rafmacalaba/datause-encoder-data huggingface dataset:
percentage points less likely to be stunted than one who has not. Wealth reduces the probability of stunting by 26 percentage points, but only when the household is in the topmost quintile.

In the overall sample, a high level of E. coli at household point increases the probability of stunting by 6 percentage points (1.2d). Mother’s completion of secondary education and breastfeeding significantly decrease the incidence of stunting by 13 and 28 percentage points, respectively. In addition, improved sanitation and water treatment have a strong correlation with lower stunting. Appropriate water treatment decreases the likelihood of stunting by around 22 percentage points while improved sanitation that is private lowers stunting by 11 percentage points. As discussed before, water treatment is not a common practice in Bangladesh and was not effective in reducing contamination in households that use piped water and tube wells. Here, the significance of water treatment might reflect a greater hygiene awareness, education level, or wealth in the household and not necessarily the effectiveness of the practice.

Table 3 shows results from specification (2) which examines the likelihood of stunting when exposed to E. coli at the source point. In the 24-60 month group, the likelihood of stunting increases from 6 percentage points (2.1d) to 10 percentage points (2.2d) when the contamination variable is redefined from some contamination to high contamination. Similarly, in the 0-60 month group, the incidence of stunting almost doubles from 5 percentage points in (2.1e) to 9 percentage points in (2.2e). Improved sanitation, mother’s education and breastfeeding are strongly correlated with lower stunting for the 24-60 month group and the total sample. Mother’s completion of secondary education has the highest correlation with an over 25 percentage point decrease in the likelihood of stunting.

In both specifications, exposure to high levels of E. coli, breastfeeding, being in the topmost quintile, improved sanitation, and mother’s education have the most significant relationship with stunting for the 0-60 months age group. Being in the topmost wealth quintile is also significant in both specifications but only in the age group 23 to 60 months.

Annex Figures 6 and 7 present the predicted probabilities of stunting from specifications (1.1e) and (1.2e) as well as (2.1e) and (2.2e). The predicted probabilities show a consistent trend across all four specifications: children from the poorest quintile are most likely to be stunted if they are exposed to 11
_Global results_ . Now the question can be asked: Can the same methodology be applied globally? The answer is yes, but with one important caveat. When this methodology was applied to individual countries, it made sense also in implicitly assessing how rapacious or successful was the elite in extracting the surplus. Combined with an analysis of class structure of a given society (e.g., the share of the middle classes), it provided an insight into the social and political structure of a polity. At the global level, however, there is no single elite and no single government (then or now), and the application of the methodology makes sense certainly in an accounting sense, but perhaps much less as a tool that would enable us to gain further insight into the social structure of a given society (with society being here “the globe”). Yet, perhaps depending on one’s perspective (e.g., taking a “world-systems” perspective) the global inequality extraction ratio could be seen as more than an accounting tool. In effect, the results obtained below will, to some extent, lead us to pose, if not to answer, that question.

The “accounting” part is useful because we may want to know whether the low measured Gini in the early 19<sup>th</sup> century (around 43 Gini points) can in part be “explained” by the overall low level of global income. Or differently, whether the increase in global inequality that we observe in the last two centuries is associated with the removal of the constraint that low income sets to the maximum feasible inequality.

Using the data on global average income (GDP per capita) from Table 2 (line 3) and assuming the subsistence minimum to be $PPP 300 (as in Milanovic, Lindert and Williamson, 2009) we can directly calculate G* and T* (see Table 3).

17
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more than 3 percent of GSDP by 2009. Using its power to set annual borrowing ceilings for the states, the federal government effectively raised the states’ fiscal deficit ceiling to 3.5 percent in 2009 and 4.0 percent in 2010.

