rafmacalaba/datause-encoder-data huggingface dataset:
# **4.6. Administrative Data**

We received a list from JUCEMG of all firms which registered with the chamber of commerce in Belo Horizonte between October 25, 2011 (the day the communication treatment started) and September 19, 2012. This list contained the official business name, fantasy name, street address, phone number, and CNPJ of the business, and also contained registrations for MEI status as well as SIMPLES. In addition, we obtained a list from PBH of all firms registering for an ALF license during this same period. We used a matching algorithm and manual checking to match this to the listing survey data, described in Appendix 4. For each formalization measure, we define “definite” matches as cases where there are sufficient data in both data sources to make it almost certain we are matching the same firm, and “definite or probable matches” to also include cases where it seems very likely to be the same firm, but less data are available for confirming the match. We also construct an overall measure of whether a firm has formalized which measures whether they are a definite or probable match for at least one of these three forms of formalization (MEI, SIMPLES, or ALF).

We cross-checked the accuracy of this matching procedure with two subsamples of data. The first was reports from the inspectors, who recorded the CNPJ and ALF of firms which registered between the inspectors’ first visit (when they issued warnings to informal firms) and their second visit (where they came back to see whether firms had formalized or were taking steps towards doing so). Secondly, in November 2012 the survey firm attempted to re-contact firms that were in the follow-up survey and ask them for their CNPJ and ALF numbers and registration dates.<sup>11</sup> Both these checks revealed the matching process to be accurate at matching firms which had formalized – it was only a handful of cases where individuals had registered at different addresses that we had not detected.

# **5. Results**

We begin by discussing implementation of the different interventions, and use our survey data to examine whether this resulted in changes in knowledge or in inspection frequencies. We then examine whether these interventions changed the rate of business closure, and then ultimately, the impact on different aspects of formalization. To estimate treatment effects, we estimate versions of equations (2) and (3) for different outcomes. Since baseline data are not available for the inspector

> 11 The survey firm was only able to re-contact 1166 firms in doing this check (after accidentally inserting a skip pattern which skipped asking the ALF and CNPJ numbers for most firms in the full follow-up survey), and so given this large attrition rate we do not use these data for measuring the formalization outcome, but instead rely on administrative records, only using these data as a cross-check to ensure our administrative matching procedure is reliable.

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# 1. Introduction

The most immediate consequences of natural hazards are the fatalities and casualties, and the first priority of disaster risk management is to save lives. But natural disasters also have economic consequences, which affect wellbeing and need to be accounted for and managed (Cavallo and Noy, 2011; Rose, 2009; Skoufias, 2003). These economic consequences and wellbeing losses depend on the value of what is lost or damaged, and on many other factors, including how long it takes to rebuild, how asset losses translate into income losses, and how coping mechanisms and ex‐post support (from insurance to social protection) protect the victims and help smooth consumption losses (Carter et al., 2007; Le De et al., 2013). In addition, wellbeing losses are larger when losses are concentrated, especially when concentrated on poor people – as is often the case (Hallegatte et al., 2016a). Disasters can also have irreversible and long‐term health consequences, particularly on children (Dercon, 2004; Maccini and Yang, 2009).

Many policies can minimize wellbeing losses and protect the population: from building dikes and restoring mangroves to better land‐use planning and early warning, to evacuation, insurance and social safety nets. Risk management policies are best designed as holistic strategies that combine many of these levers (World Bank, 2013).

Designing such a consistent policy package is challenging; here we build on Hallegatte et al. (2016b) and propose an approach and a model to support this process. In this work‐in‐progress, we combine data on natural hazards, population and asset location, asset vulnerability, and socioeconomic characteristics and combine insights from natural and social sciences to assess how natural disasters affect wellbeing, measured using a _social welfare function_ ( _welfare_ is the metric used by economists to measure wellbeing). We first present the model, and the global data used to calibrate it. We then use the model to define and assess the socioeconomic resilience of 117 countries to natural disasters, identify policy priorities to reduce the impact of disasters on wellbeing, with a focus on adaptive social protection, and help design holistic risk management strategies tailored to each country. The model and the data are available online.<sup>1</sup> We propose a new quantifiable definition of _socioeconomic resilience_ , the ratio of _asset_ losses to _welfare_ losses<sup>2</sup> : Socioeconomic resilience �<sup>Asset losses</sup>

## Welfare losses

With this definition, socioeconomic resilience can be considered as a driver of the risk to wellbeing – measured through the expected welfare losses due to natural disasters – along with the three usual drivers, hazard (the probability an event occurs), exposure (the population and assets located in the affected area) and asset vulnerability (the fraction of asset value lost when affected by a flood): 1 The code and data set are available at github.com/adrivsh/resilience_indicator_public/

> 2 This definition combines the notions of macroeconomic and microeconomic resilience previously proposed by Hallegatte (2014).

