rafmacalaba/data-use-mentions-extended huggingface dataset:
19 Appx. Fig. A.6). CPR16 add their own estimates from different studies to the estimates provided by PP18. The CPR16 database consists of 178 estimates defined c. 1995 or 2005. Using population weights, the estimated CPR16 average returns are about 9.0 for both developed observations (N = 48) and developing observations (130), respectively. The correlation between our data and their data is about 0.4 (117; see Web Appx. Fig. A.6). Comparability is likely limited because both PP18 and CPR16 rely on different studies that use different methodologies (e.g., 89 in CPR16) whereas we directly estimate our returns using a consistently harmonized global dataset and a consistent methodology.

**4.6. High-Income vs. Middle-Income vs. Low-Income Countries**

In this subsection, we show that, using our best specification and accounting for various issues, the relationship between returns to experience and economic development is monotonic. While the returns are twice higher in developed countries than in developing countries, among developing countries returns are only slightly higher in middle-income countries than in low-income countries, which explains why the relationship is not fully monotonic. Returns to experience thus only greatly increases as economies become developed, which suggests that returns to experience may proxy for an economy’s ability to converge and catch up to high-income economies.

**Graphical results.** Figure 2 suggests that the relationship between returns to experience and economic development is U-shaped, with low-income countries (most of them in Africa) having higher returns than middle-income countries. We examine if this U- shape pattern holds across the various specifications investigated previously or whether it is a statistical artefact resulting from selective mortality raising the returns of poorer countries _and_ transitions to capitalism having made experience more obsolete in exCommunist countries which are now majoritarily middle-income countries.<sup>34</sup>

In Web Appx. Table A.16, we examine the Spearman rank-order correlation between the returns and log per capita
true, a plot of log ( _y_ ) against _−_ log (1 _− F_ 2( _y_ )) should reveal a linear relationship with a slope parameter equal to<sup><u>1</u></sup> _θ_<sup>.Figure1provides</sup> this plot using the top 10 percent of the household expenditure data from the HIECS. For the majority of data points a linear relationship seems to provide a reasonable fit. A deviation from linearity can be observed however toward the far end of the income spectrum, where the slope appears to fall. Consequently, we should expect estimates of _θ_ to come out higher if we were to increase the income threshold above which observations are included.

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Our robustness exercise revolves around three potential sources of biases in our results: the relationship between population attributes and trust; the impact of _non-foreign_ assistance on trust and the nature of reporting bias in our measure of the presence of foreign organizations.

# <u>Trust and Population Attributes</u>

# <u>Risk Aversion</u>

A substantial experimental (both field and lab based) literature studies the relationship between risk aversion and trust; although the general finding from this literature is not conclusive (see for instance, Eckel and Wilson 2004), it is possible that our results could arise from preexisting differences risk-aversion or the way that that the earthquake changed people’s risk attitudes.<sup>14</sup> If the experience of an earthquake increased risk aversion and increases in risk aversion lead to a loss of trust in other locals (relative to outsiders), we would observe the patterns documented above.

In our household survey, we asked respondents to play a hypothetical game (real money was not involved) where they had to select s single scheme from among a menu of risky options. Specifically, we posited that: _In every scheme, you have a 50/50 chance of making a small profit or a big profit. Out of these schemes, which one would you choose?_ The options started from a profit of Rs.50 regardless of the draw (0 risk) increasing in risk and return to Rs.0 with 50 percent probability and Rs.200 with 50 percent probability. The options were ordered in an ordinal fashion to represent higher risks. In Table 11, we add in household choices in this game as indicator variables, increasing in their risk attitudes, to the base specification. Note that there is a drop in the number of observations because 25 percent of our

> 14 For instance, Ben-ner and Putterman (2001): “Consider next the determinants of trusting. The main factors are A’s information about B (and his trustworthiness
# **3 Conceptual Issues**

What is different about trade in services compared with trade in goods?<sup>7</sup> Services are very heterogeneous, and span a wide range of economic activities. Conceptually, this diversity masks a fundamental economic function that many services perform: they are inputs into the transaction side of production. One dimension of this ’input function’ is that services facilitate transactions through space (transport, telecommunications) or time (financial services) (Melvin, 1989). This transaction aspect is additional to the role of services as inputs in production. Services are frequently direct inputs into economic activities that determine the productivity of the ’fundamental’ factors of production - labor and capital - and that generate knowledge, goods and other services. Education, R&D and health services are examples of inputs into the production of human capital. In this section, we focus conceptually on channels or mechanisms by which services may be important in an economy wide context, and on the mechanics of international services trade. The following section builds on this discussion when mapping from the issues spelled out here to discuss practical implementation in numerical models.

## **3.1 Downstream linkages and direct cost channels**

For applied general equilibrium modeling, the relevance of a focus on services largely hinges on the strength of economy-wide linkages. To illustrate the strength of such linkages, in this sub-section we discuss recent evidence, focusing on how value added structures, and services linkages to trade have evolved over time. Thanks to repeated updating of the GTAP dataset, we have data for both cross border linkages in recent years, and how these have changed since the early 1990s.