**Table 12 Annual Changes in Real Expenditures and Fiscal Deficits in India, by State, 2003–11**

**<u>(Percent)</u>**

|_State_|_Expenditu_<br>_(real a_<br>_Precrisis_|_re and net loans_<br>_nnual change)_<br> <br>_Postcrisis_|_Fiscal_<br>_(percent_<br>_domesti_<br>_[GS_|_deficit_<br>_gross state_<br>_c product_<br>_DP])_|_Fiscal_<br>_Precrisis_|_correction_<sup>_a_</sup><br>_Postcrisis_|
|---|---|---|---|---|---|---|
||_(2003–07)_|_(2009–11)_|_(2009–10)_|_(2010–11)_|_(2003–07)_|_(2009–11)_|
|Andhra Pradesh|7.9|–6.5|2.9|2.3|1.7|0.6|
|Bihar|6.1|12.8|3.0|4.0|4.3|–2.4|
|Gujarat|5.2|5.5|3.6|3.1|2.0|–0.3|
|Haryana|7.3|6.8|4.5|3.3|3.0|0.3|
|Karnataka|12.1|6.9|2.9|2.9|2.1|–0.1|
|Kerala|3.1|1.8|3.4|2.8|2.9|0.4|
|Madhya Pradesh|6.9|13.6|2.7|3.3|2.5|–1.1|
|Maharashtra|6.8|9.9|2.9|2.4|2.4|–0.6|
|Odisha (Orissa)|5.2|7.9|1.4|2.5|6.5|–2.3|
|Punjab|2.3|6.7|3.1|3.1|1.8|0.7|
|Rajasthan|5.4|0.4|3.9|2.3|4.1|0.7|
|Tamil Nadu|6.9|14.1|2.1|3.2|2.7|–1.1|
|Uttar Pradesh|10.8|6.6|3.6|3.8|1.5|0.8|
|West Bengal<br>Small and special<br>category states|4.4<br>8.1|7.7<br>14.2|6.2|4.4|1.9|–0.5|
|All states|6.9|7.6|||||
|Major coastal states|6.7|6.3|—|—|—|—|
|Major landlocked|7.2|7.6|—|—|—|—|

_Sources: (i)_ Reserve Bank of India state finances; (ii) individual state accounts; (iii) Central Statistical Organization _. Note:_ Base year is 2004/05. — not available.

- a. Figures show change in fiscal deficit to GSDP ratio; negative values indicate deficit expansion.

A significant number of states made use of the extra borrowing room: 8 of 14 states ran deficits in excess of 3 percent of gross state domestic product in 2009 and 2010 (Table 12). Five states in 2009 and three in 2010 had deficits of at least 3.5 percent. These findings vindicate the policy decision of the central government, in the sense that the states that needed it could and did make use of the additional borrowing room to stimulate aggregate demand and national output. Increases in states’ aggregate expenditure between 2008 and 31
# **Table 3: Urban Strata**

||_Nakhchirvan_<br>_AR_|_Absheron-_<br>_Guba_|_Mugan -_<br>_Salyan_|_Ganja-_<br>_Gazakh_|_Sheki_|_Lankaran_|_Shirvan_|_Karabah_|_Baku_|
|---|---|---|---|---|---|---|---|---|---|
|Period 2|-0.017|0.129|0.128|0.047|-0.060|-0.002|0.021|-0.011|0.051|
||(0.34)|(4.032)***|(2.23)***|(1.68)|(1.56)|(0.06)|(0.55)|(0.30)|(2.64)**|
|Period 3|0.087<br>(1.70)*|0.184<br>(5.756)***|0.271<br>(5.19)***|0.116<br>(3.99)***|0.102<br>(2.68)**|0.088<br>(2.15)**|0.076<br>(1.81)*|0.105<br>(2.83)**|0.066<br>(3.12)**|
|Period 4|0.042|0.209|0.317|0.098|0.090|0.062|0.102|0.062|0.148|
||(0.84)|(6.463)***|(5.72)***|(3.42)***|(2.33)**|(1.52)|(2.49)**|(1.56)|(7.27)***|
|**Demographic**||||||||||
|Proportion of hh member b/w 15 to 59|-0.555<br>(6.49)***|0.205<br>(3.178)***|-0.588<br>(7.86)***|-0.289<br>(7.06)***|-0.366<br>(3.78)***|||||
|Proportion of hh member b/w 15 to 59 and||||||-0.163||||
|male||||||(0.569)||||
|Proportion of hh member b/w 15 to 59 and||||||0.652||||
|Male squared||||||(1.69)***||||
|Proportion of hh member above 60 years old|-0.608||||-0.321|||0..076||
||(5.98)***||||(2.77)**|||(0.46)||
|Proportion of hh member above 60 years old||||||||0.467||
|squared||||||||(2.04)**||
|Proportion of hh member above 60 years old<br>and male|||-1.061<br>(6.05)***|||||||
|Household Adult Equivalent|-0.111|||-0.065||-0.219|-0.035||-0.227|
||(7.66)***|||(6.85)***||(5.43)***|(0.64)||(10.54)***|
|Household Adult Equivalent squared||||||0.012<br>(3.046)***|0.014<br>(2.68)**||0.015<br>(5.79)***|
|Household head married||||||||||
|Total living area (meter square)|||0.002<br>(1.70)**|||0.001<br>(2.35)**||||
|Number of hh member b/w 13 to 17||-0.071<br>(4.577)***||0.029<br>(2.10)***||||||
|Female household head|||||-0.090<br>(1.66)*|||||
|Total household member above 17 years old||-0.081||||||-0.313||
|||(9.14)***||||||(7.00)***||

