rafmacalaba/data-use-mentions-extended huggingface dataset:
_Services PTAs_ Figure 3: Increasing number of bilateral pairs covered by Services PTAs <!-- Start of picture text --> 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>PR China Australia Brazil S. Africa<br>30<br>20<br>10<br>Number of PTA partners<br>0<br><!-- End of picture text --> Authors’ elaboration using the World Bank DTA 2.0 Database.

services trade integration. A few economies are party to many services PTAs (e.g. Chile: 20; Panama: 14; Peru: 13; Costa Rica: 12; or Mexico: 11). That is, these countries are well connected, both externally as well as within the region.<sup>2</sup> At the same time, the largest economies such as Argentina or Brazil do not actively pursue services integration and are party to only few agreements (MERCOSUR in this case) with shallow provisions.

In this paper we study the effect of services PTAs on cross-border services trade and countries’ engagement in international services value chains. We do so by exploiting, for outcome variables of interest, the newly released ITPD-E data on bilateral services trade (Borchert et al., 2021), and the 2018 edition of the Trade in Value added (TiVA)

> 2For instance, Chile and Panama have bilateral agreements covering services with each other as well as with Costa Rica, Guatemala, Honduras, Nicaragua and El Salvador. Mexico has also signed a number of intra-regional services PTAs.

6
BRI-recipient countries in Sub-Saharan Africa, it identifies fewer projects and much lower amounts of investment (about 300 billion, compared to about 500 billion in the Bank’s database) in 2017 and 2018.

Ideally, this paper should use debt financing related to identified BRI projects, but the information is not readily available. Two publicly available large databases track lending from China. These are Aid Data from compiled by researchers at William and Mary College<sup>9</sup> and the China Africa Research Initiative (CARI) from John Hopkins University School of Advanced International Studies.<sup>10</sup> Unfortunately, both sources of lending data have severe limitations for this paper: both databases identify borrowing or commitment only up to 2014, just one year after the announcement of BRI; no loan is identified as related to BRI; and the CARI tracks lending only to Africa, which includes only a small portion of BRI-related investment, as defined in this paper.

There is very sparse information on the financing of BRI-related projects. A full debt sustainability analysis and the analysis of fiscal risks would require detailed, country-specific information on the financing structure of BRI-related projects. According to the WIND data base, about half of the BRI investment is in electricity and 1⁄4 in transport and shipping (see Annex II). Information from Pakistan suggests that electricity investments would be conducted by Independent Power Producers (IPP). These contracts usually include public sector guarantees on loan financing and a power purchasing agreement by government entities (e.g. the Ministry of Water and Power). Infrastructure contracts in Djibouti and Pakistan are either direct lending from China to the government or to a state-owned enterprise (SOE), with explicit guarantees of the government. In both contractual structures, the BRI-recipient governments are likely to guarantee a substantial amount of the private or public financing provided. It seems therefore reasonable to assume that a large share of the BRI investment would be financed as public or publicly guaranteed debt (PPG).<
t + εs,c,t. (2) As measures for the national characteristics, zc, we employ variables capturing a country’s infrastructure, connectivity, investment and trade policy, business climate and institutions, financial and labor markets, skills and education, innovation and product standards, as well as labor, social, and environmental standards. Due to sometimes incomplete data and to minimize reverse causality, we use the average over the sample period.

We repeat this exercise by splitting the sample into three categories based on the World Bank’s income classification. The categories are low- and lower-middle-, upper-middle-, and high-income countries. As robustness, we also split the countries into three evenly sized groups based on GDP per capita to avoid sample size issues. For a country application of the empirical model, see appendix 1.

It is important to emphasize that our empirical setting does not generate causal estimates on the role of domestic policies for economic upgrading through GVC integration, but rather provides evidence to guide future more directed work. A main advantage of this study is its comprehensive nature covering an extensive part of policy space to detect which policies should be analyzed more closely and which policies might be of second order. However, we do not claim to expose a causal relationship.

# **3.2. Data**

# **_GVC Indicators_**

To calculate our GVC indicators we rely on the OECD ICIO database, which covers 61 countries, 34 industries, and the years 1995, 2000, 2005, and 2008–11. The advantage of the database compared to other ICIOs is the extended country coverage, especially regarding low- and middle-income countries. This is especially important since it allows us to test implicitly if the role of certain policies depends on a country’s stage of development. Since the average per capita income level in the OECD ICIO database is significantly below the level of, for instance, the World Input Output Database (WIOD)—a new ICIO database—
– structural transformation employment transitions from agriculture to manufacturing – and services with economic development is primarily driven by men. Women follow a different pattern: at low levels of development, they exit agriculture and primarily transition out of the labor force. At higher levels of development, women re-enter the labor force, primarily in the service sector. Unlike men, women’s representation in manufacturing remains low across all development levels. We further document gender differences in occupational choices _within_ sectors, showing that men increasingly work in professional and industrial jobs in richer countries, while women’s employment (relative to men) expands faster in clerical jobs but slower in professional and industrial jobs.

