rafmacalaba/datause-encoder-data huggingface dataset:
Data Appendix:

# A. Current Population Survey

We use the CPS Merged Outgoing Rotation Groups for years 1979 to 2002. Note that our analysis relies on the MORG copy prepared by the Center for Policy and Economic Research (CEPR). The CEPR MORG files are created from the National Bureau of Economic Research version of these data, and we rely on the processing performed by CEPR to produce consistent variables for wages, education, and other demographic characteristics of the MORG sample. Our sample includes wage/salary for workers ages 16 to 64 in current employment. Earnings weights, equal to the product of CPS sampling weights and hours worked in the prior week, are used in all calculations. Hourly wages are the logarithm of reported hourly earnings for those paid by the hour, and the logarithm of usual weekly earnings divided by usual weekly hours among salaried workers. Overtime, tips, and commissions are included in wages, and top-coded wages are computed by assuming a lognormal distribution for weekly earnings as described by Schmitt (2003). The calculated nominal hourly wage is converted to a real wage using the Consumer Price Index for 2006, and then trimmed to values between $1 and $100 per hour.

Source: Schmitt, John. 2003. “Creating a consistent hourly wage series from the Current Population Survey's Outgoing Rotation Group, 1979-2002.” Available for download at http://www.ceprdata.org/cps/cps_documentation.php.

# B. Bureau of Economic Analysis

Our data on offshoring is based on the most comprehensive available data and is based on firm-level surveys on US direct investment abroad, collected each year by the Bureau of Economic Analysis (BEA) of the US Department of Commerce. The BEA collects confidential data on the activities of US-based multinationals. Multinationals are defined a parent company, the US entity that made at least one direct investment in a foreign affiliate, defined as a foreign business enterprise. We use the data collected on majority-owned, non-bank foreign affiliates and non-bank US parents for the years from 1982 to 2002. The foreign affiliate survey forms that US multinationals are required to complete on an annual basis include detailed information on the number of employees hired abroad. In previous work we have cross-checked these data with national survey data from other countries and found the employment numbers to be remarkably similar. Using these data, we construct a panel of number of employees hired abroad by country by year.

# C. Trade Data

Our data on import penetration were made available at the 4-digit ISIC level by Bernard et al. (2006). We also include a measure of the share of import penetration from low-wage countries, also computed by these authors.

D. Data on Occupational Characteristics (United States Department of Labor, supplied by David Autor) Definitions of the Nature of Tasks, taken from Autor et al. (2003)
Mu, Ren and Dominique van de Walle. 2011. “Rural Roads and Local Market Development in Vietnam.” _Journal of Development Studies_ 47(5): 709–734.

Nepal Central Bureau of Statistics. 2004. _Nepal Living Standards Survey: Statistical Report_ . Nepal Central Bureau of Statistics.

Qureshi, Mahvash. 2008. “Africa’s Oil Abundance and External Competitiveness: Do Institutions Matter?” IMF Working Paper WP/08/172, International Monetary Fund, Washington, DC.

Rand, John. “Evaluating the Employment-generating Impact of Rural Roads in Nicaragua.” _Journal of Development Effectiveness_ 1(3): 28–43.

Roberts, P., K. C. Shyam and C. Rastogi. 2006. “Rural Access Index: A Key Development Indicator.” The World Bank Group, Transport Papers, TP10.

Shirley, Chad and Clifford Winston. 2004. “Firm Inventory Behavior and the Returns from Highway Infrastructure Investments.” _Journal of Urban Economics_ 55(2): 398–415.

Smith, Jeffrey and Petra Todd. 2005. “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?” _Journal of Econometrics_ . 125(1–2): 305–353.

Van de Walle, Dominique. 2009. “Impact Evaluation of Rural Road Projects.” _Journal of Development Effectiveness_ 1(1): 15–36.

WHO and UNICEF (World Health Organization and United Nations Children’s Fund). 2005. _Water for Life: Making It Happen_ . WHO and UNICEF. Geneva.

World Bank. 2009. _World Development Report: Reshaping Economic Geography_ . Washington, DC: World Bank.

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business” categories. The current labor market statuses are captured in the “employed”, “unemployed” and “out of the labor force” columns of table 2. Among the four main sources of job separation, workers who experienced a plant closing are more likely to be currently employed. For example, for workers in the plant closing category, 72.0 percent are currently employed, compared to only 61.0 percent of those who were fired from their last job. Similarly, among plant closing workers, 9.6 percent are currently unemployed, compared to 14.6 percent of those who were fired. Table 2 also suggests that workers who were fired are more likely to leave the labor force, as 24.5 percent of the fired category are currently out of the labor force, compared to only 18.5 percent from the plant closing category. The differences in current labor status between the voluntary and involuntary separation categories are also significant. In general, workers in the voluntary separation categories (either quit or closed own business) are less likely to be currently employed in a salaried job and around half of them are currently out of the labor force, 51.6 and 49.8 percent for quit and closed own business, respectively.