52
Table 9

# Wage Prernium for Public Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|Cote<br>d'Ivoire|Mali|Senegal|
|---|---|---|---|---|---|---|
|Berthelemy and<br>Bourguignon<br>(1992; Table<br>4.4, page 98)|OLS<br>with dummy<br>variable<br>for govern-<br>ment jobs|||- 14.3 %<br>[CILSS, 1985;<br>N = 311]|||
|Lachaud<br>(1993a; Table<br>6, page 20)|OLS<br>plus Probit<br>for self-<br>selection<br>of jobs|20.5 %<br>[IIES, 1990-91;<br>N =234]|38.4 %<br>[VIES, 1990-<br>91; N =293]|12.6 %<br>[VIES, 1986-<br>87; N = 277]|34.8 %<br>[lIES, 1991;<br>N =379]|19.0 %<br>[IIES, 1991; N<br>=243]|
|Lachaud<br>(1993c; Table|OLS<br>with**dLummy**<br>variable<br>for public|||- 28.0 %<br>[Census, 1988;|||
|B, pages 45-46)|sector jobs|||N = 181,7571|||

(Continued) 44
- **Livestock** : Biofuels will have a mixed effect on the livestock sector. In cases where crops used for feed are diverted to ethanol, higher feed prices and higher livestock prices will result. The U. S. Department of Agriculture also predicts higher price of crops like soy, sorghum, alfalfa, and hay which are displaced due to greater planting of corn to meet biofuel demand. However, higher feed prices will be partially offset by increased supplies of DDGS, which is a coproduct of ethanol production and is a substitute for corn. The response by the livestock sector to changes in feed prices depends on the relative importance of protein (primarily soybean meal) versus energy (primarily corn) and the size of the price changes associated with these feed components. Since these production and price adjustments are small, increases in biofuels production over the near term is not expected to have a major effect on the livestock sector. In the future livestock facilities may relocate near biofuel refineries or vice-versa in order to lower transportation costs. In developing countries where the livestock depends more on agricultural wastes than on feed, the effects will be much smaller. However commercialization of cellulosic technologies that can convert agricultural wastes into ethanol can increase fodder scarcity.

• **Land** : Allocating land to biofuels means taking land away from other uses like food or environmental preservation. The demand for agricultural land will benefit landowners. In fact, it has been hypothesized tenant farmers may end up losing much of the benefits to landowners in the form of increased rent. Increased demand for land for farming could lead to expansion of the agricultural land base. This might result in marginal and environmentally sensitive lands being brought under production such as CRP lands in the United States, set-aside lands in Europe, and tropical rainforests in Indonesia (Ogg 2007, Curran 2004). In other cases biofuel plantations may come up at the expense of pastureland rather than cropland or forestland. It is estimated that in Brazil about 30 million hectares out of the 220 million hectares could migrate to crops with little impact on meat production due to technological advance (FBDS 2007). The conversion of such lands to crop production will release carbon sequestered in the soil into the atmosphere, which will offset some of the carbon benefits. One concern that has 53
the second follow-up.

In the pooled sample, the index shows an improvement equal to 8.0% of a standard deviation in the rst follow up, which is signicant at the 5.2% level. This improvement is maintained and increased to 9.4% of a standard deviation in the second follow up, which has a p-value of 7.2%. Thus overall the program improved outcomes in the schools. When we break down the index to the individual tests we administered and adjust the p-values for multiple testing we nd that all test scores improved in the rst follow up by similar amounts and their adjusted p-values are less than 10%. In the second follow up the improvement in math was lost but the one in French and the oral remained and are both signicant at least at the 10% level.

