rafmacalaba/datause-encoder-data huggingface dataset:
section (N) is higher than the time series unit in panels (as in our case), the GMM system is more appropriate (Roodman, 2009).

# **4. Trends in Infrastructure Development across the Major Indian States**

This section ranks 18 major Indian states based on each of the three infrastructure development indices - physical, social, and financial - over the 2005 to 2019 period. The states are also ranked based on selected infrastructure facilities, such as road, rail, telecommunication, and electric power under physical infrastructure; school density, number of teachers, hospital beds, and number of doctors under social infrastructure; and bank accounts, bank branches, and post offices under financial infrastructure (see Tables A1, A2, and A3 in Appendix A).

**_Physical Infrastructure:_** The rankings of the states concerning overall physical infrastructure development for three different points of time (2005, 2010, and 2019) are presented in Table 2. It is evident from the index score that there exist wide interstate variations in terms of the availability of physical infrastructure facilities even though the coefficient of variation (CV) marginally dropped over the period. For the year 2005, Punjab topped the rankings, followed by Kerala and Himachal Pradesh. In contrast, Chhattisgarh, Bihar, and Jharkhand occupied the bottom three positions. The relative positions of the states remained more or less the same in 2010 and 2019, with minor changes in the top positions.<sup>10</sup> However, in the year 2019, Andhra Pradesh dropped significantly in the rankings to occupy one of the bottom positions along with Bihar and Jharkhand. This is due mainly to the division of states in 2014-15. The division of the state was followed by a dismal performance in the road network and power sector.  It also suffered from the deteriorating performance of the banking sector. On the other hand, Chhattisgarh has seen a significant improvement in its relative position mainly due to improved electricity capacity. Chhattisgarh has increased access to electricity to almost 100 percent of households by 2019.<sup>11</sup>

**Table 2: Relative Performances of States for Physical Infrastructure Index**

||**20**|**05**||**2010**|||**2019**||
|---|---|---|---|---|---|---|---|---|
|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_Score_**|**_Rank_**|**_State_**|**_Score_**|**_Rank_**|
|Punjab|0.532|1|Kerala|0.941|1|HP|1.791|1|
|Kerala|0.513|2|Punjab|0.896|2|Punjab|1.729|2|

> 10 However, in the year 2019, Andhra Pradesh dropped significantly in the rankings, possibly due to division of the state, to occupy one of the bottom positions along with Bihar and Jharkhand.

> 11 The state has augmented coal-fired  power generation along with smart city infrastructure, <u>https://csis-website-</u> - <u>prod.s3.amazonaws.com/s3fs public/publication/190920_U.S.IndiaState_Chhattisgarh_FINAL_update_v2.pdf.</u>

16
Zahian, A. "The Problem of Arab Brain Drain." In _PvcedW of_ a _Semnar on Aib Brai Drain_ organized by **EcwA.** Beirut Center of Arab Unity Studies, 1981, pp. 21-38. (In Arabic) Zahlan, A. "Arab Scientific Output." In _1roceedkjr of a Semnar on Paepring the Arab Indvdu to Coiw In Scenee,_ Amman, Jordan, May 13-16, 1985. (In Arabic) Zughoul, M.R. & Hussein, k.F. "English for Higher Education in the Arab World. A Case Study of Needs Analysis at Yarmouk University." _7he ES loumal, 4,_ 198S, pp. 133-152.

_33_
for the most efficient TK (Table 4). Higher total costs of brick production are found with the higher carbon tax, e.g., with a carbon tax of US$50 per ton of CO2, the total cost of production for ZZK increases by 12%, and with a carbon tax of US$100 per ton of CO2, the production cost increases by 27%. A carbon tax on coal is one of many economic policy tools that can be implemented to encourage coal kiln owners to switch to cleaner fuels and modern kilns in the country. The carbon tax on coal-using brick kilns is also sensitive to the type of technologies used in brick-making. This is because the CO2 emission intensity (e.g., gCO2/kg of fired brick produced) varies widely by the types of technologies used for brick-making. For example, coal-based clamp kilns have the highest average production-based emission intensity, while VSBKs have the lowest CO2 emission intensity (Figure 6). While TKs have advantages such as higher production capacity and more consistent firing results, they are more carbon-intensive than FC BTKs due to their higher fuel consumption and continuous operation. The non-firing blocks (CSEB and HCB) have the lowest CO2 emission intensity.

