rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Matching Estimates for SBCs

|Employment <sup>_a_</sup><br>Physical capital <sup>_a_</sup><br> **Subsidiaries and Independent Firms**<br>(3 nearest neighbours)|
|---|
|ATE<br>0.443***<br>0.785***|
|(0.099)<br>(0.128)|
|ATT<br>0.467***<br>1.000***|
|(0.103)<br>(0.108)|
|**Subsidiaries Firms**|
|(3 nearest neighbours)|
|ATE<br>0.680**<br>0.196|
|(0.281)<br>(0.205)|
|ATT<br>0.732***<br>0.256|
|(0.282)<br>(0.206)|
|**Independent Firms**|
|(3 nearest neighbours)|
|ATE<br>0.832***<br>0.797***|
|(0.125)<br>(0.152)|
|ATT<br>0.989***<br>1.104***|
|(0.100)<br>(0.125)|

_Notes:_ Robust standard errors in parenthesis.

*(*)[***] stands for significance at the 10%(5%)[1%] level. _a_ Matching is based on level of TFP, TFP growth, profits, exports, value added, sub-industry and time fixed effects.

Our various matching estimators commonly restrict the comparison to the same type of firms in the same year. This guarantees that we only compare subsidiaries of vertical production chains and independent firms with each other, respectively. Moreover, due to the cyclical behavior of the whole economy during our observation period, we only compare labor and capital inputs within the same years. Additionally, for our analysis of employment and physical capital, we match firms based on their level of TFP, TFP growth, profits, value added, revenues and the firm's industrial sub-sector as well as its regional location. Here, TFP is calculated as the average 27
This allows evaluation of the first hypothesis for this study (H1), which is that levels of conditional support for subsidy reform will be higher than levels of unconditional support. Multivariate regression analysis is conducted to illustrate the strength of the association between support for reform and respondents’ characteristics, knowledge, and beliefs. While the associations between these variables are not causal, they still provide valuable information about the patterns of public support across countries. To maximize the generalizability of the descriptive findings, survey weights are applied that adjust the results to match the general population in terms of age, sex, location, income, and education level. Specifically, raking is used to construct weights based on the actual distribution of sex, age, location, education level, and household income in the national population in each country. To correct for outliers, these raked weights are winsorized at the 1st and 99th percentiles of the weight distribution in each country. The unweighted findings show qualitatively similar patterns.

# **3.3.2 Experimental analysis**

The main analysis in this study involves the use of a randomized survey experiment to examine what information shifts public support for subsidy reform across 12 middle-income countries. Randomization allows for the impact of the information treatments to be determined by comparing differences in mean outcomes between the control group and treatment groups. The randomization process was stratified by the age group and gender of respondents to ensure balance across these characteristics. A balance table (Table C1) is provided in Appendix C for all answers before experimental variation was introduced into the survey (Q1–Q14), including measures of both individual and joint significance (i.e., both t-statistics for every variable and an f-statistic across all variables within a given country). As outlined in the discussion regarding the sample size, the experiment is designed so that there will be adequate statistical power (with the conventional levels of type I and type II errors of 0.05 and 0.2 respectively) to detect a meaningful effect from the treatments if it exists. This approach is based on the sample average treatment effect where survey weights are not applied (i.e., each respondent is treated equally), and as a robustness check, the weighted average 26
- Woolfolk Hoy, A., & Weinstein, C. (2011). Student and teacher perspectives on classroom management. In Evertson, C, & Weinstein, C. (Eds.), _Handbook of classroom management, research, practice, and contemporary issues_ (pp. 181-219). New York, NY: Routledge.

- World Bank. (2018). _World Development Report 2018: Learning to Realize Education’s Promise_ . Washington, DC: World BankWragg, E. C. (1993). _Questioning in the primary classroom_ . London: Routledge.

- Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. _Educational Psychologist_ , _47_ (4), 302-314.

- Yerushalmi, E., & Polingher, C. (2006). Guiding students to learn from mistakes. _Physics Education_ , _41_ (6), 532–538.

- Zahn-Waxler, C. (1993). Warriors and worriers: Gender and psychopathology. _Development and Psychopathology_ , _5_ (1-2), 79-89.