The improvement in overall test scores is largely driven by eects in third but not fth grade. There are large impacts of school grants on third grade test scores across all tests. Test scores increased by almost 3 percentage points, which is a large eect in light of the means (and standard deviations) of test scores. When we look at this aggregate index of the three tests, the school grant increases third grade school performance by 0.126 of a standard deviation at the rst follow-up. The eect on the index survives at 0.16 of a standard deviation through the end of grade three, indicating that the program impacts persisted two years after the grant was disbursed to schools.

It is interesting that a relatively small grant is able to improve children's learning outcomes to this extent. By contrast, in Glewwe and Kremer's (2006) survey of the recent literature on the eectiveness of improvements in school resources on students' learning in developing countries, there are several interventions that show no signicant impact. In developed countries, there are even fewer examples of successful school resource interventions (Hanushek (2006)).

It is possible that the intervention improved outcomes because it provided cash in a decentralized way to local decision makers, who could then put these funds to an ecient use. Nevertheless, there is abundant evidence of leakages in other similar grant programs across the world (Reinikka and Svensson (2004), Bruns et al. (2011)). If the extent of local capture of these funds is also substantial in Senegal then the results in this paper are even more remarkable because they would have been produced with minimal resources.

However, these eects are absent for fth graders: the impacts are numerically close to zero and statistically insignicant by any criterion. The standard errors of the estimates are similar across grades, but the point estimates are much smaller.<sup>13</sup> This is perhaps surprising. However,

> 13Therefore, the lack of statistically signicant results in grade 5 (but not in grade 3) does not appear to be due

14
# _Firm Performance_

In Table 5 we study the effect of the adoption of ICT on firm performance. In Panel A we estimate equation (1) with the change in the log of labor productivity on the left‐hand side and investment in ICT on the right. We focus on labor productivity as the firm‐survey does not contain information on the capital stock and we cannot compute total factor productivity. The first column reports the fixed effects estimate. As expected, ICT increases the productivity of workers. Investment in ICT causes labor productivity to increase by 7.4 percent. This result is robust to adding firm‐specific trends in column (2), which shows an increase in productivity of 7 percent. Columns (3) and (4) report results using fixed effects and instruments (column 4 controls for firm specific trends). As discussed in the previous section, the instruments are the availability of information interacted with firm‐size group. When ICT is instrumented, its estimated effects on labor productivity are of 21 and 20 percent. For completeness we also study the effect of ICT on firm size, as increases in productivity should be reflected in increases in sales. Panel B reports the effect of ICT on total firm revenue. The estimated effects when using instruments are of 165 and 159 percent.

The effects of ICT need not be equal across firms. The increase in labor productivity depends on how well the firm employees interact with the new stock of ICT which in turn depend on worker and firm characteristics. To study heterogeneous effects across firms, our estimating regression is ∆��� ���∆����� ���∆���������� ������ ��� ���� . (3) The variable ���� represents firm type defined as firm characteristics in the initial year of data. The coefficients of interest are �� and �� . The coefficient �� captures the average effect of ICT whereas the coefficient �� captures effects of ICT that vary by firm type. Results are reported in Table 6.

We start by studying heterogeneous effects according to whether firms are initially of high‐productivity type. High productivity is defined as labor productivity above the industry median. Results show that investment in ICT by high‐productivity firms causes an additional increase of 23 percent (column 4) relative to low‐productivity firms. In fact, the effect of ICT on productivity for low‐productivity firms is not statistically significant. The implication of this 14
# **Does Democratization Promote Competition?**

# **Evidence from Indonesia**<sup>**†**</sup>

Mary Hallward-Driemeier<sup>*****</sup> Anna Kochanova<sup>**</sup> Bob Rijkers<sup>***</sup> Originally published in the Policy Research Working Paper Series on _January 2020._ This version is updated on _February 2021._ To obtain the originally published version, please email prwp@worldbank.org.

## **JEL** : L11, O38, O47, N45, P16, P26, L60

**Keywords** : Corruption, cronyism, political connections, firm dynamics, Indonesia, manufacturing, state– business relationships, political turnover, democratization

> * Corresponding author: Bob Rijkers, The World Bank, 1818 H Street, N.W., Washington, DC 20433. Phone: +1(202) 4730426. E-mail: brijkers@worldbank.org.

