rafmacalaba/data-use-mentions-extended huggingface dataset:
intervention, which we will refer to as baseline toilet ownership.<sup>20</sup> For households who reported having a toilet, survey enumerators verified it directly and made observations on its appearance, overground quality, and cleanliness.

## **3.2.2 Administrative Data**

In addition to the primary endline survey of clients and their households, we rely on two sources of administrative data in our analysis. The first is detailed loan data from our lending partner on all clients residing in the study areas. In particular, we have available information on all loans taken from the partner MFI by any client residing in our study GPs during the study period, including amount borrowed, repayment amount, the date of disbursement, tenure, purpose of the loan and default. This provides us with reliable information on the disbursement of sanitation loans and other loans, and allows us to track trends for loan uptake over time.

The second source of administrative data is credit bureau data from a leading provider, used by the MFI partner when making decisions on loan applications. Following new regulations introduced by the Reserve Bank of India in 2011, all micro-finance institutions are required to report on all loans outstanding for each client on a monthly basis. We obtained this information for around 88 percent of clients in our sample.<sup>21</sup> Reassuringly, impact results are consistent for the sample for whom we have credit bureau information, as shown in TableOA-B.1 in the Online Appendix.<sup>22</sup> As a result, these data allow us to construct a more accurate measure of household borrowing from micro-finance institutions that is free from recall and measurement error.

# **3.3 Sample Descriptives and Sample Balance**

Table 2 presents summary statistics for the main characteristics of clients and their households. These variables have been constructed from survey data collected at endline. We thus concentrate on variables that are unlikely to have been affected by the intervention itself. For
variety of incentives and initiatives at a more macro level. These could entail, for example, tax breaks for Internet-related capital expenditures, tax breaks for ISO certified firms, and/or broadening the scope of exports facilitating financing activities by governmental agencies such as the Export-Import Bank or the Overseas Private In vestment Bank to cover capital expenditures necessary to obtain these tools by exporters and perhaps even their major customers abroad.

Clearly there is a close relationship of firms that have ISO certification and those that are able to export. This finding has important implications for possible export promotion programs and/or aid for trade. Particularly, ISO certification provides one of the few scenarios in trade related projects to implement natural random experiments. A natural random experiment is the optimal tool to measure the effectiveness of a program as it integrates a control and a treatment group from the design of a project and therefore makes it possible to pick up the causality and direct effect of such a project. Because of the public nature of trade projects, this methodology is usually not applicable; however, in this case it is. Certain firms can be assisted in obtaining this type of certification and others not allowing for a direct impact evaluation of such project.

Because I only have a cross-section of data, I cannot determine the direction of causality. I cannot determine if firms that have ISO certification and/or a website are the ones that are able to become exporters or if exporting firms are the ones that obtain these marketing and signaling tools. In Section 4 I provided some evidence that suggests the direction of tools helping firms become exporters as the exporter productivity premium is smaller among firms that use these tools; however, a more detailed study of causation is needed once panel data become available from the Enterprise Survey.

25
The Johannesburg futures market (SAFEX) provides the reference price for white maize in southern and east Africa. This price is quoted in rand. For rice, the most commonly used reference price is the Bangkok spot price<sup>13</sup> which is quoted in US dollars. Monthly SAFEX

> 10 Staples comprise all cereals, tubers and starchy products whether consumed crude or processed. They include wheat, rice, sorghum, millet, maize, cooking bananas, Irish and sweet potatoes and yams. Pulses (beans, peas and oilseeds such as groundnuts) are not included. Shares are calculated from the Tanzania (2008‐2009) LSMS‐ISA household survey.

> 11 In a companion paper, Gilbert, Christiaensen and Kaminski (2015), provide a systematic analysis of the extent of price seasonality across an array of staple and non‐staple food and food products in seven African countries.

> 12 Price seasonality estimates using nominal prices are very similar.

> 13 White milled 5 percent broken. Source: IMF, _International Financial Statistics._

14
also includes questions on the rights of indigenous peoples to obtain collective land titles under access to property and questions on whether legal provisions on bilingual education exist, among others.

Under the indicator for access to institutions, the survey on religious minorities includes questions that address constitutional provisions that guarantee the freedom of belief and religion. Under access to education, the survey includes questions that attempt to identify whether legal quotas exist for religious minorities in primary, secondary, and tertiary education. The section on protection from hate crimes and hate speech addresses the existence of criminal provisions on hate speech that incites religious hatred and hate crimes motivated by religious bias.

