rafmacalaba/datause-encoder-data huggingface dataset:
|**Country**|**Intervention**|**Effect**<br>**Sizes**|**Country**<br>**GNI Per**<br>**Capita,**<br>**2015 PPP**|**Labor**<br>**Share of**<br>**Income,**<br>**latest**<br>**available**<br>**year**|**Average**<br>**income,**<br>**2015**<br>**PPP**|**Return to**<br>**literacy**|**Percentage**<br>**change in**<br>**earnings**|**NPV$**|
|---|---|---|---|---|---|---|---|---|
|Kenya|The Primary Math and<br>Reading (PRIMR) Rural<br>Expansion Programme|0.23|3,060|0.35|1,079|0.59|0.14|2,366|
|Brazil|Decentralised schooling<br>system|0.29|15,020|0.44|6,552|0.72|0.21|24,369|
|Uganda|Northern Uganda Literacy<br>Project (NULP)|0.3|1,780|0.25|447|0.59|0.18|1,278|
|India|Synthetic Phonics: kids<br>teached to read using<br>decoding and synthetic<br>phonics skills, teacher<br>training|0.48|6,020|0.29|1,769|0.51|0.24|7,020|
|Cambodia|The School Readiness<br>Programme (SRP)|0.5|3,290|0.29|951|0.25|0.13|1,824|
|Kenya|The Primary Math and<br>Reading (PRIMR) Rural<br>Expansion Programme|0.56|3,060|0.35|1,079|0.59|0.33|5,762|
|Kenya|The Primary Math and<br>Reading (PRIMR) Rural<br>Expansion Programme|0.57|3,060|0.35|1,079|0.59|0.34|5,865|

46
**Krause, Benjamin.** 2020. “Balancing Purse and Peace: Tax Collection, Public Goods and Protests.” _Mimeo_ .

- **Leung, Michael P.** 2022. “Rate-optimal cluster-randomized designs for spatial interference.” _The Annals of Statistics_ , 50(5): 3064 – 3087.

- **Liu, Jizhou.** 2023. “Inference for Two-stage Experiments under Covariate-Adaptive Randomization.” _arXiv:2301.09016_ .

**Melas, Viatcheslav B.** 2006. _Functional Approach to Optimal Experimental Design._ Springer New York.

- **Miguel, Edward, and Michael Kremer.** 2004. “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities.” _Econometrica_ , 72(1): 159–217.

**Moffit, Robert.** 2001. “Policy Interventions, Low-level Equilibria and Social Interactions.” In _Social Dynamics_ . , ed. Stephen N. Durlauf and Peyton Young, 45–82. MIT Press.

- **Moulton, Brent R.** 1986. “Random group effects and the precision of regression estimates.” _Journal of Econometrics_ , 32(3): 385–397.

- **Pomeranz, Dina.** 2015. “No Taxation without Information : Deterrence and Self-Enforcement in the Value Added Tax.” _American Economic Review_ , 105(8): 2539–2569.

- **Pomeranz, Dina, and Jos ́e Vila-Belda.** 2019. “Taking State-Capacity Research to the Field: Insights from Collaborations with Tax Authorities.” _Annual Review of Economics_ , 11(1): 755–781.

- **Puelz, David, Guillaume Basse, Avi Feller, and Panos Toulis.** 2022. “A graph-theoretic approach to randomization tests of causal effects under general interference.” _Journal of the Royal Statistical Society: Series B_ , 84(1): 174–204.

- **Sasaki, Yuya, and Yulong Wang.** 2022. “Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method.” _arXiv:2210.16991_ .

- **Silvey, Samuel D.** 1980. _Optimal Design: An Introduction to the Theory for Parameter Estimation._ Springer Netherlands.

- **Vazquez-Bare, Gonzalo.** 2023. “Identification and Estimation of Spillover Effects in Randomized Experiments.” _Journal of Econometrics_ , 237(1): 105237.

**Viviano, Davide.** 2024. “Policy design in experiments with unknown interference.” _working paper_ .

- **Weigel, Jonathan L.** 2020. “The Participation Dividend of Taxation: How Citizens in Congo Engage More with the State When it Tries to Tax Them.” _Quarterly Journal of Economics_ , 135(4): 1849–1903.

30
governing the storage policy. The policy is not designed to operate as a price band, with stocks released to prevent high prices; rather, stocks are released in a fixed amount every period to supply the PDS. In contrast, the trade policy mostly truncates high domestic prices. Together both policies are extremely effective in stabilizing the domestic price, because of the complementarity between the instruments. Trade policy prevents public stocks from being exported when the world price spikes, preventing any leakage from the operation of the storage policy to the world market.

