rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Real Economy and Public Finance Implications of Alternative Fiscal Rules <!-- Start of picture text --> Infrastructure Ivestment<br>(% of Initial GDP)<br>9.5<br>9<br>8.5<br>8<br>7.5<br>7<br>6.5<br>6<br>5.5<br>5<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text --> <!-- Start of picture text --> Education Investment<br>(% of Initial GDP)<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text --> <!-- Start of picture text --> Health Investment<br>(% of Initial GDP)<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text --> <!-- Start of picture text --> Average Infrastructure Efficiency<br><!-- End of picture text --> <!-- Start of picture text --> Public Capital<br>(% Deviation from Initial Steady state)<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text --> <!-- Start of picture text --> Non-Oil Output<br>(% Deviation from Initial Steady state)<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text --> <!-- Start of picture text --> Balanced composition<br><!-- End of picture text --> <!-- Start of picture text --> Public Debt<br>(% of GDP)<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br>Aggressive infrastructure composition<br><!-- End of picture text -->

**Sovereign Wealth Fun Savings**

<!-- Start of picture text --> 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075<br><!-- End of picture text -->
# Reducing Bias in Phone Survey Samples

_Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames in Four African Countries_<sup>_1_</sup> _Alemayehu Ambel_<sup>_2_</sup> _, Kevin McGee_<sup>_3_</sup> _, Asmelash Tsegay_<sup>_4_</sup>

**JEL Codes:** C81, C83.

**Keywords:** COVID-19, survey methodology, survey sampling, nonresponse bias, coverage bias, sampling representativeness, weighting methods, Ethiopia, Malawi, Nigeria, Uganda, SubSaharan Africa.

> 1 We would like to thank Kristen Himelein, Gero Carletto, Diego Zardetto, Talip Kilic, Philip Wollburg, Josh Brubaker, and Isabela Coelho for valuable comments on earlier drafts. Any error or omission is our responsibility alone.

> 2 Development Data Group, World Bank, Washington, DC, aambel@worldbank.org

> 3 Development Data Group, World Bank, Washington, DC, kmcgee@worldbank.org

> 4 Development Data Group, World Bank, Washington, DC, atsegay@worldbank.org
<u>Panel B: Moments</u> Panel A: <u>parameters</u>

|Block 1||(1)<br>Value||||
|---|---|---|---|---|---|
|_μ_|CRRA coefcient|2.00||||
|_σp_|CES private sector|3.00||||
|_σp_|CES government|3.00|predetermined|||
|_β_|Discount factor|0.94||||
|_δ_|Depreciation rate|0.10||||
|_ρs_|AR(1) correlation|0.80||||
|_σs_|AR(1) variance|0.30||||
|Block 2||||Data|Model|
|_φa_|borrowing const. (_a_)|2.81|Credit/K|0.55|0.55|
|_φp_|borrowing const. (_ppyp_)<br>|0.27|reg. coefcient (_φp_)<br>|0.30|0.30|
|_φg_|borrowing const. (_pgyg_)|0.41|reg. coefcient (_φg_)|0.22|0.22|
|Block 3||||||
|_η_0|probability function (level)|0.78|Consistency of _g_(_b_) with _mg_|–||
|_η_1|probability function (slope)|0.62|Procurement premium|0.72|0.71|
|_Yg_|demand shifter|0.83|Share of procurement in GDP|0.12|0.12|
|_mg_|measure of procurement goods|0.12|Percentage of procurement frms|12%|12%|
|Block 4||||||
|D|Government lending|0.86|Interest rate|5%|5%|
| ̄_s_|Productivity shifter|-6.51|K/Y (aggregate)|3.88|3.84|
|_θ_|Survival probability|0.95|Exit rate|5%|5%|

**Notes:** This table summarizes our baseline calibration. Government wealth _D_ is expressed as a fraction of total credit in the model economy. In column (1), we show the parameter values in our baseline calibration.

**Table 1.** Calibration

equation (16) for firms with at least one procurement contract in our sample, using the same sample that we use in Section 3. Columns (1) and (2) show the estimated coefficients when including and not including firm FEs, respectively. Because equation (16) should hold with equality only for firms whose financial constraint is binding, in columns (3) and (4), we run the same regression using a sample of firms that are likely to be financially constrained. In particular, we run the regressions for firms with leverage ratios above the median, which, through the lens of our model, is a strong proxy for whether the firm is financially constrained. We find the same two results across all the specifications. First, the estimated coefficient _β_ 1 is positive, which is evidence of earnings-based borrowing constraints ( _φp_ > 0). Second, the estimated coefficient _β_ 2 is also positive, which is evidence that revenues from the public sector can be pledged to a higher extend than earnings from the privare sector ( _φg_ > _φp_ ).

