rafmacalaba/datause-encoder-data huggingface dataset:
timate the local average treatment effect (LATE) for districts near the cutoff (Imbens and Angrist, 1994). Figures 2(a)-2(f) in the Appendix show that there were no other discontinuities around the same cut-off in terms of the following 1991 district-level characteristics: employment rate, share of Scheduled Caste (SC) population, male-female sex ratio, number of primary schools per capita in the average village, and the share of villages that have paved roads and post offices. These figures further support the validity of our fuzzy RDD estimation strategy.<sup>12</sup> We implement the augmented procedure in Calonico et al. (2019) that permits the inclusion of additional covariates and inference using bias-corrected cluster-robust standard errors. Let the cut-off be denoted by _c_ , and the DPEP treatment assignment rule for district _i_ be _Ti_ = 1 ( _Xi < c_ ), where _Xi_ denotes district _i_ ’s female literacy rate in 1991. Further, let _Di_ be the observed treatment status of district _i_ . For outcome of interest, _Yi_ , assuming perfect district compliance with the treatment assignment criteria, the impact of the DPEP can be estimated by fitting the weighted least squares regressions of _Yi_ on _Xi_ above and below _c_ using the sample of _n_ districts: and, where **_β_**<sup>ˆ</sup> _−_ = ( _β_<sup>ˆ</sup> _−,_ 0 _, β_<sup>ˆ</sup> _−,_ 1) and **_β_**<sup>ˆ</sup> + = ( _β_<sup>ˆ</sup> + _,_ 0 _, β_<sup>ˆ</sup> + _,_ 1); _K_ ( _._ ) is the triangular kernel function, and _h_ is the mean squared error optimal bandwidth as computed in Calonico et al. (2019).

Assuming perfect compliance, the sharp RDD estimator of the DPEP’s impact on _Yi_ is ˆ then the difference between the estimated intercepts, or _τSRD_ ( _h_ ) = _β_<sup>ˆ</sup> _−,_ 0 _− β_<sup>ˆ</sup> + _,_ 0. However, in our case, district compliance with the treatment criterion is imperfect. So, we instead compute the fuzzy RDD estimator for program impact on _Yi_ as: where _ρ_ ˆ _SRD_ ( _h_ ) is the sharp RDD estimator using observed treatment status _Di_ for district _i_ as the outcome variable rather than _Yi_ . In other words, _ρ_ ˆ _SRD_ ( _h_ ) estimates the effect of crossing the cut-off _c_ on the probability of receiving treatment. The estimator _τ_ ˆ _FRD_ ( _h_ ) in (10) is unbiased and consistent under the assumption that crossing the cut-off _c_ only affects

> 12Our methodological approach is similar to that employed in other studies related to the DPEP, such as Khanna (2023) and Sunder (2020). These papers estimate program impacts on a different set of outcomes.

15
economy _i_ , %Internet users _i_ , log GDP per capita ln(GDPpc _i_ ), and youth (age 20-35) population share, Youth share _i_ . This specification allows us to control for the scale effect and interpret the results as the correlation of other independent variables with the intensity of generative AI usage. We include the potential channels that may affect generative AI usage at the country level, indicated as vector _Xi_ . We also include the month fixed effects _δt_ to control for seasonality as well as global macro conditions in this panel regression specification. Notably, we use the GDP per capita and other indicators from 2022 or the most recent available data prior to 2022. This selection is driven by data availability and aims to mitigate reverse causality where the widespread adoption of generative AI adoption might influence key economic indicators, such as GDP per capita and exports of digitally delivered services. The summary statistics for the dependent variables are presented in Table A1, and the summary statistics and data source of independent variables are listed in Appendix Table A2.

