rafmacalaba/data-use-mentions-extended huggingface dataset:
25 wage and employment patterns in Malaysia. It is important to note that earnings analysis typically uses labor force survey data, which consists of data on the sources and levels of income for a particular worker as well as his/her individual characteristics. Unfortunately, only household-level data is available for this analysis on Malaysia; this distinction does not diminish the validity of the results, however.

In order to analyze the determinants of income, it is necessary to establish at the outset the appropriate measures of income. The analysis considers two dependent variables, the logs of (i) total labor income and (ii) net household income. The reasons are two-fold. In order to determine the effect of immigration and labor market policy on employment and wages, I use total labor income, which is equal to earnings from wages and other employment. This allows me to detect pay practices that discriminate against non-Malaysians, for example, as well as wage discrimination (positive or negative) against ethnic groups among Malaysian citizens. On the other hand, I do not wish to exclude other types of income that may also be affected by labor management policies. I therefore use household income including all sources, thus creating a more complete picture of various economic activities and the associated remuneration across population groups. After-tax income, i.e. net household income, represents a more appropriate measure of disposable income and at the same time avoids biasing the results with respect to non-citizens, who generally have a smaller tax burden. Two separate regressions are run, one for each dependent variable.

What factors affect household income? Earnings vary by occupation, experience, skill level and employment status (i.e. employee, employer or self-employed). The independent variables used in this regression analysis are consistent with the vast literature on earnings functions. They include: age and age<sup>2</sup> , years of schooling, region of employment, status of employment, household size and category of principle occupation. Because age and work experience
Allen et al., 2015; Tsivanidis, 2019) by adding intersectoral distortions and factor misallocation to an urban framework. Following Baqaee and Farhi (2020), I provide a formula that decomposes the welfare gains from transit developments into a “direct” effect and an allocative efficiency term. This latter term captures two different components: factor misallocation and agglomeration forces when they differ between the formal and informal sectors.<sup>4</sup> Mexico City constitutes a relevant and informative case study for several reasons. First, it has a dense concentration of economic activity, accounting for around 8.9 million people. Second, the Mexican case is typical of developing countries, especially in Latin America, where more than 50% of the urban labor force and 70% of business establishments are informal.<sup>5</sup> Furthermore, the city constructed a new primary subway line in the early 2000s, connecting remote areas in the north with the center of the city. The entire line was not initially planned by the Government and there were multiple delays in its construction, suggesting that the opening dates were uncorrelated with local demand and supply shocks. Moreover, Mexico City collects unique data that I use to estimate the impact of transit improvements on informality. Throughout, I use the standard definition of informality: a worker is informal if he/she does not receive social security benefits based on the contractual relationship with his/her employer.

At the center of the analysis is a rich collection of administrative microdata. I observe the geography of jobs and worker residences for both the formal and informal sectors at the high granular census tract level. In the analysis, I use four main sources of data. First, I use confidential microdata from several reports of the Mexican Economic Census, covering the universe of business establishments located in Mexico City. Second, I use the microdata of the Mexican Population Census to determine the residence of both formal and informal workers. Third, I use detailed information on the
the government financed some of the subsidies. Household survey data shows that between 1985/6 and 1996/7, the budget share of water and electricity rise sharply for the lower deciles. In real terms electricity prices for residential users, inclusive of taxes, remained stable, while pre-tax prices are 12% higher. At the same time, prices for industrial users decreased by about 7%. Prices were still low by international standards. Access to service for the poorest groups rose sharply: residential service in Buenos Aires area increased from 65% in 1985/6 to 99% in 1996/7. The estimated change in consumer surplus between 1985/86 and 1996/97 shows substantial welfare gains, especially for lower income deciles due to increased access and lower prices.

