rafmacalaba/datause-encoder-data huggingface dataset:
with NFHS as benchmark. A candidate reason for this discrepancy is the difference in instrument design (these indicators are not in the set of targeting variables). In the NFHS, access to water and toilet within the household premises are collected through a detailed list of options, eliciting specific types of water sources and toilet waste disposal technologies available to the household. The CPHS in contrast, collects this information through binary yes or no questions without distinguishing between sources or disposal methods.

Comparison of CPHS and NFHS in 2019 (restricted to 14 states where asset ownership and public service access data is presently available) serves as a validation, as the reweighting for this year does not include asset ownership or access to services as target variables (these indicators are not available in the PLFS-2019). The results in Panel (b) of Figure 3 confirms the bias correction that is achieved for these non-target variables.

The largest gap in asset ownership between the CPHS and NFHS 2019 is for households owning television sets (10 percentage point) and air conditioning units (6 percentage points). The reweighting procedure does however reduce the bias by a significant margin: without reweighting, households owning TV sets would be 24 percentage points higher in the CPHS.

_Education levels:_ Undereducated people are severely under-represented in the CPHS with only 2 percent of the 2018 adult population (ages 15 to 49 years) having not received a formal education. By comparison, the periodic labor force survey (PLFS) from the same year estimates that the share of adults without formal education is 17 percent. By 2019, adults without formal education are virtually eliminated from the CPHS sample, while the PLFS-2019 continues to estimate this share of the population at approximately 17 percent. Somanchi (2021) similarly observes that female illiteracy is estimated with a significant bias in the CPHS (in selected states the mean values from the CPHS-2019 are as much as 45 percentage points lower than what is observed in the NFHS-5).

Figure 4 compares adult education levels (ages 15 to 49) in CPHS and PLFS for 2017 to 2019. The share of adult education attainment at the state level observed in the CPHS is plotted against the shares observed in the benchmark PLFS survey. Estimates above (below) the diagonal indicate states where education shares are estimated to be higher (lower) in the CPHS relative to the PLFS. Panel (a) of Figure 4 shows population shares of adults with below primary level education (which includes those with nonformal education as well non-literates). Panels (b) to (d) compare state level shares of primary, secondary and higher educated adults, while panel (e) plots the share of adults with graduate, certificate or post-graduate levels of education.

15
**Table 1: Transitions in Orphan Status among Non-orphans 6-15 at Baseline**

||N|Percentage|
|---|---|---|
|Both parents alive at age 15|585|81.4%|
|One parent died before age 15|133|18.5%|
|Mother died before age 15|52|7.2%|
|Father died before age 15|98|13.6%|
|Double orphan by age 15|17|2.3%|
|Mean age when orphaned among orphans|11.3||
|Number of observations|718||

_Notes_ : Lost mother and lost father categories include double orphans, hence rows do not add up to 100%. For double-orphans the younger age is taken to calculate mean age orphaned.

15
An overall view of the performance of the reform strategy is given by a qualitative assessment of the steps taken in the restructuring of the electricity sector and by a measure of the degree of privatization that has taken place.<sup>_6_,7</sup> Table _3_ indicates that the Philippines have proceeded much further with unbundling than comparator countries and the international benchmark. The high score relative to other countries for horizontal unbundling, which enables competition between entities, is particularly notable. As regards privatization, Peru scores slightly higher than the Philippines, but other comparator countries and the international benchmark are much lower. The performance of distribution, as in other countries, is considerably lower than that for generation and transmission. In the case of the Philippines the lower share of privatized distribution can be attributed in part to the special role of the cooperatives.<sup>8</sup> Table 3: Utility restructuring and private sector participation in the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchamrk**|
|---|---|---|---|---|---|
|**Utility Restructuring**|**35%**|**73%**|**100%**|**47%**|**45%**|
|Vertical Unbundling|70%|80%|100%|60%|55%|
|Horizontal Unbundling|0%|67%|100%|33%|34%|
|**Pvt sectorparticipation**|**49%**|**61%**|**62%**|**10%**|**24%**|
|PSP in Generation|63%|78%|84%|31%|41%|
|PSP in Distribution|35%|19%|39%|0%|16%|
|PSP in Transmission|50%|88%|66%|0%|14%|