Table 3 juxtaposes factors affecting the usage of ChatGPT and Google as a comparison. In Column (1), we regress ChatGPT’s monthly traffic (log) on four basic variables of an economy: GDP per capita, the percentage of population using the internet, population, and the percentage of the population aged 20-35. The coefficient of log population is slightly over 1, indicating an approximate unity scale effect. Internet user share is strongly related to monthly visits. A one percentage point increase in internet user share is associated with a 3.8% rise in ChatGPT visits. This coefficient is above 1%, implying a potential spillover effect among internet users: ChatGPT spreads faster and is more intensively used as the internet user base grows. We include the percentage of the population aged 20-35 to control for the demographic structure of an economy. The positive coefficient is consistent with the previous descriptive evidence that youth population is the main user of ChatGPT. Before adding other factors, the coefficient of log GDP per capita reads 0.538. The same specification is applied to Google’s monthly traffic in Column (6), yielding results very close to Column (1), except that the younger population is negatively correlated with Google’s traffic, as Google users are distributed more evenly across age groups (Figure 6).

We group other control variables into three categories: (1) digital infrastructure, (2) economy and industry structure, and (3) human capital. We explore the correlation between each category and ChatGPT’s usage in Columns (2)-(4) of Table 3. Fixed broadband penetration and median download speed are both positively correlated with ChatGPT usage. The significant correlation 31
# **5.2 Results**

Table 3 reports main results obtained from the identification equation. Columns (1) and (4) report correlation between measures of market power and the main coefficient of interest, _λ_ 3. In summary, the results show a decrease of market power in the product market following the reduction of protection levels, whereas there was an increase in market power in the labor market after trade liberalization. The results are further reinforced when control variables are added, as done in the remaining columns.

Table 3: Main Results

||Pr|oduct Mark|et|la|bor Marke|t|
|---|---|---|---|---|---|---|
|VARIABLES|_μ_<sup>_m_</sup><br>_ijt_<br>(1)|_μ_<sup>_m_</sup><br>_ijt_<br>(2)|_μ_<sup>_m_</sup><br>_ijt_<br>(3)|_γijt_<br>(4)|_γijt_<br>(5)|_γijt_<br>(6)|
|_τ_1991_× Post_1995|-0.00805**|-0.0181**|-0.0182**|0.00921***|0.0108***|0.0107***|
||(0.00361)|(0.00690)|(0.00693)|(0.00246)|(0.00302)|(0.00289)|
|_ωit_||1.570***|1.554***||0.0838**|0.0833**|
|||(0.171)|(0.173)||(0.0286)|(0.0279)|
|Skill Ratio||0.0665|0.0817||0.145|0.150|
|||(0.155)|(0.154)||(0.165)|(0.170)|
|Small size frms||0.101|-0.0901*||0.289***|0.258***|
|||(0.0840)|(0.0432)||(0.0441)|(0.0638)|
|Medium size frms||0.0559|-0.0906**||0.0909**|0.0683***|
|||(0.0664)|(0.0340)||(0.0359)|(0.0176)|
|_ωit ×_small size frms|||0.0232***|||0.00350|
||||(0.00353)|||(0.00683)|
|_ωit ×_medium size frms|||0.0177*|||0.00264|
||||(0.00830)|||(0.00362)|
|Inverse Mills Ratio||||0.0903*|0.142*|0.140*|
|||||(0.0465)|(0.0635)|(0.0666)|
|Constant|1.523***|-12.44***|-12.30***|0.237***|-0.836**|-0.832**|
||(0.0701)|(1.522)|(1.537)|(0.0780)|(0.349)|(0.338)|
|Observations|1,579|1,574|1,574|601|601|601|
|_R_<sup>2</sup>|0.024|0.483|0.484|0.046|0.120|0.121|
|Number of frm|223|223|223|152|152|152|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table report main results of specification 10. Columns (1) - (3) report results related to the product market while Columns (4) - (6) that of labor market. Robust standard errors clustered at three digit industry level in parentheses in all columns. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

Firm-level productive efficiency, _ωit_ , is positive and significant, indicating that firms 21
In the next stage, we employ the TVEM method to find the dynamic evolution for the estimated coefficients in a specification with the _f_lfp_r_ as the dependent variable and the first principal component as the explanatory variable (Figure 4). Throughout the entire period, the effect of the socalled human capital and demographic component is positive, albeit decreasing. The beginning of the period showed the highest impact, as the female participation was low. Later, the effect is reduced, though remaining positive, as the _f_lfp_r_ increases. Specifically, the human capital effect remained high during the first decade (1960-1969), with a coefficient of 6.8 percentage points (pp), whereas during the decade (2000-2018), the estimated coefficient was 3.1pp, on average. It is worth noticing that during 1960-1990 the behavior of the component is similar to the evolution of the fertility rate.

