rafmacalaba/data-use-mentions-extended huggingface dataset:
billion in 2004 to RMB 29,160 billion in 2017, a compound annual growth rate of 30 percent.<sup>5</sup> Express mail service exceeded 30 billion pieces in 2016,<sup>6</sup> of which some 60 percent is related to e-commerce.<sup>7</sup> According to a 2017 McKinsey report, China’s worldwide e-commerce transaction value grew from less than 1 percent a decade ago to over 40 percent now, exceeding that of France, Germany, Japan, the United Kingdom, and the United States combined (Woetzel et al. 2017). Online retail sales have grown even faster, from RMB 125.7 billion in 2008 to RMB 5,155.6 billion in 2016 – only 1 percent of total retail sales of consumer goods was purchased online in China in 2008, compared with 16 percent in 2016. The number of packages sent through online sales increased tenfold from one billion in 2006 to 10 billion in 2014 (Goldman Sachs 2018). According to the State Post Office, Chinese express firms delivered 40 billion packages in 2017, the majority related to e-commerce.<sup>8</sup> The development of online retailing, however, remained uneven across Chinese provinces. In 2015, in Beijing, 45 percent of the total retail sales of consumer goods was purchased online, followed by nearly 40 percent in Shanghai, 35 percent in Zhejiang, and 28 percent in Guangdong. However, this share was much lower (less than 2 percent) in nine inland provinces (Figure 1), as measured by the Online Business Index (OBI) and the Online Shopping Index (OSI) developed by AliResearch (map 1 and map 2).<sup>9</sup>

> 5 Ministry of Commerce of China. E-Commerce in China (2015, 2016, and 2017).

> 6 Source: National Bureau of Statistics of the People’s Republic of China, <u>http:/
into the commercial banking system. Specifically, Agarwal et al. (2023) show that a large-scale microcredit expansion program in Rwanda—together with a credit bureau accessible to all both banks and microfinance institutions—can enable unbanked borrowers to build a credit history, facilitating their transition from microcredit to commercial bank loans. A sizable share of firsttime borrowers switched to commercial banks, which cream-skim less risky borrowers and grant them larger, cheaper, and longer-maturity loans, based on the information provided through the credit bureau.

Another potential method for promoting financial inclusion in sparsely populated areas is agent banking, which can overcome not only geographic but also socio-cultural barriers that might prevent low-income population segments from entering formal bank offices (Buri et al., 2023). For example, Finca DRC’s over 500 agents handle more than 65% of the transactions of one of the largest microfinance institutions in the DRC, agents that have been especially successful in reaching the urban poor (Cull et al., 2018). Not only microfinance institutions but also banks have looked to agent networks to extend their reach to under-served areas and market segments. Again, in Kenya, Equity Bank moved forcefully into agent banking, expanding its number of agents from under 1,000 in early 2011 to over 6,000 by the end of 2012. By that point, agents accounted for over 30% of Equity Bank’s total transactions. In addition, the mobile money agents of nonfinancial firms expanded forcefully throughout Africa. By 2014, Findex data revealed that 12 percent of African adults had mobile money accounts. In 2021, that figure reached 33 percent of adults, and respondents were as likely to use a mobile money account to save as they were to use an account at a formal financial institution (see further discussion of mobile money and digital 26
social protection for workers by ensuring access to social security for the unemployed through various funds and solidarity mechanisms.

A notable finding from this review of programs is the lack of national measures addressing barriers to accessing care and redistributing care work within households, including the promotion of the care economy, which disproportionately falls on women with lower levels of education. Existing programs, managed by the Colombian Institute of Family Welfare (ICBF), primarily focus on childcare and time release, without providing options for elderly care or complementary measures that facilitate the effective integration of caregivers into the care and labor sectors.

# **8. Conclusions**

The gender gap in labor markets presents a significant challenge, particularly impacting vulnerable women, and Colombia is no exception, with the pandemic exacerbating this issue. This study examines recent trends in Colombian labor outcomes within the context of COVID-19. Our focus is primarily on gender gaps in the labor market and the factors influencing them, particularly in sectors where women are more vulnerable, such as home care.

Utilizing data from the GEIH, decomposition methodologies, and probabilistic models, we find that the economic recovery observed in 2021 did not close the gender gaps, indicating underlying structural factors. Moreover, the pre-pandemic gender gaps in terms of participation and unemployment were intensified by the crisis. Interestingly, gender gaps in labor income and hours worked decreased during the crisis, but this was primarily driven by a significant decline in men's levels rather than improved conditions for women. Another contributing factor to women's slow recovery in the labor market is the moderate recovery of the informal sector in 2021, in which women have a significant presence. However, there are indications of a more favorable labor market recovery for women in 2022.