The sexual minorities’ survey includes questions that address legal discrimination and legal protection from discrimination for lesbian, gay, bisexual, transgender, and intersex (LGBTI) communities. Under each indicator, questions attempt to capture the laws related to those communities and whether those same laws apply to the majority of the population (that is, heterosexual or cisgender). For example, the survey determines whether the country has a comprehensive law that specifically mentions sexual orientation and gender identity as protective characteristics. The equal treatment of LGBTI communities in schools is addressed under access to education, which assesses whether the state has introduced laws and policies to combat bullying of LGBTI students in schools. The topic of access to public services and social protection mechanisms is especially important for LGBTI communities, and it is covered through questions about access to health care, human immunodeficiency virus (HIV) preventive services, and gender reassignment surgery, among others. The indicator for hate crimes and hate speech includes questions on the criminalization of hate crimes and hate speech

> 18 For further details on the Doing Business database and reports, see http://www.doingbusiness.org/.

> 19 For further details on the Women, Business, and the Law database and reports, see http://wbl.worldbank.org/.

> 20 The Doing Business indicators are recognized as being extremely influential and successful in attracting the
# **5.3.- Performance indicators of airport infrastructures**

The performance indicators presented in this work are commonly used in the airport industry. However, in some cases it might be necessary to make a selection or an _ad hoc_ design according to the special airport features and services to be assessed.<sup>Although the</sup> proposed list is not exhaustive, it intends to cover those aspects or areas that might<sup>be</sup> problematic for regulators or managers. There are particular areas where<sup>the infringement</sup> of public interests is more likely. For example, at airports subject to price regulation, there are problems regarding incentives for investing in new capacity and with the quality of service. This is the result of strong operator tendencies toward reducing costs at the<sup>expense</sup> of service quality. Therefore, it is important to have a set of financial<sup>and economic</sup> indicators available that would help in analyzing airport performance. These could include costs coverage, profitability, asset investments and the use of available resources.

In Table 5. 1, a set of financial indicators is presented. Two groups are distinguished. The first are the _strategic_ indicators that are needed to evaluate policies with medium and long term effects, such as return on capital investments. Secondly, _other financial indicators,_ where measures such as cash-flow provide an accurate evaluation of the day to day financial situation of the airport.

**Table 5.1. Financial performance indicators**

||*Rcturn on capital investment<br>*Payback period|
|---|---|
|Strategic indicators|*Current assets/Liabilities<br>*Self financing ratio<br>*Debtors and creditors ratio|
||**C -hiows**|

Source: Lemaitre (1997) Efficiency economic indicators are shown at Table _5.2._ These are classified into six
paper: 1) country and region<sup>4</sup> , 2) year of financial closure and total life period of the project, 3) total investment (in both monetary value and physical capacity), 4) sector, subsector, segment, and technology (fuel) of each project.<sup>5</sup> The database includes observations for 105 developing countries from 16 years from 1993 to 2008.

**The macro data are collected from other central databases** , including i) the _World Development Indicators (WDI) 2009_ for macroeconomic variable and the _World Governance Indicators_ and Polity IV to select a few indicators of governance ii) the _Energy Balances and Statistics 2009_ of the International Energy Agency (IEA) to derive indicators of energy import dependence and per capita CO2 emissions and the electricity databases of the Energy Information Agency (EIA) of Department of Energy (DoE) of the US government (Annex 1) to countries‟ total installed capacity and their composition, total electricity generation, consumption, net import, and distribution losses. Finally, we also use REN21 to compile the list of countries adopting feed-in tariffs and for publicly available information on the list of countries with an autonomous electricity regulator.

**Fig. 6 PPP Investment in renewable-based energy, by region**

<!-- Start of picture text --> 3%<br>13%<br>27% EAP<br>5% ECA<br>LAC<br>MNA<br>SAR<br>16%<br>SSA<br>37%<br><!-- End of picture text --> _Source:_ World Bank/PPIAF PPI Database

**The key summary statistics show PPP investment highly concentrated in few regions.** As Figure 6 illustrates of all the PPP investment reported in the PPI data the large majority benefitted the LAC and EAP region (where investments are respectively just below 40% and above 30%
# Figure 9: Employment growth rates by ex-ante type of vulnerability jobs

# a. Essential Jobs

# b. Teleworkable Jobs

<!-- Start of picture text --> 3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>-4 -4<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Essential sector Non-essential sector Teleworkable Non-teleworkable<br>Annual growth rate) Annual growth rate)<br><!-- End of picture text -->

# c. Income-risk

# d. Health-risk

<!-- Start of picture text --> 3 4<br>2 3<br>1 2<br>0 1<br>-1 0<br>-2 -1<br>-3 -2<br>-4 -3<br>-5 -4<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>Income-safe Income-risk Health-safe Health-risk<br>Annual growth rate) Annual growth rate)<br><!-- End of picture text --> 40
scale Cobb Douglas production function for the sample of firms in the Enterprise Survey suggests that that the cost share of capital is closer to _s_ = 1/10.<sup>16</sup> Table 8 displays the results of the second stage probit and tobit regressions using the approximate measure of total factor productivity, ATFP2, estimated with a cost share of capital equal to 1/10.