The paper, however, points out that the results can be sensitive to the choice of years. The collective fiscal effect of privatisation of electricity and other industries was also significant. The privatisation proceeds amounted to about one-third of the government bonds outstanding in 1990. The sale of assets allowed the government to repurchase some bonds and, thereby, reduce interest payments. The falling public debt and reduced expenditures on utilities was accompanied by increased social expenditures. At the same time, between 1992 and 1999 the dividends paid by the privatised firms amounted to an average of 38.5% (increasing from 12% ($US 159 mill.) in 1992 to 64.1% ($US 1,670 mill.) in 1999) of the all dividends paid. However, it is argued that the distributional impact of all dividends has been limited as some individuals and employees owned part of the issue shares.

32
self-respondent interviews.

The effect of proxy respondents on the measurement of assets in low-income contexts has recently been explored by the EDGE initiative and the Living Standards Measurement Study-Plus (LSMS+) project.<sup>3</sup> The United Nations Guidelines for Producing Statistics on Asset Ownership from a Gender Perspective (henceforth referred to as ‘UN Guidelines’; UNSD, 2019), based on empirical evidence amassed through the EDGE project, explicitly state: “individual-level data on asset ownership should be reported by self rather than proxy, owing to large discrepancies between proxy and self-responses...”. Evidence from the Uganda Methodological Survey Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA), which fed into the development of the UN EDGE guidelines, illustrates the asymmetric impacts of respondent approach by gender. MEXA found that the use of self-reports increased women’s reported ownership of agricultural land by 5 percentage points relative to proxy reports, while the increase for men’s reported ownership was 10 percentage points (Kilic and Moylan, 2016; UNSD, 2019). In line with this and as part of the LSMS+ project, Kilic, Moylan, and Koolwal (2020) found that the use of proxy respondents relative to self-respondent implementation resulted in higher rates of exclusive ownership of agricultural land for men, and lower rates for women. Thus, these gender-differentiated biases based on respondent strategy may not only lead to inaccurate estimates of land tenure for the population at large, but they also have the potential to distort estimates of the gender gap in rights.

On the relationship between data quality and level of data collection, there is a dearth of research on the implications of collecting land tenure data at the parcel level vis-à-vis the aggregate level. However, related research on the effects of aggregation raises concerns for data quality implications and motivates the need for methodological validation. Research by Beegle et al. (2012), for example, explored the impacts of the level of detail in a consumption questionnaire on the resulting consumption
an experiment on college returns and costs. _Journal of Public Economics_ , _157_ , 184–211.

- Bonilla, L., Bottan, N. L., & Ham, A. (2017). _Information Policies and Higher Education Choices: Experimental Evidence from Colombia_ (SSRN Scholarly Paper No. ID 2546835). Rochester, NY: Social Science Research Network.

- Borenstein, M. (Ed.). (2009). _Introduction to meta‐analysis_ . Chichester, U.K: John Wiley & Sons.

35
revenues are recycled via mode (c) as described in Section 3.3. All reported values are population-weighted averages and adjusted for behavioral responses to higher consumer prices (see also Section 3.2).

# **4.4.2 Incidence across economic sectors**

As economic activity transitions, there is a corresponding transition in the production structure, with fossil fuel-intensive sectors losing and energy-efficient or renewable-intensive sectors gaining. This may directly impact firms and workers to the extent that firms see cost increases—given more expensive after-tax energy inputs. To quantify these cost increases for non-fossil fuel/electricity sectors, we assume away full passthrough of producer cost increases onto consumer prices (i.e., producers bear the full incidence of the carbon tax<sup>38</sup> ) and use the same input-output data and methods outlined under Section 3.2 above.

The impacts of the US$25/ton CO2e carbon tax on industry input costs are moderate, except for a few sectors in a few economies (Figure 10). While the sectors with the highest cost increases in 2030 vary by South Asian economy, the general pattern is that the largest sectors - in terms of their share in total output - are the least likely to experience substantial cost increases. Cement and aviation see the highest average cost increases in 2030 (aviation is linked to tourism which is an important exporting sector for the smaller economies such as Maldives and Nepal).