**Table 3. Unpacking current policies, statistics (quantities in million tons)**

|**Variables**|**Laissez‐faire**|**Tradepolicy**|**Storage policy**|**Both instruments**|
|---|---|---|---|---|
|**Indiaprice(Rs/t)**|||||
|**Mean**|9,809|9,527|9,882|9,486|
|**CV(%)**|14.39|10.68|10.12|3.13|
|**Quantile 10%**|8,223|8,307|9,417|9,417|
|**Quantile 90%**|11,544|10,910|11,085|9,417|
|**RoWprice(US$/t)**|||||
|**Mean**|200.5|202.3|199.7|202.5|
|**CV(%)**|20.70|23.99|19.57|23.28|
|**Quantile 10%**|160.2|160.2|159.8|160.3|
|**Quantile 90%**|258.8|269.2|251.6|272.3|
|**India demand**|||||
|**Mean**|85.77|86.39|85.41|86.33|
|**CV(%)**|3.98|3.11|2.58|0.84|
|**Mean Indiaproduction**|87.13|86.66|87.37|86.62|
|**Mean India storage **|||||
|**Private**|0.10|0.02|0.00|0.00|
|**Public**|||4.21|10.38|
|**Mean RoWprivate storage **|4.05|4.78|3.69|4.75|
|**Mean exports**|1.38|0.27|1.96|0.18|
|**Mean imports**|0.02|0.00|0.01|0.00|

The policies have little effect on India’s wheat production. Since producers are assumed to be risk neutral in their behavior and their marginal cost linear in the production level, their production decisions depend not on price volatility but on the expectations of prices times the yield shock (equation (2)), which vary little between the policies considered, as evidenced by the mean price changes.<sup>11</sup> Under all scenarios, the model allows for the possibility of competitive domestic storage in India. In practice, stock holders find few profit opportunities under scenarios that include active trade and storage stabilization policies, since these policies rarely allow speculators to cover their costs. A significant level of private storage arises only when both storage and trade policies are

> 11 This result is confirmed in a context of risk-averse producers; see Annex 4. India’s wheat production is largely unaffected by the stabilization policies, unless producers are assumed to have a very high level of risk aversion.

23
# **1 Introduction**

After decades of uneven privatization across countries, the recent Covid-19 pandemic has brought back discussions about the role of state-owned enterprises (SOEs) and firms with state participation in the economy. Increasing economies’ resilience to shocks, using state participation as a mechanism for the diffusion of technologies for climate mitigation and adaptation, and ensuring the production of critical health products, technologies, and infrastructure in the midst of growing geopolitical tensions are being used to justify an increased presence of Businesses of the State (BOS) - defined as firms with direct or indirect state participation of at least 10% - in the economy.

In the case of Brazil, despite the large privatization program in the 1990s and the recent privatization processes carried out by the latest administrations, BOS are still important in some sectors - especially in infrastructure, extractive industries, and postal activities - and the state footprint has expanded to investments to private firms in competitive sectors. However, little is known about the impact of these BOS on markets and how effectively they play some of the roles they are supposed to play. This paper studies the effects of BOS in Brazil from different angles, particularly their effects on the labor market and business dynamism. The contribution of this paper is twofold. First, in addition to the traditional analysis of SOEs, the analysis uses a unique dataset that allows describing BOS in their different modalities, from fully public enterprises to private companies with minority state participation operating in competitive sectors. Second, in addition to measuring the effects of specific roles of the state footprint on the labor market, we also study the impact that BOS presence has on business dynamism.

Specifically, we first provide a descriptive analysis of BOS and find that, on average, BOS pay higher wages, are older, and are larger than their private peers. Next, we perform a series of estimates controlling for firms’ characteristics to examine how BOS differ from private firms in terms of innovation, employment and employment growth. Using data from 2016 to 2020, we find that BOS are more innovative and larger than fully private companies, consistent with findings in China (Lo et al., 2022). In particular, participated companies - private companies with public investments - are more innovative and have higher employment relative to other types of BOS, thus suggesting the public sector invests essentially in some key sectors and innovative private companies. Furthermore, BOS grew more in terms of employment, although it is only statistically significant for participated firms.

Following this initial assessment, we explore the link between BOS and the labor market by testing for a wage premium associated with BOS. BOS, especially public enterprises, have different wage-setting mechanisms, commonly resulting in a wage premium, which, in 2
disbursed. A second cause is the T&D losses that distribution companies are allowed by NEPRA for tariff determination are lower than the actual levels (GoP, 2013). State Electricity Regulatory Commissions (SERCs) grapple with a similar situation in India (Singh, 2006).

