rafmacalaba/datause-encoder-data huggingface dataset:
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"Hong Kong Pays More Attention to Tech" 2004. _Asian Wall Street Journal._ October 27. Honohan, Patrick. 2004. "Finance in China: Removing Ambiguity Over Government's Role." Paper prepared for the Report on 11th Five Year Plan. Washington, DC: World Bank. Howell, Jude 1993. _China Opens Its Doors_ . Colorado: Lynne Rienner Publisher's Inc.

Hu, Zhigang and Xiowen Bao 2003. _Classic Managing Models for China's Top Enterprises_ .

Beijing: Central Compilation & Translation Press.

Hutchings, Graham 2001. _Modern China: A Guide to A Century of Change_ . Cambridge, Massachusetts: Harvard University Press.

Jefferson, Gary H. and Kaifeng Zhong. 2004. "An Investigation of Firm-Level R&D Capabilities in East Asia," in Yusuf, Shahid, Kaoru Nabeshima, and M. Anjum Altaf (eds.), _Global Production Networking and Technological Change in East Asia_ . New York: Oxford University Press.

Keister, Lisa A. and Jin Lu. 2001. "The Transformation Countries: The Status of Chinese StateOwned Enterprises at the Start of Millennium." _NBR Analysis_ 12(3): 5-31.

Khanna, Tarun and Krishna Palepu. 2002. "Emerging Giants: Building World-Class Companies in Emerging Markets." _Harvard Business Review_ (October).

Lardy, Nicholas R. 1992. _Foreign Trade and Economic Reform in 1978-1990_ . Cambridge, U.K.: Cambridge University Press.

----- 2002. _Integrating China into the Global Economy_ . Washington, DC: Brookings Institute. Liang, Zai, Hy Van Luong, and Yiu Por Chen. 2005. "Urbanization in China in the 1990s:

Patterns and Regional Variations." Presented at Urban China in Transition, New Orleans, Louisiana, January 15, 2005.

Lin, Justin Yifu and Zhiqiang Liu. 2000. "Fiscal Decentralization and Economic Growth in China." _Economic Development and Cultural Change_ 49(1): 1-21.

Lin, Shuanglin and Shunfeng Song. 2002. "Urban Economic Growth in China: Theory and Evidence." _Urban Studies_ 39(12): 2251-2266.

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capital of the next generation, as measured in the HCI, and the human capital stock that enters the production function, need to be linked. This can be done by considering the scenarios outlined in the main text. Imagine first a “status quo” scenario in which the expected learning‐adjusted years of school and health as measured in the HCI today persist into the future. Over time, new entrants to the workforce with “status quo” health and education will replace current members of the workforce, until eventually the entire workforce of the future has the expected learning‐adjusted years of school and level of health captured in the current human capital index. Let k�,�� �e<sup>���������</sup> denote the future human capital stock in this baseline scenario. Contrast this with a scenario which the entire future workforce benefits from complete education and enjoys full health, resulting in a higher human capital stock k�∗ �e��<sup>∗</sup> ���<sup>∗</sup> .

It is possible to compare eventual steady‐state GDP per worker levels in the two scenarios using Equation (14), assuming that levels of TFP and the physical capital‐to‐output ratio are the same in the two scenarios, to obtain: This expression is the same as the human capital index in Equation (3), except for the term corresponding to survival to age 5 (since children who do not survive do not become part of the future workforce). This creates a close link between the human capital index and growth. Disregarding the (small) contribution of the survival probability to the HCI, Equation (15) says that a country with an HCI equal to x could have future GDP per worker that would be 1/x times higher in the future if its citizens enjoyed complete education and full health (corresponding to x�1 ). For example, a country such as Morocco with a HCI value of aroudn 0.5 could in the long run have future GDP per worker in this � scenario of complete education and full health that is �2 times higher than in the status quo �.� scenario. What this means in terms of average annual growth rates of course depends on how “long” the long run is. For example, under the assumption that it takes 50 years for these scenarios to the four unskilled workers are undoubtedly more productive when it comes to moving a piano. See Jones (2014) for alternative human capital aggregators that relax the assumption of perfect substitutability. Jones (2014) argues that allowing for complementarities between workers of different skill levels substantially increases the role of human capital in accounting for cross‐country income differences. However, Caselli and Ciccone (2017) point out that this interpretation ignores the important role of cross‐country differences in productivity in driving the skill premia that in turn drive the conclusions in Jones (2014).

