rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Global Policy Selectivity and CPIA (1991–2015) Dependent Variable: CPIA, log

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Lagged GPS|0.032<sup>_∗∗∗_</sup><br>(0.008)|0.043<sup>_∗∗∗_</sup><br>(0.008)|0.045<sup>_∗∗∗_</sup><br>(0.011)|0.067<sup>_∗∗∗_</sup><br>(0.015)|
|=1 if year before 1998|-0.140<sup>_∗∗∗_</sup><br>(0.012)|-0.066<sup>_∗∗∗_</sup><br>(0.016)|-0.129<sup>_∗∗∗_</sup><br>(0.014)|-0.046<sup>_∗∗_</sup><br>(0.019)|
|Region-Year FE|No|Yes|No|Yes|
|GPS without outliers|No|No|Yes|Yes|
|N|3064|3064|3064|3064|
|R-squared|0.084|0.201|0.084|0.201|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally. A dummy variable that is equal to one for all years before 1998 is included to capture the scale change in the CPIA measure. All regressions use recipient-year observations and include a constant term that is not reported. Regressions (2) and (3) include region and region-year fixed effects that are not reported. Year level cluster-robust standard errors in (1) and (3) and region-year level cluster-robust standard errors in (2) and (4) reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent Table 5: Global Policy Selectivity and CPIA (1998–2015) Dependent Variable: CPIA, log

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Lagged GPS|0.042<sup>_∗∗∗_</sup>|0.037<sup>_∗∗∗_</sup>|0.119<sup>_∗∗∗_</sup>|0.134<sup>_∗∗∗_</sup>|
||(0.013)|(0.013)|(0.038)|(0.041)|
|Region-Year FE|No|Yes|No|Yes|
|GPS without outliers|No|No|Yes|Yes|
|N|2323|2323|2323|2323|
|R-squared|0.002|0.145|0.003|0.146|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally. All regressions use recipient-year observations and include a constant term that is not reported. Regressions (2) and (3) include region and region-year fixed effects that are not reported. Year level cluster-robust standard errors in (1) and (3) and region-year level cluster-robust standard errors in (2) and (4) reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent levels in recipient countries. The effect is small, however. The coefficient reported in (4) in Table 4 implies that a 10 percent increase in the share of policy selective aid is associated with a 0.67 percent increase in the CPIA index. In this specification we control for region-year fixed effects and we use our GPS measure without outliers. We notice that this effect doubles when we use the 1998-2015 time 18
# **9. References**

- Acemoglu, D., and D. H. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” _In_ O. _Ashenfelter and D. E. Card (Eds.) Handbook of Labor Economics, Volume 4_ : 1043–1171. Amsterdam: Elsevier.

- Aedo, C., J. Hentschel, J. Luque, and M. Moreno. 2013. “From Occupations to Embedded Skills: A Cross-Country Comparison.” _World Bank Policy Research Working Paper_ 6560. Washington, DC: World Bank.

- Almlund, M., A. L. Duckworth, J. J. Heckman, and T. Kautz. 2011. “Personality Psychology and Economics.” _In E. A. Hanushek (Ed.). Handbook of the Economics of Education, Vol. 4_ , 1–181. Amsterdam: North Holland.

- Apedo-Amah, M. C., B. Avdiu, X. Cirera, M. Cruz, E. Davies, A. Grover, L. Iacovone, U. Kilinc, D. Medvedev, F. O. Maduko, S. Poupakis, J. Torres, and T. T. Tran. 2021. “Unmasking the Impact of COVID-19 on Businesses: Firm Level Evidence from Across the World.” _World Bank Policy Research Working Paper_ 9434. Washington, DC: World Bank.

- Apella, I., and G. Zunino. 2018. “Nonstandard Forms of Employment in Developing Countries: A Study for a Set of Selected Countries in Latin America and the Caribbean and Europe and Central Asia.” _World Bank Policy Research Working Paper_ 8581. Washington, DC: World Bank.