_(iv) Limited involvement of the private sector_ . Attracting foreign and domestic capital through private sector participation in the power sector was one of the major objectives of reforms in SAR (Victor and Heller, 2007). Hence, IPPs were allowed to sell electricity to the grid under state supported long-term contracts in the SAR. Reforms in the region thus have had some success in attracting private sector participation in power generation. For example, in both Nepal and Sri Lanka, IPPs’ share of installed generation capacity increased from 21% in 2004<sup>16</sup> to around 33% in 2012 (see Table 3 above). In India, the share of the private sector in the electricity generation capacity has grown to more than 34% by the year 2014 (CEA, 2014). In Pakistan, the share of the private sector increased from 30% in 2004-05 to 49.34% by June 2013 (GoP, 2013). In Bangladesh, the share of private sector in total generation capacity was 40% in July 2014.<sup>17</sup> Nonetheless, many IPP projects built rapidly to address electricity shortages are small in scale and not cost-effective.

Privatization of electricity distribution remains a politically sensitive decision in the region. The Indian state of Orissa was home to the first experiment, in 1999, with privatization of electricity distribution in the region. The initial hiccups in this exercise postponed further attempts at privatization elsewhere. Privatization of electricity distribution in Delhi, in 2002 was remodeled based on the experience in Orissa: it incorporated performance milestones based bidding criteria with declining government support. Evidence indicates that these and other privatized distribution companies have considerable decreases in technical and commercial power losses (IDFC, 2010). Privatization of electricity distribution in Karachi faced political hurdles, making it difficult to implement similar process elsewhere in the country.

> 16See Bhattacharya (2007) for the 2004 data on IPPs’ share in generation capacity.

> 17Most of the private sector investments in the electricity sector in South Asia are based on the private sector choosing to invest by itself, though there also have been some Public-Private Partnerships (PPPs). The PPPs have mainly occurred in the power generation segment under build-operate-operate (BOO) arrangements such as in India.

18
# The Quest for Subsidy Reforms in Libya

_Abdelkrim Araar_<sup>_1_</sup> _, Nada Choueiri_<sup>_2_</sup> _and Paolo Verme_<sup>_3_</sup>

**JEL:** D1, D3, D4, D6, H2, H4, H6, I3, O2 .

**Keywords** : Libya, subsidies, energy, food, welfare, income distribution.

> 1 University of Laval.

> 2 International Monetary Fund

> 3 World Bank. The authors would like to thank the Libyan authorities for the information, comments and advice provided throughout the study. In particular, we would like to thank the staff at the Ministry of Economy, Ministry of Finance, Ministry of Planning and the Central Bank who provided excellent support during the various missions to Tripoli.  The World Bank country and regional teams provided essential logistical support and various World Bank staff contributed with comments and advice including Marouane El Abassi, Bernard Funck, Khalid El-Massnaoui, Fanny Missfeldt-Ringius, Maria Vagliasindi and Heba Elgazzar. All remaining errors are the sole responsibility of the authors.
# **Table 6 Summary Statistics**

|**Variable**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
||**1980**|**-1989**||||
|**Log of GDP per capita**|71|8.52|0.95|6.05|10.09|
|**Openness Index**|71|50.91|31.11|7.53|142.98|
|**Log of Distance to the US**|71|8.45|0.55|6.98|9.15|
|**English**|71|0.41|0.50|0.00|1.00|
|**Military conflict**|71|0.38|0.49|0.00|1.00|
|**Communism index**|71|0.14|0.35|0.00|1.00|
||**1970**|**-1979**||||
|**Log of GDP per capita**|72|8.34|0.93|6.20|10.01|
|**Openness Index**|72|51.10|36.09|7.16|189.99|
|**Log of Distance to the US**|72|8.43|0.56|6.98|9.15|
|**English**|72|0.42|0.50|0.00|1.00|
|**Military conflict**|72|0.40|0.49|0.00|1.00|
|**Communism index**|72|0.07|0.26|0.00|1.00|