_Source_ : Data collected for the Rethinking Power Sector reform Project

> 6 The privatization (PSP) scores are based on the World Bank’s PPI database. Each economy is scored on the basis on the number of projects that have been privatized (divested) in the sector. Full divestiture receives a higher score as compared to partial divestiture. The tabulated scores for generation, transmission and distribution are then multiplied by the respective share of the private sector in each of the operational areas. This gives individual scores for PSP in generation, distribution and transmission. The scores are averaged to get to the PSP score.

> 7 Utilities where unbundling involves ownership separation receive the highest scores while utilities where unbundling involves just functional separation receive the lowest. Both horizontal and vertical unbundling are scored separately for each area of operation- generation, distribution and transmission. The restructuring score is the average of vertical and horizontal unbundling.

> 8 Cooperatives are private companies owned by consumers and highly supervised by the government. In this sense the distribution sector is 100 percent private, but the table gives a value equating privatized to investor owned.

25
questions: “Who owns this [ASSET]?” (identifying reported owners); “If this [ASSET] were to be sold/rented out today, who would decide how the money is used” (identifying economic owners); and “Whose names are listed as owners on the ownership document for this [ASSET]?” (identifying documented owners).

In contrast, Arm 5 only inquired about the assets owned by the respondent, exclusively or jointly with others within or outside the household, in each asset class. Another household member’s potential joint ownership of an asset was identified only conditional on the respondent’s identification of himself or herself as an owner of that asset. For example, in the case of inquiring regarding the dwelling reported ownership, Arm 5 would have asked first “Are you among the owners of this dwelling?” followed by “Who else owns this dwelling?” to capture, if applicable, other household and/or non-household members that may be joint reported owners.

Finally, Arm 4 and Arm 5 had each respondent create an independent roster of assets in each asset class with the idea that the analysis team would attempt to create a panel of assets across the respondents of the same household based strictly on the household survey data. This decision was thought to better capture assets that may be hidden from other household members, and still did not compromise the objective of creating a householdlevel wealth aggregate that would ultimately feed into the System of National Accounts.

# **3.3 Gender, Agriculture, and Assets Project**

The Gender, Agriculture, and Assets Project (GAAP), led by the International Food Policy Research Institute (IFPRI) and the International Livestock Research Institute (ILRI), and funded by the Bill and Melinda Gates Foundation, aimed to better understand the dynamics of gender and assets in agricultural development programs. The GAAP portfolio included eight agricultural development projects from South Asia and Africa from 2010 to 2014. Each project collected both qualitative and quantitative information on individual asset ownership and control, but each data collection effort was tailored to the individual project, rather than standardized. The person chosen as the respondent also varied by project. The purpose was to collect data for impact evaluation, rather than to obtain representative data on patterns of asset ownership.

Most of the GAAP surveys collected information on the number and value of assets owned by the household and by individuals within the household. Asset types included livestock, agricultural and non-agricultural productive assets, and non-productive assets. In addition to gathering data on who owns assets, either individually or jointly, the surveys gathered data on who holds certain rights over assets, such as the right to use, rent out, sell, lend, or prevent others from using the asset; who can decide how to spend money generated from 11
economic growth by using synthetic summary indices of a relevant range of regulation areas, and the aggregate national ranks of the corresponding Doing Business Indicators.<sup>16</sup> It should be noted that many of the DB variables are indicative of competition-related entry barriers and hurdles, so that their impacts are apt to be different over subsamples of firms sorted by size and age. Hoped-for new business expansions that may result from opportunities created by R&D and by process, organizational and marketing innovations might also be vulnerable to the same barriers and hurdles that afflict new and small firms. On the other hand, well-established firms may benefit from an environment with more entry barriers. Increased difficulty and riskiness in getting started, and impediments to access to credit and skilled employees could be advantageous for well-established firms, so DB variables reflecting the lack of competitiveness of the business environment can also be interpreted as correlates of entry barriers that protect them, inasmuch as well-established firms are over the hurdles that these variables also indicate.