**Table 2: Type of job displacement and current labor force status**

|||**Cu**<br>|**rrent labor forc**<br>|**e status**<br>|
|---|---|---|---|---|
|||**Employed**|**Unemployed**|**Out of labor force**|
||**Plant**<br>**Closing**|72.0|9.6|18.5|
|**Type of job**|**Fired**|60.9|14.6|24.5|
|**displacement**|**Quit**|44.4|4.0|51.6|
||**Closed own**<br>**business**|44.8|5.4|49.8|

9
closely matching services with citizen preferences, and by moving governments closer to the people they are intended to serve, which ensures greater accountability of the public sector. The paper is organized as follows. Section II presents an approach to the measurement of governance quality. Section III applies this measurement to the decentralization debate. Section IV notes limitations of the approach and a final section highlights main conclusions of this paper.

# **II. Measuring Governance Quality**

Governance is a multi-faceted concept encompassing all aspects of the exercise of authority through formal and informal institutions in the management of the resource endowment of a state. The quality of governance is thus determined by the impact of this exercise of power on the quality of life enjoyed by its citizens. There is growing awareness in the development community that a comprehensive look at the enabling environment of institutions (World Bank 1992, 1994, Picciotto, 1995, Hansen, 1996, Huther, Roberts and Shah, 1996), interests (Shah, 1996), and policies is needed in determining the net impact of the state on the well-being of its citizens. While no single index can conceptually capture all aspects of this enabling environment, a focus on key observable aspects of the governance dimensions can be helpful in providing a comparative perspective on differentials in the quality of governance among different nations. The key observable aspects of the governance dimension considered in this paper are: citizen voice and exit; government orientation; social development and economic management. Accordingly, the governance index we have composed has four composite indices which have been chosen to provide an indication of a government's ability to: 1) ensure political transparency and voice for all citizens, 2) provide efficient and effective public services, 3) promote the health and well-being of its citizens, and 4) create a favorable climate for stable economic growth. These factors are among those cited in the World Bank's (1992) booklet, _Governance and Development_ as representing the most important goals that ought to be faced by governments. It is important to note that these are goals which _all_ governments can be expected to pursue regardless of their 2
<!-- Start of picture text --> Figure 6. Policy effect 2010-17, by types of Figure 7. Policy effect 2010-17, by market income<br>households, age groups and locations, change in deciles, change in consumable income, %<br>consumable income, % 10<br>10<br>5<br>5<br>0 0<br>-5<br>-5<br>-10<br>-10<br>1 2 3 4 5 6 7 8 9 10 Total<br>direct taxes and SIC pensions<br>other transfers indirect taxes<br>direct taxes and SIC pensions total<br>other transfers indirect taxes<br>total Source: Authors’ calculations using RUSMOD-2010 and<br>2017 and 2010-2016 RLMS-HSE-data.<br>Source: Authors’ calculations using RUSMOD-2010 and<br>2017 and 2010-2016 RLMS-HSE-data.<br><!-- End of picture text --> While the total increase in average consumable income in Russia over the period 2010-2017 was around 60 percent, the policy effect was negative on average. The average consumable income contraction driven by tax-benefit policy related factors in line with the above explained methodology contracted by 2 percent. Most of the population groups lost because of policy changes (Figure 6 and Figure 7). However, it turns out that the only big group that benefited from the changes in tax-benefit policies in Russia during 2010-2017 were pensioners.

As the largest increase in disposable income due to policy changes in Russia over his period can be attributed to a growth in pensions (by 4 percent in real terms), the only net beneficiaries of the policy changes over 2010-17 were people of the retirement age and households with pensioners (Figure 6 and Figure 7). As most of pensioners do not receive any market income, the beneficiaries of policy effect are mostly concentrated in the lower part of the distribution if deciles are defined based on the market income. Other types of households positively affected by changes in social transfers —but to a much smaller degree—were couples with two and three or more children. However, for these groups positive changes were offset by income reduction due to direct and indirect taxes. In terms of geographical location, rural areas had smaller negative effect than metropolis of fiscal policy change during the period under analysis (Figure 6).

