rafmacalaba/data-use-mentions-extended huggingface dataset:
and Timmis (2023) find the adoption of cloud computing is linked to firm-level productivity gains. Using data from a survey of 179 publicly traded firms in the United States about their business practices, information systems, and the analysis of information and publicly available financial data, Brynjolfsson et al. (2011) find that, on average, a one-standard-deviation increase in datadriven decision-making is associated with a 4.6 percent productivity gain.

There are also specific case studies on the effects of ICT on productivity in the United States. Jin and McElheran (2017) show how the purchase of cloud computing services improves the productivity of young companies, particularly in IT-intensive and high-uncertainty manufacturing industries. Agrawal and Goldfarb (2008) show how shared computer networks increased academic productivity at middle-tier universities. In health care, Miller and Tucker (2014) and McCullough, Parente, and Town (2016) show that electronic medical records (EMRs) are found to improve patient outcomes, and Lee, McCullough, and Town (2013) show that EMRs increase hospital productivity.

Studies from developing economies also document the productivity effects of the first wave of digitalization. Covering firms in the manufacturing sector across 82 developing economies, Cusolito, Lederman, and Peña (2020) find evidence of a larger productivity premium from using email or a website than from improving managerial capability or from exporting.<sup>6</sup> Evidence from approximately 20 developing economies shows that differences in technology sophistication, ranging from manual to advanced digital processes, account for 31 percent of cross-establishment dispersion in productivity (Cirera, Comin, and Cruz 2024). For Chinese-listed manufacturing firms, Lo et al. (2023) show that digital technology boosts productivity by reducing operating costs, improving resource allocation, and enhancing innovation capability. Greater internet connectivity led to a large and significant increase in net firm entry in South Africa, notably in sectors that use ICT extensively (e.g., finance), and in the productivity of existing manufacturing firms in Ethiopia (Hjort and Poulsen
# **1 Introduction**

Lower income countries, on average, collect less taxes than higher income countries, limiting their ability to invest in infrastructure and public services. This tax gap occurs despite comparable statutory rates and accounting for different levels of economic activity, suggesting the presence of deficits in lower income states’ tax administration capacity (Besley and Persson, 2014). This paper distinguishes three key components of tax capacity that states must invest in to collect taxes effectively: identifying taxpayers (identification capacity), determining how much they owe (detection capacity), and taking in these liabilities to the state coffers (collection capacity). Whereas traditional tax evasion models (e.g. Allingham and Sandmo (1972)) and a resulting large literature on third-party reporting emphasize detection capacity, this paper focuses on the two other dimensions of tax capacity, identification and collection, which are particularly relevant in lower income countries.

Identifying taxpayers, a presumption in most developed economies, is a first order concern for tax collection in many lower income countries characterized by a large informal sector and the lack of proper identification systems for individuals, businesses, property and other taxable entities/ tax bases. For example, 45 percent of people in Sub Saharan Africa do not have an official proof of identity (World Bank, 2022), and majority of lower income countries have sparse and inaccurate taxpayer databases (Nyanga, 2021).<sup>1</sup> Many contexts are reminiscent of Scott’s (1999) description of the premodern state as “in many crucial respects, partially blind; it knew precious little about its subjects, their wealth, their landholdings and yields, their location, their very identity.” In order to tax, the premodern state sought to make society “legible” through the creation of permanent last names, the establishment of cadastral surveys and population registers, etc. Similarly, modern governments seeking to understand their potential tax base must increase coverage of foundational identification systems, business registries and property
alike. Furthermore, while ODA financing provides annual financing estimates, some financing will be capital expenditures with long term benefits. As such, the benefits of ODA allocations may be realized over many years, but categorized as financing in just one year.

# (ii)

education, primary data collection was conducted for this report. This was achieved through extensive engagement with government, non-government, and international organizations working in the 25 largest refugee hosting LICs and MICs. These efforts yielded estimates on host government financing for refugee education in 20 of the largest 25 hosting countries, which combined hosted 77 percent of all school-age refugees in LICs and MICs in 2021. Given the deficiencies in the recording of such information at country level, as outlined in the methodology, assumptions were required to estimate host government financing for the majority of estimates produced. To ensure the validity of these estimates, officials from international organizations, host country governments, and/or non-government organizations (NGOs) either verified or worked with the authors to produce this studies estimates.

examined, there is little commonality in the methods used to estimate host government financing for refugee education by country. Rather, approaches were adapted depending on data availability.