**Figure 4: First principal component**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Component 1<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

The second component, primarily linked to violence, had a relatively stable impact on the _f_lfp_r_ until the early 1990s, when it began to increase (Figure 5). Between 1960 and 1991, the coefficient averaged 0.7 pp. However, between 1992 and 2018, there was an upward trend with 3.0 pp on average. The results indicate that men were more affected by political and drug-related violence than women, increasing women's participation, as discussed in more detail in the next section.

10
87 Psacharopoulos, George (1985). “Returns to Education: A Further International Update and Implications.” _The Journal of Human Resources_ . Vol. 20, No. 4, pp. 583604.

Psacharopoulos, George (1994). “Returns to Investment in Education: A Global Update.” _World Development_ . Vol. 22, No. 9, pp. 1325-1343.

Reardon, James, Ron Hasty and Barbara Coe (1996). “The Effect of Information Technology on Productivity in Retailing.” _Journal of Retailing_ . Vol. 72, No. 4, pp. 445461.

Romer, Paul M. (1986). “Increasing Returns and Long-Run Growth.” _Journal of Political Economy_ . Vol. 94, pp. 1002-37, October.

Romer, Paul M. (1990a). “Endogenous Technical Change.” _Journal of Political Economy_ . Vol. 98, pp. S71-S102.

Romer, Paul M. (1990b). “Human Capital and Growth: Theory and Evidence.” _Carnegie-Rochester Conference Series on Public Policy_ . Vol. 32, No. 0, pp. 251-86.

Sachs, Jeffrey D. and Andrew M. Warner (1995). “Economic Reform and the Process of Global Integration.” _Brookings Papers on Economic Activity_ . Vol. 1 (August), pp. 1-118.

Schreyer, Paul (2000). “The Contribution of Information and Communication Technology to Output Growth: A Study of the G7 Countries.” _STI Working Paper 2000/2_ , Organisation for Economic Co-operation and Development, Paris.

Solow, Robert M. (1956). “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics. Vol. 70 (February), pp. 65-94.

Solow, Robert M. (1957). “Technical Change and the Aggregate Production Function.” _Review of Economics and Statistics_ . Vol. 39, pp. 312-320.

Temple, Jonathan (1999a). “The New Growth Evidence.” _Journal of Economic Literature_ . Vol. 37, March, pp. 112-156.

Temple, Jonathan (1999b). “A Positive Effect of Human Capital on Growth.” _Economics Letters_ . Vol. 65, pp. 131-134.

Whelan, Karl (2000). “Computers, Obsolescence, and Productivity.” _Finance and Economics Discussion Series_ . 2000-6, Federal Reserve Board, Washington, DC.

World Bank (2003a). _Engendering ICT: Ensuring Gender Equality in ICT for Development_ . Washington, D.C., September.
-10-

**Table 3: The Structure of Income: Bulgaria and Selected Comparators**

||Wages and<br>Salaries|Social<br>Transfers|Self-<br>employment|Other<br>Income|
|---|---|---|---|---|
|Bulgaria:<br>1992a|38.1|24.2|31.6|6.1|
|1989b|56.5|21.2|14.7|7.6|
|E. European Countries'|||||
|Czechoslovakia|69.5|25.4|3.4|1.7|
|Hungary|55.0|22.4|14.0|8.6|
|Poland|57.2|22.1|19.6|1.1|
|Average|60.6|23.3|12.3|38|
|OECD Countriesb|||||
|Australia|71.2|9.8|8.5|8.0|
|Germany|63.1|16.5|16.7|1.1|
|Sweden|64.5|29.3|3.7|2.7|
|UK|72.0|17.2|4.5|2.7|
|USA|75.8|8.1|6.7|5.8|
|Average|69. 3|16.2|84|1|

**_'_** Bank staff calculations from 1992 Individual Budget of Households. _b_ See Milanovic (1992), Tables I and 4.

**_Cl_** Includes self-employment, in-kind income, farm produce and home-made goods (see Table 2)

income, far larger than both other East European countries and the OECD.