The analysis reveals that several factors disproportionately affect women and impact their probability of participating in the labor market. Having children in the household, low levels of schooling, and household income are among the key determinants. Women with children can experience a significant decline in the probability of labor
Policy Research Working Paper 8585

# **Abstract**

According to the official statistics published by the National Institute of Statistics of Rwanda, the country registered a decline in poverty from 46 percent in 2010/11 to 39 percent in 2013/14. This declining poverty trend was broadly debated and repeatedly questioned in national and international forums, which provided the primary motivation for this study. Using data from the third and fourth rounds of the Integrated Household Living Conditions Surveys, this paper revisits the national poverty numbers and corroborates the poverty rates published by the National Institute of Statistics of Rwanda. Underlying the paper’s conclusions is a detailed theoretical and analytical framework for making poverty comparisons over time. Furthermore, the paper shows that after adjusting for spatial and temporal price differences, the poverty rate based on the international poverty line of $1.90 per day per capita shows that there was a reduction in poverty between 2010/11 and 2013/14.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ffatima@worldbank.org and nyoshida@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced
(e.g. Harding and Javorcik 2011), or rely on newspaper-reported greenfield FDI announcements (e.g. Crescenzi, Di Cataldo and Giua 2021). In both cases, this only accounts for a subset of FDI and can deviate significantly from actual FDI dynamics. In addition, their models commonly do not account for the relative effects that IPA targeting may have on relative competitiveness vis-à-vis alternative host countries. Such omissions can bring about endogeneity concerns and potentially over-estimate the effect of investment promotion on FDI inflows. This challenge was first raised by Anderson and van Wincoop (2003), showing the importance of ‘multilateral resistance’ general equilibrium effects in correctly estimating the response of trade flows to trade costs.

In contrast, this paper makes use of a wide set of FDI source countries and makes use of actual FDI stock data (rather than newspaper reported FDI announcements). It also explicitly considers how an IPA’s targeting of a priority sector in one country may affect the relative effectiveness of IPAs in _other countries_ targeting the same sector, by including sectoral IPA targeting within a structural gravity model framework.

The paper also contributes to the more qualitative literature around identifying high-performing IPAs and considering which characteristics contribute to their performance. Other such studies have historically relied on IPAs’ self-reported statistics (e.g. Lim, 2018) which can be biased, or have defined IPA performance based on a set of “good practices” exhibited by their favored IPAs, which then runs the risk of circular reasoning (e.g. Whyte, Ortega and Griffin, 2011). Other, more empirical estimates tended to correlate FDI inflows with a narrow indicator of IPAs (e.g. Harding and Javorcik, 2012 show a relationship between an IPA’s information handling and aggregate FDI inflows). Yet, it can be difficult and misleading to reduce the effectiveness of an IPA to a single outcome indicator.

This paper avoids some of these limitations by taking a data-driven approach to identifying ‘effective’ IPAs. Namely,
an annual allocation of 46.1 million for survey implementation and 16.9 million for technical assistance.

The paper is organized as follows. Section II discusses data gaps that need to be filled for effective monitoring of poverty and shared prosperity. Section III details the nature of the surveys to be implemented for filling the data gaps as well as the assumptions that underlie our costing exercise. Section IV provides the estimates of costs based on several scenarios and Section V concludes.

# **II. Defining data gaps: Why focus on surveys in IDA countries, every three years?**

The starting point for monitoring progress in poverty reduction and enhancing shared prosperity is to have household consumption survey data that are not only available at reasonably frequent intervals, but that are also comparable over time. Alongside household consumption survey data, complementary data are needed for tracking poverty, especially when comparing poverty across countries in a given year (World Bank, 2015a). This includes data from population censuses, cost of living adjustments across countries or Purchasing Power Parities (PPPs), inflation, and economic growth. However, the main bottleneck for monitoring poverty and shared prosperity is typically lack of household surveys (Serajuddin et al., 2015).

To assess a country’s ability to monitor poverty and shared prosperity, the frequency of poverty data matters; however, setting a standard on the ideal interval is not straightforward. Though valuable for decision making, surveys typically impose significant demands on financial and human resources. The General Data Dissemination System (GDDS), developed as an attempt to improve the quality of statistics, recommends that poverty statistics be updated in at least 3 to 5 year intervals.<sup>9</sup> This can serve as a useful benchmark of household survey periodicity. For the 2002-2011 time period, Serajuddin et al. (2015) note that 63 of 155 countries had three or more data points during those ten years, and another 15 countries met the GDDS minimum requirement for data needs with two
diversification away from land- and capitalintensive bulk commodities with limited value addition and employment generation by allowing use of agricultural land as collateral for credit. Together with land sales market opening, laws were put in place to mandate public reporting of land prices and establish a public land market monitoring system; restructure institutions to increase transparency via digital registry-cadaster interoperability; improve public land management by transferring ownership of such land to local governments subject to central land use controls; require transfer of lease rights to public land via electronic auction;<sup>5</sup> and establish a partial credit guarantee facility to ease small producers’ access to financial markets.<sup>6</sup>

# **2.2 Data sources and variable construction**

To explore land price determinants since 2021 in land rental and, after the moratorium had been lifted for individuals or legal entities, land sales markets, we combine a range of administrative data and use remotely sensed imagery to generate land use maps and field boundaries using machine learning approaches.