In addition to gender employment gaps, we also analyze the evolution of gender wage gaps for a select set of countries which provide high-quality micro-data on hourly earnings. We find that wage gaps have narrowed over time across all occupation-sector pairs. However, women in the 2010s still earned, on average, only 70 cents for every dollar earned by men. Furthermore, we observe a weak correlation between gender wage – gaps and countries’ development levels in contrast to gender employment gaps that tend to decrease in richer countries. Additionally, gender employment and wage gaps do not necessarily correlate across occupation-sector pairs.

We then propose a model of occupational and sectoral choice that can speak to these empirical findings and that builds on Hsieh et al. (2019). The documented changes in gendered employment and wage patterns – both across countries and within countries over time – can be partly driven by previously studied economic channels, such as income effects in consumption, sector- or factor-specific technological change, or shifts in the supply of, or returns to human capital.<sup>2</sup> Specifically, technological change and income effects can increase labor demand in the “female-gendered” service sector, disproportionately benefiting women (Ngai and Petrongolo, 2017; Fan et al., 2023). Additionally, the narrowing of gender education gaps has expanded the supply of women’
In Table 2, we present the correlation between district-level spending on social and religious obligations and perceived household decile. The estimates in Column (2) show that as mean spending on religious and social obligation in the district rises, household perceived decile also rises, conditional on the household’s objective decile. Since the estimate is conditional on the household’s own spending on religious and social obligations, it reflects the relationship between perceived decile and a greater supply of religious and social goods from other households in the district. Overall, the evidence is consistent with the interpretation that households perceive themselves as richer in districts with a greater supply of religious and social goods. As a robustness check, shown in Table S6, we estimate Equation (2) dropping all districts that have a sample size of less than 10 in the CPHS and find virtually identical results.

||(1)||(2)|
|---|---|---|---|
||Perceived<br>decile|HH|Perceived<br>HH<br>decile|
|District: Mean HH spendingon religious/social obligations|||0.000819**|
||||(0.000396)|
|Objective HH decile|0.239***||0.238***|
||(0.0119)||(0.0118)|
|Proportion of HH spendingon religious/social obligations|2.455*||-0.118|
||(1.417)||(0.862)|
|Observations|97,439||97,439|

Notes: All specifications include controls for state, religion, caste, and household size. The unit of analysis is the household, and all observations were weighted by the inverse of the district sample size in the CPHS. Robust standard errors clustered at the district level in parentheses. *** p<0.01
Yagan (2019) for the United States (US), while that of Bolsa Familia is lower than that of welfare programs in the US found by Autor et al. (2013, 2014).<sup>3</sup> Our estimates suggest that training programs do not help to mitigate the impacts of foreign shocks on workers.

The types of adjustment that we estimate require a certain degree of labor market flexibility. Using data from Ecuador (a less flexible labor market), we find qualitatively similar effects: persistent reductions in worker employment occurring only for the lower-skilled as a result of GFC-related foreign shocks to Ecuadorian firms. However, negative effects on worker average months worked are smaller in magnitude than those in Brazil and effects on wages

> 3Autor et al. (2013, 2014) show that welfare programs compensate for 10 percent of earnings losses in the US in response to the China competition shock, with increases in the uptake of social security disability insurance being substantially larger than the uptake of unemployment insurance and trade adjustment assistance. Trade adjustment assistance differs from unemployment insurance and cash transfers in that its main benefit is the coverage of training costs for workers whose jobs are displaced by import competition or offshoring. Hyman (2018) shows a substantial positive impact of trade adjustment assistance on worker earnings in the US.

4
of vacancies associated with a job posting. Job descriptions are a key source of data for skill demand, and the paper mostly focuses on analyzing them.