Table 1 provides a broad overview of services exports (excluding transport and distribution) on a gross and value added basis for Brazil, China, Korea, and the United States (Francois, Manchin, and Tomberger 2012). There are three sets of values in the table. The first, ”Gross Values”
|0.803|0.801|
|Endline control mean|0.4|70|0.|524|0.|540|0.5|95|0.3|98|0.|450|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.011|0.135|0.139|0.236|0.006|0.102|0.062|0.135|0.016|0.129|0.192|0.306|
|Observations|1,534|1,534|396|396|1,534|1,534|396|396|1,534|1,534|396|396|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and their matched controls. Matching variables are those listed in notes of Table 2 (ITT regressions). Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

14
order to draw implications regarding progressivity.

These are obviously blunt instruments, as no detail is provided on tax burdens borne by poor vs rich taxpayers. This is particularly important in the case of the PIT where, for example, a flat tax regime could be applied that raises the same total revenue as a highly progressive tax, but this would not be differentiated using the tax revenue data. The remaining indicators therefore provide greater detail on the PIT.

# 6.2 Effective progressivity indicators

To examine the effective progressivity and redistribution achieved by PIT systems in IDA countries, it is proposed that Kakwani and Reynolds-Smolensky effective progressivity (and redistribution) indicators be calculated for each IDA country. These indicators could be constructed from a full microsimulation model. However, building a full microsimulation model even for a single country is highly data and resource intensive.<sup>37</sup> In the context of producing progressivity assessments for more than 60 IDA countries, it is proposed that a simplified modelling approach is more appropriate.

36 The aggregate tax revenue figures effectively incorporate both the tax rate and breadth-of-base information into the one indicator.

37 The FPSG unit has constructed PIT microsimulation models for a number of developing countries as part of its general microsimulation capacity building work. However, no models have been developed as yet for IDA countries. Constructing these models requires a significant resource investment, and so their development is more suited to country-specific projects rather than for an analysis of 60+ countries. Full microsimulation models also have a broader use than just progressivity assessment. Specifically, they are constructed to provide Finance Ministries with the ability to assess both the revenue and distributional impacts of potential tax reform options. To maximize accuracy of revenue figures, they are typically based on tax return microdata rather than income survey data (and so do not account for the full income distribution – which is necessary for constructing progressivity measures). That said, work is underway to adapt the unit’s tax return data-based models to utilize income survey data, and to develop
constitutes a global benchmark of each sector’s need for external financing. The authors calculate the external financial dependence ratios by industry using Compustat data on U.S. firms for the years 1980 to 1989. We simply use their data for this measure.

# **Table 1: Definition and source of the variables.**

|Variable|Description|
|---|---|
|Growth|Average annual real growth rate of value added in a particular sector in a particular country<br>over the period 1980 to 1990. The sectors are classified on the basis of ISIC. Source: Rajan<br>and Zingales (1998). Original source: United Nations Database on Industrial Statistics.|
|Growth8097|Average annual real growth rate of value added in a particular sector in a particular country<br>over the period 1980 to 1997, if available. The sectors are classified on the basis of ISIC.<br>Source: Authors’ calculations based on data from the United Nations Database on<br>Industrial Statistics.|
|Share in value added|Fraction of ISIC sector in value added of total manufacturing sector in 1980. Source: Rajan<br>and Zingales (1998).|
|Financial dependence|External financial dependence of U.S. firms by ISIC sector averaged over the period 1980<br>to 1989. Source: Rajan and Zingales (1998).|
|Private credit|Private credit divided by GDP in 1980. Source: Rajan and Zingales (1998).|
|Market cap|Stock market capitalization divided by GDP in 1980. Source: Rajan and Zingales (1998).|
|Total capitalization|Sum of the private credit and stock market capitalization variables.|
|Concentration|Measure of bank concentration. Sum of the market shares measured in total assets of the<br>three largest banks for the period 1989 to 1996, when available. Source: Cetorelli and<br>Gambera (2001).
Looking at Table 2, we find that the choice of survey approach primarily affects reporting of exclusive as opposed to joint ownership, as well as joint rights to sell/bequeath (with the exception of women household heads, where the survey approach primarily affects exclusive rights, likely because their households have fewer adult household members).

**Figure 1.  Bundles of Ownership/Rights, by Survey Approach (T1/IHS4 vs T5/IHPS) and Gender Reported versus Economic Ownership Right to Bequeath and/or Sell**

**Right to Bequeath and/or Sell** ( **Among Reported/Economic Owners)**

# <u>Notes:</u>

- (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The estimates are weighted by the response weight. The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

- (2) In T5/IHPS, the parcel-level question on economic ownership was, by design, asked conditional on having been identified as a reported owner.

- (3) The rights-related variables are defined irrespective of the reported need to obtain consent/permission from anyone – a topic that the IHPS collected additional information on. Less than two percent of individuals across surveys had rights to bequeath/sell if they were neither reported nor economic owners.