In particular, the largest cost increases are seen in India, at around 11 to 12 percent (iron/steel and cement), given its heavy use of coal. Pakistan and Nepal also see relatively large cost increases in these sectors (at around 2-4 percent), while economic sectors in Bangladesh, Bhutan, Maldives, and Sri Lanka experience negligible cost increases overall. In fact, the 2030 average effective input cost increase for South Asia, weighted by the share of the sectors in regional output (and each economy’s share in regional GDP
over time. The IEG methodology provides a granular metric based on six potential scores, ranging from “Highly unsatisfactory” to “Highly satisfactory.” Before being publicly disclosed on the portal, project review reports, along with evaluation ratings, undergo thorough reviews and approvals from designated review coordinators, managers, and World Bank global practices.<sup>8</sup>

**_Construction of the PIQ index._** For the purposes of our analysis, we convert the qualitative project outcome ratings into numerical scores that range between 1 and 6: 1 = “Highly unsatisfactory”, 2 = “Unsatisfactory”, 3 = “Moderately unsatisfactory”, 4 = “Moderately satisfactory”, 5 = “Satisfactory”, and 6 = “Highly satisfactory”. As we focus on public investment, we only use the data on projects in the Investment Project Financing (IPF) category.<sup>9</sup> These investment projects finance activities that create the physical and social infrastructure needed for sustainable economic development over medium- to long-term horizons (5-10 years).<sup>10</sup> We augment the IEG project

> 6 IEG is an independent unit within the World Bank Group responsible for assessing its programs and activities. All the data used to build the index are available on the website of the Independent Evaluation Group – World Bank Project Performance Ratings: http://ieg.worldbankgroup.org/ratings.

> 7 As of August 2022. For the estimations we use the period 2000-2021.

> 8 Additional details on the methodology of project evaluations with guidelines for each evaluation step are reported in IEG (2017) and are available at the dedicated web portal: https://ieg.worldbankgroup.org/methodology.

> 9 We thus do not incorporate other project categories, including Program-for-Results Financing, Development Policy Financing.

> 10 Most of these projects focus on infrastructure, human development, agriculture, and public administration. One should note, however, that, while
**Sales:** Firms’ total turnover. Sales are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level sales for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Employment:** Number of persons employed. Firm-level employment for Georgia is collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, measured as the average number of persons employed (employees, employed shareholders and employed family members in case of family owned enterprise) in the enterprise during the year.

**Female employment:** Number of female persons employed. Firm-level female employment for Georgia is collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, measured as the average number of female persons employed (employees, employed shareholders and employed family members in case of family owned enterprise) in the enterprise during the year.

**Wages:** Total annual cost of labor or remuneration (including wage, salary, premium, bonus, social payments, etc. of both permanent and temporary employees) which was accrued or paid in kind to employees (including income tax) during the year divided by employment. Wages are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level average wages for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Female wages:** Remuneration (including wage, salary, premium, bonus, social payments, etc.) which was accrued or paid in kind to female employees during the year divided by female employment. Female wages are in real 2005 national currency units using the consumer price index reported by the World Bank World Development Indicators. Firm-level average female wages for Georgia are calculated from the GEOSTAT Statistics Survey of Enterprises for 2006-2017.

**Nominal effective exchange rate:** A measure of the value of a currency against a trade-weighted average of several foreign currencies. The nominal effective exchange rate for Georgia for each destination market
3

**sheets and adjusting their business models to varying degrees** . European banks gradually started deleveraging as part of the process of cleaning up their balance sheets and shrank by 10– 15 percent. Risk-weighted assets (RWAs), which stood at 12.4 times capital in 2007, fell to 9.5 times in 2010 (figure 4). Banks also sought more stable sources of funding such as retail deposits to substitute for more fickle, short-term wholesale funding sources, which helped reduce their loan to deposit (L/D) ratios from 175 percent to 138 percent from  2007 to 2010 (figure 5). Banks also bolstered capital cushions by 20 percent on average to ease solvency concerns, in part in response to market pressures, as well as to prepare for the phasing-in of the tighter Basel III capital rules over the 2013–19 period.