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IIAG). The SPI, the ODIN, the GDB (and ODB), and the IIAG offer data points on country performance over time, while the self-assessment tools (including European Snapshot Tool, the UN NQAF self-check tool, and the Paris21 NSDS) are designed for self-assessment and do not offer such data.

The tools’ methods of data collection differ, with the SPI obtaining most of the data from curated international databases, the ODIN exclusively from NSO websites, the GDB mostly from expert surveys, the IIAG from a mix of different sources, and the three self-assessment tools from expert self-assessment tools. These different methods come with their own strengths and weaknesses. Data that are provided by the curators of the international databases potentially ensure data quality and comparability, especially in countries with weak statistical capacity where data are not well-maintained or where data might at times be subject to weaker quality control or even manipulation (as discussed earlier). But such data are dependent on the curators’ production cycles and hence at times can be a bit dated. On the other hand, collecting data directly from NSO websites or an expert survey could provide more in-depth analyses and uncover finer details, but it incurs high costs and could be far more time consuming. In addition, direct interviews of government officials might bias responses and complicate comparability across countries since government officials may have an incentive to overestimate their country’s statistical capacity where concerns exist regarding their ability to deliver, or they may underestimate their country’s statistical capacity where they are requesting additional international aid.

Notably, the SPI offers an explicit discussion of its conceptual and mathematical foundations in peer-reviewed academic publications (Cameron _et al._ , 2021; Lokshin, 2022; Dang _et al._ , 2023; Dang _et al._ , forthcoming). We employ the SPI’s five pillars as a reference point in the subsequent comparison of the different tools. We provide further discussion on the SPI framework in the Method section.

# **Further comparison of the tools**

# _Country coverage, dimension, indicators, and data sources_

We compare in Table 1 several other features of the tools (indexes), including their coverage of countries, dimensions, indicators and their data sources. For completeness, we show in this table both the current tools and their predecessors. In addition, we also broaden this comparison to include some self-assessment tools that have been employed by international organizations for 8
and subsidies are considered, the poverty rate for the corresponding income concept (consumable income) increases, leading to a global poverty increase for the fiscal system.

**Figure 10. Effect of Fiscal Policy on Poverty in Mali and Niger, 2014**

<!-- Start of picture text --> 47.02<br>44.54 44.46<br>42.99<br>40.59 40.42<br>Mali Niger<br>Market Income Disposable Income Consumable income<br>Source: Authors.<br><!-- End of picture text --> Direct taxes (−0.01 in Mali and Niger) and transfers (0.02 in Mali, 0 in Niger) have little effect on poverty because the richest households pay most of the direct taxes, and direct transfers are relatively small. In addition, in Niger, direct transfers are not well targeted at the poor. In contrast, **− −** indirect taxes have a strong impoverishment effect ( 3.66 in Mali, 2.44 in Niger) that is, in absolute value, greater than the poverty reduction effect (0.79 in Mali, 0.02 in Niger) of indirect subsidies (Figure C.4). Of indirect tax categories, VATs have the strongest impoverishment effect **− −** ( 1.79 in Mali, 0.94 in Niger). As a result, poverty headcount measured according to consumable income is far above poverty headcount measured according to market income, confirming that fiscal policy is impoverishing in Mali and Niger.