The Distribution of Income. The distribution of household income per capita by income decile is shown in Table 4. The Gini coefficient is 25.8 percent, indicating a low
# **Table 2. Scale elasticity parameters, RLMS 1994-2017**

||**Dependent variable:****_subjective wea_**|**_lth_**|
|---|---|---|
|**Scale parameters**<br>|**Pooled Ordered Logit**<br>**FE OLS**|**BUC**|
|Baseline elasticity<br>|0.636***<br>0.399***|0.407***|
|ea= βea/β<br>|_(0.032)_<br>_(0.060)_|_(0.088)_|
|<br>Additional child<br> <br>|0.078***<br>0.050**|0.048*|
|<br>b=βb/β<br>|_(0.012)_<br>_(0.021)_|_(0.026)_|
|<br>**_Overall elasticity _**e<br>**Note:**Standard errors in p<br>education level marital|0.636-0.078*_k_<br>0.399-0.050*_k_<br>arentheses are calculated using delta-method. All regressi<br>tatus employment status respondent`s poor health du|0.407-0.048*_k_<br>ons include age groups,<br>mmy whether there are|

**Note:** Standard errors in parentheses are calculated using delta-method. All regressions include age groups, education level, marital status, employment status, respondent`s poor health, dummy whether there are other household members in poor health, dummy indicating whether the person was employed at survey time and per capita living space and time effects as additional variables. Pooled model additionally includes gender, nationality and regional state effects.

28
**_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**|
|---|---|---|---|---|---|
|**Bacon and**<br>**Besant-Jones**<br>**(2001)**|_H0: Country policy and_<br>_institutional indicator are_<br>_positively correlated with_<br>_reform score._<br>_H1: Country risk is_<br>_negatively associated with_<br>_occurrence of reform score._|-reform scores: number of<br>reform steps taken by each<br>country in ESMAP (1999):<br>corporatisation,<br>restructuring, law,<br>regulator, IPP entry,<br>divestiture<br>(RM)|-country policy and institutional<br>indicator: based on 20 indicators,<br>which focus on macro<br>management and sustainability of<br>reforms, policies for sustainable<br>and equitable growth, policies for<br>reducing inequalities, and public<br>sector management<br>(CL)<br>_Result: significant (at 5% level of_<br>_significance)_<br>-country risk indicator : based on<br>a weighted average of 9 indices,<br>of which political risk and<br>economic performance each<br>account for 25% of the weighting<br>(CL)<br>_Result: significant (at 10% level_<br>_of significance)_<br>-GDP per capita (US$ per capita)<br>(CL)<br>_Result: insignificant_<br>-aid/GDP ratio<br>(CL)<br>_Result: insignificant_<br>-commercial energy use per<br>capita (MWh per capita)<br>(SE)<br>-annual growth rate of<br>commercial energy use per capita<br>(SE)|-regional dummies for<br>Latin America, Africa,<br>Asia, etc.<br>(CL)<br>_Result: very significant_|Cross-section data on the 115<br>developing countries<br>surveyed in ESMAP (1999).<br>The dependent variable is<br>from the study. The source of<br>other data (policy, risk, and<br>energy use data) is not<br>specified.|
|**Drillisch and**<br>**Riechmann**<br>**(1998)**|_H0: There is a correlation_<br>_between energy dependency_<br>_and choice of liberalisation_<br>_model._|-liberalisation index,<br>(RM)<br>composed by the following<br>elements:|-prices (US$),<br>(PI)<br>_Result: insignificant_|-“UK/ commonwealth”<br>dummy<br>(CL)<br>_Result: insignificant_|Data on countries: Australia,<br>Germany, Denmark, Spain,<br>France, Japan,<br>Norway, Netherlands, New|

50
positive impact of microfinance on various dimensions of household welfare in Bangladesh.<sup>3</sup> While there are differing views among studies about whether microfinance has significant impact on poverty of member households (see Morduch 1999), there is a broad consensus that microcredit improves welfare by reducing the _variability_ of consumption of borrowers and cushioning the impact of income shocks on households.<sup>4</sup>

# [Insert Table 5 here]

Thus a number of household level characteristics – notably household size and composition, occupation and education of household head, ownership of land and whether the household receives foreign remittances or not – appear to be strongly associated with its poverty status. Below we examine these correlations in a multivariate framework, which is useful for quantifying the relative importance of each household/location attribute in influencing household consumption.