- Zarrella, I., Lonigro, A., Perrella, R., Caviglia, G., & Laghi, F. (2018). Social behaviour, socio-cognitive skills and attachment style in school-aged children: What is the relation with academic outcomes? _Early Child Development and Care, 188_ (10), 1442-1453.

Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press: San Diego Zentall, S. R., & Morris, B. J. (2010). “Good job, you’re so smart”: The effects of inconsistency of praise type on young children’s motivation. _Journal of Experimental Child Psychology_ , _107_ (2), 155-163.

- Zimmerman, B. J., & Schunk, D. H. (Eds.). (1989). Springer series in cognitive development. Self-regulated learning and academic achievement: Theory, research, and practice. New York, NY, US: Springer-Verlag Publishing

- <mark>Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview.</mark> _<mark>Theory into Practice</mark>_ <mark>,</mark> _<mark>41</mark>_ <mark>(2), 64-70.</mark>

Zimmerman, B.J. (2010). Attainment of self-regulation: A social cognitive perspective. In M.Boekaerts, P.R. Pintrich & M.

63
**Figure 1: Sovereign Foreign Currency Upgrades, Downgrades, and Outlooks, 2004‐Present**

<!-- Start of picture text --> Upgrades (trailing‐12‐month sum) Downgrades (trailing‐12‐month sum)<br>Positive outlooks at month‐end Negative outlooks at month‐end<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Jan‐04 May‐04 Sep‐04 Jan‐05 May‐05 Sep‐05 Jan‐06 May‐06 Sep‐06 Jan‐07 May‐07 Sep‐07 Jan‐08 May‐08 Sep‐08 Jan‐09 May‐09 Sep‐09 Jan‐10<br><!-- End of picture text --> Source: Standard and Poor’s Despite the recognized need for such institutional strengthening, according to some measures debt management performance in LICs has stagnated or even deteriorated in recent years.<sup>6</sup> In light of this, it is important to assess the priority areas in improving DeM performance in a way tailored to country specific circumstances and stages of development. For countries without the necessary legal and institutional framework to support effective DeM, the focus must be on identifying and addressing these weaknesses. For countries that have the institutional underpinnings of a functional DeM framework in place, it will be critical to focus on capacity building to develop medium to long‐term DeM strategies that examine the cost‐risk tradeoffs in order to safeguard future debt sustainability. This is particularly important in preparing for future crises as LICs gradually accumulate increasingly complex public debt portfolios combining a wider array of financial instruments from both public and private creditors.

It is in this context that this paper examines the application of two global public goods in LICs: the Debt Management Performance Assessment (DeMPA) and the Medium‐Term Debt Management Strategy (MTDS) tools<sup>7</sup> . Drawing upon results from the application of these tools from 2007‐2009 provides valuable information to policymakers and other stakeholders on the development of sound public DeM practices and analytical capacity, with the goal of strengthening the public balance sheet and reducing vulnerability to financial crises. This paper builds on the results of a 2006 World Bank analysis on Public Debt Management in Low‐Income Countries that provided the empirical justification for the need to improve DeM capacity in LICs.<sup>8</sup> The results showed that DeM policy and high indebtedness are

> 6 World Bank (2006).  As measured by the CPIA Debt Policy Indicator

> 7The DeMPA was developed by WB and the MTDS jointly by the Bank and the Fund. Both tools were developed through  a broad consultative process seeking suggestions and inputs from client countries and international technical assistance providers that are active in the field.

> 8 World Bank (2006)