# _5.1.2 Substitution of coal with biomass pellets for ZZK brick kilns_

To reduce dependency on coal imports for brick production, various forms of alternate or renewable sources of energy must be developed. Biomass fuels, such as briquettes, pellets, wood chips, sawdust, and bagasse, in combination with coal, are commonly used in many traditional brick kilns in many South Asian countries. Biomass, such as pellets, is considered carbon-neutral because the CO2 released during its combustion is offset by the CO2 absorbed by the plants during their growth (assuming the plants are managed sustainably and not contributing to deforestation). When biomass is co-fired with coal, the overall carbon footprint would be smaller than that of burning coal alone. A survey study (Timilsina and Malla, 2023) observes that some of the brick kilns, particularly in the western part of Nepal, use biomass along with coal in the same kiln.<sup>13</sup> However, large-scale use of biomass is constrained by its limited availability due to strict forest regulations (Timilsina and Malla, 2023). If the biomass supply constraint is relaxed and it is used along with coal at various proportions, the costs of brick production change. Table 5 presents the costs of brick production when part of the coal is substituted by carbon-neutral biomass fuels, such

> 13 Small amounts of biomass fuels are always used for the initial firing of the coal in most of the brick kilns (Timilsina and Malla, 2023).

18
Table 11

# Cotton Productivity

Sensitivity to the Definition of the Reform

|||Model 1|||Model 2||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_φ_|0_._009|0_._009|0_._011|0_._01|0_._009|0_._011|
||0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|
|_F_ <sup>1</sup>|_−_0_._503|||_−_0_._505|||
||0_._058_∗∗∗_|||0_._058_∗∗∗_|||
|_F_ <sup>2</sup>|0_._193|||0_._19|||
||0_._039_∗∗∗_|||0_._039_∗∗∗_|||
|_R_<sup>1</sup>||_−_0_._426|||_−_0_._425||
|||0_._048_∗∗∗_|||0_._048_∗∗∗_||
|_R_<sup>2</sup>||0_._185|||0_._183||
|||0_._039_∗∗∗_|||0_._039_∗∗∗_||
|Dummy 1998|||0_._326|||0_._328|
||||0_._058_∗∗∗_|||0_._058_∗∗∗_|
|Dummy 1999|||_−_0_._143|||_−_0_._141|
||||0_._104|||0_._103|
|Dummy 2000|||_−_0_._368|||_−_0_._369|
||||0_._064_∗∗∗_|||0_._064_∗∗∗_|
|Dummy 2001|||0_._172|||0_._168|
||||0_._061_∗∗∗_|||0_._061_∗∗∗_|
|Dummy 2002|||0_._457|||0_._457|
||||0_._057_∗∗∗_|||0_._057_∗∗∗_|
|Constant|_−_0_._951|0_._706|0_._252|_−_0_._948|0_._708|0_._253|
||0_._309_∗∗∗_|0_._331_∗∗_|0_._534|0_._309_∗∗∗_|0_._331_∗∗_|0_._534|
|Observations|3418|3418|3418|3418|3418|3418|
|_R_<sup>2</sup>|0.15|0.15|0.16|0.15|0.15|0.17|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Model 1: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in monetary units. Model 2: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in physical units.

35
Figure 9: Roads and Armed Conflict in DRC <!-- Start of picture text --> 2000 2005 2010 2015 2020<br>year<br>road length completed events<br>400 800<br>300 600<br>200 400<br>road length completed<br>100 200 number of violence events<br>0 0<br><!-- End of picture text -->

**Notes** : The Figure shows the number of violent events sourced from UCDP and the sum of completed road projects per year for the DRC.

