rafmacalaba/data-use-mentions-extended huggingface dataset:
# **_1. Introduction_**

Developing East Asia – here defined as the following set of countries: Cambodia, China, Indonesia, Lao People’s Democratic Republic, Malaysia, Mongolia, Myanmar, the Philippines, Thailand and Vietnam – has been experiencing a consistent and steady pattern of economic growth in the past two decades. This successful performance can be attributed to a broad set of policies that can be grouped in three key pillars: outward orientation, investment in basic human capital and sound economic governance (Mason and Shetty, 2018). From a GDP growth accounting perspective, data for this set of countries show that physical and human capital accumulation (particularly labor quantity) were the main drivers of growth, while (total factor) productivity growth played a less important role.

**Figure 1. Contribution to GDP growth in developing East Asia* (percent, 2000-2017)**

<!-- Start of picture text --> 10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>Cambodia China Indonesia Malaysia Myanmar Philippines Thailand Vietnam<br>(Official)<br>Labor Quantity Contribution Labor Quality Contribution Total Capital Contribution<br>Total Factor Productivity Growth of GDP<br><!-- End of picture text --> Source: Data from Total Economy Database. Note: *there was no data available for Mongolia and Lao PDR.

Looking forward, developing East Asian countries face a common challenge: the need to increase productivity which will depend on their ability to innovate. In this context, as these countries attempt to climb up the income ladder while being exposed to increasing competitive pressure, economic growth will depend on innovation, and specifically on their ability to build the resources and capabilities to innovate. This will be the ultimate driver to support diversification into new (and higher value added) areas of
18 It reduces the poverty rate by roughly one-quarter in Thailand and Vietnam, for example. Overall, the regional and total average extent of poverty alleviation is around four times larger in this scenario than when the poor are also assumed to be levied with income taxes to replace lost trade tax revenue. The unweighted average poverty headcount reduction for the three regions shown in the final column of Table 5 are in line with the population-weighted averages from the LINKAGE model reported in Table 4 above with a similar tax-replacement assumption: the latter’s 30 percent for Asia excluding China and India and 6.8 percent for Latin America are above the GTAP model’s 14 percent and 5.7 percent, while their 3.8 percent for Sub-Saharan Africa is just below the 4.5 percent obtained for the Hertel and Keeney sample.

# **4. SYNOPSIS OF EMPIRICAL FINDINGS: NATIONAL MODEL RESULTS**

We turn now to see how the results from nine more-detailed individual country case studies compare with the above results from global models.<sup>6</sup> Like the two global models, they focus on price-distorting policies as of 2004, even though the database for their CGE models and their household survey data typically date back a little earlier in the decade. They all include more sectoral and product disaggregation than the global models, and have multiple types of

> 6 The nine national studies are for Brazil (Ferreira Filho and Horridge (2010), China (Zhai and Hertel 2010), Indonesia (Warr 2010a), Mozambique (Arndt and Thurlow 2010), Nicaragua (Sanchez and Vos 2010), Pakistan (Cororaton and Orden 2010), Philippines (Cororaton, Corong and Cockburn 2010), South Africa (Herault and Thurlow (2010), and Thailand (Warr 2010b). Results were also generated for Argentina (Cicowiez, DiazBonilla and Diaz-Bonilla (2010), but they are not included here because they are based on a household survey that unfortunately did not include rural areas.
remain the top five destination provinces for migrants who moved across province boundaries during this period. All these destinations, except Beijing, are located in coastal and eastern regions.

A policy response to regional inequality, the Western Development Project, which started in 2000, directed substantial public investment to western and inland regions (Naughton 2004). With more investment and more employment opportunities, China’s western and interior regions also experienced a rise in the migrant population during the first decade of the 21st century (Liang, et al. 2014). For more recent trends, the RCRE data show that Guangdong, Hubei, Shannxi, Liaoning, and Jiangxi have experienced the largest intra-provincial migration movements over the period 2009-2017. In sum, the coastal and eastern regions remain the major destinations for inter-provincial migrants, while the increase in the migrant population in the central and western regions seems to be driven predominantly by intraprovincial migrants.