3
Gunasekera et al., 2015; Rentschler and Salhab, 2020; Ward et al., 2020). Gross Domestic Product (GDP) is a critical economic indicator in the measurement and monitoring of an economy in a country that is typically only available at national and occasionally subnational levels. Regional indicators play a key role in the necessary variation to forecast regional GDP (Lehmann and Wohlrabe, 2015) and food security (Andree et al., 2020). Previous efforts to estimate local GDP use high resolution spatial auxiliary information such as luminosity or population data to provide local variation. Methods by Kummu, Taka, and Guillaume (2018), Murakami and Yamagata (2019), Nordhaus (2006), and World Bank and UNEP (2011) took advantage of gridded population data, which is the result of a model disaggregating the most detailed level population data into grids (e.g. see review in Leyk et al., 2019). However, wealth is not evenly distributed among people nor infrastructure (Berg, Blankespoor, and Selod, 2018). In fact, the divide between the rich and poor is even widening in our time (Dabla-Norris et al., 2015). The method used in World Bank and UNEP (2011) stratify the population by rural and urban, yet definition of these geographic areas can vary based on the selection of the population model (Leyk et al., 2019). These measurements matter in application to stylized facts such as the strong negative correlation of the level of urbanization with the size of its agricultural sector (Roberts et al., 2017). Also, the uniform distribution of labor in agriculture is another key concern (Gollin, Lagakos, and Waugh, 2014). Other methods used land cover such as vegetation and built-up indices, however did not incorporate types of agriculture like cropland and livestock (Goldblatt, Heilmann, and Vaizman, 2019; Rashmin Gunasekera et al., 2015).

Other methods to estimate GDP at a local level take advantage of the lights at night dataset. Doll, Muller, and Morley (2006) and Elvidge et al. (2009) found nighttime lights to provide a uniform, consistent, and independent estimate for economic activity, and several other studies (e.g. Bundervoet, Maiyo, and Sanghi, 2015; Chen and Nordhaus, 2011; Eberenz et al., 2020; Ghosh et al., 2010; Henderson, Storeygard, and Weil, 2012; Wang, Sutton, and Qi, 2019) utilized this striking correlation between luminosity and economic activities to estimate economic output on the ground. While night light is a good reflection of economic activities in manufacturing and urban areas, night light data may not capture the agricultural activity as it requires areas to emit light. Bundervoet, Maiyo, and Sanghi (2015) suggest that agricultural indicators rather than rural population could improve the estimation of GDP given the importance of agriculture in many of the economies in their sample of Africa. Gibson et al. (2021) find that night time lights data are a poor predictor of economic activity in low density rural areas.

In this paper, we present a high resolution gridded Agricultural GDP (henceforth AgGDP) dataset that is produced through a spatial allocation model by distributing national 4
**Table A1 ‐ Correlation matrix of variables**

# **SET OF FAST GROWING COUNTRIES**

||Pub<br>spe<br>|<br>nding<br><br>Fisc<br>|al<br>||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||GDPPCGR<br>/GD|P<br>bala|nce/GDP Pro/|TEXP|Soc/TEXP|Eco/TEXP|Edu/TEXP|Hea/TEXP|TRA/TEXP|CORE/TEXP|
|GDPPCGR|1.00||||||||||
|Pub spending/GDP|0.13|1.00|||||||||
|Fiscal balance/GDP|0.09|‐0.07|1.00||||||||
|Pro/TEXP|0.33|‐0.04|0.49|1.00|||||||
|Soc/TEXP|‐0.23|0.30|0.04|0.34|1.00||||||
|Eco/TEXP|0.22|0.00|‐0.11|0.43|0.04|1.00|||||
|Edu/TEXP|0.00|0.23|0.27|0.59|0.39|0.05|1.00||||
|Hea/TEXP|‐0.04|0.40|‐0.14|0.18|0.72|‐0.16|0.49|1.00|||
|TRA/TEXP|0.38|0.10|0.04|0.46|0.02|0.88|0.14|‐0.05|1.0|0|
|CORE/TEXP|0.35|0.21|0.10|0.69|0.60|0.57|0.30|0.36|0.6|1<br>1.00|