Calculation of the poverty indexes before and after application of the fiscal policy shows that the increase in poverty is due to taxation in the two countries, but it does not show the proportion of households that have become poorer or richer because of the various fiscal interventions. Although payment of taxes impoverishes households, the benefits from public spending enrich them, and the net effect may therefore be positive (enrichment) or negative (impoverishment). The indicators that Lustig and Higgins (2016) propose are used to assess fiscal impoverishment of and fiscal gains to poor people. Fiscal policy is considered to have impoverished individuals if they were not poor before the policy was applied and became poor after its application or if they were already poor and dropped further below the poverty line after the policy’s application.

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section, we briefly discuss implications for policy and research. There, we highlight the scale of the problems facing policy makers and other stakeholders, discuss how the findings can help improve our understanding of the growing experimental literature on education in developing countries, and point to avenues for future research.

# **2. Measuring teacher effort, knowledge, and skills: The Service Delivery Indicators**

The Service Delivery Indicators (SDI)—an ongoing Africa-wide program with the aim of collecting informative and standardized measures of what primary teachers know, what they do, and what they have to work with—grew out of concern about poor learning outcomes observed in various student tests as well as evident shortcomings in fast-expanding systems of education. This policy failure is evident throughout the education service delivery chain, but it is most clearly (and perhaps most damagingly) manifested at the school level.

The delivery of education in many low-income countries is characterized by centralized, but typically weak, state control and often low-capacity, locally governed institutions for education provision. At the same time, the institutional incentives for performance are largely missing, with both career progression and financial rewards delinked from performance. Hiring, salaries, and promotions are largely determined by teachers’ seniority and initial educational qualifications, and are unrelated to effort or performance. In most settings, parents have little influence on how teachers are hired or schools are managed, and the various state and local authorities provide limited technical support or supervision.

Teacher salaries account for the largest single item in education expenditure, in countries at all income levels. In Sub-Saharan Africa, salaries for teachers and education officials account for more than 70 percent of the expenditure in education (UIS/ISU 2013) and approximately 12 percent of total government expenditure. Teachers in Sub-Saharan African countries earn on the order of 4 times average GDP per capita (UIS 2011) whereas in high-income countries the ratio is closer to 1.5 (Bruns and Luque 2014).

In Africa, the public sector is the dominant actor in primary education. However, while public spending on education has increased in the last decade, so has the number of private schools. Recent data suggest that private schools—both informal and formal—account for around 20 percent of total primary school enrollment in low-income countries (Baum et al. 2014).

The SDI program has to date been implemented in Tanzania and Senegal in 2010 (Bold et al, 2011), Kenya (2012), Mozambique (2014), Nigeria (2013), Tanzania (2010, 2014), Togo (2013), and Uganda (2013), bringing the total to eight surveys in seven countries. The surveys 5
elicitation exercise measures _wii_ and _wij_ , while the belief update interventions identify the impact of _p_<sup>_l_</sup> _ij_<sup>and</sup><sup>_pt_</sup> _ij_<sup>on</sup><sup>_wij_.</sup> Finally, it is important to notice that (9) embeds a selfish assumption: agents are only willing to pay for others to acquire the technology as long as it benefits them. I interpret a participants’ willingness-to-pay for another farmer as the total value to the participant of the externalities produced by the other farmer; as a result, the selfish assumption is crucial to identify spillovers. If participants had altruistic preferences, their willingnessto-pay would also include the value a participant attaches to providing the technology to others. I will further discuss the selfish assumption in the results section to provide evidence in its favor.

# **4 Data and Sample**

The sample includes 780 farmers from 103 villages located in the districts of Manafa, Mbale, and Tororo in Eastern Uganda. This sample had been selected for a field experiment carried out in previous years by a research team that included the author (Burchardi et al., 2022). The three contiguous districts were selected for their high suitability to maize cultivation according to the FAO GAEZ data.<sup>10</sup> The 103 villages<sup>11</sup> were selected by a randomization algorithm from the pool of villages in the 2010 national census with a population density lower than 100 households per square kilometer (to ensure they were in rural areas), stratifying by population density. In each village, we enlisted all residing households through a researcher-run census survey. We selected six to nine research participants (maximum one per enlisted household, always the head of the household) according to the following criteria: _i)_ engaging in commercial farming; _ii)_ having cultivated or planning to cultivate maize; _iii)_ reporting land holdings from two to six acres; _iv)_ having a mobile money account. The participants in the Burchardi et al. (2022) experiment were enrolled at the end of 2016 and had participated in up to four survey interviews and one willingness-to-pay elicitation by the time the field work for the experiment in this paper started; all research activities connected to Burchardi et al. (2022) were completed by 2017.