This could obviously be a coincidence–or contingent on external factors such as peace agreements–but if we look at specific provinces, the link becomes even stronger. Figure 10 shows the same data as Figure 9 but just for North Kivu, arguably the most conflictaffected provinces in eastern Congo, and we see an even stronger negative association between conflict events and road projects. Completed road projects seem to reduce violence. The same picture emerges if we focus on South Kivu. As previously discussed, this is not due to a strong selection away from violence in terms of provinces (Figure 5).

Figure 10: Roads and Armed Conflict in North Kivu <!-- Start of picture text --> 2000 2005 2010 2015 2020<br>year<br>road projects events<br>6 400<br>300<br>4<br>200<br>2<br>road project recent<br>100 number of violence events<br>0 0<br><!-- End of picture text -->

**Notes** : The Figure shows the number of violent events sourced from UCDP and the number of PRIO cells with a newly or recently (last year) completed road project for Nord-Kivu.

If the completion of road rehabilitation seems to correlate with an ensuing reduction of violent events, it is obviously of interest how long rehabilitated roads retain their peace effect. To visualize the effect of the discount factor we derived through remote sensing, we 19
**Annex Table A4: Exogenous determinants of household credit and nonfinancial corporation credit**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. Var.|ln_totalcredit|ln_hhcredit|ln_nfccredit|ln_totalcredit|ln_hhcredit|ln_nfccredit|
|legor_uk|0.3484**|0.9140***|0.1512|0.4330***|1.2452***|0.1827|
||(0.020)|(0.000)|(0.312)|(0.001)|(0.000)|(0.167)|
|legor_fr|0.0528|0.3394|-0.0550|0.2714**|0.7066***|0.1703|
||(0.717)|(0.164)|(0.719)|(0.029)|(0.004)|(0.179)|
|legor_ge|0.3456**|0.7814***|0.2050|0.5431***|1.2496***|0.3504***|
||(0.024)|(0.002)|(0.196)|(0.000)|(0.000)|(0.009)|
|legor_sc|0.4005*|0.2757|0.7588***|0.4383**|0.5925*|0.6596***|
||(0.086)|(0.421)|(0.002)|(0.012)|(0.054)|(0.001)|
|religion_protestant|-0.0026<br>(0.197)|0.0028<br>(0.313)|-0.0082***<br>(0.000)|-0.0025*<br>(0.061)|0.0026<br>(0.216)|-0.0068***<br>(0.000)|
|religion_catholic|-0.0032**|-0.0035*|-0.0028*|-0.0027*|-0.0027|-0.0023|
||(0.024)|(0.077)|(0.055)|(0.053)|(0.178)|(0.105)|
|religion_muslim|-0.0114***<br>(0.000)|-0.0192***<br>(0.000)|-0.0099***<br>(0.000)|-0.0105***<br>(0.000)|-0.0170***<br>(0.000)|-0.0096***<br>(0.000)|
|fdi|0.0080**|0.0070|0.0102**|0.0047*|-0.0032|0.0068**|
||(0.028)|(0.133)|(0.012)|(0.086)|(0.597)|(0.011)|
|ln_initialgdppc|0.3344***<br>(0.000)|0.4733***<br>(0.000)|0.2829***<br>(0.000)|0.2982***<br>(0.000)|0.3011***<br>(0.000)|0.2715***<br>(0.000)|
|financialopenness|0.0000***<br>(0.006)|-0.0000**<br>(0.047)|0.0000***<br>(0.001)||||
|tot|-0.0028|-0.0024|-0.0026||||
||(0.127)|(0.311)|(0.146)||||
|enrolment||||0.0037**<br>(0.037)|0.0112***<br>(0.000)|0.0020<br>(0.295)|
|tradeopenness||||0.0024***<br>(0.000)|0.0028***<br>(0.009)|0.0026***<br>(0.000)|
|Constant|3.9451***|2.0677***|3.7556***|3.0696***|0.5884*|3.0253***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.092)|(0.000)|
|Observations|141|141|141|203|205|203|
|R-squared|0.712|0.696|0.648|0.693|0.697|0.595|
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|
|F-stat legal origin|3.01|11.34|4.11|5.11|15.93|7.30|
|F-stat religion|19.37|11.12|21.38|17.97|8.96|22.26|