The results using ATFP and ATFP2 are identical for the second stage. Both measures of productivity affect the probability a firm exports and its export intensity as well. Size, foreign ownership, ISO certification, and the use of email still remain positive and significant and the magnitudes on the coefficients for these variables are identical. _7.2 Labor Productivity vs. ATFP_ There are frequent concerns of the quality of the data on capital and inputs from developing countries that is required to estimate ATFP and other productivity measures. To check that the results of Table 6 and Table 7 are not driven by measurement errors of these variables, I run the same regressions controlling for labor productivity (i.e., total sales per worker) instead of ATFP. Table 9 displays the results. Some of the coefficients slightly change in magnitude but overall results remain unchanged.

_7.3 One Stage vs. Two Stages_ In order to test that the results are not driven by the two-step specification, I include ATFP and not its residual in the estimation of the probit and tobit regressions. Table 10 displays the results. Even though the magnitude some of the variables of interest decrease in absolute terms, the results remain unchanged. An interesting finding from this set of regressions is that having a website is only significant in the probit regression whereas, using email is only significant in the tobit regression. This confirms previous results that

> 16 If only labor and capital are used in the production function, excluding materials as an input,  then the coefficients on capital are closer to 0.3.

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policy measures include each harmonization directive and MRA concluded between the countries in the set. We concord the policy measures, which often pertain to a speci...c attribute (e.g. safety) of a variety of products, with trade data at the SITC (revision 2) 3-digit industry level. We then estimate the signi...cance of the impacts of these measures on bilateral trade across countries and over time, controlling for other in‡uences.

Our evidence broadly con...rms the conclusions drawn from the model. Regional harmonization signi...cantly increases intra-regional trade in a¤ected industries. Exports to the region of excluded developed countries also increase, but exports of excluded developing countries decline. These asymmetric e¤ects may arise because developing country ...rms are hurt more by an increase in the stringency of standards and bene...t less from economies of scale in integrated markets. Mutual Recognition Agreements (MRAs) promote trade both within the region and with the rest of the world. But when they contain restrictive rules of origin, then intra-regional trade increases at the expense of trade with other, especially developing, countries.

To place our contribution in the context of the existing literature,<sup>9</sup> the analytical section builds on the work of Baldwin (2000) and Ganslandt and Markusen (2001). In particular, Baldwin (2000) anticipated some of the results of this paper on MRAs, but assumed identical countries with identical ...xed costs of complying with standards. So the implications of harmonization and asymmetric e¤ects on excluded countries were beyond the scope of that model. As far as we know, only two previous studies have empirically explored the impact of shared standards on trade. Swann et al (1996) regressed British net exports, exports and imports over the period 1985-1991 on counts of voluntary national ("idiosyncratic") and international standards recognized by the United Kingdom and Germany. They discovered
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# IV. **How Much Data Does a Regulator Really Need?**

## **Data Requirements**

A main concern in most reforming developing countries is the limited access to data. This implies that a careful assessment of the data capacity of a regulator is probably the first stage in the development of a toolkit for any regulator. The following table'<sup>2</sup> shows the input requirements of the HCPM, compared to those in the accounting approach.

|**Data**<br>**Requirements**|**Accounting**<br>**Approach**|**HCPM**|
|---|---|---|
|Customer data|Only aggregate<br>historical data used.|Uses and processes<br>data at any level of<br>aggregation.|
|Input Prices|Highly aggregated;<br>not forward looking;<br>accounting<br>categories not<br>relevant to economic<br>issues.|Highly<br>disaggregated input<br>data possible.|
|Engineering|Engineering inputs<br>other than historical<br>experience not<br>possible.|Based on network<br>optimization<br>principles; can<br>design to variety of<br>engineering<br>standards.|
|Time to<br>implement|Accounting data<br>available now in<br>many countries, but<br>not in others.|Logic available<br>now; inputs may<br>require six months<br>or more.|