Job postings data, however, may not be representative of labor demand across entire economies. Recent evidence from the roll-out of LinkedIn data for policy use (e.g., at the World Bank) suggests that online job platform data are skewed towards high-skilled jobs and that the degree of labor market representativeness highly varies across countries (Zhu et al., 2018). Correcting for such bias is not straightforward. Kureková et al. (2015) highlight that due to the absence of information on the precise size and structure of the population of vacancies in the job market of many countries, traditional correction methods (e.g., weighting) are often not feasible to achieve representativeness. In the case of Kosovo, we highlight three aspects with regard to representativeness. First, close to 40 percent of the employed population is estimated to work informally (i.e., without a labor contract).<sup>14</sup> Assessing the extent to which this leads to a bias in representativeness is difficult, as traditional data sets at the firm level (such as firm registries) do not reflect jobs created in the informal sector, therefore compromising comparisons with job portals data. Second, and similar to other middle-income countries, even within the formal sector not all vacancies appear on job portals, in part because the majority of jobs tend to be filled through informal channels (Cojocaru, 2017; World Bank, 2019a). Employers report that informal channels are their main recruitment channel, with 64 percent using such channels to recruit for high-skilled positions, and 59 percent for medium- to low-skilled positions (World Bank, 2019a). Finally, because online job advertisement is carried out by both private and public providers, even among online vacancies the true number and characteristics of posted jobs are not known. Kureková et al. (2015) stress
non-food inflation, as well as the item level budget shares ‘for the same items’ in the COLI and CPI basket can prove to be very different. This represents one the key differences between the national CPI for Rwanda and the COLI used in poverty estimation.

Lastly, it is not clear as to whether the calculation/aggregation methods used in COLI and CPI are similar.<sup>14</sup>

# **VI. Revisiting the debate on the poverty trend**

Results from Rwanda’s fourth Integrated Household Living Conditions Survey (the 2013/14 EICV4) were published in September 2015. It showed a continued reduction in poverty and extreme poverty between 2011 and 2014. Soon after the launch, a debate on the comparability of poverty estimates from EICV3 and EICV4 to the previous series ensued. For instance, Reyntjens (2015) questioned the comparability of poverty rates between EICV3 and EICV4 in NISR (2015) as mentioned below.

> .<sup>13</sup> The corresponding shares are 47 and 55 percent for national and rural CPIs if the category of ‘restaurants and hotels’ is included.

> 14 NISR states that the official CPI uses a Modified Laspeyres formula (NISR, CPI 2016).

19
gross debt liabilities was much smaller for the other countries. An important exception is Slovenia, which shows gross debt levels that are more similar to other higher-income members of the euro area. For the new member states, foreign reserve assets are quite substantial (with the exceptions of those who had joined the euro area during this period), providing a buffer against foreign-currency shortages.

Turning to the group of other countries, gross debt liabilities actually fell for these countries between 2002 and 2007. However, there was some deterioration between 2007 and 2009 for most of these countries, while Ukraine saw a very large jump in gross debt liabilities (relative to GDP). In terms of foreign reserve assets, these countries look very similar to the new member states in maintaining foreign reserve assets at around 20 percent of GDP.

Table 3.3a shows the comparative cross-regional data. All emerging regions show gross external debt liabilities that are far below the levels of the Euro Periphery, reflecting a much lower degree of cross-border financial integration. However, across the emerging regions, it is striking that Latin America and Asia witnessed sizeable declines in gross debt liabilities in the 2002-09 period, while the new member states underwent a doubling in gross debt liabilities.

**<u>Table 3.3a. Gross Debt and Foreign Reserves, Percent of GDP</u>**

||G<br>|ross Debt<br>||Foreign E<br>|xchange Rese<br>|rves<br>|
|---|---|---|---|---|---|---|
|Country|2002|2007|2009|2002|2007|2009|
|Euro Periphery|184|315|370|6|0|1|
|Emerging Latin America|61|34|32|10|13|15|
|Emerging Asia|46|37|36|29|36|43|
|New
, and more likely to be female. Also, these plots belong to households that are on average smaller. These plots are therefore not randomly selected, which is what one would expect, since taking the actual GPS measurement of a plot requires a household member and enumerator to move for the site of the main interview to the location of the plot to take the GPS measurement. More distant, less accessible plots are therefore more likely to be excluded. These statistically significant differences in plot characteristics between the part of the sample we use and the part we had to discard suggest the presence of possible sample selection issues arising from these restrictions and impacting on the analysis of farm productivity. We will return to this point in the result section, to show how these issues to do not in fact appear to be affecting our results. The nonrandom selection of GPS-measured plots, however, does represent a challenge that future surveys employing these methodologies will have to find ways to confront.