We take column (4) as our preferred specification. This delivers estimates _β_ 1 = 0 _._ 292 and we do not have labor in our model, that variable is equal to the firm’s value added _piptyipt_ + _pigtyigt_ . However, in the data, we compute the counterpart of that variable as: Because we do not observe the use of factors separately for what is used to deliver sales to the private vs. the government sector. To compute value added generated by selling to the government, we assume that the intermediate goods and the labor share in total expenditure is constant within the firm, i.e., it does not change depending on whether the firm sells to the private sector or the government.

25
the next highest levels of account ownership illustrates that the Japanese case is indeed an exception. In Ireland and Luxembourg, a little over 30 percent of adults report owning a post office account. Furthermore, in most countries, fewer than 10 percent of adults have a post office account. With respect to the percentage of adults who _only_ own an account at the post office, Italy stands out with 16 percent of adults who report owning an account at the post office only. In contrast, in most other countries, less than five percent of adults have a post office account only. We report the percentages for all three types of account ownership by country in Appendix 1.

In addition to sharp differences in account penetration across countries, there are also important disparities in account penetration by individual characteristics. Allen et al. (2012) document that men and more educated, wealthier, and older adults, as well as adults residing in urban areas, are more likely to own an account. We test whether demographic characteristics also matter for the type of account that adults own. Table 1 reports the univariate statistics of type of account by individual demographics which are included in the Gallup World Poll Survey.

Because there are three account ownership types – financial institution only, both financial institution and post office, and post office only – we present two sets of statistics. First, we compare individuals with an account at a financial institution or both financial institution and post office to those with an account at the post office only. We find that individuals with an account at a post office only tend to be significantly poorer, older, less educated, less likely to be married and less likely to be employed. This suggests that post offices may play an important role in providing financial services to segments of the population that might be particularly likely to be financially excluded. Notably, we find no statistically significant gender or rural-urban gap between adults with an account at a financial institution or financial institution and post office 6
words, varimax rotation produces results which make it the most likely to identify each variable with a single factor. This approach greatly enhances our ability to make substantive interpretation of the main factors. Table 9 presents the factor loadings, where variables with large loadings (N>0.4) for a given factor are highlighted in bold.

**TABLE 9: Factor loadings of indexes after varimax rotation**

|Variable|Factor 1|Factor 2|Factor 3|Unexplained|
|---|---|---|---|---|
|Tariff regulation Index|-0.026|-0.085|**0.772**|0.120|
|Informal Autonomy Index|**0.480**|-0.113|0.032|0.541|
|Informal Transparency Index|0.370|0.288|0.022|0.281|
|Informal Accountability Index|**0.560**|-0.011|-0.109|0.384|
|Tools/Capacities Index|**0.561**|-0.037|0.062|0.261|
|Formal Autonomy Index|0.037|0.135|**0.621**|0.162|
|Formal Transparency Index|0.003|**0.655**|-0.040|0.183|
|Formal AccountabilityIndex|-0.051|**0.670**|0.010|0.171|

As seen in Table 9, Factor 1 reflects informal governance aspects in a regulatory agency, as it is correlated with informal autonomy, informal transparency, informal accountability, and tools and capacities. Nevertheless, good informal governance does not necessarily imply good overall performance; it may also be a manifestation of the agencies behavior as to balance low formal governance. Factor 2 reflects formal aspects of regulatory governance and is highly correlated with formal transparency and formal accountability. Factor 3 reflects formal aspects of autonomy and the formal power of the agency to determine tariff’s structure and level. This factor is highly correlated with the Tariff Regulatory index and the Formal Autonomy one.

We included these three factors in the models and the results are presented in Table 10.

As we did for the ERGI, the results may be better interpreted when we compute the impact on performance given an increase of one standard deviation for each factor. Standard deviations resulted in 1.51, 1.41, and 1.28 for each of the three principal components, respectively. Most of the coefficients for the three principal components resulted significant and with the expected signs in most of the cases; however, it seems that each of them have a distinct effect on each of the performance indicators. For instance, a standard deviation in the formal component has a higher effect on improving labor productivity by 15.9 percent and reducing frequency of interruptions and the residential tariffs by 13.8 and 19.0 percent, respectively. A standard deviation improvement in the third component that is related to formal autonomy and the attributions of the agency in terms of setting tariffs is associated with higher labor productivity by 11.4 percent and a 17.2 percent reduction in the average duration of interruptions. Furthermore, it produced a reduction in operation expenditure between 42.8 and 49.3 percent with consequent improvements in the cost recovery ratio. Finally, the first component resulted with less influence given that only three out of eleven coefficients resulted significant.