# (iii)

OECD Creditor Reporting System (CRS), the grant aggregator database _Candid_ , and a survey of International Education Funders Group (IEFG) members conducted for this paper. The OECD’s CRS forms the basis of the accompanying dataset’s philanthropic financing data. This information is then supplemented by additional project data recorded in the grant aggregator database _Candid,_ and primary data collected through the September 2023 World Bank and IEFG survey of IEFG members on refugee education (IEFG and World Bank, 2023). These data were then triangulated to produce estimates of the philanthropic sector’s financing for refugee education in LICs and MICs. While this paper uses the latest available information to estimate philanthropic financing for refugee education, given the lack of transparency in this sector, these figures are likely to underestimate the extent of philanthropic financing
-27-

**TABLE 2.7**

**US** Eaployment and Productivity in 1975 According to Annual Survey of Manufactures and National Accounts

|E|ASM<br>mployment<br>(1000)|Nat. Accounts<br>Employment<br>(1000)|ASM Gross<br>Value Added<br>Per Person<br>Employed|National<br>Accounts<br>Adjusted GDP<br>per Person<br>Employed|
|---|---|---|---|---|
|Food & Kindred Products|1,321|}1,687|}31,538|}18,657|
|Beverages|204|}|}|}|
|Tobacco Products|66|73|56,388|37,342|
|Textile Mill Products|835|872|17,663|11,172|
|Apparel, Other Textile|||||
|Products|1,214|1,258|12,149|8,810|
|Lumber & Wood Products|588|684|17,612|14,914|
|Furniture & Fixtures|396|425|15,884|11,464|
|Paper & Allied Products|589|643|30,465|21,449|
|Printing & Publishing|1,070|1,129|23,029|15,883|
|<br>Chemicals, Allied Products|<br>842|1,C25|53,416|27,394|
|Petroleum & Coal Products|<br>141|189|74,465|32,931|
|Rubber, Miscellaneous|||||
|Plastic Products|585|603|23,247|15,372|
|Leather, Leather Products|<br>240|247|13,280|9,769|
|Stone
# **4. Policy implications for Turkey**

As discussed in section 2, Turkey traditionally struggled to attract foreign direct investment, until the country saw a change in fortune in 2005. As discussed in more detail in Sánchez-Martín et al. (2014), the jump in FDI inflows observed in 2005-08 is likely to have to do with the signaling caused by the official launch of _EU_ accession negotiations, among other factors. This seems to be corroborated by the significant coefficient of the EU membership dummy variable present in the cross-country empirical analysis conducted for this paper.

However, in the context of the international economic crisis and the crunch in Southern EU members, starting in 2009, foreign direct investment slowed down. Net FDI inflows to Turkey halved to 1.6 percent of GDP in 2009-2013, from 3 percent of GDP in 2005-2008. This compares to a regional median for countries in Eastern Europe of 3 and 6.2 percent of GDP, respectively, over the same periods. Interestingly, other developing countries (e.g. large Latin American economies) have not experienced a significant decline in FDI inflows over the past five years, which seems to suggest that new EU member countries and other economies with strong ties with the European Union may be suffering from the convulsed economic and political situation traditional investor or partner countries are going through.

In the case of Turkey, EU27 originated FDI inflows decreased from a value of close to US$14 billion in 2006-08 to roughly US$5.5 billion in 2009-10. Foreign direct investment inflows after the 2009 crisis have been mainly sustained thanks to capital accruing to the energy and utilities sector in a context of liberalization. The empirical analysis has shown that in fact privatization seems to have played a significant role in FDI attraction in Eastern European countries. However, as _privatization_ and liberalization processes in Turkey are completed in most sectors, this will no longer be a driving force for FDI inflows.