The data I use come from four surveys: an in-person baseline survey, an in-person willingness-to-pay elicitation survey, a phone follow-up survey, an in-person follow-up survey. All surveys have been written by the research team and approved by the Ugandan ethics authorities. The enumerators who administered the survey have at least a high school diploma, speak the Eastern local languages (Ateso, Japadhola, Luganda, Lugisho, Swahili) and were recruited through a local research firm. The timeline of data collection is summarized in Figure A.1, while Appendix F provides details about each variable used

> 10www.gaez.fao.org

> 11Villages are commonly referred to as LC1 (Local Council 1) in Uganda, and they are the smallest administrative unit, governed by a chairperson (LC1 chairperson) and nine executive committee members.

10
order to reduce carbon emissions. The fourth specification with interactions shows that the increase in material costs is larger among firms with a larger allocation of permits.

Table 5: Results on Unit Material Cost for Power Plants

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|0.125***|0.0693***|0.0479***|0.0532|
||(7.88)|(6.92)|(3.82)|(1.16)|
|Phase 2 Participation|0.110***|0.0895***|0.0820***|0.00664|
||(5.92)|(6.80)|(4.41)|(0.10)|
|Phase 1×Log(Surrendered)||||-0.00448<br>(-0.85)|
|Phase 2×Log(Surrendered)||||-0.0124<br>(-1.17)|
|Phase 1×Log(Allocated)||||0.00428<br>(1.06)|
|Phase 2×Log(Allocated)||||0.0191*<br>(1.75)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations<br>|3712|3712|3712|3712|
|Adjusted_R_<sup>_2_</sup>|0.036|0.042|0.101|0.107|

Note: Dependent variable is the unit material cost, defined as the ratio of total material cost to operating revenue. Standard errors are clustered at the firm level and _t_ statistics are shown in the respective parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

The results for firm turnover are shown in table 6. The first two specifications suggest that the program increased turnover in both phases while the third specification with firm fixed effects and country-year fixed effects shows no statistically significant effect on average in the first phase and an almost 30 percent increase in the second phase. The increase in turnover could come from an increase in output or in price or in both components. Intuitively, an increase in material costs could lead to an increase in electricity prices. Since electricity demand is highly inelastic in the short-run, the price increase could translate into an increase in turnover. The fourth specification with interaction terms suggests that the increase in turnover is larger among firms with a larger usage in both phases. On the other hand, a larger allocation of allowances is associated with a smaller turnover in the second phase. Both findings could reflect utilities passing the cost (from compliance) to consumers.

15
<!-- Start of picture text --> ARGENTINA: REAL EFFECTIVE EXCHANGE RATE<br>(Cobinidu cmwr. wholosele prie"l<br>520,<br>300Lt<br>280<br>2850<br>240<br>20<br>si.~ ~<br>K/,U<br>0 n7 Ju17 J4 u40 Jl46 Jn0<br>Ja7 - Jn<br>s Offical * Pall o htbU at<br><!-- End of picture text -->
**Figure 7:** _Public and private investment_ (as a share of GDP)

<!-- Start of picture text --> Russia Kazakhstan Moldova<br>30%<br>20%<br>10%<br>0%<br>1992 1997 2002 2007 1992 1997 2002 2007 1992 1997 2002 2007<br> Private Investment Public Investment<br><!-- End of picture text --> Sources: Penn World Tables (total investment); IMF World Economic Outlook (share of investment); World Development Indicators (GDP). Note: Public investment (in infrastructure only) = 7 percent of GDP in fast-growing Asian economies.