**Figure 2. Leverage and Reliance on Wholesale Funding**

<!-- Start of picture text --> Asset to Equity Ratio Tier 1 Capital Ratio<br>Q1 2007 and Q4 2010 2007 and 2010<br>25 20<br>20<br>15<br>15<br>10<br>10<br>5<br>5<br>0 0<br>ASIA CEE MENA SSA LATAM EUR OTHER ASIA CEE MENA SSA LATAM EUR OTHER<br>2010 2007 2010 2007<br>Loan to Deposit Ratio<br>Q1 2007 and Q4 2010<br>200<br>150<br>100<br>50<br>0<br>ASIA CEE MENA SSA LATAM EUR OTHER<br>Q4 2010 Q1 2007<br>Percent Percent<br>Percent<br><!-- End of picture text --> Source: Bankscope, Bureau van Dijk. EUR data from European Central Bank (ECB) Consolidated Banking Data Note: ASIA: Asia. CEE: Central and Eastern Europe MENA: Middle East and North Africa
opinion surveys. We expect stronger public support for development aid to increase a bilateral donor agency‟s use of country systems. Where support for aid is relatively weak, aid agency officials will be under more pressure to show that the funds they are provided produce visible results directly attributable to its efforts, so will provide more aid in the form of projects using parallel systems and less in the form of budget support.

Data for testing this hypothesis are available from three different public opinion surveys: Gallup International‟s 2002 “Voice of the People” survey, the 1995-1998 round of World Values Surveys (WVS), and the 2004 Eurobarometer. These surveys each cover a somewhat different sample of donor countries, as shown in Appendix Table A2. All 24 bilateral donors are represented in one or more of the surveys, but only four (Finland, Germany, Spain and Sweden) are included in all three. The question inquiring about support for development aid is worded somewhat differently in each survey. The percentage of respondents indicating greater support for aid in the WVS is correlated at .85 with the corresponding percentage from Gallup International (GI), and at .49 with the percentage supporting aid in Eurobarometer. Support for aid in GI and Eurobarometer, however, are correlated at only .18. The two donors with the weakest support for foreign aid in both the WVS and GI are the U.S. and Japan (neither is included in Eurobarometer).

To maximize the information content from these three surveys and to preserve the sample size, we construct an index of public support for aid. Values from each survey are first standardized to have a mean of 0 and standard deviation of 1. Index scores, reported in Table 2, are then computed as the unweighted mean of the index components that are available for each country. The alpha coefficient of index reliability from this procedure is a very respectable .72, 14
**Table 6: Cutting Robots and Export Quality**

**Table 7: Handing Robots and Export Quality**

|Robot Measure:<br>Outcome:|(1)<br>(2)<br>**Cutting Robots**<br>Quality|Robot Measure:<br>Outcome:|(1)<br>**Handlin**<br>Qu|(2)<br>**g Robots**<br>ality|
|---|---|---|---|---|
|Mechanical cutting robot|0.082**<br>(0.038)|Pallet handling robot|0.145***<br>(0.052)||
|Laser cutting robot|0.085***<br>(0.032)|Handling & inspection robot|-0.023<br>(0.041)||
|Mechanical cutting robot - Developing|0.246***<br>(0.071)|Pallet handling robot - Developing||0.300***<br>(0.099)|
|Mechanical cutting robot - Developed|-0.012<br>(0.022)|Pallet handling robot - Developed||0.049*<br>(0.028)|
|Laser cutting robot - Developing|-0.001<br>(0.041)|Handling & inspection - Developing||-0.062<br>(0.073)|
|Laser cutting robot - Developed|0.084**<br>(0.035)|Handling & inspection - Developed||-0.032<br>(0.031)|
|Observations|864,039<br>864,039|Observations|864,039|864,
barriers; and (d) insufficient financial or human resources. Whether or not statistically valid samples are drawn, they should be designed so that they reflect the survey's objectives, especially household characteris¶ics which are thought to be relevant to energy consumption.