2
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA xvi A detailed description of the methodology can be found in Annex, available upon request. xvii Telephone conversations included in Annex, available upon request xviii Based on the definition by the Serbian Government. For further details see Annex, available upon request. xix <mark>The results did not vary systematically depending on geographic region, municipality size (expressed by the number of primary schools), or school location (on the outskirts or in the town center); hence these variables are not reported.</mark> xx For example: Kilem Gwet, Handbook of Inter‐Rater Reliability _: The Definitive Guide to Measuring the Extent of Agreement Among Multiple Raters_ , 4th ed., (Gaithersburg: Advanced Analytics, LLC, 2014), http://www.agreestat.com/book4/. xxi Pearson Chi‐Square = 5.52, df = 1, p < 0.05. xxii Pearson Chi‐Square = 21.97, df = 1, p < 0.001. xxiii Laura Durso and Gary Gates, “Serving Our Youth: Findings from a National Survey of Service Providers Working with Lesbian, Gay, Bisexual and Transgender Youth Who Are Homeless or At Risk of Becoming Homeless,” Los Angeles, The Williams Institute with True Colors Fund and The Palette Fund, 2012, https://williamsinstitute.law.ucla.edu/research/safe‐ schools‐and‐youth/serving‐our‐youth‐july‐2012/. xxiv See the Annex for a fuller description of the scenario. xxv Kilem Gwet, _Handbook of Inter‐Rater Reliability._ xxvi Pearson Chi‐Square = 41.63, df = 3, p < 0.001. xxvii Pearson Chi‐Square = 12.172, df = 1, p < 0.001 . xxviii Gay and corresponding heterosexual group: Pearson Chi‐Square = 7.36, df = 1, p < 0.01. xxix Lesbian and corresponding heterosexual group: Pearson Chi‐Square = 3.11, df = 1, p = 0.08. xxx Pearson Chi‐Square = 2.36, df = 1, p = 0.001.

xxxi In spite of the fact that the difference in the percentage of refusals of gay and lesbian couples might seem to be different, the statistical test shows that difference is not systematic. Pearson Chi‐Square = 10.84, df = 1, p = 0.12. 32 UNESCO, “From Insult to Inclusion.” 33 The Government of the Republic of Serbia, “Anti‐Discrimination Strategy 2013‐2018” (unofficial translation), 44, <u>https://rm.coe.int/16801e8db9.</u>

> 34 “ _Specifically ensure protection of the right of children and youth to education in a safe environment, without violence, harassment, social exclusion or other forms of discriminatory or degrading treatment based on sexual orientation or gender identity._ ” Government of the Republic of Serbia, “Anti‐Discrimination Strategy,” 47.

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Appendix A. CLR (2003) argue that prudential supervision should be triggered when an MFI accepts retail deposits from the public and in our sample 74% of those that accept deposits face onsite supervision compared with only 14% for those that do not accept deposits. A much higher share of those with onsite supervision are non-NGOs that lend to individuals (rather than groups), make larger loans, lend less to women, and have a higher share of staff concentrated in the head office (and thus fewer staff with contact with clients in the field). Supervised MFIs also tend to be larger (in terms of assets), and a bit older and more profitable (in terms of Financial Self Sufficiency) than unsupervised MFIs. However, these differences are not significant for total assets or FSS. The profile that emerges is that more commercially-oriented MFIs tend to be supervised, while the more outreach-oriented MFIs are not.

These summary statistics foreshadow our main regression results. Namely, while there is no significant difference in profitability, there is significantly less outreach (larger loan sizes and less lending to women) for supervised MFIs than unsupervised ones. These simple statistics are therefore also consistent with the idea that supervised MFIs are compelled to curtail outreach to maintain profitability.

Operating expense ratios for supervised institutions tend to be lower than for the unsupervised. At first blush this might seem to contradict an important part of our story, namely that complying with regulation and supervision is costly for MFIs. However, operating expense ratios tend to be higher for institutions that make smaller loans and lend more to women precisely because those market segments are harder to reach (Gonzalez, 2007; Cull, Demirgüç-Kunt, and Morduch, 2007, 2009). Table 2 therefore indicates that the costs of complying with onsite supervision are not large enough to push the cost profiles of the more commercially-oriented MFIs beyond those of the outreachoriented MFIs. Yet this does not necessarily indicate that the costs of complying with supervision are negligible or that commercially-oriented MFIs do not seek to defray those costs by making larger loans and lending less to women. In any event, the operating cost levels for the two groups are not significantly different from one another.

