rafmacalaba/data-use-mentions-extended huggingface dataset:
household identifier, household number and individual line number. Based on this naive match criteria, a high non-matching rate results, as survey respondents who move out of a housing unit are replaced in the sample by those who move in and given the same unique identifiers, as well as nonresponse, mortality and migration. A naive match is then dropped if it does not match on sex, race or age criterion. Based on these criteria, the match rate is 50%, with 871,917 individuals matched, or 1,743,834 observations out of a possible 3,481,692 observations.

# H. Imputing Computer Use Rates to CPS Workers

We calculate average industry and occupation computer use rates from the October 1984, 1989, 1993, 1997, and August 2000 CPS Internet and Computer Use Supplement files. The use rate within an industry or occupation is the fraction of respondents who report using a computer or the internet at work. We impute computer use rates for the remaining years by linearizing the percent change within an occupation or industry between available years. We use the linear trend from 1984-1989 to impute computer use rates for 1982-1983, setting a lower bound of 0%. We use the linear trend from 1997-2000 to impute computer use rates for 2001-2002, setting an upper bound of 100%.

# I. Creating Price Indices by Industry

We create a set of price indices at the industry level using data from the Bureau of Labor Statistics (BLS) and the US Department of Agriculture (USDA). For most industries, a match is made to the industry-specific BLS Producer Price Index (PPI) data. For the four agricultural industries, we use an industry-specific Producer Price Index from the USDA. For 46 industries, a consistent PPI is not available across time; we instead use a product-specific Consumer Price Index from the BLS. For an additional 46 industries (mainly in the service sector), no consistent PPI or CPI is available for our entire time period; we instead use the economy-wide PPI from
, the better-off Phase III districts have better educational outcomes. Conditional on being enrolled, 92 percent of the children pass exams in grade 5 in Phase III districts, whereas 88 percent do in Phase I districts.

> 13These data are collected using a district level administrative structure. School principals fill a standardized survey about the school. The data are manually checked at various levels for completeness, accuracy, and inconsistencies. States also implement checks. NEUPA has commissioned an external audit of the school data. These audits check 5 percent of the schools chosen randomly from at least 10 percent of the districts from each state. The auditors also visit the schools. These audits have established that the enrollment data reported by the principals are remarkably accurate.

8
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developed financial markets, the financial assets of these institutions are not more than 18 percent of GDP, while in countries with less developed financial markets, such as Greece, Italy, Turkey, and Hungary, non-life insurance financial assets represent less than 5 percent of GDP in 1996.

**Figure 3: Non-life insurance financial assets (GDP %, 1996)**

<!-- Start of picture text --> 20S 1<br>18%.<br>16%<br>14%<br>12%<br>10%<br>I~~~ t 3 g B<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Table 1 shows how the vast majority of personal sector financial assets, across selected countries in 1990, is held in liquid financial assets like cash and deposits. Instead, Figure 4 shows that contractual savings tend to hold a smaller share of financial assets in cash and deposits and a larger share of these assets in stocks, loans, and bonds. Notice how during the 1982-96 period, cash and deposits never exceeded 6 percent of total financial assets. Bills and bonds decreased from about 50 percent to about 43 Table **Tablew** 1:w Asset **vstrucoitur of personal sector,** 1990ilases ars percent, and shares increased from about IS percent to around 30 percent at the expense selecte cofnre **total** shl i iuiiacilaseslk **cash and deositr.** assets, have declined their participation from about 25 percent in the early period to about 15 in the latter.

|**assets**|**Equity**|**Bonds**<br>**deposi**|**Loans**<br>**ts**|**s**|**ons**|
|---
Eqns (1) and (2) assume that conditional on the propensity score, participation in the insurance program is independent of the _change_ in outcome between the year before the program was introduced and the date of the second wave of the survey, five years later. Eqns (1) and (2) are compatible with individual-specific time-invariant unobservables, so that our estimates will not be contaminated by any selection on time-invariant unobservables. We have also allowed for a common unobservable growth component in the outcome growth equations. Under our assumptions, the only driver of differential change between the insured and matched uninsureds is the fact that the former are insured and the latter are not. The risk we run is that the unobserved time trends are not—contrary to what we assume—independent of insurance status. If the insured have a more favorable time trend than the uninsured, we will end up attributing differences in changes in _y_ to insurance when they are, in fact, attributable—at least in part—to different unobserved time trends.