With increases in rural-to-urban migration, we also observe important demographic features of the migrant population, some of which have undergone notable changes over time. First, migration trends differ by gender and age.<sup>8</sup> As shown in Figure 2, the likelihood of working as a migrant increase with age, peaks for cohorts aged 25 to 30, and then decreases with age. Below age 70, a larger share of rural men migrate than women. The gender gap in the migrant share of the local population remains stable with no sign of converging over time.<sup>9</sup> Even though this broad gender and age pattern has been consistent for years, a couple of important changes are notable in the RCRE data. First, migration at younger ages (1620) has declined for both men and women, and the decline is more pronounced for women. One possible explanation is that a higher proportion of people in this age group are in school in later years, rather than in the labor market (Figure 3). In 2003, about one-third of young women
# **5 Discussion and Conclusion**

## **5.1 Explanations for the findings of the study**

This study has illustrated that, on average, the sales of formal firms in Zambia have recovered from the pandemic far more strongly than their employment levels. These findings suggest that formal firms have adjusted their operations in a way that reduces their need for labor to achieve the same level of sales. In other words, there has been a shift in the capital–labor mix that formal firms use, away from labor. If this is a persistent change, lower levels of formal employment in low- and lower-middle-income countries may be a concerning consequence of the COVID-19 pandemic that lingers for years to come. This pattern of a weak recovery of formal employment has also been observed in several emerging-market economies (Andaloussi et al. 2022). Ultimately, this means more people are in vulnerable employment situations that lack a formal safety net and there is less income tax being paid to fund the provision of public goods and services. Detailed investigation of the survey and tax administrative data does not suggest that the overall findings are due to improvements in formal sector labor productivity. There is no evidence of existing firms reporting higher average wages for the employees they retained or large shifts between relying on full-time compared to casual employees. In addition, the tax administrative data does not suggest there have been a large number of new firms entering the formal sector.<sup>5</sup> Rather, a more compelling explanation for what we observe is that many firms have tried to rebound from the crisis by reducing (in some instances potentially unproductive) labor inputs, which can often be challenging in a more stable setting. An unavoidable limitation of this study is that the focus is on only formal sector employment, which means that it is entirely possible that people who were previously in formal employment have moved to the informal sector and maintained (or even increased) their productivity.

> 5If this was the case,
farmers’ perceptions. Notably again, the paper also explores the scope for integrated approaches that combine self-reported and sensor data to harness the strength of each.

For our analysis, we draw on a diverse set of data sources. Our main data source is rich plot-level household survey panel data<sup>18</sup> for two Sub-Saharan African countries, Ethiopia and Malawi, supported by the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The LSMS-ISA data have a distinct focus on agriculture and contain, among others, detailed data on harvest, input use, crop management, as well as a large set of community, household, and individual characteristics. The agricultural modules of the surveys feature a twovisit structure in which each household was visited after planting and again after the harvest had been completed. At the first visit, among others, self-reported information on input use, plot characteristics, and expected production are elicited while the second visit asks farmers about their

> 17 This is in contrast to other approaches including the “default” angle of the FAO methodology which aim to measure crop losses at the farm-level or higher.

> 18 The LSMS-ISA data we use are _farm-level_ panels but not necessarily _plot-level_ panels due to constant changes in plot dimensions and the crops cultivated on them (Carletto, Gourlay and Winters, 2015). In this version of the paper, we therefore do not exploit the panel dimension of our data and treat each wave as a separate cross section. However, this does not mean that the fact that we are dealing with panel data does not carry advantages for our analysis. For example, it should attenuate concerns about changes in the sample when comparing descriptives across rounds. An exception is ESS18 in Ethiopia which received a new baseline sample in this round and is thus not strictly comparable (from a panel perspective) to the sample from previous rounds.

9
childbirth compared with those who had not heard about the voucher (69 percent and 57 percent respectively), suggesting that the program not only created awareness about the vouchers but other reproductive health indicators as well. However, no significant difference was seen in knowledge of maternal health conditions between the health SPs from HealthyBaby voucher facilities who were interviewed in 2010–11 and those from nonvoucher sites (table 3).

Table 3 Percentage of Providers Interviewed in 2010–11 by Knowledge of Selected Maternal Health Conditions

|Maternal condition|HealthyBaby<br>voucher<br>facilities<br>(_N_= 63)<br>(%)|Comparison<br>facilities<br>(_N_= 49)<br>(%)|_P_value|
|---|---|---|---|
|Birth<br>preparedness<br>and<br>complications<br>readiness<sup>a</sup>|78|84|0.418|
|Danger signs in pregnancy|89|86|0.694|
|Danger signs during labor, delivery, and<br>afterward|86|89|0.659|
|Number of ANC visits women should make<br>during pregnancy|75|80|0.198|
|Gestational age at which a woman should make<br>first ANC visit|52|53|0.668|

_Source:_ Population Council, Health Facility Survey 2010–11.

_Note:_ ANC= antenatal care; _N_ = number of SPs interviewed.

a This statement was read to the respondents so they could indicate their level of knowledge regarding it.

# **<<B>>** Health Service Utilization

The net increase in private facility deliveries among women who had used the HealthyBaby voucher was greater than the reduction in public facility deliveries or home births (table 4). The proportion of private facility births among women who had ever used the HealthyBaby
by 7.9% and that moving from the minimum to the maximum rule of law countries (which, in our sample, is from Russia to Norway) increases TFP by 21.6%. Our findings therefore highlight a quantitatively important channel through which institutional differences drive productivity differences across countries and regions.