# **III. Determinants of poverty from multivariate regressions**

Regressions of (log of) per capita expenditures – a proxy for household welfare – on a set of household and location-specific attributes are run separately for urban and rural samples of HIES 2005 data. The regression specification is as follows: Where yi is real consumption per capita and Xi is a vector of independent variables that influence consumption. The independent variables contain household and locationspecific variables. To identify the effect of location on household consumption, fixed

> 3 To cite one example, using a specialized survey, Khandker (2005) finds that both poverty and extreme poverty rates dropped faster among microcredit borrowers than among non-borrowers, with nearly half of the borrowers’ poverty reduction attributable to microcredit alone.

> 4 See Morduch (1999), Pitt and Khandker (1998), Zaman (1999) and Khandker (2005).

10
|Introduction<br>................................<br>1|
|---|
|Secondary Schooling in the Philippines<br>..........................<br>2<br>Comparative Effectiveness of Government and Private Schools<br>.....2|
|What Accotvat.;<br>for the Greater Effectiveness of Private<br>Schools?<br>..................................................<br>5|
|Method<br>...............................................................<br>10|
|Background<br>.....................................................<br>10|
|Models<br>.........................................................<br>12|
|Sample and Data ................................................<br>13|
|Student Variables<br>..............................................<br>14|
|School Variables<br>...............................................<br>15|
|Results<br>.18|
|Achievement Differences Between Schools<br>..... ..................<br>18|
|Other Differences Between Schools<br>.................<br>19|
|Explaining Differences Between Schools<br>.................<br>21|
|Summary and Conclusion<br>.................<br>31|
|Annex A .................<br>34|
|References<br>.................<br>35|

The World Bank does not accept responsibility for the views expressed herein, which are those of the authors and should not be attributed to the World Bank or to its affiliated organizations. The findings, interpretations, and conclusions are the results of research or analysis supported by the Bank; they do not necessarily represent official policy of the Bank. The comments of Linda Dove, Paul Glewwe, Stephen Heyneman, William Loxley, Neville Postlethwaite and the contribution of the IEA in making the data available to us are gratefully acknowledged.
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(5) Total annual regionally adjusted household expenditure (local currency, regionally deflated).

(6) Total food expenditure (nondeflated).

- (7) Total housing expenditure (nondeflated).

- (8) Total health and education expenditure (nondeflated).

(9) Total household energy expenditure (gas and electricity) (nondeflated).

*** p<0.01, **p<0.05, *p<0.1. All regressions control for year and district fixed effects, urban dummy, age, and years of education.

# **7. Robustness for district-level estimations**

# **7.2 Using production levels**

We continue by exploring the effects of mining intensity as proxied by district-level production volumes. The estimation will be similar to equation (2), but we replace the indicator variable for being an active mining district with annual gold production in the district: The measure of gold production is in 10 tons of gold produced, and gold_production�� is either _gold_year_district_ , which equals the total production of all mines in a district in the different survey years, or _gold_period_district_ , which equals total production for the years before the survey. For the 1993 survey, the period is 1990–93, for 1998 it is 1994–98, and so on.

Using production levels instead of an indicator of having any production in the district has the advantage of capturing the intensity of mining production. Since it is somewhat unclear when mining production spills over to other types of employment, we use two measures of mining production. Panel A of table 13 shows the results of mining production in the period before the survey, including the survey year, on female employment, and we see that mining production leads to less agricultural employment but more employment in services and sales, as well as in professional work. Panel B shows that the effects are larger but not as precisely estimated for the yearly measure. That they are larger is not surprising, since a 10-ton increase one year is much more than a 10-ton increase over a longer time period. The precision is also probably lower since it is unclear what year the production spills over to other activities. In any case, we see that the effects are similar across these two specifications.<sup>18</sup>

> 18 The effects for infant health and infant mortality are also stronger when we add production levels (results available upon request).

33