rafmacalaba/data-use-mentions-extended huggingface dataset:
sectors: energy, water, ports and railways. We only used publicly available information on the Web. Indeed, various commercial databases and a Web search provided us with the balance sheets, financial statements and related information for 120 companies. The information was checked whenever possible either on the site of the companies or on the sites of their regulators in the countries in which they are operating. We also relied on reports generated by credit rating agencies or investment banks to cross reference the information. The sample covers 31 developing countries in five regions: Sub-Saharan Africa & MENA (11), South Asia (12), East Asia (30), Europe & Central Asia (12), Latin America and the Caribbean (55). Latin America and East Asia provide, as expected, the largest number of companies since these are the two regions which generate over 75% of public-private partnerships over the 1990s. The distribution of the sample per income groups or geographical regions, per sector is summarized in Table 1.

The table points clearly to the limited statistical significance of the sectoral observations for the water, port and rail sample in Africa, South Asia and Eastern Europe. Classifications by country income group (low-income, lower-middle-income, upper-middle-income) were less problematic because they take all countries with the same average risk level and compare them with appropriate hurdle rates, which have the same average risk level as well. The sample size for the low income group is however also limited for all sectors, except energy.

2
For profits, we find comparable results that the magnitude is higher in administrative data than in the AAA tool, but the difference in this case is statistically significant for Ghana. However, the difference in magnitudes is not statistically different in Uganda. Overall, these findings confirm that if we believe administrative data to be the closest “true” business performance indicator, then the AAA tool comes very close to matching it in terms of magnitude and precision. Note that all traditional measures of sales and profits have significantly lower magnitude than AAA estimates with AAA sales of 1,608 USD and 1,306 USD in Ghana and Uganda compared to 1,384 USD and 1,205 USD, respectively; and AAA profits of 374 USD and 215 USD in Ghana and Uganda compared to 245 USD and 92 USD, respectively.

Furthermore, as discussed earlier administrative data for the vast majority of small firms in developing countries simply does not exist and would be very expensive and time-consuming to collect. In comparison, the AAA methodology provides a compelling alternative, offering comparable value much more efficiently and cheaply.

# **3.5. Method or Medium?**

As a final test of the validity of the AAA tool, we explicitly conduct a field experiment in Ghana to assess the measurement methodology. Specifically, we randomly assign half the survey respondents from a baseline business survey the AAA electronic tool and the other half an identical paper version of the tool.<sup>10</sup> Both survey instruments include the same set of questions and steps for measuring firm sales and profits. All surveyors received identical training and each surveyor implemented the electronic tool or the paper tool to a respondent on a randomized basis; each surveyor was randomly assigned 50% electronic instruments and 50% paper instruments to complete (thus the only thing that differed was the 'approach' of using an electronic tool). This approach allows us to examine differences between the questioning techniques (reported versus anchored/adjusted) while isolating any effects due to
# **E Key descriptive statistics using tax records**

In this section we present a set of descriptive statistics related to our tax record only – meaning, before merging the distributions between survey and tax data, and before any adjustments to national account aggregates.

Counts of observations in our tax administrative records, separately presented by income source, are presented above in Table 1: in more recent years we observe halfmillion individuals receiving some form of income and/or listed as shareholders of a corporation filing income taxes in that year. Approximately 110,00 individuals each year file an income tax tax declaration, which is not mandatory for individuals whose sole income sources are withheld at source. In terms of individuals withheld at source, close to 120,000 are wage workers whose salaries are withheld by their employers; 45 - 50,000 are withheld for service provision activities; 200,000 for receiving some form of dividend<sup>5</sup> and 300,000 for receiving interest. Approximately 20 - 30,000 individuals each year declare capital gains, with the majority of them declaring very small amounts, and a few hundred file a separate declaration of rental income for individual properties. Between 90 - 100,000 individuals in recent years are also included in other types of third-party information for commercial activities, such as exports, sales through credit cards or sales to the government. As we discuss above, we use this data to compare self-reported amounts with third-party ones, and increase the imputed income to individuals according to the largest amount among the two sources. Finally, in most recent years we observe between 70,000 and 80,000 shareholders in firms that filed a CIT declaration, and are thus assigned non-zero income based on undistributed profits (profits net of dividend distribution).

In Figure E1a, we present the distribution of total income in the tax records, excluding the imputed undistributed profits. As we discuss in section 3, one key limitation of the administrative tax records we observe is the
coordinating the assistance effort. In particular, it is to be noted that, although aid coverage has reached many targets, it is still necessary to reach the poorest and most vulnerable people.