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52. Early in 1980, a comprehensive program of loan portfolio classification according to risk of default was established. It was recognized that the most important risk to the solvency of a banking institution came from the loan portfolio, and that a sound assessment of the solvency and profitability of a bank could not be made without having a clear idea of the quality of its loan portfolio. Accordingly, banks were required to classify their loan portfolios in one of four categories according to the risk of default. The risk of default was to be determined considering three elements: (i) repayment capacity of the borrower; (ii) knowledge of the borrower and its previous creditworthiness record; and (iii) pledged collateral. To discourage unsound lending and to cover potential loan losses, banks were required to provision those loans that received a low classification. This represented a complete departure from the traditional principle that only past due loans should be provisioned.

53. A better knowledge of the quality of the loan portfolio was instrumental in the design of the different mechanisms to reschedule debts and recapitalize banks. It provided the necessary information to estimate the size of existing losses, to design the corrective action that was needed, and to assess the expected cost for the Government.

54. In 1982, when the system of the loan portfolio classification was well advanced in its implementation, an early warning system was also established. The system was based on financial ratio analysis and permitted to assess the current and projected financial condition of each bank. According to these assessments, banks were grouped in different risk categories. The variables considered to make this classification were adequacy of the capital, quality of the asset,., quality of the management, profitability, and liquidity. Tht} system was derived from the CAMEL system, the acronym for Capital, Assets, Management, Earnings, and Liquidity, used by t.s US regulatory agencies since the late 1970s.

55. Information disclosure was also considered a key element in a more deregulated banking system. With timely and reliable information depositors could better discriminate among different institutions and exert market discipline. Accordingly, the Superintendency of Banks gradually increased the disclosure of information, including detailed monthly balance sheets and income statements, and information on loan-portfolio concentration. Moreover, in 1983, the CAMEL indicators started to be published to facilitate the analysis of specific institutions and comparisons with the rest of the system.

56. A new banking law was passed in 1986 strengthening the monitoring and enforcement powers of the Superintendency of
notably China (Blanc-Brude and Jensen 2004), driven by both international and local investors and elsewhere in Asia and in Latin America, regional and national companies are increasingly active. Local investors seem willing to take on financial risks that the largest international water companies are not, and are expanding rapidly into new markets.

**Figure 3: Number of Projects Announced with Foreign Investors by Region and Year (1991-2004)**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Africa Asia Latin America Eastern Europe<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Using data from our 60-country sample, Figure 4 shows the increasing role of local investors in Latin America and Asia, who are taking the place of international companies, which contrasts with the established view that the water sector is dominated by a small group of European companies. In fact, projects without international involvement constitute the majority of projects in Malaysia and China and recent deals in Latin America (Chile and Peru) also point to the ascendancy of domestic companies. However, in Europe there are no projects without international involvement and there is only one project of this kind in Africa. Figure 4 also shows that most of the new investments in Latin America involve only domestic companies while the level of international involvement has collapsed. In contrast, in Asia, the growth in project numbers is due to both domestic and foreign investors. Moreover, the latter include many regional investors such as Malaysian companies investing in China.

**Figure 4: Number of Projects Announced with Local Investors by Region and Year (1991-2004)**

<!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Africa Asia Latin America<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> In this review of the descriptive statistics, we have noted the recovery in investment levels in 2004 and their high degree of concentration in a handful of countries. The WATSAN data also show that the common belief that investors are no longer engaging in concession-type contracts is not accurate and that local private corporations are increasingly important players in the water and sanitation markets. In the next section, we review the literature pertinent to our analysis of PSP in the water sector, before developing and testing our model of PSP with our data.

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differences in the allocation of output by branch. In some cases the two different sources may draw a different boundary between manufacturing and 1 non-manufacturing as well as between branches within manufacturing .

A further difference between the two sources is that BEA includes firms without employees whereas these are not included in the Census, but output of these firms was less than 0.5 per cent of the manufacturing total.

Table 2.7 presents a confrontation of employment and productivity derived from the Annual Survey of Manufactures (ASM), and the National Income and ProducTty Accounts (NIPA). The proiuctivity figures from ASM are higher than NIPA for two reasons. The ASM measure of output is bigger as already noted, and the ASM employment figure is lower than in NIPA. The reason for the latter difference is not clear. Both sources exclude unpaid f'amily helpers, and although NIPA includes self-employed people without employees, this accounts for only a small part of the difference.

Table 2.8 presents a consolidated version of the US input-output table for 1977. The input-output industry classification corresponds very closely with that used in the census, but there is some reclassification of secondary products, i.e. the "the secondary products and associated inputs are excluded from the industry that produced it and included in the industry in which it was primary" (US Dept. of Commerce, 1984a, p. 51). The inputs are for current use only, e.g. construction inputs are only those for repair and maintenance (not for new capital formation). Broadly speaking the inputs in the first five columns of table 2.8 are those which are excluded from the US Census definition of value added, whereas the national accounts definition of value added also involves deduction of service inputs (column (6)). In table 2.8 total value added (USS 487,203 million) is the national accounts definition, the US census definition would be more or less equivalent to 659,622 million US dollars (i.e. the national accounts value added - US$ 487,203 million - plus service inputs - US$ 172,419 million-). The ratio of the national accounts to the US census concept of value added would therefore be 73.9 per cent for 1977, i.e. not too different from the 1975 ratio of 76.6 in the bottom right of table 2.6.