- Autor, D. H., F. Levy, and R. Murnane. 2003. “The Skill Content of Recent Technological Change: An Empirical Exploration.” _Quarterly Journal of Economics_ 118(4): 1279–1333.

Bellemare, M. F. 2015. “A Rant on Estimation with Binary Dependent Variables.” _Personal blog_ .

- ______. 2018. “Causal Inference with Observational Data: Nonstandard Dependent Variables.” _Lecture_ . University of Copenhagen, Denmark.

- Burning Glass Technologies. 2019. “Mapping the Genome of Jobs: The Burning Glass Skills Taxonomy.” _White Paper_ . Boston, MA: Burning Glass Technologies.

- Cammeraat, E., and M. Squicciarini. 2021. “Burning Glass Technologies’ data use in policy-relevant analysis: An occupation-level assessment.” _OECD Science, Technology and Industry Working Papers_ 2021/05. Paris: OECD.

- Caunedo, J., E. Keller, and Y. Shin. 2021. “Technology and the Task Content of Jobs across the Development Spectrum.” _NBER Working Paper_ 28681. Cambridge, MA: National Bureau of Economic Research.

- CSC (Malaysia’s Critical Skills Monitoring Committee). 2019. _Critical Occupations List 2018/2019: Technical Report_ . Kuala Lumpur: TalentCorp. Available at: https://bit.ly/3uZ6G4E

- Cirera, X., A. D. Mason, F. de Nicola, S. Kuriakose, D. S. Mare, and T. T. Tran. 2021. _The Innovation Imperative for Developing East Asia_ . World Bank East Asia and Pacific Regional Report. Washington, DC: World Bank.

- Cunningham, W. 2018. “Meeting the Jobs Challenge in Southeast Asia: Lessons from Cambodia, Myanmar, and Vietnam” in World Bank (eds). _Navigating Uncertainty: East Asia and Pacific Economic Update._ World Bank East Asia and Pacific Regional Report. Washington, DC: World Bank.

37
IBM/JDM-021 6 Table <!-- Start of picture text --> Model Structure<br>1. Substitution in Production and Demand<br>Domestic Supply<br>l.a Production and Its Allccation<br>Gross Output CET<br>!I Export Supply<br>Leontief<br>Value Added Intermediate<br>I I<br>CES Leontief<br>/ \ Composite Composite<br>Capital I.Ir Intermediate Intermediate<br>I I<br>.ES ~~~~~~CES<br>Domestic Imoted Domestic Imported<br>Intermediate Intermediate Intermediate Intermediate<br>l.b Consumer Demand<br>Consumption<br>I<br>LES<br>Domestic Imported<br><!-- End of picture text --> 2. Imoort SuOD1Y and Foreign Export Demand Import Supply: Infinitely elastic (except autos) Foreign Export Demand: Infinitely elastic (except agriculture) a
<!-- Start of picture text --> Female (base=Male)<br>Experts e ot) @<br>Experts + Celebrities Ooatho<br>Experts + Religious leaders ® COmoe<br>Safety @ @ joo<br>Efficacy and Pro-Social oo baaeao<br>Dynamic Social Norms ° O@ Dc ° °<br>Endemic @® CE®@<br>Variants cog 0 CO<br>-16 -1.2 -08 -04 00 04 08 41.2 1.<br>log Odds ratio<br><!-- End of picture text --> <!-- Start of picture text --> Age 40+ (base=Age 39-)<br>Experts e © emmjo00 ©<br>Experts + Celebrities © cajco<br>Experts + Religious leaders e OPCO 00<br>Safety ° wo ® |o eo<br>Efficacy and Pro-Social @® O@cD °<br>Dynamic Social Norms @Moo0pmoo o<br>Endemic ° a e °<br>Variants ° @o<br>-16 -1.2 -08 -04 00 04 08 41.2 1.<br>log Odds ratio<br><!-- End of picture text -->