The link with standard exogeneity assumptions and the attractions of the matching approach can be seen as follows (cf. Imbens 2004). Suppose one were prepared to assume that the treatment effect is constant, and that the outcome among the uninsured is linear in _Xi_ . With cross-section data one could try to recover the treatment effect from a regression of the outcome on an insurance status dummy ( _I_ ) and the vector _Xi_ : Estimation might proceed on the assumption that _I_ is exogenous in the sense that conditional on _X_ , _I_ and ε are uncorrelated, which is the regression equivalent of the ‘unconfoundedness’ or ‘ignorable treatment assignment’ assumption made in the treatment effects literature when crosssection data are used. Or one might use a Heckman selection model and rely either on functional form or exclusion restrictions to identify the insurance effect. Both have been used in the insurance literature, but neither is altogether satisfactory.<sup>7</sup> In the present context, with a two-wave panel
five of the six countries had made a major change in the design of the module collecting consumption data in their surveys.<sup>3</sup> Only South Africa had not changed the consumption survey design across the two years in the database, although they did in 2005. As we will show in this paper, these design changes can result in large changes in both mean consumption and distributional measures.

Our findings come from a study specifically intended to provide more comprehensive evidence on the sensitivity of consumption estimates to varying survey designs. We developed eight alternative consumption questionnaires that were randomly distributed across 4,000 households surveyed in Tanzania. These eight designs all represent common approaches to consumption measurement that have been implemented in numerous settings. We consider the various strengths and weaknesses of each approach to survey-based consumption measurement, including the types of errors that are likely to be more prominent. While in this experiment there are no validation data available for the study households, one resource intensive variant – a personal consumption diary with intensive and frequent supervision – is believed the most accurate and in many ways represents a “gold standard” for field based consumption measurement in survey format. We investigate how alternative survey designs compare with our benchmark, with a focus on the patterns of over or under-reporting of consumption expenditure with respect to household characteristics, and poverty and inequality measures.

The paper is organized as follows. A review of related literature is presented in Section 2. Section 3 describes the experiment, the Survey of Household Welfare and Labour in Tanzania, and construction of the consumption aggregates. Section 4 discusses the results. We compare estimates of total and component consumption for each of the modules, explore how household characteristics affect reporting, and consider the impact of alternative survey designs on inequality and poverty measures. Section 5 discusses the resource requirements for each survey design. The final section reviews the lessons learned herein.

> 3 The variation in the type of consumption survey for these countries included: bounded recall and usual consumption (5 surveys Côte
The empirical analysis is performed in urban areas because rural areas are underrepresented in the PNADs, the law may not be fully enforced in rural areas, and because rural areas lack well-developed school systems and labor markets. According to the 1999 PNAD, around 82 percent of Brazil’s population lives in urban areas.

# **5.1. Descriptive Statistics**

This section presents the descriptive statistics of the main sample used for the short-term analysis and some stylized facts of the unaffected cohort that turned 14 immediately before the law change.

Our main sample for the short-term analysis consists of all male individuals living in urban areas and had turned 14 between April 1998 and September 1999. The basic descriptive statistics for the 1999 PNAD sample of males are presented in table 1. There are 4549 observations, 47 percent of boys are white, about two-thirds of boys working (19 percent) were in paid activities, 92 percent were in school, and 15 percent worked while in school. The average years of schooling of mothers and fathers suggest they did not hold a primary education degree. The average household size is about five members.

The figures 1.a and 1.b depict the incidences of time allocation in school and work for boys and girls separately. The sample consists of all individuals in the comparison group, that is, those who turned 14 in the last nine months of 1998. The outcomes examined are work incidence in paid and unpaid work,<sup>21</sup> and school attendance.<sup>22</sup> We also look at these outcomes in six exclusive combinations: working only in paid activities, working only in unpaid activities, only studying, paid work and studying, unpaid work and studying, and neither working nor studying. None of the definitions above include household chores. We excluded household chores because these are performed predominantly by girls and our estimates focus only on boys.

Figure 1.a shows that 90 percent of 14-year-olds were enrolled in school, but boys
# **Box 4: An illustrative econometric estimation of the components in the nominal lending interest rate**

The decomposition of the nominal lending interest rate is calculated using a panel data regression model with country fixed effects and first-order error structure to adjust for serial correlation. We use the method of Baltagi and Wu (1999) to estimate coefficients. The Baltagi and Wu (1999) method uses feasible generalized least squares (FGLS). We use annual data between 2005 and 2017.

**Independent variables:** the basic model has the following RHS variables:

- <u>Macro-fiscal conditions: inflation rate, inflation volatility (rollover standard deviation of</u> inflation rate for a window of last 12 months), public debt (% GDP), savings rate (% GDP), GDP per capita, and policy interest rate. Sources: IMF-IFS, IMF Global Debt Database, World Bank World Development Indicators.

- <u>Banking sector characteristics: overhead cost (% of total assets), non-performing loans (% of</u> total gross loans), non-interest income (% total income), bank asset concentration for three major banks (% total banking system assets), and private credit (% GDP). Source: World Bank Finstats 2019.