Drawing from the cross-country empirical results presented in the previous section, we try to
average five months later. The earliest purchase by a low-income country was in February 2021, nine months later than the earliest purchase by a high-income country. ACT-A did make an advance commitment to procure vaccine doses in June 2020 but did not manage to raise resources to scale up purchases until after December 2020 (see Agarwal and Gopinath, forthcoming). The Africa Vaccine

> 5 Data on contract dates are from the IMF-WHO COVID-19 Vaccine Supply Tracker, and data on delivery dates for those contracts are from the UNICEF COVID-19 Vaccine Market Dashboard.

7
Since 2005/06 spending on education has increased in Liberia, a result of the government‘s raising public spending and increases in education financing by international donors, rather than an increasing prioritization in the sector (World Bank 2010). At 2.9% of GDP in 2007/8, public spending on education in Liberia is lower than in other conflict countries in the region and well below the share of public spending in Sub-Saharan Africa (13% vs. 20% of public spending, respectively). The contribution of households to finance education is very substantive. World Bank (2010) reports that in 2007/8 total resources in the public education system of US$ 77.2 million, accruing from the government --US$12.2 million–– were only half those provided by households -- US$27 million –– (and the US$38 million by international donors).

In 2002, the Education Law made primary education free and compulsory, although user fees are still reported at a large scale. Enrolment in primary and secondary levels have increased since the end of war, but the country is still far from universal primary education and there are clear gender, regional and income disparities in access to education (World Bank 2008).

In this context, two issues are relevant when selecting indicators for education that can be considered ―opportunities‖. First, indicators must be available from existing data sources and can be consistently collected over time in order to conduct inter-temporal comparisons. There are two potential data sources, the Demographic and Health Survey (DHS) for 1986 and 2007 and the Core Welfare Indicators Questionnaire (CWIQ) for 2007 and 2010. Given that the DHS of 1986 differs significantly from later DHS surveys and that CWIQ provides more recent information, the HOI analysis for Liberia is conducted using CWIQ. Also CWIQ data matches 16
|SEVAT<br>||||||
|---|---|---|---|---|---|
|Non-referral failure|23.4|22.0|20.8|19.8|16.9|
|Non-referral success|76.6|78.0|79.2|80.2|83.1|
|Referral failure|45.8|36.2|35.7|31.2|28.3|
|Referral success|54.2|63.8|64.3|68.8|71.7|
|Non-referral of eligible cases|92.8|84.7|78.1|73.2|59.7|
|Referral of non-eligible cases|2.0|2.8|3.9|3.9|5.2|

_Source_ : Authors’ calculation using the Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

# 4. Results

This section presents the results from applying the pseudo-PMT models within the legacy database of current humanitarian cash assistance beneficiaries. The total revised legacy caseload at the time of the analysis was 37,457 households corresponding to 224,736 individuals. Initial analysis suggests strong potential for a sequenced referral process. CLCI’s CT program only operates in the districts affected by the conflict in the Northern governorates of Iraq. The CLCI’s legacy database and, hence, the results presented in this section are focused on the Northern region only.<sup>13</sup> As discussed under the methodology section, there are two dimensions of analytical variation. First, the likely size of the MoLSA budgets will determine how many new households can enter the program. Second, the degree of confidence of a successful referral is taken into account. Referring more people will increase the absolute number of households being determined eligible after MoLSA assessment but will also increase the absolute
,671,656|71,703|8,820|

a) converted at official exchange rate of 8.13 cruzeiros to one US S.

b) excludes head office and auxiliary units located outside establishments. At the end of 1975 these activities employed 152,682 persons, who earned 6,550 million cruzeiros.

Source: Figures derived from Censo Industrial (1981a), column (1) from table 1, column (2) derived from tables 1, 15 and 35, column (4) from table 2 (annual average of monthly figures).
to impute poverty into the census. Using survey data, we predict household per capita expenditures for all households in the census using assets. We then classify households as poor (or not) based on the quantile of their imputed expenditures per capita and the poverty rate in the household survey. We set a poverty rate of approximately 50 percent in Malawi and 21 percent in Tanzania, which matches the national poverty rate in the household surveys. Appendix B provides more details on the imputation procedure, as well as the calculation of the asset indices across countries.