Regressions (1) – (3) are the first stage regressions for the 2SLS regressions in column (1) of Table 3A, 3B, 4A and 4B. Regressions (4) – (6) are the first stage regressions for the 2SLS regressions in column (1) of Table 5A and 5B. Credit variables and initial GDP per capita are in logs. All the other variables are in levels. Five-year average data are used. Robust p-values are reported in parentheses. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

24
One reason why this question might be asked is that the regulatory literature derived from Levy and Spiller (1994) emphasises country-specific constitutional, legal, economic, and political differences as being crucial for the success or failure of utility regulation. Hence, a highly reduced form model that abstracts from all those issues may well fail to reflect these local issues that seem to be so important in practice.

The answer to both these concerns lies in the importance of the country-specific _fixed effects_ . With 28 countries each having up to 21 years of data, we can obtain estimates of the fixed effects which should capture most if not all of the factors identified by Levy and Spiller and the subsequent literature. Hence, the estimated impact of eg enacting a regulatory law plus an autonomous regulator in Chile or Sudan (both countries in our sample) will be very different. That impact is the combination of (a) the predicted effect of the relevant regulatory variables **_plus_** (b) each country’s predicted fixed effect. The Chilean fixed effect is strongly positive relative to the sample average whereas that for Sudan is strongly negative.

In other words, the coefficients that we report are ‘highest common factor’ estimates of the impact of regulatory governance indicators where the fixed effects not just control for but effectively “wash out” all the Levy and Spiller and similar factors. But, this means that the regulatory governance effects that we report are not just _average_ cross-country sample effects but that they refer to a country with _average scores on country-specific fixed effects_ . Moreover, they are the impacts that one might expect, looking forward, for a country:

- With an average country specific fixed effect

- Implementing an average quality law

- Establishing an average quality autonomous regulator, etc.

It is for such a country that one might expect that implementing a best quality electricity regulator would increase per capita generation capacity in the long run by 15-25%.

The policy implication of this is, firstly, that the quality of overall country governance matters considerably for the impact of regulation on outcomes (eg as in the rule of law); and secondly, that countries cannot expect to achieve the gains we have estimated by enacting low quality regulatory laws or introducing autonomous regulatory agencies with very low staffing levels. But, the corollary is that the potential gains from introducing an electricity regulator could be significantly higher than the average level for countries with good overall governance who deliberately try to introduce best practice regulatory agencies and practices.

The argument above follows not just from the logic of our fixed effect modelling but is supported by the suggestive implications of a potential interactive and/or independent impact of overall country governance (eg as measured by the Kaufmann index) on our regulatory governance measure. (See Section 5.1.2.2 above.) These arguments do not, of course, apply just to this paper. They also apply to the similar models of Gutierrez, Edwards and Waverman, etc.

39
_CHAPTER 8. CHALLENGES AND POTENTIAL RESEARCH TOPICS_ 64 3. _Estimation of error variances in the FH model and associated MSE estimates:_ Error variances in the FH model are assumed to be known, because they cannot be estimated with area-level data. Typically, these error variances are replaced with the estimated sampling variances of the direct estimators acting as model response variables. However, these variance estimators are also based in the area-specific survey data, making them inefficient in small areas. Moreover, the usual estimators of the MSE for FH estimators do not account for the uncertainty due to the estimation of these error variances, which might be substantial. More research is needed to obtain efficient error variances and derive MSE estimators that account for the uncertainty due to the estimation of these variances.