The main drawback of our study is the lack of exogenous variation in (proxies for) the quality of contracting institutions. While we perform an exercise using historical institutions as a potential exogenous driver of contemporary trust as in Tabellini (2010) (described below), in general it is a challenge throughout the literature to find an instrument for trust that is is wholly uncorrelated with regional unobservables. Nevertheless, we believe our results advance the literature by providing new empirical patterns that control for country-level unobservables and region characteristics. Indeed, our cross-country estimates are twice as large in the absence of these controls. More broadly, since the patterns borne out in the data are consistent with our model’s multiple predictions, they support its narrative that legal institutions lower aggregate performance by causing more firms to sort into less productive family management.

There are several other limitations which we address through robustness checks. First, we lack a measure of legal institutions at the region level. In support of using trust as a proxy, we show that it is highly correlated with measures of the rule of law (from the Worldwide Governance Indicators) and contract enforcement (from the World Bank Doing Business Survey) across countries. Second, since there are a number of ways to define family management we show our results are robust to a variety of alternatives. Third, we address the difficulty in measuring the operating location of multi-plant firms by showing the results hold on a sample of smaller firms who are less likely to operate multiple establishments. Fourth, we re-run our main specifications under a number of alternative sampling rules to show the way in which we construct our main sample does not drive our results. Fifth, we show our results hold if
Policy Research Working Paper 9264

# Applied Services Trade Policy

A Guide to the Services Trade Policy Database and the Services Trade Restrictions Index _Ingo Borchert Batshur Gootiiz Joscelyn Magdeleine Juan A. Marchetti Aaditya Mattoo Ester Rubio Evgeniia Shannon_ Development Economics Development Research Group & East Asia and Pacific Region Office of the Chief Economist June 2020
0074||.0118||
|Pseudo R2|.225||.357||.240||.291||

Notes: z-scores in parentheses in Columns (1). (2), (3), and (4). % changes in columns la, 2a, 3a. 4a computed at the means of the independent variables. Prior Instability is the coefficient of variation (standard deviation divided by mean) for M2/GDP over the three years prior to the adoption of explicit deposit insurance. For those countries that retained implicit insurance, the coefficient of variation is calculated over all three-year periods from 1980-95 for which data were available. Recent Financial Crisis is a dumnnv variable = 1, if a systemic financial crisis occurred within the five years prior to the deposit insurance decision. For those with implicit deposit insurance, the five year period was 1989-93. Legal Tradition is a six-point index created by the Intemational Country Risk Guide. Higher scores indicate "sound political institutions, a strong court system, and provisions for an orderly succession of power." Legal tradition data averaged over 1985-1991. "% Neighbors adopted EDI" is the percentage of countries within a region that adopted explicit deposit insurance after the date specified (above). Regional pressure figures exclude the country in question from the calculation. The five regions for developing countries were Africa, Asia, Latin America and the Caribbean, the Middle East, and Transitional Socialist Economiies. Developed countries were all grouped into one category (region).

16
# Classification of measures

Drawing from the World Bank's experience with its original Services Trade Restrictions Database (STRD) and developments since then, notably the OECD STRI, we developed a new classification of measures affecting trade in services. We organized measures into four main policy categories, namely a) conditions on market entry, b) conditions on operations, c) measures affecting competition, and d) regulatory environment and administrative procedures. Relevant policy measures that potentially affect trade but do not fit neatly into any of these four categories are included in a fifth category – miscellaneous measures (see Box 1).

<!-- Start of picture text --> Box 1: Structure of STPD classification of measures<br>A Conditions on market entry<br>A1 Legal forms of entry (including foreign equity limits)<br>A2 Quantitative measures (for firms and natural persons)<br>A3 Conditions on licensing/investment screening/qualifications relating to market entry<br>(firms /natural persons)<br>A4 Other conditions on market entry<br>B Conditions on operations<br>B1 Conditions on supply of services<br>B2 Conditions on service supplier<br>B3 Conditions on government procurement<br>B4 Other conditions on operations<br>C Measures affecting competition<br>C1 Conditions on conduct by firms<br>C2 Governmental rights/prerogatives (including public ownership)<br>C3 Other measures affecting competition<br>D Administrative procedures and regulatory transparency<br>D1 Administrative procedures<br>D2 Regulatory transparency (including licensing)<br>D3 Nature of regulatory authority (measures related to nature of regulator)<br>D4 International standards<br>D5 Other regulatory environment and administrative procedures<br>E Miscellaneous<br><!-- End of picture text --> All measures are classified according to the taxonomy above in binary form (yes or no), except for the few measures that require a quantitative
size from tax records, which we estimate for single-bookkeeping firms from our data. We observe earnings used for benefit calculation, allowing us to trace the impact of reported earnings on expected benefits.<sup>7</sup> We focus on the 2012 to 2017 period because in our data earnings before 2012 were smoothed within employment spells by employer-employee-year. We limit to individual-month observations with at most 5 sick days and no maternity payments. We adjust monthly earnings for sick days and to their full-time equivalent, using contractual work hours.