34
Table 8: Estimations of manufacturing <u>production functions and district specialization/diversity</u>

||||DV: Log out|put in manu|facturing est|ablishment,|2000 sample|||
|---|---|---|---|---|---|---|---|---|---|
||Full|Full|Full|Full|Full|Full|Traditional|Modern|Full|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Log employment in establishment|0.510<br>(0.028)**|0.512<br>(0.028)**|0.509<br>(0.028)**|0.513<br>(0.028)**|0.508<br>(0.025)**|0.508<br>(0.025)**|0.514<br>(0.028)**|0.393<br>(0.058)**|0.512<br>(0.028)**|
|Log capital in establishment|0.059<br>(0.008)**|0.059<br>(0.008)**|0.058<br>(0.008)**|0.058<br>(0.008)**|0.059<br>(0.008)**|0.059<br>(0.008)**|0.057<br>(0.008)**|0.080<br>(0.015)**|0.057<br>(0.008)**|
|Log materials in establishment|0.508<br>(0.016)**|0.507<br>(0.016)**|0.509<br>(0.016)**|0.507<br>(0.016)**|0.507<br>(0.015)**|0.507<br>(0.015)**|0.503<br>(0.016)**|0.568<br>(0.018)**|0.507<br>(0.015)**|
|District specialization index||0.007<br>(0.006)||0.015<br>(0.005)**|0.013<br>(0.005)*|0.013<br>(0.005)**|0.015<br>(0.005)**|0.015<br>(0.010)|0.018<br>(0.004)**|
|District diversity index|||0.008<br>(0.006)|0.015<br>(0.006)**|0.014<br>(0.005)*|0.015<br>(0.005)**|0.016<br>(0.006)**|0.003<br>(0.008)|0.016<br>(0.006)**|
|Manufacturing share of local||||||0.012||||
|employment||||||(0.016)||||
|(0,1) Plant in specialized industry x|||||||||-0.024|
|District specialization index|||||||||(0.024)|
|(0,1) Plant in specialized industry x|||||||||-0.012|
|District diversity index|||||||||(0.018)|
|(0,1) Plant in specialized industry|||||||||0.201<br>(0.155)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Additional urban traits|||||Yes|Yes||||
|Observations|137,090|137,090|137,090|137,090|137,090|137,090|124,022|13,068|137,090|

Notes: Estimations consider simple production functions for manufacturing establishments in India. Indian data are taken from Annual Survey of Industries and National Sample Statistics. Specialization and diversity indices are on a ten-point scale representing deciles for districts. Additional urban traits include Log manufacturing employment in district; Log manufacturing employment in a plant's district-industry; Log share of local manufacturing employment in the unorganized sector; and Log share of district-industry manufacturing employment in the unorganized sector. Estimations report standard errors clustered by district, include industry fixed effects, and weight observations by sample weights. + significant at 10% level; * significant at 5% level; ** significant at 1% level.
V. Wong will provide a conceptual framework for planning implementation of a Safe Motherhood program.

# BREAK: 3:30-3:45

# 2. COMPONENTS OF A SAFE MOTHERHQOD PROGRAM: 3:45-5:15. Room J-4009

- J. McCarthy will discuss Safe Motherhood programs and the indicators of effectiveness.

G. Walker will comment on the WHO Safe Motherhood research results and potential contributions to cost and effectiveness information.

Session Questions, V. Wong moderating:

1. What is the core package of interventions that make up a Safe Motherhood program?

2. Are there alternative compositions of Safe Motherhood programs?

3. What are the outputs? How do we measure effectiveness and impact?

4. What are the principal unanswered questions on the core program?

# 3. SPECIAL ISSUES IN COSTING METHODOLOGY: April 9. 9:30-11. Room H-2300

- B. Herz will discuss the basic approach to costing, as applied to The Safe Motherhood Initiative.

L. Forgy will discuss what is known about Safe Motherhood costing in work to date.

J. Walsh will present a brief description of work for the Bank's Health Sector Priorities Review project.

Session Questions, B. Herz moderating:

1. What major interactions exist among parts of the Safe Motherhood program that are important for determination of costs and effectiveness?

2. To what extent do optimal program costs change with the intended scale of operation, or with social and demographic factors? Are there threshold levels of operations that are important for effectiveness?

3. What type of inputs are used in Safe Motherhood programs? Which are shared with other programs, and how can the portion used for Safe Motherhood be determined?

29
11 Two standard indicators to gauge the overall use of NTMs across countries are the coverage ratio and the frequency index. The first captures the percentage of trade subject to non-tariff measures, while the latter shows the percentage of products to which one or more NTMs apply.<sup>9</sup> While the use of non-tariff measures tends to vary considerably across countries, these simple inventory indexes show the importance of NTMs in terms of trade and sectors affected, justifying the growing interest of economists in this area. Based on NTM data from TRAINS, coverage ratios for the countries in the sample vary between 30 and 100 percent, with the median country (Brazil) having 71.7 percent of trade covered by NTMs. Frequency indexes have a higher variability (between 5 and 100 percent), while the share of products subject to at least one NTM is 66.9 percent for the median country (China).<sup>10</sup> Key questions on NTMs are: what countries are more likely to use them, in what sectors are they employed more frequently, what specific NTMs are more common, and how have they behaved over time. In what follows, we use the different sets of data discussed above to shed some light on these issues.