<!-- Start of picture text --> Secondary or below (base= Tertiary)<br>Experts COOGHEEID<br>Experts + Celebrities mp Oo<br>Experts + Religious leaders Oo capa<br>Safety Oo oOofpMo0O fome)<br>Efficacy and Pro-Social e@ © @@moo00<br>Dynamic Social Norms o@ OM0® e e<br>Endemic a @ o<br>Variants o|}@ @o<br>-16 -1.2 -08 -04 00 04 08 41.2 1.4<br>log Odds ratio<br><!-- End of picture text -->

<!-- Start of picture text --> Health worker (base= rest)<br>Experts 4 @ ° codammao aD<br>Experts + Celebrities oo @<br>Experts + Religious leaders @ho<br>Safety coomop Oo Oo<br>Efficacy and Pro-Social @ 0G@)@oo<br>Dynamic Social Norms] @0 © 0 0O@/O 00 @<br>Endemic e o oO ° °<br>Variants e 00 0<br>-1.6 -1.2 -08 -04 00 04 08 1.2 1.6<br>log Odds ratio<br><!-- End of picture text -->
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE on a 24/7 basis in Ouagadougou, the water delivered increased by 70–80 percent, and collection bills from private customers increased from 85 percent to 95 percent (table 9).

With this remarkable performance, ONEA’s hidden cost of operational inefficiencies (box 1) has been reduced to nearly zero—making Burkina’s utility, by this measure, the most-efficient utility in SubSaharan Africa (figures 13 and 14).

# **Box 1. Hidden costs in utilities**

A monetary value can be attributed to observable operational inefficiencies—mispricing, unaccounted-for losses, and undercollection of bills, to mention three of the most conspicuous—by using the opportunity costs of operational inefficiencies, that is, tariffs for uncollected bills and production costs for mispricing and unaccountedfor losses. These costs are considered hidden since they are not explicitly captured by the financial flows of the operator. Hidden costs are calculated by comparing a specific inefficiency against the value of that operational parameter in a well-functioning utility (or the respective engineering norm), and multiplying the difference by the opportunity costs of the operational inefficiency.

_<u>Source:</u>_ <u>Briceño-Garmendia, Smits, and Foster 2009.</u>

**Figure 13. Evolution of hidden costs in Burkina’s water sector**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Collection Inefficiencies Underpricing Losses<br>Percentage of the revenues<br><!-- End of picture text --> _Source:_ Derived from Banerjee and others (2008).

**Figure 14. Hidden costs of selected water utilities, as percentage of the revenues**

<!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Mali Liberia Niger Cote d'Ivoire Ghana Benin Senegal Burkina Faso<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

24
# **Table 6 Impacts of sea level rise: Sub-Saharan Africa**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (T**<br>|**otal= 16,137,43**<br>|**8 sq. km.)**<br>|||
|Impacted area|18,641|28,083|42,645|59,661|77,253|
|% of total area|0.12|0.17|0.26|0.37|0.48|
||**Popula**<br>|**tion (Total= 46**<br>|**3,121,000)**<br>|||
|Impacted population|2,098,795|3,651,629|4,303,289|8,471,790|11,040,978|
|% of total population|0.45|0.79|0.93|1.83|2.38|
|<br>|**GDP (To**<br>|**tal= 963,974 m**<br>|**illion USD)**<br>|||
|Impacted GDP (USD)|2,228|4,368|7,143|10,897|13,722|
|% of total GDP|0.23|0.45|0.74|1.13|1.42|
||**Urban ext**<br>|**ent (Total= 109**<br>|**,372 sq. km.**<br>|**)**<br>||
|Impacted area|430|742|1,268|1,853|2,449|
|% of total area|0.39|0.68|1.16|1.69|2.24|
||**Agricultural e**<br>|**xtent (Total= 4,**<br>|**236,159 sq.**<br>|**km.)**<br>||
|Impacted area|1,646|3,404|6,595|11,231|16,145|
|% of total area|0.04|0.08|0.16|0.27|0.38|
||**Wetlands a**|**rea (Total= 805**|**,030 sq. km**|**.)**||
|Impacted area|8,902|13,551|20,625|29,078|37,864|
|% oftotalarea|1.11|1.68|2.56|3.61|4.70|