Most typically, regulators of privatized utilities will start from scratch and initially have only a very modest source of information to work with. As regulators build on this information, they will need to consider the policy issues that they will ultimately have to address. These issues will determine the form of the
SWFs to contribute more to long-term financing of development in Africa? In terms of the literature, Triki and Faye (2011) count at least 15 SWFs, and analyze how African economies can benefit from SWFs and use them as a channel to tap into international financial markets. They use data taken from different sources (Sovereign brands survey, JPMorgan) to discuss the landscape of African SWFs, as well as SWF interventions on the continent, and find that African Funds are small, suffer from poor governance structures, and mainly focus on stabilizing local economies. However, this paper does not analyze the determinants of SWF investments, nor the empirical relationship between SWF investments, economic growth and financial development. Turkisch (2011) analyzes the opportunities and barriers for SWF investments in Africa, and suggests that the introduction of SWFs in African economies facilitates up to 50 percent of the investment needed for infrastructure. However, this paper also identifies the main barriers for SWFs in Africa, which include macroeconomic risks and high volatility of returns, the lack of technological knowledge, and the size and liquidity of African economies. Moreover, the latter study did not use a theoretical model or empirical estimates to obtain these findings. This lack of studies motivated us to explore the landscape and determinants of SWF investments in Africa. In doing this, we use an enriched simple mean-variance portfolio model to derive testable results. First, we theoretically show that the determinants of SWF investments in Africa are rate of return, risk, and networks. More precisely, we find that the rate of return has a positive and significant effect on SWF investments, while risk exerts a negative effect.

2
correlation between sudden stops and recessions require the presence of real rigidities that overcome this pressure.

This paper used industry-level data from emerging and industrial countries to provide evidence of the likely nature of these rigidities by documenting how the differential response of industries to sudden stops relates to industry characteristics that proxy for their vulnerability to some of their rigidities. In particular, we show that industries that require higher levels of external financing suffer more from these episodes, especially when, at the aggregate level, the sudden stop coincides with recessions, and particularly so among less financially developed countries. This strongly suggests that a feature of these episodes is the tightening of financing constraints and that financing rigidities at the micro level are behind the negative consequences of sudden stops.

The paper also explored the role of two common policy actions aimed at preventing and reducing the impact of sudden stops: reserve accumulation and monetary loosening. Our findings suggest that while reserve accumulation may help reducing the probability that a sudden stop will coincide with a recession, it does not change the pattern of responses once they coincide. Nonetheless, we also find that a monetary loosening may indeed smooth the average and differential impact of sudden stops across industries, both by reducing the correlation between sudden stops and recessions, and by dampening their consequences when the recession actually occurs.

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||
||_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|||
|ETH|5,819|6,409|918|1,035|58.23|40.71|||***|N/A|
|NGA|1,287|1,231|3,484|3,692|[18.30]||63.45||[***]||
|SOM|2,698|2,761|5,162|5,182|[7.31]||48.47|44.09|***|[***]|
|SSD|198|230|1,368|1,595|||42.78|[34.31]|***||
|SUD|1,869|1,837|3,933|4,030|35.41|28.18|18.83|17.58|***|*|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

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# FIGURE 8 Pass-through associated with global shocks

# A. Global demand shocks

<!-- Start of picture text --> Ratio<br>1.2<br>Interquartile range Median<br>0.8<br>0.4<br>0.0<br>-0.4<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# C. Global supply shocks

<!-- Start of picture text --> Ratio<br>0.4<br>Interquartile range Median<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# E. Oil price shocks

<!-- Start of picture text --> Ratio<br>0.5 Interquartile range<br>Median<br>0.0<br>-0.5<br>-1.0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text -->

# B. EMDEs: Global demand shocks

<!-- Start of picture text --> Ratio<br>3.0 Interquartile range Median<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>D. EMDEs: Global supply shocks<br>Ratio Interquartile range Median<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8<br>
341 increase represents a significant increase. Storeygard (2016) constructs a panel of Sub-Saharan African cities using nighttime lights data and studies the effect of trade costs on economic activity. Asher and Novosad (2020) find effects of rural roads on non-farm employment but not on agricultural outcomes, income, or assets. Asher et al. (2020) estimate the effect of road construction on deforestation using satellite-based measures of forest cover. Faber (2014) finds that a national highway program in China aimed at connecting provincial capitals caused a reduction in GDP growth in small peripheral counties that the highways passed through. We add to this literature on transportation infrastructure investments by using satellite imagery to study development in a context where urbanization rates are low and where we observe a large-scale national investment program that was gradually rolled out over regions and time.

Third, we also contribute to the urban and regional economics literature on land use more generally.<sup>4</sup> For example, Deng et al. (2008) use high-resolution satellite imagery data to study urban expansion in China. This literature often builds on the monocentric city model, where the land use in and around a central business district depends on transportation costs. Michaels et al. (2012) consider a model of urbanization and structural transformation where land is allocated to residential, agricultural, or non-agricultural land use. Motamed et al. (2014) show that the transition from rural to urban activity depends on trade costs as

> 2The data is publicly available at `https://www.esa-landcover-cci.org/?q=node/175` .

> 3See Redding and Turner (2014) and Donaldson (2015) for surveys of the literature.

> 4See Duranton and Puga (2015) for an overview of theoretical and empirical literature on urban land use.

5