# **COMPARISON GROUP**

||Pub<br>spe<br>|<br>nding<br><br>Fisc<br>|al<br>|||||||
|---|---|---|---|---|---|---|---|---|---|
||GDPPCGR<br>/GD|P<br>bala|nce/GDP Pro|/TEXP|Soc/TEXP<br>Eco/TEXP|Edu/TEXP|Hea/TEXP|TRA/TEXP|CORE/TEXP|
|GDPPCGR|1.00|||||||||
|Pub spending/GDP|‐0.04|1.00||||||||
|Fiscal balance/GDP|‐0.21|‐0.40|1.00|||||||
|Pro/TEXP|‐0.06|‐0.54|0.39|1.00||||||
|Soc/TEXP|0.16|0.30|0.27|‐0.24|1.00|||||
|Eco/TEXP|‐0.05|‐0.61|0.24|0.60|‐0.58<br>1.00|||||
|Edu/TEXP|0.01|‐0.56|0.29|0.48|0.00<br>0.18|1.00||||
|Hea/TEXP|‐0.03|0.01|0.28|0.28|0.54<br>‐0.21|0.31|1.00|||
|TRA/TEXP|0.00|‐0.57|0.26|0.68|‐0.38<br>0.88|0.25|‐0.04|1.0|0|
|CORE/TEXP|‐0.08|‐0.60|0.36|0.96|‐0.20<br>0.58|0.62|0.38|0.6|5<br>1.00|

Note: GDPPCGR is the growth rate of GDP per capita in real terms. Pub spending/GDP is the ratio of total public spending in %. Fiscal balance is the ratio of fiscal balance to GDP in %. Pro/TEXP is public productive expenditure in total public expenditure in %. Soc/TEXP is public social expenditure in total public expenditure in %. Eco/TEXP is public economic expenditure in total public expenditure in %. Edu/TEXP is public education expenditure in total public expenditure in %. Hea/TEXP is public health expenditure in total public expenditure in %. Tra/TEXP is public transportation expenditure in total public expenditure in %. CORE/EXP is the core spending in percent of total expenditure.

30
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- b. Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points

<!-- Start of picture text --> 5<br>Main Telephone Lines<br>Electricity Generating Capacity<br>4<br>Length of Road Network<br>3<br>2<br>1<br>0<br>-1<br>-2<br>per capita from improving infrastructure to level of African leader (Mauritius)<br>Potential changes in growth<br><!-- End of picture text --> _Source:_ Calderón 2009.

Looking ahead, simulations suggest that if Zimbabwe‘s infrastructure could be improved to the level of the African leader—Mauritius—annual per capita growth rates would be 2.4 percentage points higher than they are at present. This impact would come from improvements not only in ICT, but also from an increase in power-generating capacity and better road infrastructure (figure 2b).

# The state of Zimbabwe’s infrastructure

Zimbabwe‘s population of around 11.3 million is relatively evenly distributed across the country, though more densely populated urban areas surround the capital Harare and other major cities, and there are more scarcely populated areas in the western and southern parts of the country (figure 3a). Geographically, Zimbabwe is located on a plateau that forms a watershed between the Zambezi and Limpopo river systems. Consequently, it has significant agriculture, forestry, and tourism potential. But agriculture, which used to be the backbone of the economy, has declined as a result of the near collapse of commercial farming during the past decade, and has been further aggravated by apparent neglect of rural roads in high-value agricultural areas.

Zimbabwe is endowed with more than 40 minerals<sup>4</sup> —including gold, diamonds, ferrochrome, copper, and coal—clustered along the mining belt running across the country from north to south (figure 3c). Gold used to be the country‘s key foreign currency source, but its production has declined in recent years.

By African standards, Zimbabwe has impressive backbone infrastructure, including power, roads, ICT, and water. The country is historically well integrated with its neighbors when it comes to power and transport networks, allowing Zimbabwe to participate in regional trade and act as a critical transit country for landlocked neighbors Zambia and Botswana and a key link in the north-south surface corridors (figures 3d–h). But the deteriorating quality of this infrastructure is currently jeopardizing the functionality of the regional and national links.

> 4 www.state.gov/r/pa/ei/bgn/5479.htm.

5
<!-- Start of picture text --> Figure 1: Korea and Brazil – catching up with the OECD<br>Korea: Catching up with high income countries - trend in GDP per capita<br>30000<br>27500<br>25000<br>Singapore<br>22500<br>20000 High income: OECD<br>17500 High income:<br>nonOECD<br>15000<br>12500<br>Korea, R ep .<br>10000<br>7500<br>5000 Upper middle income<br>Brazil<br>2500<br>Low er middle income<br>0<br>Upper middle income High income: OECD Low er middle income<br>Korea, Rep. High income: nonOECD Singapore<br>Brazil<br>Source: WDI<br>19661968 197019721974 19761978 198019821984 19861988 199019921994 19961998 200020022004<br>2000 constant USD<br><!-- End of picture text --> Since the 1980s, many developing countries, both low and middle-income, have liberalized their economies and pursued greater integration with global markets with the expectation of becoming more prosperous, but only a handful are catching up with OECD economies. What can they learn from the experience of the BICs and of Korea? Moreover, among the BICs, what is China doing differently to overtake Brazil and India?