The use of unit-record census data remains the preferred gold standard option when recent census data are available. However, census data tend to be collected infrequently in most developing countries, and small area estimates based on satellite indicators are a preferred alternative to reporting direct survey estimates when census data are old or there have been rapid changes in spatial welfare patterns. We focus on satellite and other remotely sensed data because they are widely available and predictive of spatial variation in welfare. Importantly, their wide coverage means that these indicators cover all areas of a given country, which is a prerequisite for unbiased small area estimates.

> 5For example, only four of the countries are classified as lower income, while the geography varies from the Sahel to Southeast Asia.

> 6The sub-area in Mozambique is below the admin4 level, while the sub-area in all other countries is the admin4.

> 7The size of the sub-sample varies from 10 to 20 percent.

8
sup>***</sup>|0.133<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|<br>595|<br>595|<br>595|<br>589|<br>589|<br>589|
|_AR(1)_|0.003|0.239|0.000|0.002|0.678|0.000|
|<br>_AR(2)_|0.516|0.006|0.878|0.676|0.009|0.948|
|<br>_Hansen_<br>_p_-values in parentheses<br>* _p_ <0.05,** _p_ <0.01,*** _p_ <|0.724<br>0.001|0.417|0.515|0.689|0.464|0.538|

Our coefficient estimates in columns [5] and [6] of Table 5 suggest the following economic interpretation: first, doubling trade in manufacturing goods would accelerate growth of physical capital per worker and TFP growth in Sub-Saharan Africa by 1.3 and 0.8 percentage points per year, respectively. Second, doubling trade in primary goods would decelerate growth of physical capital per worker and TFP growth in Sub-Saharan Africa by 0.4 and 0.2 percentage points per year, respectively. We should note that the evidence is consistent with: first, the success of manufacturing export-led growth strategies in East Asia, where exports provided a basis for learning (transfer of technology, knowledge spillovers to 19
has generally focused on temporal and spatial deflation independently. Notably, for what concerns welfare measurement, the focus has been prominently on spatial adjustment, i.e. how to properly compare welfare of households living in different regions, overlooking the temporal dimension, i.e. how to compare households that are interviewed at different points in time. For instance, in Deaton and Zaidi (2002), on addressing issues related to the construction of the consumption aggregate, they observe “when we are working with a single crosssectional household survey, the price variation is less temporal than spatial; people who live in different parts of the country pay different prices for comparable goods.” Deaton and Zaidi (2002: 7) 19
**Table 3. Marginal Impact of Social Factors and Disciplinary Climate on Mathematics Performance (PISA)**

||2000|2009|2012|2015|MENA|Non-<br>MENA|
|---|---|---|---|---|---|---|
|Social factors (individual)|21.6|21.6|20.5|30.0|17.1|21.6|
|Social factors (school)|63|59.5|57|57.5|55,2|57.9|
|Disciplinary climate (ind.)|4.1|5|8.9|6.4|6,4|6.3|
|Disciplinary climate (school)|21.6|23.2|24.8|18.5|39,4|23.1|
|N. Obs.|93,390|420,565|256,143|345,576|47,856|722,242|
|R<sup>2</sup>|0.43|0.48|0.45|0.51|0,36|0.45|

Source: Analysis using PISA 2000-2015. Country-fixed effects are included. Reading: Pooling data for the MENA countries participating in PISA from 2000 to 2015, an increase in one standard deviation in the disciplinary climate index at the school level is associated, all other things equal, with an increase in math performance amounting to 39.4 points.

School disciplinary climate has been proven to be a major determinant of math performance in countries outside the MENA region as well. For instance, school disciplinary climate had a positive and significant impact in distinct cultural settings using PISA 2003 data in the Republic of Korea, Japan and the United States (Shin and others 2009). Evidence showed that the educational context
# **Online Appendix B: The Evolution of the Value of Suharto Connections**

# **(Extending Fisman, 2001)**

A key assumption of our paper is that the fall of Suharto reduced the value of political connections to him. Given that he was replaced by his protégé, B. J. Habibie, and that many people, including Suharto family members, managed to maintain their positions of power and prominence, it is important to assess whether this is indeed the case. If political connections to the Suharto regime remained valuable – or if our measure of political connections is a measure of generic political connectedness, rather than connectedness to Suharto specifically, we might anticipate that these connections continued to impact how firms’ stock prices responded to political developments (at least to some extent). By contrast, if these connections lost their relevance, then political connections to the Suharto regime should not predict firms’ share price responses to news about political developments.