- <u>Business environment: credit bureau coverage (% of adults), time to resolve insolvency (years).</u> and rule of law index. Sources: World Bank Doing Business, and Governance Indicators (Kaufmann et al., 2010).

**Decomposition of interest rate (level):** Once the coefficients are estimated, we attribute each estimated coefficient to a value of each driver. For instance, to decompose the average lending interest rate in 2017, we calculate the average value of each driver for that year across 64 countries and multiply them to the corresponding coefficient. Later, the results of that multiplication are added based on the three main driving groups (i.
based text analysis of all O*NET green task statements, terms within the skills/competences from the ESCO taxonomy,<sup>46</sup> and terms in Eurostat manuals for data collection and analysis (1999 and 2009). Second, it also included commonly mentioned terms that frequently appeared in more than 72 papers and reports in the labor environmental economics literature. The green dictionary was then compared with dictionaries shared by Lightcast (formerly Burning Glass Technologies, BG), a list of 81 job titles within O*NET, and a list of 154 words or phrases from Janser (2019). These dictionaries were informative in the sense that they confirmed the appropriateness of many terms already selected. It should be noted that these dictionaries also included terms that may or may not be environmentally friendly depending on the context applied (e.g., energy engineers, thermograph, battery technology) which received different treatment as explained below or were not included. The desk review resulted in the evaluation of 451 terms, organized into 14 main green categories according to topic areas— categories that frequently appeared in the consulted sources (e.g., GTP survey questionnaire).<sup>47</sup> Appendix C contains a full list of all the consulted sources and table 3.1 provides some examples of terms present in the cited sources that were considered for inclusion.

**Table 3.1. Examples of terms under consideration**

|**Consulted sources**|**Examples of termspresent in cited sources**|
|---|---|
|BLS GTP survey|Renewable source, renewable energy, solar energy, wind<br>energy, landfill gas, energy efficient, carbon offset, composting,<br>remanufacturing, greenhousegas,LEED|
|Most common environmental words in<br>O*NETgreen tasks|Environmental, energy, solar, green, waste, wind, carbon,<br>biofuels,recycling,hazardous,
# **A Cautionary Tale: An Experiment on The Stability of Business Environment Perceptions in a Firm Survey**

Asif M. Islam Jorge Rodriguez Meza<sup>1</sup>

**JEL** : C83, D84, D73, L20

**Keywords** : Firm Surveys, Survey Bias, Subjective Response, Question Order

> 1 Islam is a Senior Economist with the Office of the Chief Economist in the Middle East and North Africa Region of the World Bank (aislam@worldbank.org). Rodriguez Meza is the Program Manager for the Enterprise Analysis Unit in the Development Economics Global Indicators Group of the World Bank (jrodriguezmeza@worldbank.org). The authors are grateful for feedback from Aart Kraay, Norman Loayza, Ulrich Zachau, and Donato De Rosa. The paper is largely an exploration of the validity of survey questions of a specific nature. Although the paper is based on an Enterprise Survey undertaken in the República Bolivariana de Venezuela in 2006, its survey methodology findings have general relevance for other country contexts. All errors and omissions are the authors’ responsibility. The opinions expressed in this paper do not represent the views of the World Bank Group, its Board of Directors, or the Governments they represent.
# **1 Introduction**

A country engages in fiscal consolidation programs, increasing taxes or cut government expenditures, when the national debt level is deemed high enough to influence the ability of the country to borrow in the future or to prevent the government of the country to undertake necessary public spending programs. If the need for fiscal consolidation arises when the economy is at relative stagnation or decline, policymakers usually worry that fiscal consolidation efforts present an unnecessary drag on aggregate demand. It is therefore important to understand the output or consumption costs of fiscal austerity to weigh it against the benefit of reducing the national debt stock. The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase, or at least not decrease output or consumption when a country is under heavy debt burdens because it sends positive signal about the country’s solvency situation and long-term economic wellbeing. If this hypothesis is true, policy implications for fiscal austerity programs are clear: the cost of austerity relative to its benefit will be low if debt burden is high enough, and more austerity programs should be undertaken in this case. The question remains whether there is sufficient support for the EFC hypothesis in the data.