† We are very grateful to Ahmed Mushfiq Mobarak and Denni Purbasari for sharing their data on political connections to Suharto with us. We would like to thank Nicolas Gomez Parra and Nicolas Santos Villagran for excellent research assistance. Research for this paper has been supported in part by the Knowledge for Change Program, the Multidonor Trust Fund for Trade and Development, and the Strategic Research Program of the World Bank. It also benefitted from funding from the World Bank’s Research Support Budget. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent.

> * The World Bank, 1818 H Street, N.W., Washington, DC 20433. Phone: +1(202) 473-9120. E-mail: mhallward@worldbank.org.

**  Cardiff Business School, Aberconway Building, Colum Dr., CF10 3EU, Cardiff, United Kingdom. Phone: +44(0) 29-20876627. E-mail: kochanovaa@cardiff.ac.uk.

*** The World Bank, 1818 H Street, N.W., Washington, DC 20433. Phone: +1(202) 473-0426. E-mail: brijkers@worldbank.org.

1
for non-COVID related effects that may confound the _β_ 2020<sup>_ω_estimates.</sup> Therefore, by using the average pre-post (first) difference in consumption in the previous non-COVID years (2018-2019) as the counterfactual, our difference-in-differences estimate is expressed as: where _DID_<sup>_ω_</sup> essentially measures the changes in electricity consumption during the COVID months relative to the typical consumption levels in the same periods during the previous years. Standard errors are clustered at the household (firm) level.

# **5 Results**

In this section, we present the results on the effect of the COVID pandemic on electricity consumption in the two countries, as well as the role of electricity subsidies in Ghana.

## **5.1 Rwanda**

We start by showing the average effects of the COVID pandemic on electricity consumption in Rwanda, estimated separately for the various user types. For each user type, we estimate two variant specifications: with and without climate controls (average monthly temperature and total precipitation). Our preferred specifications are where we include the full set of climate controls, customer and district-year fixed effects, albeit the results are largely robust to the exclusion of the climate controls. Results are shown in Table 1.

The parameters of interest are the DID estimates, which compare the pre-post differences in consumption during the period March-December 2020 with the average pre-post differences in consumption over the same period in the pre-pandemic years. Thus, conditional on the fixed effects and the climate controls, pre-post differences in consumption over the same in the are assumed to be the in con- period pre-pandemic years changes sumption levels in the absence of the pandemic, i.e., counterfactual.

The results in Table 1 suggest that the pandemic led to a modest, 1.7% increase in (monthly) electricity consumption in Rwanda (column 2). However, there appears to be significant heterogeneity in the impact of COVID on consumption patterns across various users. For instance, while electricity consumption in the residential sector increased 9
At baseline, the average mother of a stipend-eligible child in a treatment district household in the LEAPS sample reports spending 516 minutes on housework on a typical school day. Column 7 – the specification with the full set of controls – suggests that the availability of the stipend program increases the amount of time mothers in stipend-eligible households spend on housework by approximately 100 minutes (1 hour and 40 minutes). This point estimate corresponds to an effect of roughly 19 percent when compared with mothers in stipend ineligible households.<sup>17</sup> This increase is the same order of magnitude regardless of whether I look at round 3 of the LEAPS survey or round 4. I fail to reject the null hypothesis that the two coefficients are equal. Together this is evidence that the effect of the stipend program persists from one year to the next.

Table 6 reports the results from a similar exercise conducted on mother’s time spent on paid work. In contrast to the results on housework, I find no appreciable change in the amount of time reported on paid work. I do find evidence of a substantial difference on the extensive margin however: mothers from stipend-eligible households in the treatment district are substantially more likely to stop reporting time spent on paid work over time when compared to mothers from similar households in the comparison districts.

While housework and paid work are typically the outcomes of interest in most studies, Table 7 exploits the availability of detailed data in the LEAPS survey and focuses on time spent looking after children’s needs. As in the case of paid work, there is a substantial reduction in the fraction of mothers reporting time spent on children’s needs in the treatment district relative to the comparison districts. On average, over time mothers report spending roughly 1 hour less on this type of activity if they live in the treatment district than if they live in the comparison districts.

> 17 I divide the point estimate by the sample average at baseline and multiply by 100 to arrive at this figure.

18