Specifically, the business-as-usual approach significantly increases exclusive reported ownership among men overall (columns 7-8), and women heads of household (columns 1-2). Within the business-as-usual approach, nearly all (95 percent) of women household heads responded for themselves. The business-as-usual approach also raises exclusive economic ownership for men, as well as non-household head women, but results in lower exclusive economic ownership for women heads. For women heads in particular, Figure 2 (focused on own-reporting) also shows wider positive effects of the business-as-usual approach on exclusive reported ownership among younger groups less than 50 years, while the negative effects on
- Linking a worker's pay to firm performance. Supposedly a close link should increase employee incentives.

Understand internal incentives may help policy makers provide better advice on private sector reforms. For instance, suppose surveys reveal little correlation between pay and firm performance in a particular country. Such a finding may imply that the labor force is not properly motivated, perhaps due to poor performance measurement or the many layers of hierarchy. Alternatively, if all employees perceive their jobs as permanent, increasing the share of contract workers may be a valid option to motivate workers.

Many World Bank surveys do not measure internal incentives at all. Some have a minimum number of measures, such as the percentage of work being contracted out, employment benefits, bonuses, payment method (e.g., piece rate or hourly rate), total wages, non-wage costs, and labor turnover rates. The RPED covers internal incentives most extensively. It asks many useful questions, including the payment scheme (piece rate, time rate, or based on firms' sales), the number of people fired, the presence of a union, overtime pay, and detailed wage data by worker category. An especially innovative feature of the RPED is its survey of a subsample of workers in the surveyed firms, which asked workers about issues such as bonuses, wages, and housing allowances.

Despite the strong advances made by the RPED, Bank surveys leave many questions unexplored. For example, we still know little about pay differences between different levels of workers, and between managers and production workers. Likewise, in few countries and industries do we have any sense of the involuntary turnover rate. Without such data we cannot understand the effects and determinants of firm internal incentives, and whether performance improves with better internal incentives in different countries.

# **B. Human Capital**

Human capital theory emphasizes the importance of schooling, labor market experience, seniority, and training in explaining individual productivity (Willis 1986). In the context of firms we should
# **III. Data**

The study is based on two‐period panel data collected by the IHDS, which was jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. This nationally representative survey covers a wide‐ranging set of topics, including energy use, income, expenditure, education, health, and employment. The survey covers all of India’s key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out in 2004–05 (mostly in 2005) and collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second one, conducted in 2011–12 (mostly in 2012), re‐interviewed 83 percent of the original households and split households (if located within the same village or town), and interviewed 2,134 new households, for a total of 42,152 households.<sup>4</sup> Besides collecting detailed information on income, consumption, and other household‐level welfare measures, the IHDS includes elaborate questions on household energy consumption behavior, such as fuel use, cash expenditures for fuels, time spent collecting biomass fuels, and types of stoves and electric appliances used in the household. It also asks questions related to the reliability of power supply and the source of household electricity. These detailed questions allow us to analyze the impact of electricity supply and its quality on a broad range of household economic outcomes.

The survey also covers key features of the villages where surveyed households are located. It is important to control for village‐level characteristics in the analysis because they can directly affect both the outcomes of interest (such as employment, income, and poverty status) and the probability of electricity being present in the village. While the survey was carried out in both urban and rural areas, community characteristics are available only
unconditional convergence in manufacturing.

An earlier study by Wong (2006) decomposes the pattern of _β_ -convergence into within- and between-sector contributions and show that both the within-sector growth and between-sector reallocations. Among the OECD countries during 1970-1990, Wong (2006) shows sizable contributions of productivity growth in agriculture and services to aggregate convergence, but limited role of labor reallocation and productivity growth in manufacturing. Relatedly, Dieppe and Matsuoka (2021) apply the same technique on a larger sample of advanced and developing countries and more recent years<sup>7</sup> and show that both the within-sector growth and between-sector reallocations

> 6 Rodrik (2013) clearly indicates the context in which to interpret the findings; they apply to formal manufacturing.

> 7 Their results are based on sectoral data for 91 countries over 1995-2018, including a longer horizon (between 1975 and 2018) for a sub-sample of 60 countries.

6
commuting.

## **E.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **F Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0
the spike in food prices. Most studies on the global food crisis use pre-crisis national household survey data to simulate the short-run effects of price increases on poverty. See for example, Robles and Torero (forthcoming) who examine simulated results from four Latin American countries,<sup>2</sup> Woden _et al._ (2008) who summarize simulations for 12 African countries,<sup>3</sup> Ul Haq _et al._ (2008) who simulate welfare changes in

> 1 According to the Economic Research Service ( _Food Security Assessment, 2008-2009_ ), in 2008 Afghanistan had a nutrition gap – the difference between available food and food needed to support a 2,100 per capita calorie intake – of 1,999 thousand tons. Only North Korea was estimated to have a larger nutrition gap in that year. 2 The countries are Guatemala, Honduras, Peru and Nicaragua.

> 3 The countries are Burkina Faso, Democratic Republic of Congo, Ghana, Gabon, Guinea, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. Their findings suggest overall increases in poverty as a result of the food crisis, with a large share of the increases in poverty due to a worsened situation for the already poor.

2