Moreover, Haitians have been showing quite remarkable levels of resilience so far, which leads us to think that they will devise unforeseen coping strategies to address the challenges that lay ahead. Aid efforts should definitely take this factor into consideration, tap into and build on these capacities, in order to deliver more targeted, effective and sustainable humanitarian and recovery forms of aid.

# **3. Methodological Framework**

Addressing the political economy of aid allocation often proves to be a major issue. Indeed, in the post-disaster context in Haiti—as in other countries—, it appears crucial to know whether assistance benefited more influential households. For instance, Francken et al. (2009) study the allocation of emergency assistance after cyclone Gafilo hit Madagascar in 2004, based on the USAID-funded survey conducted after the disaster. They find that, while assistance efforts also reached some untouched towns and villages, directly affected areas were more likely to receive aid. Also, they showed how donors’ assistance efforts were greater in richer areas and how mass media coverage made it more likely to receive assistance. Finally, areas which had been supporting the current government in previous elections received more assistance, and this assistance was often allocated to easily accessible communities, even when they had not been hit by the cyclone.

In the case of Haiti, CNSA (2010a) reports that food aid might not have been allocated in an equal and unbiased way, particularly when local boards had been entrusted with it or had simply taken it over by themselves.

Indeed, strict rules such as those described by Morris and Wodon (2003) are not necessarily implemented or applied when allocating assistance. In their model, assistance is used by the policymaker (be it a government or an agency) to equalize the level of post-disaster assets (including emergency assistance) of the poorest
play an important role as young (connected) firms benefitted the most (least) from foreign bank entry.<sup>2</sup> Exploiting sectoral data from more than 90 countries between 1995 and 2007, Claessens, Hassib, and van Horen (2015) investigate whether the local presence of foreign banks stimulates exports. This paper is closely related to ours. Foreign banks can promote trade but they focus on the financing roles these banks play in increasing exports by relying on the sectors dependence on external finance from Rajan and Zingales (1996) in their identification strategy. In contrast, our paper focuses on how the presence and footprint of foreign banks affect the sub-national exports of one country.

Our paper highlights the information roles foreign banks can play in promoting trade. Turkey presents a distinct case to examine the information role of foreign banks as firms in Turkey are less reliant on foreign banks for trade financing. As discussed in Acar (2009) above, over 70% of firms use their own capital or retain earnings to finance production activities and Turkish exporters are financed by cash on delivery.<sup>3</sup> In fact, the survey of Turkish firms by Acar (2009) finds that the decline in Turkish exports during the financial crisis is a result of decreases in foreign demand rather than issues with trade finance (See Ahn (2015) for a theoretical discussion). Demir and Javorcik (2016) also confirm the findings in the survey with data from the Turkish Statistical Institute for a subset of exports. They show that in 2002 a majority of textiles and clothing from Turkey to the EU (92%) and nonEU countries (76%) were financed by open account transactions and documentary collection methods.<sup>4</sup> These firms do not rely on the banks to provide credit financing.

Second, we contribute to the emerging literature that examines how networks can overcome information barriers in international trade. Information asymmetries and the lack of information about the final destination present
Policy Research Working Paper 9485

# **Abstract**

This paper estimates the impact of a differential relaxation of COVID-19 containment policies on aggregate economic activity in India. Following a uniform national lockdown, the Government of India classified all districts into three zones with varying containment measures in May 2020. Using a difference-in-differences approach, the paper estimates the impact of these restrictions on nighttime light intensity, a standard high-frequency proxy for economic activity. To conduct this analysis, pandemic-era, district-level data from a range of novel sources are combined —monthly nighttime lights from global satellites; Facebook’s mobility data from individual smartphone locations; and high-frequency, household-level survey data on income and consumption, supplemented with data from the Indian Census and the Reserve Bank of India. The analysis finds that nighttime light intensity in May was 12.4 percent lower for districts with the most severe restrictions and 1.7 percent lower for districts with intermediate restrictions, compared with districts with the least restrictions. The differences were largest in May, when the different policies were in place, and slowly tapered in June and July. Restricted mobility and lower household income are plausible channels for these results. Stricter containment measures had larger impacts in districts with greater population density of older residents, as well as more services employment and bank credit.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rcmbeyer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be
the target areas – in Mexico.<sup>6</sup> Third, it assesses whether the combination of survey and geospatial data generate estimates that improve on small area estimates generated using a census enumerated five years prior as well as a widely available wealth index created by Meta (Chi et al., 2022).<sup>7</sup> Finally, it compares estimates from three different types of small area estimation methods: a household unit-context model that models household per capita income as a function of sub-area and area-level predictors, a sub-area model that utilizes a nested error model at the level of the sub-area, and a Fay-Herriot area-level model (Fay and Herriot, 1979).