- 23 -
# Table of Contents

||Paae|
|---|---|
|Introduction<br>......................................|I|
|Public Works and Work Relief .<br>.....................................|2|
|Programs in Developing Countries .........................<br>Programs in Industrial Countries ............<br>Lessons for Developing Countries<br>.........................|. 3<br>............<br>12<br>16|
|Public Service Employment .<br>.....................................|17|
|Subsidized Private Employment<br>......................................|21|
|Key Program Dimensions ....................................|22|
|<br>Lessons for Developing Countries .<br>.......................|.......<br>25|
|Conclusion<br>.......................................|26|
|References<br>..................................|...................<br>30|
details, see Appendix A.4) and by the expansion of academic programs<sup>16</sup> . Academic programs refer to the number of concentrations across universities that offer a particular academic degree<sup>17</sup> , such as economics, engineering, and other programs. I account for these changes in the empirical specifications by including various fixed effects. Finally, in principle, there may be discrepancies between matriculated applicants and those eventually attending public IHEs. Nevertheless, the gap is likely negligible considering the institutional features (e.g., limited transfer possibility, free tuition, and dormitories) and future return on education. It should not pose a significant threat to the consistency of the findings.

# **4 Data**

I compiled three datasets to assess the gender quota impact on outcomes of interest. First, I attained the Kankor matriculation dataset for 2013–2018, from publicly available sources to compute variables of interest. Second, I obtained district-level nightlights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Stray Light Corrected Nighttime Day/Night Band Composites Version 1 via Google Earth at the second administrative unit level. Finally, I used the school database in Afghanistan available on the Ministry of Education (MoE) website. It contains information on school names and district locations, allowing me to match test-takers to their school districts.

First, in the Kankor dataset, I observed applicants’ gender, final test scores, high school name, high school graduation year, the province where the applicants took the exam, the institution’s name for those who matriculated into 2-year associate degrees, and university-unique-concentration identifier for applicants enrolled into 4-year public IHEs on 1,151,042 Kankor test-takers for 2013–2018. I limited the data to those applicants enrolled into the 4-year public universities (29% of all test-takers). The

> 16See Table A2.1 for the conceptual difference between concentrations, academic programs, and universities.

> 17See Table A3.2 for the share of ever-treated concentrations across academic programs.

14
# **<mark>Table 7: Plot Size and Value (all plots controlled by the respondent)</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impa|ct byRound|(OLS)|Pooled|Impact|byRound|(SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N<br>(1)|R3<br>(2)|R1<br>(3)|R2<br>(4)|R3<br>(5)|Impact<br>(6)|R1<br>(7)|R2<br>(8)|R3<br>(9)|
|**_Panel A: Plots Controlled by Women_**||||||||||
|Number of plots, by owner|2,401|.893|.041<br>(.057)|-.066<br>(.057)|-.169***<br>(.054)|-.068**<br>(.033)|.069<br>(.055)|-.022<br>(.055)|-.110**<br>(.053)|
|Average plot size, by plot [in ha]|1,572|.490|.013<br>(.057)|-.044<br>(.052)|-.135**<br>(.053)|-.057<br>(.038)||||
|Total average plot size, by owner [in ha]|1,289|.962|<br>-.113|<br>-.217**|<br>-.267***|<br>-.073*|-.007|-.061|-.081|
|Value of land per ha, by plot [in Cedis]|1,070|43,107|(.096)<br>-2,709<br>(4,083)|(.102)<br>5,916<br>(5,990)|(.100)<br>25,293**<br>(10,226)|(.043)<br>11,065**<br>(4,550)|(.048)|(.053)|(.057)|
|**_Panel B: Plots Controlled by Men_**||||||||||
|Number of plots, by owner|1,847|1.70|-.100<br>(.089)|-.093<br>(.089)|-.117<br>(.088)|-.103**<br>(.052)|-.048<br>(.087)|-.003<br>(.087)|-.042<br>(.086)|
|Average plot size, by plot [in ha]|2,346|.616|-.134**<br>(.066)|-.052<br>(.061)|-.108**<br>(.054)|-.097**<br>(.048)||||
|Total average plot size, by owner [in ha]|1,612|1.39|-.481***<br>(.130)|-.357***<br>(.136)|-.398***<br>(.138)|-.192***<br>(.039)|-.160***<br>(.054)|-.11*<br>(.058)|<br>-.100*<br> <br>(.057)|
|Value of land per ha, by plot [in Cedis]|1,795|54,046|5,043<br>(8,964)|-5,862<br>(9,506)|19,385*<br>(9,919)|6,999<br>(6,285)||||

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on the existence of a female (male) plot. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

29
computerization to that of a reduction in stamp duties can ideally provide a bound on the magnitude of the associated reduction in transaction cost.

# **3. Data and estimation strategy**

Data on credit disbursed by commercial banks and the number of total registered transactions for 19972007 point towards significant increases in supply of credit as well as land sales. To assess whether any of these increases can be attributed to computerization, we use the roll-out of this intervention across SROs to identify the impact of computerization on credit supply and transaction frequency in a way that can be compared to that of stamp duty reductions. Below we discuss the approach as well as the assumptions required to hold in order to be able to do so.