In the case of our detailed comparisons involving the USA in chapter III, it was not operationally possible to use a national accounts concept of value added, as we would have preferred. This was due to the impossibility of reconciling the US census material with the national accounts and the input-output table. However, we were able to put our comparison of Brazil/USA and Mexico/USA on an national accounts basis at the branch level and for manufacturing as a whole.

A reconciliation of the census and national accounts approach for major branches of manufacturing for 1977 and 1982 can be found in Board of Governors of the Federal Reserve System (1986), pp. 52-7.
Table 1.

# **4 Empirical Strategy**

## **4.1 Measuring GDP manipulation**

To infer true GDP, we rely on a proxy for economic activity that is objectively measured and highly correlated with true output. Since Henderson, Storeygard, and Weil (2012) developed an original statistical framework to use satellite data on night lights to improve official economic growth measures, nighttime lights have often been used as a proxy for economic activity (Hodler and Raschky 2014; Michalopoulos and Papaioannou 2014; Henderson et al. 2018). To predict the true GDP growth using nighttime lights data, Henderson, Storeygard, and Weil (2012) run the following regression: ln( _GDPi,t_ ) = _α_ 0 + _α_ 1 ln( _Night lightsi,t_ ) + _μi_ + _δt_ + _εi,t_ (1) where ln(GDPi,t) is the logarithm of real GDP, and ln(Night lightsi,t) the logarithm of the night light luminosity of the region, measured as the area-weighted average of DN values across pixels within the region. _μi_ and _δt_ are the region and the year fixed effects (FEs), respectively. To improve the precision of estimated economic growth, they combine the fitted values from equation (1) with the official GDP growth to arrive at a composite estimate that would have a lower error than that based on either measure separately. That is _Predicted true growthi,t_ = _λ Official growthi,t_ + (1 − _λ_ ) _Light-predicted growthi,t_ (2) where _λ_ is the optimal weight that minimizes the variance of measurement errors in the above estimate relative to the true value of economic growth. Under this framework, GDP manipulation is the difference between the officially reported growth and the predicted true growth in Eq. (2): (1 − _λ_ ) ( _Official growthi,t_ − _Light-predicted growthi,t_ ).<sup>28</sup> However, the relationship between nighttime lights and real GDP may differ for regions at different stages of development (Hu and Yao 2019). For example, regions at more advanced stages may focus more on technological innovation rather than infrastructure development, thus their GDP may be less associated with their nighttime lights. We thus apply the statistical framework of Henderson, Storeygard, and Weil (2012) and modify Eq. (1) to allow for region-specific slopes of lights to consider the different levels of development across regions. We classify China into eight economic regions based on their geographic proximity and levels of economic development following

> 28 Note that different values of _λ_ will only lead to changes in the absolute values of our measure, thereby not affecting the regression results except inflating the coefficients (or effects) by the same amount. In our case, although we allow _λ_ to be region-specific, since the variation is small and thus _λ_ can be viewed as almost constant, the above logic applies to our setting. Our estimated _λ_ is generally larger than those in the literature (e.g., Henderson, Storeygard, and Weil 2012).

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Total (quotas) 3.61 2.63 2.08 1.65

1.07

2.07 Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

The importance of food subsidies for poorer households is even more apparent when looking at the distribution of expenditure shares by population percentiles. This can be seen in Figure 1 which plots the share of expenditure on food products at subsidized prices, relative to total expenditure, by population percentiles. The negative slopes indicate that poorer households devote a larger share of their total spending on food bought under the quota system than richer households (for all products except milk for children.) In other words, food is a larger component of the consumption basket of poorer households.

**Figure 1: Household Expenditure on Food Bought at Subsidized Prices (% of total expenditures, quotas only)**

<!-- Start of picture text --> .015<br>Flour<br>Flour-bread<br>Semolina<br>Rice<br>Sugar<br>.01<br>Tea<br>Expen<br>diture Macaroni<br>shares<br>Vegetable Oil<br>Paste tomatoes<br>Milk for children<br>.005 Milk (concentrated)<br>0<br>.01 .198 .386 .574 .762 .95<br>Household Percentiles<br><!-- End of picture text --> Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

The poorest quintiles benefit the most from the monetary value of subsidies (Table 9). This is true for each product with the exception of milk for children. This result sets Libya apart from other countries in the region, where food subsidies tend to be slightly regressive because richer households consume more food overall and because subsidies are universal, unconstrained by a quota system.

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