4. _The Alpha model in unit-level models:_ The traditional ELL method includes heteroscedastic idiosyncratic errors, and EB or Census EB under the nested error model allows for the inclusion of known heteroscedasticity factors _kdi_<sup>2,althoughthispossibilityisnotimple-</sup> mented in the `sae` R package. In the ELL method, the error variances _σdci_<sup>2are firstly predicted through the so-called “Alpha model”,</sup> which uses a regression model in logistic form for the squared residuals _e_<sup>2in termsof possibly different covariates than those included</sup> _dci_<sup>,</sup> later in the regression of the welfare variable. Heteroscedasticity is likely to be needed in applications where the statistical units are households instead of individuals, due to the different households sizes. In general, before applying the Alpha model, it would be recommendable to check for evidences of heteroscedasticity in the particular data set using a formal test such as Wald test (Greene 2000, Section 12.5.3), and consider the Alpha model only when necessary. In any case, further research is needed on proper variable selection methods in models for heteroscedasticity within the SAE context.

5. _Machine learning techniques:_ Semi- or non-parametric machine learn-
through the increase in the probability of having a program placed in the village as well as through the included income, education, and community variables. We include tests to ascertain the validity of the proposed instruments.

The ancillary regression used to instrument the (endogenous) feeding post / Partage variable is reported in Table A1 in Appendix 1.<sup>6</sup> The coefficient estimates show that the intervention targeted villages with worse conditions such as villages with a low ratio of vaccinated children. This suggests that, unless instrumented, the OLS coefficient associated with the presence of a program will be downward biased, a finding in line with recent empirical findings of Behrman, Cheng, and Todd (2004) and Attanasio and Vera-Hernandez (2004) who also evaluated the impact of a nutritional program participation on the health outcomes of children. Their evidence suggest that, without taking into account endogeneity, the impact of the program would be underestimated.

We also instrument per capita consumption to take into account measurement errors in per capita household consumption that could lead to attenuation bias towards zero. We use per capita household income and the quality of the roof of the house where the child resides as instruments. The use of income as an instrument does not, however, address the possibility that a child’s health may affect income, possibly more through adult time allocation than the direct loss of child labor given the age of the children we are considering. The approach used here presumes that this is a minor issue relative to the problem of measurement error.

F-tests indicate that the proposed instruments have predictive power to explain the presence of programs and that they have no predictive power for malnutrition beyond their influence through the nutrition program variable. The latter test is based on regressing the height of the child on all regressors and instruments. This test allows to detect whether instruments have additional power on the current height of the child, beyond through the prediction of the program probability. The value associated to this F test is 1.40, suggesting that our instruments do not have a direct effect on malnutrition. Thus it is valid to exclude them from the malnutrition equation.<sup>7</sup> The results of the instrumenting equation, which are

> 6 As the placement of the program is at village level, the ancillary regression has been run on the sample based on the four round panel dataset made of village level observations; the dataset, thus, being different then the one used for our main regression.

> 7 We also derived a chi squared test following Wooldridge (2001). According to the test result we cannot reject the overidentifying restrictions. Different set of instruments have been tried, the coefficient associated with Partage ranging from 0.3 to 1.2.