To discriminate between these competing hypotheses, we use Fisman’s (2001) event study methodology and data to assess how firms with connections to him respond to political news after his fall, with the caveat that we have to confine our attention to firms that survived up until 2019.<sup>26</sup> We use the Castle Suharto Dependency Index which Ray Fisman generously shared with us. This indicator is a numerical rating of the degree to which the profitability of the 25 largest industrial groups in Indonesia was dependent on political connections to Suharto, ranging from 1 to 5, that is based on the subjective assessments of consultants at the Castle Group, a leading economics consultancy firm based in Jakarta. Rank 5 is given to conglomerates with direct ownership links to the Suharto family. Most of these groups had multiple companies listed on the Jakarta Stock Exchange. The total sample of firms for which we can revisit Fisman’s analysis is 51, which is lower than the original sample of 79 firms analyzed by Fisman (2001) because Bloomberg
system operating costs.

The unbundling mechanism resulted in two large interconnected systems; the Central Interconnected System, which accounts for approximately 75% of the country’s power generation and serves mostly households, and the Big North Interconnected System, which serves mostly mining operations. Generators that are connected to the grid are subject to the instructions of the Independent Co-ordinator of the National Electricity System (ICNES). Generators can sell surplus electricity in the spot market at marginal cost, or the cost of production of energy by the least efficient generation facility per hour. They may also sell to private companies whose capacity exceeds 5 MW at an agreed upon price. Finally, generators can sell to distribution companies at a price determined by a public bid.<sup>5</sup> Most of Chile’s generation is provided by five producers (Jim ́enez et al., 2020). According to Article 7 of the Electricity Law, national transmission companies cannot participate in generation. Interestingly, in spite of pro-competitive laws, no large generators have entered the market (in the central system that supplies most of the electricity). This is partly due to high fixed costs, but also due to opposition from civil society organizations (Serra, 2022).<sup>6</sup> There are emissions standards in place for the maximum PM2.5 emissions from thermal plants. A clean national intervention in 2010 states that all companies need to generate electricity from a given percentage of renewables (5% in 2010 – but scaling up in subsequent years) (Jim ́enez et al., 2020).<sup>7</sup> Although regulated, thermal power units create large levels of pollution. The correlation between coal-fired power stations and pollution will be used as an instrument in what follows. Although coal-fired power plants create large amounts of pollution, the largest sources of pollution according to the Global Carbon Project in Hannah Ritchie and Rosado (2020) are oil and gas. For our instrument to be
(9)|(10)|
||SSP 1|SSP 2|SSP 3|SSP 4|SSP 5|
|_Panel B: Urban expansion to 2100_||||||
|Urban expansion rate in 2100|291,822***|302,128***|442,168***|293,287***|163,586***|
||(1732.26)|(1684.69)|(1916.40)|(1678.8)|(1095.79)|
|Observations|2,014,887|2,014,887|2,014,887|2,014,887|2,014,887|
|R squared|0.0139|0.0157|0.0257|0.0149|0.0109|
|Mean livestock(popdensity)|13021.28|13021.28|13021.28|13021.28|13021.28|

Robust SE in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Grid-level OLS estimation, where one grid has a spatial resolution of 1/12 degree - about 9 to 10 sq km. Urban expansion rate is the percentage expansion in urban area in a given SSP scenario (ranging from 0 to 1). Livestock production is the global population densities of eight types of livestock: cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator). Here, the urbanization rate is defined as the percentage of urban population to total population, and ranges from the value of 0 to 1. The urban expansion projection data (Chen et al. 2020) are calculated using panel data regression based on historical population, urbanization rate, and gross domestic product (GDP)