect data on respondents’ wages over the past 30 days and their total wages over the past 12 months, as well as their working hours during the past 30 days. Several workers have more than one job and are asked about the wages and working hours of their main and secondary jobs. Thus, we compute total wages from the main and secondary jobs for our analysis.<sup>9</sup> The real hourly wage of women increased from 18,100 to 23,500 VND during 2010-2016. Monthly and yearly wages also increased over time. However, the gender gap in wages also increased over time. In 2010, the average annual wage for men was 9% higher than that for women but in 2016, the annual wage for men was 18% higher than that for women.<sup>10</sup>

# _3.3. Childcare and maternal employment_

Figure 2 compares by children’s age several employment variables for women whose children attend childcare versus those whose children do not. The difference in the working rate between the two groups is small. However, there is a clear gap in terms of wage-earning jobs. The gap is larger for those with smaller children than those with older children. Similarly, women whose children attend childcare have a higher proportion of formal jobs, more working hours, and higher wages than women with children who do not attend childcare. The difference in these employment variables also tends to be larger for younger children than older children.

Figure 2 shows a correlational—rather than a causal—relationship between women’s employment and childcare because there can be unobserved factors that affect both childcare attendance and maternal employment. The next sections discuss our estimation method and empirical findings on the causal effects of childcare attendance on maternal employment.

> 9 We also conduct analysis using wages from the main jobs alone, and estimation results are very similar because wages from secondary jobs are equal to only around 4% of those from the main jobs.

> 10 Further breakdown of women’s employment by urban/ rural areas (Table A.2) suggests that while the percentage of women working is higher in rural areas, the percentage of urban women working in non-farm work, skilled work, or formal work is higher. Urban women also work more hours and earn more wages per hour than rural women.

9