Our conceptual theory of inclusive growth from entrepreneurial innovation and competition is tested to explore whether it is consistent with available data through a triangular (or trapezoidal, to be more precise) system of four equations, recognizing possible roles of both enterprise and sector level influences over the key dependent variables. To focus on enterpriselevel correlates, we include fixed effects for country of establishment and for the sector of the establishment‘s main product. To explore the impacts of the competitiveness of the business environment (including the elements reflecting the ease of administrative regulations), we assess the rank-order correlations among the estimated country-level fixed effects of key outcome variables and aggregate rankings of countries‘ Doing Business Indicators.

The first of our four equations is an enterprise R&D investment equation, estimated using a probit estimator: Here, indicates whether firm _i_ in country _j_ and sector _k_ was actively engaged in research and development. The vector includes a set of variables describing the firm‘s relationship to its business environment including the firm‘s ability to fund investment and access capital as measured by its share of investment capital from local banks and the share of the firm‘s borrowing in foreign currency. This vector also includes an indicator of whether the firm competes in export markets, as a proxy correlate of the firm‘s opportunities to expand if its innovation is successful. We also include measures of the firm‘s roles in partnerships, which may

> 16 Hallward-Driemeier and Pritchett (2011) study the unstable relationships among the (de jure) Doing Business Indicators and the (de facto) indicators that emerge from the World Bank‘s Enterprise Surveys.

14
high, households will tend to spend more and keep less money on hand to avoid loss of value, pointing to a negative relationship between inflation and real narrow money. On the other hand, if inflation is relatively low, then households will keep more money on hand assuming—under static expectations—that it is better to keep more money on hand to meet any transaction costs. Accordingly, the relation will be positive in this case.

Moreover, broad money balances that have a negative relationship with depreciation of the exchange rate<sup>13</sup> due to portfolio reallocation will also be tested.

This is in addition to testing the hypothesis whether speculation motive reflects negatively on a relationship between money demand and interest rates.

The null hypothesis of a unit income elasticity of money demand will also be tested. This will test the theoretical proposition of the QTM that the volume of produced goods and services and the money supply will grow at the same speed. Rejecting this null will provide evidence that inefficiencies and rigidities related to costs of holding money and transaction motive for money demand holds for the economy under consideration and provides guidance to policy on the need for further innovation and development of the financial system (Knell and Stix 2005).

Another null hypothesis that will be tested is whether the spread between the deposit rate and the interest rate is statistically not different from zero. The discount rate reflects the interest rate that the Central Bank of Egypt charges commercial, depository banks for loans to meet temporary shortages of funds. It can be seen as a channel to influence the financial market’s liquidity. This was a primary monetary policy instrument in Egypt until the interbank system was introduced in 2001.<sup>14</sup> A significant foundation of QTM proposes that money supply growth is followed by equal changes in the inflation rate and, by the force of the Fisher effect, in the nominal interest rate. A hypothesis test of a one-to-one relationship between price level and monetary aggregate will test whether this proposition holds for Egypt.

> 13Based on the definition of: value in national currency per unit of foreign currency.

> 14 Please see Central Bank of Egypt’s Economic Review Vol. 50. No.3, 2009-10, available at <u>www.cbe.org.eg.</u>

18
Hahn, Robert (1989). “Economic Prescriptions for Environmental Problems: How the Patient Followed the Doctors Orders”. _The Journal of Economic Perspectives_ 3 (1): 95-114.

- Hahn, Robert (1990).  “Regulatory Constraints on Environmental Markets”. _Journal of Public Economics_ 42 (2): 149-177.

Harrington, Winston, Richard D. Morgenstern, and Thomas Sterner (eds.) (2004). _Choosing Environmental Policy: Comparing Instruments and Outcomes in the United States and Europe_ . Washington, D.C.: Resources for the Future.