# IV. Prospective scenario: A hypothetical tax change

## **Background and the description of “tax maneuver 22/22”**

Informal employment has been growing in Russia, similarly to other East European countries that have undergone transition to a market economy in early 1990s. Informal employment rates in Russia, according to Rosstat, were growing since the early 2000s through 2016, with a short period of stagnation or even decrease in the second half of the 2000s.<sup>10</sup> However, the aging population coupled

> 10 Rosstat’s official measure of informal employment is estimated using the Labor Force Survey (LFS) and includes workers who are employed but not at an enterprise in their primary or secondary job. Informal employment rose from 13

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parental gender bias affects both relative and absolute mobility when diminishing returns and/or complementarity are important.

A credible empirical analysis of the above ideas, however, depends critically on the quality of the data available.<sup>8</sup> Most of the existing household surveys use coresidency criteria to define household membership and thus miss children from the sample in a nonrandom fashion. This results in truncation bias in the estimates of widely used measures of intergenerational mobility such as the intergenerational regression coefficient (IGRC). As reported by Emran et al. (2018), the truncation bias due to coresidency can vary significantly across gender and countries, making it doubly hazardous to rely on the coresident sample for a comparative study of gender bias in China and India. We take advantage of rich household surveys from China and India that do not suffer from coresidency bias for our analysis. Since the theory yields interesting predictions regarding the effects of gender bias on educational investment, our empirical analysis estimates both the intergenerational persistence equation (using IHDS 2012 for India, and CFPS 2016 for China) and the investment equation (using IHDS 2005 and NSS1995 for India, and CFPS 2010 for China).

The main conclusions from the empirical analysis are as follows. In India, the intergenerational mobility equation is _concave_ irrespective of gender and geographic location, rejecting the almost universally used linear specification in the existing literature. The concavity suggests that the complementarity between financial investment and parental education emphasized recently by Becker et al. (2018) may not be important in India. There are strong diminishing returns to both financial investment and parental direct inputs in education for girls in rural India. Girls face significantly lower relative and absolute mobility when the father is uneducated, but the gender difference becomes negligible when the father is college educated.<sup>9</sup> The relative magnitudes of the estimated parameters of the 8A large literature on intergenerational income mobility in the context of developed countries emphasizes the attenuation bias in the intergenerational income elasticity estimates due to measurement error in yearly income data. See, for example, the seminal analysis by Solon (1992). Measurement error is likely to be less of a concern in education data (Deaton (1987)).

> 9If one relies on the linear CEF estimates, s _/_ he would miss the gender convergence at the right tail of the father’s schooling distribution.

3
control groups was that any confounding effects of the incentive itself on subsequent financial attitude or behavior change (i.e. Hawthorne-type effects) would be netted out.<sup>10</sup> During these calls, in addition to questions about the storyline, the surveyors asked questions related to financial knowledge, attitudes, and behavior. Since the scope of questions asked over the telephone was limited, a face-to-face follow-up survey was conducted four months after conclusion of the storyline with more comprehensive knowledge and behavior questions. In addition, administrative daily call data was obtained from the NDMA so we could track incoming calls to the financial advisory call center following the public call to action. Finally, in order to be able to understand mechanisms of impact, three qualitative focus groups were conducted after the final follow-up survey.

An important aspect of the above research design is the comparability of viewers across Scandal and Muvhango. Clearly, if the demographic profiles varied substantially, we could have significant differences in take-up among our random sample of treatment and control groups. Hence, prior to the study, we researched all available alternative soap operas to Scandal and found Muvhango to be very similar in content and in viewership profile.<sup>11</sup> Both series had approximately 2.5 million adult viewers who came predominantly from mid to low income groups (LSM 2-4, LSM 5-6 and LSM 7-8), and viewership was distributed similarly across race with most viewers being non-white and primarily Sotho speaking.

# _4.2 Study Timeline_

The study spanned a period of one year, from December 2011 to December 2012. The initial listing exercise described in the previous sub-section was conducted in early December 2011, after which our study sample was selected. An initial Computer Assisted Telephone Interview (CATI) was conducted in February 2012, prior to the airing of the storyline. The soap opera storyline then began airing from mid February to end March.

> 10 This design is similar to an experimental evaluation of radio programming to change social norms and behavior regarding dispute resolution in Rwanda (Paluck and Green, 2009).

> 11 Muvhango has run for the last three years on SABC2. It is set in a Venda community both in rural Venda and in urban Soweto. For more information see http://www.tvsa.co.za/showinfo.asp?showid=540.