Samoling Procedures Depend on Goals and Resources. There are several types of sampling procedures which can be used in surveys including random, stratified random, matched pair, selection according to a quota, and even a total census. Generally, a random, representative sample is necessary to determine total household energy consumption for a country or a region. To draw such a sample, reliable and comprehensive data must exist on the size, location and key characteristics of the population one wants to study must exist. When this is not aval able, creative alternatives may be found. For example, in the 1987 Burkina Faso urban household energy survey, census listings were unreliable so the following methodology was used: (a) cities were divided into small geographic areas; (b) a random sample of these areas was chosen for the survey, (c) a household census was conducted in the chosen areas; and (d) a random sample was drawn from the list'.d households.

Tiae focus of the survey can also help determine whether or not a random sample of the population is necessary. For example, if an evaluation of energy consumption with and without an improved stove is to be done but the stove has not significantly penetrated the market, then a representative sample selected randomly from the population may not have enough cases of the stove to permit adequate analysis. In a recent study of rural electrification in India, a matched pair design was chosen. Households with electricity were concentrated in the high income groups and those without electricity were mainly in low income groups. With a random sample, results would have been biased because there would be no control groups (poor households with electricity and rich households without it). Therefore, the sample selection matched households from
_3. A MODEL OF STRUCTURAL CHANGE AND INTEGRATION_

# **3.5 Data**

## **3.5.1 Economic activity**

**Administrative level** For Central Asian countries, we choose each location to be at the district administrative level, called _raions_ in most post-Soviet states<sup>12</sup> . There are 174 districts in Kazakhstan, 162 districts in Uzbekistan, and 45 districts in Kyrgyzstan. For China, the third administrative level is chosen with 2048 locations. Recovering the sufficient statistics (amenity, productivity, prices) for each district requires the following observed variables: employment for the export sector (agriculture or manufacturing) versus non-tradable sectors (services, local manufacturing), land area, total population, and estimates for changes in transport costs. Assuming that agriculture will be the sector to benefit from access to new corridors in Central Asian countries, we also collect data on urban and rural populations for each district.

**Land data** Land areas cover currently-used areas for productive activities, excluding large areas that are empty of population and economic activities. For Central Asian countries, we use European Satellite Agency data to keep irrigated land and urban areas for each loction. For China, the land area that is considered to be allocated between manufacturing and services is the built-up area per location.

**Employment in the tradables sector** For Central Asian countries with a comparative advantage in agriculture, employment in agriculture at the district level is not directly available. While some papers use rural shares as a proxy, workers in rural areas can also work in rural services, and such percentages vary across regions. To adjust the shares in rural population, we use available employment surveys, sufficiently representative at the provincial level, to build a correction factor to consider both the share of employment in the population and the share of employment in agriculture in total employment at the provincial level. Rural shares and employment shares in agriculture are positively but imperfectly correlated (Annex D
discusses only the legal basis, description of the statistical system, data source, and processing of the target country (UNECE, 2014). The FAO, in its guidelines for assessing country capacity in producing agricultural statistics, provides instructions on completing the questionnaires and on compiling the assessment indicator (FAO, 2014), but pays no attention to the axiomatic principles of these indicators. The U.S. Census Bureau developed and recently updated (2017) the Tool for Assessing Statistical Capacity (TASC) with a primary objective of measuring the overall capacity of an NSS by providing a breakdown of the areas of strength and weakness. However, the focus of this instrument is on measuring the capacity of an NSS to conduct household-based surveys and censuses.

> 8 We return to more discussion on the differences between our paper and Cameron _et al_ .’s (2021) study in Section 2.2.

5