Table 2 summarizes land measurement statistics at the household level. We report statistics for deciles of landholding to investigate how measurement bias plays out at different points of the distribution. This information will be important later, when we analyze the impact of land measurement bias on the IR. The last column of table 2 reports the discrepancy in percentage terms. According to this measure, the magnitude of the discrepancy appears to increase monotonically as one moves from the bottom (smallest landholders, -97 percent) to the top deciles (largest landholders, 19 percent). Up to 4.57 acres of farm size (the seventh decile), the bias is on average negative, whereas it becomes positive in the top three deciles. There is therefore a pattern in the direction of the bias, with 6
<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>.1 .1 .1<br>share of communes affected share of communes affected share of communes affected<br>.05 .05 .05<br>0 0 0<br><!-- End of picture text --> Notes: Bars show the average share of communes in each climate zone being affected by the event based on self‐reported data. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

> 15 These emergencies are listed by year and month, including crop diseases, storms, floods, droughts, fires and epidemics.

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|||**Model 2**||**Model 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|**N**|1222|1149|1221|1107|
|**Pseudo R-squared**|0.222|0.168|0.262|0.209|

Source: Authors’ calculations based on ELMPS 2012 and ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.3.2. Are informal firms like formal firms?**

Using the preceding models, we now turn to considering whether the predicted probabilities of being formal overlap between formal and informal firms. We take the extent of this overlap to be a metric of the potential to formalize. However, we recognize that this is entirely driven by the predictive power of the models, which is in turn driven by data availability. The pseudo-R-squareds of the models are another metric of this; consistent with our finding that location and industry were poor predictors of formality, Model 1, which includes these, firm size, and firm age, has pseudo-R-squareds that range from 0.0820.119. Model 2a for EcC is 0.133, and the ELMPS model 2b 0.221 (2012) and 0.168 (2018). Owner characteristics in Model 3 raise this to 0.262 (2012) and 0.209 (2018).

Consistent with the low R-squareds from Model 1, Figure 10 shows high overlap between predicted probabilities, ranging from a 67% overlap in the EcC to an 76% overlap in ELMPS 2012. Since these are the easily observable characteristics, this bodes poorly for the potential of policymakers to target firms that are
6 of the 10 largest South-North corridors in 2000 were to the United States. The two largest corridors to the United States were from Mexico and the Philippines, the largest and 12th largest developing to developed country migration corridors in the world. They accounted for 10.8 million migrants, equivalent to 31 percent of the migrant stock in the United States, or nearly 7 percent of the world migrant stock.

Western Europe has been instrumental in many of the largest migrations in history, as both a major sending and receiving region. Between 1960 and 2000, many Western European countries transformed from net migration senders to net migration receivers. Today, Western Europe remains a key destination region for migrants from every other part of the world except the high-income Middle East and North Africa region. Increasingly over the period, Western Europeans began migrating to other countries in the region. In 2000, two-fifths of Western European migrants lived elsewhere in Western Europe, driven largely by the expansion and economic and political integration of the European Union. This is a significant increase from 1960, when far greater numbers of Europeans chose to migrate to the United States and to Latin America and the Caribbean. Despite these increases, however, intra-Western European migrants are increasingly becoming a minority proportion of the migrant stock, especially after 1970 as migration from developing countries increased. Migrants from Turkey and 29 Migration corridors are discussed to highlight the most important global migrant stocks; at no point does the discussion relate to migration flows. The focus is on stock data, and the term ―migration corridor‖ simply refers to the bilateral migrant stock for a particular pair of countries.

21
were (a) an existing detailed SAM for a LIC (Uganda); (b) a macro SAM for a median LIC during the period 2005-2009 – Table A2.2 shows the corresponding macro SAM for 2013; and (c) median data for LICs on sector shares in value added. The median macro SAM is for the most part based on medians of the averages of GDP shares for individual LICs during the period 2005-2009, for some cells complemented by data in the original Uganda data set. For the estimation of a balanced SAM that draws on the original LIC SAM and replicates the macro SAM and other structural data, an entropy program was applied (Robinson _et al_ . 2001).

28
in which the settlement agreement of a conciliation

> 30 Interview with reformer involved with the Kyrgyz project.

> 31 U.S. State Department page on FY2008 assistance operations in Macedonia: <u>http://www.state.gov/</u> p/eur/rls/rpt/seedfy08/117521.htm.

> 32 See ―Secured Transactions Reform and Access to Credit‖, Frederique Dahan and John Simpson. Elgar Financial Law Series, 2009.

> 33 Doing Business Database, 2005, World Bank.

27
|
|Endline control mean|0.4|64|0.5|10|0.|541|0.5|78|0.3|97|0.|440|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.013|0.136|0.148|0.252|0.005|0.102|0.071|0.178|0.017|0.134|0.203|0.307|
|Observations|1,540|1,540|390|390|1,540|1,540|390|390|1,540|1,540|390|390|

_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. 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.