We observe the month of each birth between 2009 and 2017, defined by International Classification of Diseases (ICD-10) inpatient diagnosis codes in the health records. We focus on the first observed birth by each woman.<sup>8</sup> We restrict our sample to women who gave birth between ages 20 and 40 (90% of first childbirths in our data). We create a control group of women who did not give birth by 2017 and assign them a placebo childbirth date between ages 20 and 40 and years 2012 and 2017.

Our baseline sample consists of women working in the private sector who had a non-missing full-time equivalent wage of at least 90% of the monthly minimum wage each month between thirty to four months before giving birth or the placebo birth. Table 2 suggests that the two groups are broadly comparable, though women who give birth are somewhat more likely to have higher wages and to work in higher-skill occupations and in services.

# **4 Reporting Responses to the Contribution-Benefit Link**

## **4.1 Main Results on Reported Earnings**

In this section, we start by examining the evolution of reported earnings among expectant mothers. We document several patterns consistent with the formalization of previously underreported earnings to increase maternity benefits.

We estimate an event study regression following Kleven et al. (2019) to characterize the time pattern of reported earnings among expectant mothers relative to otherwise similar women who do not give birth: where _i_ indexes individuals,
data comparisons over time suggests that business exits were much less severe than were expected early in the crisis, likely due to swiftly enacted government policies (Crane, Decker, Flaaen, Hamins-Puertolas, and Kurz, 2021).

Thus, we argue that the swift, widespread, and relatively quickly acknowledged turnarounds in GDP growth and unemployment rates and employment, as well as the improvements in personal incomes, poverty rates, credit scores, consumer debt, and business exits clearly describe a favorable “economic surprise.” To be sure, the U.S. economy may not be completely out of the woods as of this writing in mid-2021. Supply bottlenecks, the withdrawal of economic and monetary stimuli, and most importantly the spread of the very infectious Delta variant could still lead to a deceleration and undermine recovery. However, we argue that the ultra-fast economic recovery to date qualifies as the “economic surprise” of this crisis.

# **4. The U.S. Banking “Surprise” during the COVID-19 Crisis**

The very steep slide in the U.S. and global economies during COVID-19 might have been expected to result rapidly in banking crises due to large credit losses and uncertainty about future prospects. However, more than a year later, we are surprisingly unaware of banking crises to date anywhere in the world, although there are areas of significant concern (e.g., OECD, 2021).

In this section, we dig deeper to explore the impact on the U.S. banking system primarily using raw data from regulatory Call Reports from 2019:Q4 to 2020:Q4, as well as other information. What we see supports our claim of a favorable “financial surprise” for the U.S. banking system during the COVID-19 crisis. Not only was there no banking crisis, but the banks performed very well and did not suffer any noticeable financial distress.

This is not an econometric analysis, but a rough look at how the industry performed during some very volatile economic circumstances
3 reforms covering as a rule activities and policy areas where progress can be gauged only indirectly. Moreover, they usually tend to be highly intertwined. Progress in one area can be ‘neutered’ by the lack of progress in another area. For instance, large-scale privatization may be completed. Yet, its benefits may not materialize if privatized companies are subsidized by still state owned banks, as it was the case in the Czech Republic in the mid-1990’s, or competition policy is unable to contain their abuse of dominant position. The same comment applies to various indicators measuring the quality of business climate. Huge minimum capital requirements or instabilities in tax policies, for instance, may offset simplicity of procedures for business entry.

In order to assess Bulgaria’s progress in building institutions supporting plugging-into global markets, we adopt an eclectic approach. Proxy variables for assessing the progress in structural reforms in a comparative perspective is the improvement in the quality of governance supplemented by the ‘cost of doing business’ indicators and external assessments of corruption. We shall use the methodology and data developed by the World Bank for the first two, and Transparency International’s country rankings for the latter. They are all intertwined with the progress in building institutions supporting competitive markets and improving business climate. Governance takes into account the capacity of state to provide environment conducive to business activity, i.e., transparency, accountability, enforcement of the rule of law, regulations supporting competition, etc. The ‘cost of doing business’ indicators provide measures to assess businessfriendliness of regulations, whereas corruption indicators the extent of graft as perceived by international investors. The incidence of corruption supplements an assessment of the quality of governance pointing to its symptoms.

Two caveats are important. First, the shared weakness of governance and cost of doing business indicators is that a low score in a single dimension can offset high scores on other dimensions. For instance, friendly conditions for entry of start-ups would matter little if the government preys on existing businesses. In a similar