First, we look at the question of what countries are the larger users of non-tariff measures. In Figure 2a, we plot the coverage ratio calculated using TRAINS data against the log of GDP per capita. The size of the bubble represents the percent of imports under NTMs of the country. The regression line shows a positive correlation between the level of development and the use of NTMs, suggesting that developed countries tend to be larger users of NTMs relative to developing countries. This evidence is consistent with the findings in WTO (2012) based on specific trade concerns and the results of business surveys. This finding could be consistent with two types of explanations. One possibility is that more advanced economies have substituted declining tariff

> 9 The frequency index for country _i_ is calculated as �� � ~~�~~ ∑�∑���� <u>��∙100, where �� and �� are respectively dummy</u> variables capturing the presence of one or more NTM and the presence of imports in product _j_ . Similarly, the coverage ratio for a country is calculated as �� � ~~�~~ ∑�∑����� <u>��∙100, where �� is the value of imports of product</u> _j_ . To partially offset the endogeneity problem implicit in the coverage ratio (the value of imports is lower for goods that are subject to NTMs), we use import values for three years. Beyond these simple measures, Bown (2011) presents alternative approaches to construct coverage ratios that capture both the stocks and flows of imported products covered by TTBs and calculates them for the countries covered in the TTB database. These approaches could be usefully applied to the broader set of NTMs as more data become available.

> 10 The two inventory measures are highly correlated, but the coverage ratio is generally higher than the frequency index. This shows that NTMs tend to be concentrated in highly traded products.
_ESTIMATING FOOD PRICE INFLATION_ _23_

- Andr ́ee, B. P. J., Chamorro, A., Spencer, P., Koomen, E., and Dogo, H. (2019). Revisiting the relation between economic growth and the environment; a global assessment of deforestation, pollution and carbon emission. _Renewable and Sustainable Energy Reviews_ , 114:109221.

- Andr ́ee, B. P. J., Diogo, V., and Koomen, E. (2017). Efficiency of secondgeneration biofuel crop subsidy schemes: Spatial heterogeneity and policy design. _Renewable and Sustainable Energy Reviews_ , 67:848–862.

- Andr ́ee, B. P. J., Kraay, A., Chamorro, A., Spencer, P., and Wang, D. (2020). Predicting Food Crises. _World Bank Policy Research Working Papers_ .

- Andreyeva, T., Long, M. W., and Brownell, K. D. (2010). The impact of food prices on consumption: A systematic review of research on the price elasticity of demand for food.

- Baffes, J., Mitchell, D., Riordan, E. M., Streifel, S., Timmer, H., and Shaw, W. (2008). _Global Economic Prospects: Commodities at the Crossroads 2009_ . World Bank Publications, Washington, DC.

- Baillie, R. T., Bollerslev, T., and Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. _Journal of Econometrics_ , 74(1):3–30.

- Baillie, R. T., Han, Y. W., and Kwon, T.-G. (2002). Further Long Memory Properties of Inflationary Shocks. _Southern Economic Journal_ , 68(3):496.

- Boudt, K., Peterson, B., and Croux, C. (2008). Estimation and decomposition of downside risk for portfolios with non-normal returns. _The Journal of Risk_ , 11(2):9.

- Breiman, L. (1996). Bagging predictors. _Machine Learning_ .

- Breiman, L. (2001). Random forests. _Machine Learning_ , 45(1):5–32.

- Chang, C.-L., McAleer, M., and Tansuchat, R. (2012). Modelling Long Memory Volatility In Agricultural Commodity Futures Returns. _Annals of Financial Economics_ , 07(02):1250010.

- Concei ̧c ̃ao, P. and Mendoza, R. U. (2009). Anatomy of the global food crisis. _Third World Quarterly_ .

- Corey, D. M., Dunlap, W. P., and Burke, M. J. (1998). Averaging correlations: Expected values and bias in combined pearson rs and fisher’s z transformations. _Journal of General Psychology_ , 125(3):245–261.

- Cutler, P. (1984). Famine forecasting; Prices and peasant behaviour in Northern Ethiopia. _Disasters_ , 8(1):48–56.