Hazilla, M. and R. Kropp (1990). “Social Cost of Environmental Quality Regulations: A General Equilibrium Analysis”. _The Journal of Political Economy_ 98 (4): 853872.

Kornai, János (1979). “Resource-Constrained Versus Demand-Constrained Systems”. _Econometrica_ 47 (4): 801-819.

Kwerel, Evan (1977). “To Tell the Truth: Imperfect Information and Optimal Pollution Control”. _Review of Economic Studies_ 44: 595-601.

Kritikos, Alexander S., (2004). “A Penalty System to Enforce Policy Measures under Incomplete Information”. _International Review of Law and Economics_ 24 (3): 385-403.

List, John A, and Shelby Gerking (2000). “Regulatory Federalism and Environmental Protection in the United States.” _Journal of Regional Science_ 40(3): 453-471.

McLure, Charles E. Jr. (1994). “The Tax Assignment Problem: Ends, Means, and Constraints”. _Australian Tax Forum_ 11: 153-183.

McLure, Charles E. Jr. (ed.) (1983). _Tax Assignment in Federal Countries_ . Center for Research on Federal Financial Relations.

Montero, Juan-Pablo (2004). “Tradable Permits with Incomplete Monitoring: Evidence from Santiago's Particulates Permits Program”. Mimeo, Cambridge, MA: MIT. http://tisiphone.mit.edu/RePEc/mee/wpaper/2004-015.pdf.

Montero, Juan-Pablo (2005). “Pollution Markets with Imperfectly Observed Emissions”. _The Rand Journal of Economics_ 36 (4): 645-659.

Morgenstern, Richard D., Piya Abegunawardena, Robert Anderson, Ruth Greenspan Bell, Alan J. Krupnick, Jeremy Schreifels, Cao Dong, Wang Jinan, Wang Jitian, and Steiner Larsen (2005). “Emissions Trading to Improve Air Quality in an Industrial City in the People's Republic of China”. In _China's Environment and_ 46
**<u>Table 6. Determinants of being approached by fraudsters: OLS</u>**

||(1)<br>|
|---|---|
|Y=1 if approached by fraudsters|Urban HHs|
|Credit_cons|0.028<sup>***</sup><br>(0.008)<br>|
|Lincome|0.023<sup>***</sup><br>(0.003)<br>|
|Head age|0.004<sup>***</sup><br>(0.001)|
|Head age sq|-4.4*10<sup>-5 ***</sup><br>(1.1*10<sup>-5</sup>)<br>|
|Illness|0.038<sup>***</sup><br>(0.007)|
|Alone|-0.037<sup>***</sup><br>(0.011)<br>|
|Male|-0.014<sup>**</sup>|
||(0.007)<br>|
|Schooling|0.012<sup>***</sup><br>(0.001)|
|Constant|0.226<sup>***</sup><br>(0.041)|
|County Dummy|Yes|
|Observations|24472|
|R-squared|0.161|

Note: This table shows the determinants of being approached by fraudsters. The explained variable is a dummy variable indicating weather a household has been approached by fraudster. The full urban sample containing all households is used. _Lincome_ is the logarithm of the total sum of household income, which consists of labor income, business income, agriculture income, investment income, transfer income and other income. _Schooling_ is the years of schooling for the household head. _Illness_ is a dummy variable to indicate if the household head suffers from at least one chronic disease, such as hypertension, hyperlipidemia, diabetes, stroke, heart disease, mental issue, Alzheimer's disease, and Parkinson. _Alone_ is a dummy variable to indicate whether the household head lives alone. And _Male_ equals to 1 if respondent is male. County fixed effects are controlled for in all specifications. Robust standard errors are clustered at city level and reported in parenthesis.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