The dramatic drop in public investment has shifted the composition of total investments towards the private sector; however, the volume of private investment has not been anywhere near enough to compensate for the decline in public investment spending. In Russia, the composition of total investment shifted from 90 percent public sector in 1993 to below 20 percent by 2012. Similarly, in other Eurasian countries, the public sector now accounts for 20 to 30 percent of total current investments, comparable to that in EAP12.

Why do some countries invest less than others in physical capital? For Hall and Jones (1999), differences in capital accumulation are fundamentally driven by differences in institutions and government policies across countries, which they call “social infrastructure”. By social infrastructure, they mean the institutions and government policies that determine the economic environment within which individuals accumulate skills and firms accumulate capital and produce output. Social institutions that protect the output of productive units from diversion—such as expropriation, confiscatory taxation, and corruption—tend to accumulate more capital and produce more output per worker. Hall and Jones (1999) define a social infrastructure index as a combined measure of economic institutions. This index is the average of five categories (law and order, bureaucratic quality, corruption risk, risk of expropriation, and government repudiation of contracts).<sup>5</sup> It ranges between 0 and 7, with 0 signifying the worst

> 5 Due to data revisions in 1996 and 1997, Risk of Expropriation and Government Repudiation of Contracts were combined into a new indicator, Contract Viability. The Social Infrastructure Index numbers cited in this paper are derived from an average of the three remaining indicators and the new indicator, over 2003-2012, following the basic methodology used by Hall and Jones. Benchmarks used by Battacharrya and Collier (2011) were adjusted accordingly.

16
correlates--indicators such as women's average education level, household characteristics, and place of residence on which inforrnation had been gathered in the 1993 census. In the second stage, the coefficients from these departmental regressions were applied to district-level data from the 1993 census to estimate district-level infant mortality rates (INEI 1997).

# _The FONCODES index_

Peru has a long history of developing "poverty maps" based on composite indices of unmet basic needs. The first of these maps was constructed by Webb with information from the 1961 population census (Webb 1977). This poverty map was updated with new censuses conducted in 1972, 1981, and 1993 (Amat y Le6n n.d.; Banco Central de Reserva 1981; INEI 1994). FONCODES, in turn, has developed composite poverty maps since its creation in 1991, often with technical assistance from the Inter-American Development Bank, the World Bank, and the German development agency GTZ.

The current district-level poverty map was developed by FONCODES and the Ministry of the Presidency. It is based on eight indicators--the rate of chronic malnutrition, illiteracy, schoolaged children not in school, overcrowded housing, inadequate roofing, and the proportion of the population without access to water, sewerage, and electricity. All of these indicators except the rate of chronic malnutrition were estimated with data from the 1993 population census. The rate of chronic malnutrition was estimated from a census of height and weight among school-aged children, also conducted in 1993 (FONCODES 1995 and 1996).

Composite indices invariably involve some arbitrary weighting of individual indicators. The 1996 index standardized each indicator by dividing it by its minimum value, multiplied the rate of chronic malnutrition by seven, and then added all of the individual indicators' For ease of interpretation, FONCODES then standardized the index by dividing all values by the lowest value. The resulting index ranges from a value of 1 to 36.38.

# _Imputed poverty and income_

In Peru, there are no survey-based estimates of income or expenditures at a level more disaggregated than the department: for example, household surveys conducted by INEI, which generally have sample sizes of 15,000 to 20,000 households, can only be used to compare income i This procedure had the unintended consequence that the greatest weight was given to those indicators with the greatest variance. Thus, while the intended weights were 50% for the rate of chronic malnutrition, and 7.14% each for the seven other measures, the actual weights in the index turned out to be 15.3% for the rate of chronic malnutrition, and 3.4%, 2.2%, 3.0%, 38.3%, 8.8%, 7.4%, and 21.6% for the measures of illiteracy, school attendance, overcrowding, inadequate roofing, and access to water, sewerage and electricity, respectively (World Bank 1996, p. 7).

4