Within the region, the countries with greatest land area impact would be The Gambia and, to a lesser extent, Guinea-Bissau (reference Figure 3a). However, even in these two countries, the impact remains well below 4% with a 2m SLR, and it never raises above 10% except for The Gambia with a SLR of 4m or greater.

# **Figure 3a Sub-Saharan Africa: Country area impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter 5 meter<br><!-- End of picture text --> <!-- Start of picture text --> Congo<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>12<br>10<br>8<br>% Impact (Area)<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2<br><!-- End of picture text --> <!-- Start of picture text --> Cameroon Sudan D.R. Congo<br><!-- End of picture text --> <!-- Start of picture text --> The Gambia Guinea-Bissau<br><!-- End of picture text --> <!-- Start of picture text --> Senegal Sierra Leone Mozambique<br><!-- End of picture text --> <!-- Start of picture text --> Sao Tome and Principe Benin Mauritania<br><!-- End of picture text --> <!-- Start of picture text --> Guinea Madagascar Ghana Eritrea<br><!-- End of picture text --> <!-- Start of picture text --> Djibouti Gabon Nigeria<br><!-- End of picture text --> <!-- Start of picture text --> Togo Equatorial Guinea Liberia<br><!-- End of picture text --> <!-- Start of picture text --> Ivory Coast Somalia Kenya Namibia<br><!-- End of picture text --> <!-- Start of picture text --> Angola South Africa Tanzania, United Republic of<br><!-- End of picture text --> 23
83 ESTIMATES OF COSTS AND BENEFITS

**Table 5.5i** Demand Equations

|DEPENDENT VARIABLE:<br>Log of Gros<br>DEGREES OF FREEDOM:<br>73|s<br>Rent<br>(Adjusted|)|||
|---|---|---|---|---|
|R-SQUARE:<br>.23||F:|5.18<br>||
|R-SQUARED(ADJ):<br>.19||PROB>FP|.0010||
|VARIABLE|ESTIMATE|STD<br>ERROR|T STAT|PROB>|TJ|
|Intercept|5.016|1.437|3.49|.0001|
|Log of Consumption|0.277|0.150|1.89<br>|.0694<br>|
|**Log**Length<br>of Tenure|-0.283|0.079|-3.60|.0006|
|Household<br>Size|-0.001|0.029|-0.04|.9693|
|Household<br>Size<br>Squared|0.003|0.010|0.30|**.7654**|

# F. Price Elasticity of Demand

The fourth piece of information for the Olsen model, the price elasticity of demand, could not be directly estimated from the Kumasi housing data. Malpezzi and Mayo (1985) surveyed estimates of this key parameter in a number of developing countries, and found most estimates lie between -0.5 and -1.0. Hence these two values were chosen as upper and lower bounds for the benefit-cost measures.

# G. Estimating PmOc With Hedonic Indexes

Hedonic regression models can be used to estimate the second piece of information required - - PmQc, the rent that would be commanded by the ontrolled units in the absence of controls.

Hedonic house price models have been widely used in environmental economics to estimate air quality (Ridker & Henning, 1967); neighborhood parks (Weicher & Zerbst, 1973; Hammer et al, 1974); lakes and reservoirs (Knetsch, 1964; Darling, 1973); aircraft noise (Walters, 1975); green belts<sup>(Correll et</sup> al, 1978; Willis and Whitby, 1985); land use zoning (Avrin, 1977); environmental risk and uncertainty (Brookshire, et a., 1985); and option prices and values (Smith, 1985). Hedonic price models are also common in housing policy analysis and have been extensively used to estimate the berefits of subsidized housing programs (see, for example, Kraft and Olsen, 1977; Olsen and Barton, 1982; Hammond, 1987; and Clemmer, 1984).