37
Table 3: Main result: Difference-in-differences in TSC intensity <u>predicts</u> ASER test scores

||(1)|(2)<br>restrict|(3)<br>ed sample|(4)|(5)<br>full|(6)<br> sample|
|---|---|---|---|---|---|---|
|Panel A: Reading (recognize|s letters|or better; me|an = 0.748)||||
|TSC household|0.752*|0.744*|0.689*|0.776*|0.752*|0.777*|
|latrines per capita|(0.339)|(0.339)|(0.348)|(0.355)|(0.339)|(0.355)|
|efect at overall 2003 mean|0.003|0.003|0.003|0.003|0.003|0.003|
|girl||-0.0104**|-0.00906*|-0.00922*||-0.00913*|
|||(0.00389)|(0.00381)|(0.00380)||(0.00379)|
|household, parent controls|||✓|✓||✓|
|village & district controls||||✓||✓|
|_n_ (six-year-olds)|47612|47612|47612|47612|47684|47684|
|Panel B: Math (recognizes n|umbers|1 to 9 or bett|er; mean = 0|.747)|||
|TSC household|0.754*|0.743*|0.688<sup>_†_</sup>|0.783*|0.754*|0.784*|
|latrines per capita|(0.359)|(0.360)|(0.373)|(0.378)|(0.359)|(0.378)|
|efect at overall 2003 mean|0.003|0.003|0.003|0.003|0.003|0.003|
|girl||-0.0150***|-0.0137***|-0.0138***||-0.0137***|
|||(0.00378)|(0.00373)|(0.00372)||(0.00372)|
|household, parent controls|||✓|✓||✓|
|village & district controls||||✓||✓|
|_n_ (six-year-olds)|47063|47063|47063|47063|47133|47133|
|Panel C: Math (recognizes n|umbers|10 to 99 or be|tter; mean =|0.325)|||
|TSC household|0.897*|0.889*|0.807*|0.810*|0.897*|0.810*|
|latrines per capita|(0.407)|(0.408)|(0.377)|(0.375)|(0.407)|(0.375)|
|efect at overall 2003 mean|0.004|0.004|0.004|0.004|0.004|0.004|
|girl||-0.0102*|-0.00876*|-0.00893*||-0.00901*|
|||(0.00433)|(0.00422)|(0.00421)||(0.00421)|
|household, parent controls|||✓|✓||✓|
|village & district controls||||✓||✓|
|_n_ (six-year-olds)|47063|47063|47063|47063|47133|47133|

Standard errors clustered by 573 (restricted) or 575 (full sample) clusters in parentheses. The restricted sample omits two districts with unusually high reported levels of TSC construction. Two-sided _p_ -values: _†_ * ** *** _p <_ 0 _._ 10; _p <_ 0 _._ 05; _p <_ 0 _._ 01; _p <_ 0 _._ 001. All specifications include district and year fixed effects. Scaled effects below the TSC coefficient multiply by the mean program intensity. Household and parent controls are housing material, household electrification in general and on the day of the survey, whether the child goes to “tuition” tutoring classes, whether the child’s father and mother have been to school, and whether the child’s mother is literate. Village controls are indicators for the village having electricity, a road, a health worker, a school, and a government ration shop; district controls are a quadratic polynomial of interpolated census literacy.