We consider these questions in the context of Mexico. Mexico is a useful case study for this analysis because of the richness of publicly available official data. In the Mexican context, subareas are AGEBs (Area Geoestadística Básica) and areas are municipalities. The official MCSENIGH household survey provides representative statistics at the state level, motivating the use of small area methods to generate municipal estimates. INEGI, the Mexican statistical agency, fielded a large intercensus sample of 5.8 million households in 2015. CONEVAL, the agency that produces poverty statistics in Mexico, used these data to generate official municipal level estimates of monetary poverty, which can be used as a benchmark for evaluation. CONEVAL employed an EBP using a model that included an extensive set of household and municipal characteristics. These type of small area estimates, generated using household-level auxiliary data, are rarely available between census rounds in low- and middle-income countries. They therefore provide a natural benchmark to evaluate whether combining survey and geospatial data produces municipal poverty estimates that are like those produced by a best-practice poverty mapping exercise, based on household data from a large intercensus.

We compare these 2015 benchmark estimates with five sets of municipal poverty estimates. The first is direct sample estimates taken from the 2014 survey. We generate the second, third, and fourth sets of estimates by combining
_<u>Table 6 Computer and Software Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|86.07|104.93|74.47|80.07|107.98|119.52|100.00|
|**Main Sectors**||||||||
|Manufacturing|87.79|95.28|82.74|85.19|109.94|118.88|99.84|
|Services|84.29|108.12|71.39|78.40|107.44|119.80|100.15|
|**Size**||||||||
|Small|74.56|98.00|70.49|81.13|103.21|100.71|83.12|
|Medium|100.44|131.10|84.28|71.86|104.30|140.55|117.25|
|Large|145.55|179.08|116.21|101.83|190.28|160.90|157.23|

Source: Author’s own elaboration from enterprise surveys

|_Table 7 Firms with Internet Connect_<br>Categories|_ion by Co_<br>Ghana|_untry (P_<br>DRC|_ercentage i_<br>Tanzania|_n terms of_<br>Uganda|_Firms Usi_<br>Zambia|_ng Comp_<br>Kenya|_uter)_<br>Average|
|---|---|---|---|--
_v_ ( /(a ' **_'T_** _+2 v' > v 2_ **_i '._** _+ + 2 *v ')_ So, while we can estimate the latter expression from a cross section, to move to the former requires some estimate of the permanent versus transitory innovations and the persistence of "permanent" innovations to expenditures.

As a provisional measure, we shall do two vulnerability calculations in the following sub-sections. First, using the estimate of the standard deviations of changes in expenditures, in aggregate and for groups of households. Second, using the estimate of the standard deviation of incomes scaled back by the estimated measurement error from appendix 1.

# _B) Estimating the Level of Vulnerability_

In this sub-section we estimate VPLs and headcount measures of vulnerability in both the Mini-Susenas and 100 Village Survey data. Since the level of poverty at any point in time is (more or less) arbitrary, we simply choose the poverty line so that the headcount poverty rate in both data sets is 20 percent.<sup>8</sup> Using this poverty line and the measured standard deviation of expenditure changes, we can calculate the vulnerability for the total sample. If we take a 3 year horizon as the _n_ and a vulnerability threshold of 0.5 for calculating the households who are vulnerable, the

> 8 This rate of headcount poverty is not completely unreasonable, for discussions on the poverty rates in Indonesia around the time of data collections, see Suryahadi _et al_ (2000).

13
The power sector was in the midst of a crisis, with tariffs that were double those in other developing regions and unreliable supply throughout the continent. The AICD calculated investment requirements for the next decade in the sector in 43 countries. A subset of the countries numbering 26―which together accounted for about 85 percent of the region’s population, GDP, and infrastructure inflows―were analyzed for hidden costs of power utilities arising from underpricing and operational inefficiencies (system losses, collection inefficiencies, and overstaffing).

The AICD used two different methods. The first, as reported in Eberhard et al. (2008) and in BriceñoGarmendia, Smits, and Foster (2008), allocated hidden costs to underpricing, excess T&D losses, and incomplete bill collection. The second, as reported in Eberhard et al. (2011) and Briceño-Garmendia and Shkaratan (2011), introduced a further hidden cost, that of overstaffing. The AICD found that power utilities in SSA had large numbers of employees per customer when compared to other developing countries, suggesting that the same output might be produced with less labor.

In half the sample countries, the largest single contributor to the hidden costs was underpricing, followed by under-collection of bills (7 countries) and system losses (5 countries). Overall, these three components were more significant than overstaffing in many cases, but the latter exceeded the rest combined in Chad, underpricing in Mozambique, and T&D losses in Benin and Cape Verde, and exceeded under-collection and matched underpricing in the Republic of Congo.