The purpose here, however, is to use hedonic price models to estimate PmQc (market price for controlled quantity) by comparing rents for different kinds of dwellings in the controlled and uncontrolled sector. Hedonic equations are one way that rents for different dwellings can be compared, or rents for identical dwellings in different markets can be predicted.
for each treatment arm. We then run the following specification: where _i_ denotes a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects _αs_ and compute robust standard errors.

Given the presence of the other indicators in specification (1), the coefficient _βE_ on Extension compares outcomes for farmers in _T_ 4 relative to those in the control group (H1). It therefore measures the combined impact of the recommendations and agricultural extension services. _β I_ measures the impact of recommendation specificity by comparing farmers offered plot-level recommendations ( _T_ 1) with farmers in _T_ 2 who were offered cluster-level recommendations (H2); _βG_ measures the impact of the in-kind grant on farmers in _T_ 2, compared to farmers in _T_ 4 (a subset of H3). Finally, _βF_ measures the impact of autonomy by comparing farmers in _T_ 3 with those in _T_ 2 (H4). For completeness, in all results we also provide the p-value associated with the test that _T_ 1, _T_ 2 and _T_ 3 are different from zero.<sup>28</sup> Recall that the effect of flexibility/autonomy, and the effect of individual recommendations/specificity are measured in addition on the effect of the grant. Unless otherwise stated, we use the study sample of 678 farmers and we focus on intent-to-treat (ITT) estimates and examine spillovers (and hence possible SUTVA violations) in Section 5.2. In cases where the dependent variable is sometimes missing we report whether there is differential attrition, and if so, provide Lee bounds (Lee, 2009).

# **5 Short-Term Results**

## **5.1 Take Up**

Table 5 uses the sample of farmers that received soil analyses and recommendations during the intervention (arms _T_ 1 _− T_ 4) and examines the take up of the precision drill during sowing (column 1), the two fertilizer packages (columns 2 and 3), attendance at AEW group meetings and the total number of AEW plot visits (columns 5 and 6). Column 7 uses as the dependent variable the sum of the dependent variables in columns 1-3, 5 and 6 while column 8 uses a standardized index of the outcome in column 7. The take up of these items was verified both from farmer reports as well as administrative data and as a result, mis-classification is not an important concern. The penultimate row of Table 5 also reports the mean of the dependent variable among farmers in _T_ 4 (with the corresponding standard error). The control group is not included in these regressions as no intervention was offered to them.

> 28Note that we can recover the overall impact of being in any treatment arm by combining the _β_ -coefficients. Thus, the test that _T_ 1 = 0 is equivalent to _βE_ + _β I_ + _βG_ = 0, _T_ 2 = 0 is equivalent to _βE_ + _βG_ = 0 and _T_ 3 = 0 is equivalent to _βE_ + _βG_ + _βF_ = 0. Finally, _T_ 4 = 0 is equivalent to _βE_ = 0, which is already reported.

16
analysis, we construct virtual banks, i.e. we treat banks that merged during the sample period as one unit throughout the analysis.<sup>13</sup> Doing so yields a sample of six large retail banks, five of which ended the sample period as foreign-owned, and Banorte, which remained domestic during the sample period. We identify foreign-acquired banks with a dummy variable that takes on value one for the five banks that ended the sample period as foreign-owned starting with the quarter after the acquisition (we label this variable Foreign Acquisition).