# **3.1 Data and descriptive evidence**

We have quarterly data on credit disbursed by all scheduled commercial banks to retail customers for a 11 year period in all of AP’s 1064 _taluks_ (blocks) from India’s Central Bank (Reserve Bank of India, RBI).<sup>5</sup> This provides a total of 44 observations per _taluk_ , from Q1 of 1997 to Q4 of 2007, i.e at least 7 quarters before the first batch of registry offices was computerized to 2 years after the last of the state’s SROs had shifted to operate on a fully computerized basis. The quality and reliability of these data derives from the fact that they are based on routine reports which all scheduled commercial banks are required to submit to RBI at regular intervals. To match them to information on computerization, we aggregate up from the branch to the _taluk_ level. This information is combined with annual data on the total number of registered land transactions (incl. sales and mortgages separately) for the period of 1997-2007 from the Andhra Pradesh Department of Stamps and Registration for 242 of the 387 SROs where data predating computerization were available in electronic form. As rural and urban land administration systems will differ from each other, we also use the 2001 Census of India to obtain the share of urban population in any _taluk_ or SRO.<sup>6</sup> Figure 2 provides a graphical illustration of the dynamic nature of AP’s land and credit markets over the period under concern. Overall, the volume of credit disbursed more than tripled in real terms, from approximately Rs. 700 billion in 1997 to Rs. 2,500 billion in 2007 (i.e. some Rs. 15,000 or US $ 300 per person). _Prima facie_ most of the credit expansion happened after 2003, at a time when about two thirds of SROs in the state had already been computerized for some time. This suggests that more detailed analysis will be needed to assess the extent to which computerization is a factor driving the observed credit

> 5 Scheduled commercial banks include all public and private sector banks with the exception of cooperative banks who contributed less than 10 percent to total lending from 1997 to 2006 (RBI 2008).

> 6 In a small number of cases where SROs did not match with taluk boundaries, we used lists of villages from the 2001 Census to establish a match.

9
18 from the supply-side data are broadly consistent with those of the household data, and also show that the NCMS has had significant impacts on bed-occupancy, staffing, and capital investments, at least at township-level providers. One important difference between the demand- and supplyside estimates, which may be due to the different geographic coverage of the two samples, concerns the increase in inpatient care. In contrast to the household data, the facility data suggest that the increase in inpatient episodes has primarily been at township level.

Our finding that NCMS has increased utilization of services is not especially surprising, and is consistent with the previous literature on subsidized health insurance programs and health insurance programs more generally. For example, all the studies to date find that coverage by Vietnam’s health insurance program is associated with higher rates of utilization (Jowett, Contoyannis and Vinh 2003; Trivedi 2003; Jowett, Deolalikar and Martinsson 2004; Wagstaff and Pradhan 2005; Sepehri, Simpson and Sarma 2006; Wagstaff 2006). Mexico’s _Seguro Popular_ scheme also appears to have increased utilization (Gakidou et al. 2006). In Colombia, coverage by the subsidized program has been estimated in all studies to increase preventive and ambulatory care (Panopoulu and Velez 2001; Trujillo, Portillo and Vernon 2005; Gaviria, Medina and Mejía 2006), though not—with the exception of one study (Trujillo, Portillo and Vernon 2005)—hospital utilization.<sup>33</sup> By contrast, our finding that NCMS has not reduced out-of-pocket spending or the risk of catastrophic spending is somewhat surprising, and is at odds with the literature on other countries. Most studies of Vietnam’s insurance program find that coverage reduced out-ofpocket spending and the risk of catastrophic payments (Jowett, Contoyannis and Vinh 2003; Trivedi 2003; Wagstaff and Pradhan 2005; Sepehri, Sarma and Simpson 2006; Wagstaff 2006), though in one study the effect is not significant (Trivedi 2003) and in another the effect goes from being positive to negative when unobserved heterogeneity is taken into account through a fixed effects specification (Sepehri, Sarma and Simpson 2006). In Colombia, enrollment in the subsidized insurance scheme has been found to reduce out-of-pocket spending (Panopoulu and Velez 2001), and in Mexico coverage by _Seguro Popular_ has been found to reduce the risk of catastrophic out-of-pocket spending (Gakidou et al. 2006; Knaul et al. 2006). The effects in these studies are not always large, however—around a 20% reduction in out-of-pocket spending two

> 33 It has been hypothesized by Gaviria et al. (2006) that the subsidized scheme encourages people to switch from the hospital emergency room (the cost of which is covered by the taxpayer for the uninsured) to ambulatory facilities (the cost of which is not covered by the taxpayer for the uninsured).