Where time-series data were collected, hidden costs changed markedly over time in several countries. For example, in Ghana, the Volta River Authority’s hidden costs grew from 14 percent of the total revenue in 2004 to 117 percent in 2006, halved in 2007, and then rose to 87 percent in 2008, whereas those for the Electricity Company of Ghana varied between 42 percent and 70 percent during the same period. On average, tariffs were below the cost-recovery levels
%<br>10%<br>0%<br>Existence of e- Robustness of e- Existence of Robustness of Cybercrime Cybersecurity<br>commerce commerce law personal data personal data<br>protection law protection law<br>High income Upper middle income Lower middle income Low income<br>Percentage of countries<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic. _Note: Robustness_ means that the corresponding legal framework contains all the regulatory attributes (getting a full score on all relevant questions) assessed under the Global Data Regulation diagnostic.

The contrast between the existence of laws and the robustness of those laws is particularly salient for data protection legislation (figure 11). Though more than 60 percent of upper-middle-income countries have legal frameworks on data protection, none of them has adopted all the regulatory good practices measured 25
Furthermore, this result is robust to the use of various proxies or indicators for openness. Regarding the direction of causality, the evidence points in favor of a “virtuous cycle,” by which higher openness produces growth improvements and vice versa.’ As to the channels through which openness affects economic growth, empirical studies show that this mechanism not only works through raising domestic investment, but also, most importantly, through efficiency gains (growth in total factor productivity).° Countries with more open and less distorted foreign trade sectors exhibit faster TFP growth rates over the long run. Furthermore, TFP growth improves after trade-liberalization reforms. However, the evidence also points out that the degree of improvement is not homogeneous, as some countries (for example, Chile) benefited from trade liberalization more than others (Mexico), possibly because the extent oftrade liberalization and the depth of other structural reforms were greater in the former group of countries.

In this section, two sets of indicators of openness to international trade are presented, namely, policy-instrument indicators and outcome-based indicators.

1. Policy indicators oftrade liberalization ‘Policies that present obstacles to trade are of two broad categories: tariff (and para-tariff charges) and quantitative restrictions. Both should be evaluated together, given that one type of restriction has often substituted for the other. The higher the average level and dispersion of tariffs and para-tariffs (for example, duties and customs fees), the more distorted the trade outcome. As recommended by the literature,° the measure used to evaluate the level of tariffs and para-tariffs is the weighted average rate of tariff and para-tariff charges, using as weights their respective shares in * See Harrison (1996) and Easterly, Loayza, and Montiel (1996). * See Edwards (1995). -

© See Pritchett and Sethi (1993).

7 Actually, the weights are given by the respective shares in total imports of some 120 developing countries in 1985 (see UNCTAD (1994)). Using the country’s own import shares
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period, although stepped up government expenditures more than offset the additional revenue, leading to increased deficits (Shalizi, Ghandi, and Ehdaie, 1985).

Wheeler (1984) used a number of variables to explain the economic downturns in many Sub-saharan African countries. Important factors were outbreaks of violence (or more exactly, years of peace), the terms of trade, the diversity of exports, whether the country exported minerals, the existence of foreign exchange controls, and a "habit" parameter that measures how imports respond to declines in foreign exchange. While the results presented below do not include all of his explanatory variables, they do generally confirm the effects of political instability and terms of trade on output growth. A study by Kormendi and McGuire (1985) which usod data from both developed and developing economies, suggest that other variables, such as the variability in money growth, the growth in exports, and the standard deviation of real outpu'. growth, can also explain differences across countries in output growth rates.

The sample of countries was selected by including all those which reported complete information on tax, output, and investment variables. A total of 56 observations remained; 27 countries from 1965-73 and 29 countries from 1974-82.10 This pooled cross-section time-series data set compares the growth experience of similar countries over time, and provides a larger number of observations than a simple cross-section data set. For some coefficients, such as the marginal product of capital, interactive terms are introduced which allow marginal productivity to differ across periods.

> 1Q/ In 1965-73, the countries were Benin, Cameroon, Chad, Congo, Gabon, Gambia, Guinea, Ivory Coast, Liberia, Mali, Nigeria, Senegal, Sierra Leone, Burkina Paso, Botswana, Burundi, Ethiopia, Kenya, Lesotho, Madagascar, Nalawi, Mauritius, Rwanda, Somalia, Sudan, Swaziland, and Zaire. In 1974-82, the Gambia, Guinea, Niger, and Zambia were included, while Madagascar and Gabon were dropped.