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The paper also contributes to a nascent literature on measuring expectations in developing countries. Early work by Ravallion (1987) asked rice traders in Bangladesh whether they thought the price would go up, go down, or stay the same, and the amount of change. Expectations were found to track prices fairly well on average, although there was a tendency to overestimate price changes. A series of recent studies have attempted to elicit probabilities in agricultural contexts. These include Luseno et al (2003) and Lybbert et al (2005) on rainfall expectations, Santos and Barrett (2006) on herd size, and Hill (2006) on coffee prices. These studies have generally found reasonably accurate expectations, with updating in response to new information. These existing studies have asked expectations about events in which respondents have substantial existing experience. Like these studies, we find reasonably accurate expectations when asking Tongan immigrants in New Zealand about employment and earnings in Tonga. However, in contrast, we find substantial inaccuracies when we look at expectations about an important life decision for which individuals do not have direct experience.

The remainder of the paper is structured as follows. Section 2 describes the Pacific Access Category and the survey upon which this paper is based. Section 3 outlines in detail how expectations are measured. Sections 4 and 5 compare expectations about employment and income in New Zealand to the actual distributions experienced by migrants. Section 6 then explores several explanations for the difference between actual and expected work outcomes, and Section 7 concludes.

# **2. Description of the Survey and Experimental Design**

The data used in this paper are from the Tongan component of the Pacific Island-New Zealand Migration Survey (PINZMS), a comprehensive household survey designed to measure multiple aspects of the migration process. The unique feature of PINZMS is that it is based on a natural experiment that enables the income gains from migration to be estimated free of any selection bias (McKenzie, Gibson and Stillman, 2006). New Zealand has a special immigration category, established in 2001, called the Pacific Access Category (PAC), which allows an annual quota of 250 Tongans to emigrate to New Zealand without going through other migration routes, such as categories for family 5
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# Footnotes

1. For recent discussions of the various approaches see Sen (1979, 1987), Deaton and Muellbauer (1980a, Chapter 7; 1986), Diewart (1980), Osmani (1982), King (1983), and McKenzie and Ulph (1987).

2. See, for example, Fields (1979), Kakwani (1986), and Ravallion and Chao (1987).

3. The above figures are based on our calculations from the primary data tapes of the 1981 household expenditure survey for Java. See Table 1 for details.

4. See, for example, the results of Ravallior and Chao (1987) for various Asian countries.

5. For example, the 'Sajogyo poverty line" is set at a rice equivalent which is 50 percent higher in urban areas; see for example, Sajogyo and Wiradi (1985). BPS (1984) assume an even higher differential of 66 percent.

6. Cost-of-living differences need not be the only reason for using different poverty lines; relative deprivation may also be a consideration. But the relevance of the latter to poverty comparisons within Java is questionable. The case for twin urban-rural poverty lines in this setting appears to rest heavily on cost-of-living factors.

7. See the surveys by Mayo (1981) and Halpezzi and Mayo (1987).

8. Price effects on housing demand have been mainly identified by two methods: i) That proposed by Muth (1971) based on (more readily observed) land and other input prices, and subsuming a housing production function into the demand model; for recent examples see
available resources.” This is because no form of social cost-benefit analysis has been used.

Overall, weighing the strengths and limitations of existing labor market policy research, there is a need to be humble in drawing policy conclusions. Many empirical researchers feel obliged to add a policy conclusions section to their papers. For the reasons given, more circumspection is called for.

# **<u>Five Pitfalls to be Avoided</u>**

Five pitfalls are common enough in the literature that it is worth cautioning the reader specifically against them. The first is to think of labor markets in terms of productivity rather than in terms of supply and demand. A simple example illustrates the point.

Consider a two person economy in which workers dig holes. Suppose that together, two workers working with one shovel can dig 10 holes a day, i.e., 5 holes each. Suppose one worker is replaced by a power shovel, which costs the same amount per day as the worker displaced, and that the one worker who remains employed working with a power shovel is able to dig ten holes a day. Suppose the other worker is unemployed. Productivity has doubled. “Should” the wage of the employed worker double? No – supply and demand analysis tells us that if anything the employed worker’s wage would fall, not rise.

A second pitfall is to rely on the wrong kinds of empirical studies. What we need most for policy purposes are empirical studies that give guidance on what the right theoretical labor market model should be or what the empirical magnitudes included in existing models actually are. Standard household and firm surveys are usually used to perform individual-level analysis. They can be used to perform market-level analysis instead, and indeed it would be helpful if they were used in that way more often. For example, if we want to know what the consequences of future educational expansion might be, we can derive useful policy insights by asking what were the market-level effects of past educational expansion. If, in the recent past, the supply of educated people has been increased by 100 university graduates, are 100 more people now working in “university-level” occupations? Sometimes, though, non-standard data are needed. In South Africa, the broad unemployment rate is 41%, whereas in most developing 57