Finally, our empirical analysis also incorporates a number of other country- and banklevel variables, depending on the estimations. In the aggregate, country-level analysis, we control for Mexico’s GDP per capita in constant prices. In the bank-level regressions, we control for a number of time-variant bank characteristics, such as size, loan-asset ratio, return on assets (ROA), operating costs, and net interest margins, computed from financial statements. In some bank-municipality-level regressions, we interact the foreign acquisition dummy with GDP per capita or the share of rural population at the municipality level in 1994. GDP and population data come from the Instituto Nacional de Estadística, Geografía e Informática (INEGI), Mexico’s statistical institute.<sup>14</sup>

# **IV. Methodology**

To analyze how outreach changed as a result of foreign bank acquisitions we exploit the variation in our data along the three dimensions of time, banks, and municipalities. Specifically,

> 13 Take the case of Banamex and Citibank. The latter acquired Banamex in 2001; we treat the two banks as one throughout the sample. Prior to 2001, we add the data for both banks to create one consolidated institution. We do this to avoid the artificial jump in outreach measures (branches, loans, and deposits) that we would otherwise observe at the time of the merger. We consider the merged bank to be foreign starting in 2001, since Citibank operations were very small relative to Banamex prior to 2001.

> 14 GDP data at the municipality level was constructed from value added information derived from the 1994 Economic Census conducted by INEGI. The share of rural population is defined as the share of population living in towns/villages with fewer than 2,500 inhabitants. Population data at the municipality level (both total population and the share of rural population) come from the 1995 Conteo de la Poblacion conducted by INEGI.

13
# **Table 4. Number of SEOs**

|||Full sample|||Full sample||Low- and|Middle-Incom|e Countries|Low- and|Middle-Inco|me Countries|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Stock Market Capitalization<br>(% of GDP)|0.364**<br>(0.147)|||0.372**<br>(0.143)|||0.577**<br>(0.255)|||0.571**<br>(0.242)|||
|Stock Market Total Value||0.335**|||0.337**|||0.335*|||0.370*||
|Traded (% of GDP)||(0.133)|||(0.142)|||(0.18)|||(0.188)||
|Stock Market Turnover|||0.069|||0.075|||0.056|||0.087|
|Ratio|||(0.101)|||(0.111)|||(0.121)|||(0.139)|
|Foreign Direct Investment,|-0.001|-0.001|0|0|-0.001|0|0.053|0.081|0.078|0.046|0.08|0.076|
|net inflows (% of GDP)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.069)|(0.078)|(0.071)|(0.072)|(0.079)|(0.074)|
|Political Stability and<br>Absence of Violence|-0.145|-0.166|-0.181||||-0.391|-0.654|-0.711||||
||(0.313)|(0.326)|(0.319)||||(0.522)|(0.523)|(0.537)||||
|Investment Profile||||-0.014|-0.012|-0.015||||-0.027|0.049|0.012|
|||||(0.067)|(0.089)|(0.084)||||(0.171)|(0.171)|(0.167)|
|Constant|1.889***|2.609***|3.260***|2.002**|2.715***|3.384***|0.506|1.776***|2.177***|1.069|1.807|2.520**|
||(0.557)|(0.299)|(0.293)|(0.811)|(0.747)|(0.675)|(0.976)|(0.524)|(0.558)|(1.665)|(1.372)|(1.212)|
|Country Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Time Fixed Effects|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Observations|368|296|322|359|290|316|150|135|145|147|135|145|
|R-squared|0.09|0.122|0.091|0.084|0.112|0.081|0.11|0.14|0.112|0.107|0.124|0.092|

_Note:_ The table reports fixed effects panel regressions using 5 years of data. The outcome variable is total number of SEOs in a year. FDI net flows as percentage of GDP, political stability and absence of violence/terrorism, as well as investment profile are used as controls. This analysis includes both country and year fixed effects. One-year lags (t-1) are applied to all the explanatory variables and controls. Markings *, ** and *** denote significance at 10%, 5% and 1%, respectively. Robust standard errors are presented in parentheses. All variables are described in Table A-7 and Table A-9 in the annex.

16