10
**Table 2**

**Decomposition of area, yield, and production growth by region**

||**EECA**|**SEAO**|**SSA**|**MENA**|**LAC**|**EMDE**|**AE**|**All**|
|---|---|---|---|---|---|---|---|---|
|**A. 1961-63 average**|||||||||
|_Area (million hectares)_|188.4|397.5|88.8|43.4|73.3|791.3|180.2|971.6|
|_Yield (MCal/ha)_|3.9|3.7|3.0|3.4|4.5|3.7|7.5|4.4|
|_Production (million MCal)_|726.4|1,484.4|264.6|148.5|326.7|2,950.6|1,357.3|4,308.0|
|_Area (share, percent)_|19.4|40.9|9.1|4.5|7.5|81.4|18.6|100|
|_Production (share, percent)_|16.9|34.5|6.1|3.4|7.6|68.5|31.5|100|
|**B. 2019-21 average**|||||||||
|_Area (million hectares)_|149.5|592.1|245.5|71.9|171.2|1,230.2|192.6|1,422.7|
|_Yield (MCal/ha)_|10.1|11.3|5.1|6.7|12.5|9.8|17.8|10.9|
|_Production (million MCal)_|1,513.9|6,688.4|1,241.1|482.7|2,143.3|12,069.4|3,418.8|15,488.2|
|_Area (share, percent)_|10.5|41.6|17.3|5.1|12.0|86.5|13.5|100.0|
|_Production (share, percent)_|9.8|43.2|8.0|3.1|13.8|77.9|22.1|100.0|
|**C. Growth from 1961-63 to**|**2019-21**||||||||
|_Area (log change)_|-23.2|39.9|101.8|50.5|84.8|44.1|6.6|38.1|
|_Yield (log change)_|96.5|110.7|52.8|67.6|103.3|96.8|85.7|89.8|
|_Production (log change)_|73.4|150.5|154.5|117.9|188.1|140.9|92.4|128.0|
|**D. Contribution to growth**<br>|**from 1961**|**-63 to 20**|**19-21**||||||
|_Area (percent)_|-31.5|26.5|65.8|42.9|45.1|31.3|7.2|29.8|
|_Yield (percent)_|131.4|73.5|34.1|57.3|54.9|68.7|92.8|70.2|
|_Production (percent)_|100|100|100|100|100|100|100|100|
|<br>**E. Contribution to growth f**|**rom 1961**|**-63 to 201**|**9-21 (pro**|**duction-**|**adjusted**|**share)**|||
|_Area (percent)_|-4.2|10.3|4.7|1.4|4.8|22.9|1.9|29.8|
|_Yield (percent)_|17.5|28.5|2.4|1.9|5.9|50.3|24.9|70.2|
|_Production (percent)_|13.3|38.8|7.1|3.3|10.7|73.2|26.8|100|

**Notes:** Yield is measured as MCal per hectare. The changes from 1961-63 to 2019-21 are calculated as logarithmic changes. Some numbers may not add up due to rounding. Acronyms denote regions according to the FAO classification: LAC (Latin America and the Caribbean), SEAO (South and East Asia, and Oceania), EECA (Eastern Europe and Central Asia), MENA (Middle East and North Africa), SSA (Sub-Saharan Africa). EMDE (Emerging Markets and Developing Economies) denotes the sum of the five preceding regions. AE (Advanced Economies) includes North America, Northern Europe, Western Europe, and Southern Europe. All refers to all regions.

**Source** : Authors’ calculations based on FAO data.

_— 20 —_
<!-- Start of picture text --> Figure 5: Regional<br>Figure 4: Regional<br>representation of sample<br>representation, 2009 Census<br>Kidal<br>5%<br>Kidal<br>14%<br>Timbuktu<br>Gao Timbuktu 49%<br>42% 53% Gao<br>37%<br><!-- End of picture text --> In addition to the 500 households and 180 refugees, 50 local authorities were interviewed. Of those, 18 are based in Goa, 22 in Timbuktu and 10 in Kidal. Included were 38 village chiefs, 11 mayors and 1 local notable.

**Table 3: Authorities interviewed by function and by region (%)**

||**Gao**|**Kidal**|**Timbuktu**|**Total**|
|---|---|---|---|---|
|**Village chief**|13|8|17|38|
|<br>**Mayor**|5|1|5|11|
|<br>**Local notability**|0|1|0|1|
|**Total**|**18**|**10**|**22**|**50**|

The authorities interviewed are all men aged between 30 and 86. Forty-four percent are either just literate or have no education at all. In Gao, authorities have a higher level of education compared with Kidal and Timbuktu. The majority (almost 39%) of respondents in Gao have a high school education, 22% are literate and 17% have primary education. In Kidal, 80% of respondents have no education at all. In Timbuktu, about 32% of the surveyed authorities are literate, 27% have a high school education and 23% a primary education.

**Table 4: Level of education of authorities interviewed (%)**

||**Gao**|**Kidal**|**Timbuktu**|**Total**|
|---|---|---|---|---|
|**None**|11|80|5|22|
|**Literate**|22|0|32|22|
|**Primary**|17|0|23|16|
|**Middle school**|39|10|27|28|
|**High school**|**11**|**10**|**0**|**6**|

9