44
Table 6a: Average gender activity by district, 2001-2010

|||Manufa|Share of female<br>cturing|Share of male||Ser|Share of female<br>vices|Share of male|
|---|---|---|---|---|---|---|---|---|
||Share of female-<br>owned plants|Share of female<br>employees|empl. in female-<br>owned plants|empl. in male-<br>owned plants|Share of female-<br>owned plants|Share of female<br>employees|empl. in female-<br>owned plants|empl. in male-<br>owned plants|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Percentage of pop. in rural setting|-0.030|0.039|0.002|-0.055**|-0.011|-0.010|0.039|0.004|
||(0.063)|(0.054)|(0.038)|(0.026)|(0.016)|(0.027)|(0.043)|(0.022)|
|Log district population|0.028**|0.023**|0.001|-0.020***|0.000|-0.003|0.002|0.003|
||(0.011)|(0.010)|(0.011)|(0.006)|(0.004)|(0.006)|(0.009)|(0.005)|
|Demographic dividend|0.075**|0.133***|-0.032|-0.119***|0.072***|0.150***|0.065*|-0.117***|
||(0.037)|(0.033)|(0.043)|(0.024)|(0.014)|(0.024)|(0.038)|(0.021)|
|Female-to-male ratio|0.680***|1.018***|0.418**|-0.664***|0.097**|0.416***|0.016|-0.390***|
||(0.182)|(0.160)|(0.162)|(0.078)|(0.038)|(0.067)|(0.121)|(0.058)|
|Percentage of pop. in sch. caste or tribe|0.175|0.244**|0.160|-0.145**|0.108***|0.100*|0.035|-0.025|
||(0.110)|(0.100)|(0.115)|(0.059)|(0.037)|(0.059)|(0.092)|(0.051)|
|Literacy rate|0.078|-0.095|0.019|0.152**|0.010|-0.323***|0.081|0.346***|
||(0.096)|(0.091)|(0.088)|(0.060)|(0.028)|(0.051)|(0.080)|(0.044)|
|Composite infrastructure measure|0.064***|0.012|0.032***|0.015|0.010**|0.049***|0.009|-0.042***|
||(0.015)|(0.015)|(0.012)|(0.009)|(0.004)|(0.008)|(0.013)|(0.007)|
|Log distance to national highway|-0.003|-0.003|-0.002|-0.000|0.000|-0.001|-0.006|0.000|
||(0.006)|(0.005)|(0.006)|(0.003)|(0.002)|(0.003)|(0.004)|(0.003)|
|Log distance to state highway|0.000|0.002|-0.002|-0.003|-0.000|-0.001|0.006|0.002|
||(0.006)|(0.005)|(0.005)|(0.003)|(0.002)|(0.003)|(0.006)|(0.002)|
|Log distance to railway|0.004|-0.001|0.003|0.003|0.003**|0.003|-0.004|-0.002|
||(0.006)|(0.005)|(0.004)|(0.003)|(0.001)|(0.003)|(0.005)|(0.003)|
|Prop. of plants in district using internet|-0.342|-0.471|-0.369|0.164|0.057|0.225**|-0.245*|-0.196**|
||(0.455)|(0.448)|(0.306)|(0.207)|(0.092)|(0.102)|(0.144)|(0.084)|
|(0,1) 0-200 km from big-7 city|0.074***|0.049*|0.012|-0.007|0.012*|-0.003|0.076***|0.015|
||(0.027)|(0.026)|(0.024)|(0.015)|(0.006)|(0.012)|(0.022)|(0.010)|
|(0,1) 200-400 km from big-7 city|0.050**|0.028|-0.021|0.004|0.003|-0.010|0.034|0.012|
||(0.024)|(0.023)|(0.019)|(0.013)|(0.006)|(0.009)|(0.021)|(0.008)|
|(0,1) 400-600 km from big-7 city|0.034*|0.013|-0.004|0.011|0.006|0.001|0.035|0.004|
||(0.020)|(0.021)|(0.022)|(0.014)|(0.005)|(0.008)|(0.021)|(0.008)|

Notes: Estimations include 421 observations, are weighted by log district population, and report robust standard errors.
40 Table 5: Contributions of different policy instruments to the decline in the border policy component of the agricultural Welfare Reduction Index,<sup>a</sup> by region, between 1985-89 and 2000–04 (percent)

||Africa|Asia|LAC|**DCs**|HICs|**World**|
|---|---|---|---|---|---|---|
|Export tax|21|94|93|**86**|0|**42**|
|<br>Export subsidy|8|-4|-10|**-3**|10|**2**|
|<br>Import tax|79|9|-17|**15**|88|**54**|
|Import subsidy|-8|1|34|**2**|1|**2**|
|<br>All border|||||||
|measures|100|100|100|**100**|100|**100**|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008).

a. Contributions are computed using the value of the IWRI in constant 2000 $US billions.