Standard neoclassical economic theory does not offer conclusive answers to these questions. It asserts that increasing the level of competition through trade liberalization, for example, improves efficiency, spurs innovation, and increases productivity growth. Empirical evidence confirms that productivity growth can contribute as much as fifty percent of overall economic growth (N. Loayza, P.Fajnzylber and C.Calderon (2005), D.M. De Ferranti et al. (2003)); it also suggests that productivity growth is powered by innovation. However, it does not indicate that liberalization automatically breeds innovation. In fact, in many developing countries, trade liberalization has not been followed by sustained innovation-powered productivity gains that have led to higher per capita incomes in the long term.

Economists have proposed two alternative paradigms to explain why trade liberalization or increased competition may not lead to a sustained increase in productivity. Schumpeter, for example, contended that economic rents were essential for innovation to occur. From this perspective, by dissipating rents, competition can adversely affect innovation. Recently, in advancing the new growth theory which is grounded in a Schumpeterian framework, Aghion et al. (2003, 2004, 2005, 2006 and 2008)<sup>3</sup> hypothesized that competition can have variable effects on productivity. It need not benefit all firms in an industry uniformly. Firms that are relatively technologically sophisticated and hence close to the most technologically sophisticated firm 3 P. Aghion, R. Blundell, R. Griffith, P. Howitt, and S. Prantl (2004), P. Aghion, N. Bloom, R. Blundell, R. Griffith and P. Howitt (2005), P. Aghion, R. Blundell, R. Griffith, P. Howitt and S. Prantl (2006), and D.P. Acemoglu, P. Aghion and F. Zilibotti. (2006).

3
<mark>PPP deflated estimates are not unidirectional and vary across regions and between countries. However, the large downward revisions from the 2011 ICP PPP deflated to the counterfactual PPP deflated GDP and consumption figures occur more frequently than the reverse, especially in the larger and more populated countries such as South Africa, China, Mexico and Russia. Consequently, consistent with Figures 5 and 6, a comparison of the mean and median figures between the two PPP deflated values in either table establishes the result that the 2011 ICP overestimates the real size and consumption of the world at large. A result of particular interest is that the feature that China’s dominance over India on per capita GDP is sharply reduced from the ICP PPP to the counterfactual PPP deflated values.</mark>

# <mark>[Tables 7a and 7b about here]</mark>

The ratios of per capita annual GDP of the different countries to the per capita annual GDP of India, evaluated at the alternative PPPs, are presented in <mark>Table 7a and the corresponding ratios for per capita annual aggregate consumption are given in Table 7b. These tables mirror Tables 6a, 6b in showing that in most cases the dominance over India in the per capita GDP and consumption figures shrinks, and in few cases even gets reversed, when we use the counterfactual PPP in place of the 2011 ICP PPP. The widely made claim that the 2011 ICP made the poorer countries look lot ‘less poor’ vis a via the USA can be extended to the result that India looks ‘far poorer’ on the ICP PPP than on the counterfactual PPP.</mark> An underlying assumption in both sets of PPPs that we have compared in this study is that all the income groups face the same PPP, analogous to the assumption of a single economy-wide PPP for each country’s currency that holds true for all regions. While the latter assumption is now considered untenable as reflected in the increasing emphasis on the calculation of subnational PPPs, there is still not much awareness of the need for subclass PPPs. Table 8 provides evidence on the latter and, also, on the robustness of the comparison between the 2011 ICP PPP and its counterfactual, by presenting the counterfactual PPP by income quintile based on the publicly available disaggregated household level expenditure data of Iraq, Malawi and Tanzania supplemented by the unit records from India’s National Sample Survey and Vietnam’s Living Standard Survey- see Appendix A for details.

[Table 8 about here] 28