This paper seek to bring new empirical evidence to bear on the subject of expansionary austerity by producing a new model-based definition of a high-debt regime, leveraging a newly constructed cross-country narrative dataset and using a new econometric methodology. An empirical answer on this subject will depend crucially on how one defines when the debt level is high enough for austerities to have anti-Keynesian effects and on how one defines austerities themselves. In this paper, I introduce a new definition of a high-debt regime by calibrating a model of expansionary austerity for each country in my sample, and I use this definition along with the conventional cutoff debt levels. I consider two alternative definitions of austerity: fiscal consolidations constructed by the reading of narratives, a direct measure, and structural shocks from identified VARs, an indirect measure. For the former, I use a new source of cross-country narrative
**

Our analysis focuses on city-level pollution and productivity data from Chile. As noted by Graff Zivin and Neidell (2012), several issues emerge when identifying the causal relationship between pollution and labor productivity. First, selection issues arise from how workers allocate their labor in the presence of high pollution levels. Given that most databases use annual frequency, workers who experience negative effects from local pollution may in the meantime decide to leave the area and work in a different region, or transition to a different sector that is less exposed to pollution. This labor mobility may occur within a year. Another potential source of concern is the presence of a confounder and the possibility of simultaneity bias. Increases in economic output lead to corresponding increases in energy demand. If the response of the energy supply is mainly through an increase in fossil fuel use, this could also lead to an increase in pollution. Arguably, this raises concerns about the orthogonality between pollution, PM2.5, and labor productivity. This, in turn, could affect the unbiased and consistency properties of any coefficient estimate. Several controls and instruments have been used in the literature to overcome the non-orthogonality issue. Given that we require one instrument for our explanatory variable, we can estimate the effect of pollution on labor productivity consistently using an IV-regression. In our case, the instrument needs to be strongly correlated with pollution (PM2.5) and uncorrelated with labor productivity.

We argue that the installation of fossil fuel power stations, specifically coal and diesel-fired power stations, can serve as such an instrument. Two arguments support this assertion. First, power stations plug into four regional grids, and are hence isolated from productivity developments that are specific to certain sectors and cities. Regional time fixed effects can control for regional specific effects related to productivity. Finally, as mentioned earlier, while both transportation costs for inputs and transmission costs related to proximity to demand centers are important determining factors, consistent with other countries, the location of coalfired power plants in Chile appears to have
APPENDIX

# Appendix B: Bond prices and investor returns

## B1. Database of bond prices and returns

This section summarizes the sources and coding for our database of sovereign bond prices and returns across 200 years. As explained in the main text we focus on bonds issued (i) by central governments, (ii) in foreign currency (only US dollar and British pound bonds), (iii) traded and priced on the London or New York Stock Exchanges and (iv) with a fixed coupon rate (no floating rate instruments).

We first discuss the historical data, then move to the modern sample that uses JP Morgan’s EMBI data and then describe the final, merged, 200-year dataset.

### **Historical bond prices and returns, 1818-1980**

For the 19<sup>th</sup> century we use price data of those external sovereign bonds traded on the London Stock Exchange (LSE). From the early 20<sup>th</sup> century on, these data are complemented with bonds traded on the New York Stock Exchange (NYSE). New York becomes the dominant market for trading and issuing external sovereign bonds from the mid-1920s onwards.

To identify bonds and their prices we rely on several main sources. The London price data for the early 19<sup>th</sup> century and until 1870 comes from the Money Market Review, The Economist, Circular to Bankers, Course of the Exchange, and The Banker’s Magazine. For the 1869-1929 period we mostly use LSE pricing data from the Investors Monthly Manual, as provided by Goetzmann and Rouwenhorst at the International Center for Finance at Yale. We extend their data series of LSE sovereign bond prices by 50 years, using quotations from The Economist and Financial Times from 1930 to 1980. For illustration, Figure B1 shows an example of how this source looks like. The bond price data from the NYSE was coded from the “Bank and Quotation Section” of the Commercial Financial
In order to match the aid flows from the CRS database to the seven obstacle areas from the Enterprise Survey we had to perform “key word” searches among the detailed purpose description of the aid flows. We found that the general categories from the CRS database were too broad to give us an accurate understanding of the target of the aid flow. For example, a general purpose category for aid flows is “Business support services and institutions” amongst this category we have many detailed project descriptions including: “The Trade and Transport Facilitation in Southeast Europe Project for Bosnia and Herzegovina, aims at modernizing, and strengthening customs administrations, and border control agencies...”. Under the same category we can also find a project description such as: “Facilitate the adoption of streamlined and inexpensive licensing, registration, and inspection regimes for different categories of businesses, including SMEs, joint-stock companies, and partnerships.” Obviously the nature of the two projects is very different; however, they are grouped into the same category in the CRS database. Being able to use the detailed purpose information for each aid flow clearly has its advantages as we are able to better match the aid flows to the corresponding seven obstacle categories from the Enterprise Survey. The project aimed at modernizing and improving customs was matched to the trade category (key word=customs) while the business licensing project was matched to the business environment category (key word=licensing). This process was very time consuming as we had to investigate the detailed project description of 360,000 aid flows to be able to find “key words” for each category.<sup>5</sup> Even though most project descriptions are in English we also used “key

> 5 The key words used to group the aid flows into the Enterprise Survey obstacle categories are available from the authors at request.

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