> 4 A limited class of land was exempt from the moratorium. Also, legislation requiring a minimum lease term of 7 years was passed in 2015. While this may have improved predictability for the large farm sector, it likely pushed farmers interested in a more flexible lease term into informality. 5 The mandatory shift from centralized, in-person to fully electronic auctions run by local authorities for any transfer of use rights to public land instantaneously increased lease prices by 175% (Deininger _et al._ 2023).

> 6 The numbers of relevant laws are 552 (turnover law opening agricultural land sales market), 340 (anti-raider law, including price reporting), 711 (local planning), 554 (spatial data interoperability), 985 (state support), 1423 (institutional reform of SGC and decentralization), 1444 (mandatory e-auctions for public land ), and 3205 (partial credit guarantee facility).

7
The introduction of innovation surveys, similar to those conducted in developed countries<sup>31</sup> , can also be useful in developing countries, although they would need to be adapted to the specific challenges and needs of the countries concerned.

Technology audits examining the quality and potential value of technological assets and competencies of a given country can usefully be implemented. They have to be focused on specific sectors and compare systematically available technological items to international competition, appraise the strengths and weaknesses of the supporting enterprise structure, evaluate the research and educational infrastructure, etc. Such work should be conducted by international groups with an in depth knowledge of the concerned sectors. There is so far little experience in these types of audits, and it deserves being shared and expanded<sup>32</sup> .

Another type of investigation useful for assessing innovation climates, defining policy actions and stimulating change is the auditing of obstacles to innovation as discussed earlier (section 5). This should be designed in ad hoc manner, performed by respected bodies (commissions linked to administrative and judiciary authorities) and conceived as a process by which there is a clear fo **l** ow-on, and not as a one shot event.

# **_Organizations, programs and strategies_**

With regard to the research and technology infrastructure, it is important to assess:

- technological services such as metrology, testing, quality control institutes: the level of activities of technology infrastructure has to be evaluated with respect to demand and the required sophistication

- research structures: data is needed to assess their budgetary structures and judge their openness.  The same applies to university and academic institutions to view how they are positioned in the international science (bibliometrics), or how they relate to the local communities (services, RD contracts, etc.)

- extension services operating in agriculture or manufacturing

- techno-parks which have to be evaluated from the viewpoint of enterprise numbers, employment, growth rate, etc.

For all the above, what matters is
from 0 (least patient) to 5 (most patient) as one measure of self-reported patience, and how much they agree with statements such as “When I get money, I spend it quickly” as a proxy of time inconsistency. Specific questions are displayed in Table D.3.

By reporting all measures collected in the endline survey, three-quarters of our time preference measures are hypothetical rather than based on incentivized games. For robustness purposes, in Table D.3 we also report a summary index of the incentivized games only.

# **D.4 Anti-criminal and anti-violent values**

Table D.4 displays control means and long-term treatment effects for survey questions in the anticriminal and anti-violent values index.

# **D.5 Other outcomes**

Table D.5 displays the control group means and long-term impacts of all the survey questions underlying the variables in Table 8, including prosocial behavior, post-traumatic stress, personality traits, quality of appearance, substance abuse, quality of social networks, well being, and executive function. We describe each of these measures in Section 6.6 of the main paper.

> 13Enumerators continued down the list, and (oddly) a nontrivial fraction switched multiple times. We use the first switch only. Furthermore, about 17% of respondents preferred less money in the future as a commitment device, especially if they were expecting a large purchase coming soon.

> 14Dohmen et al. (2011) and Jamison and Karlan (2011)show that basic self-reported attitudes on risk and time preferences can be externally valid.

xxvii
.204***|0.262***|
|(constant 2010 IDR)|(0.929)|(0.962)|(0.010)|(0.026)|(0.030)|
|Growth in GRP per capita|-0.020|0.007|0.002**|-0.000|0.004|
||(0.088)|(0.056)|(0.001)|(0.004)|(0.006)|
|Number of observations|238|238|238|238|238|
|R-squared|0.854|0.357|0.794|0.703|0.727|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

19
Krueger and Summers (1988).<sup>27</sup> The results show that the magnitude of the coefficient for the foreign dummy increased with the inclusion of industry variables. A potential explanation is that the jobs generated by foreign firms during the period reflected by the VHLSS 2004 are disproportionately represented by low-wage industries. Thus, controlling for industry affiliation, the estimated effect of foreign ownership may well increase as a negative omitted variable bias is removed.

Column 3 in Table 3 shows the results introducing the impact of location on wages. With the inclusion of urban and regional dummies, the effect of foreign ownership is reduced. The latter result is consistent with the view that higher wages by foreign firms are partly attributed to the relatively high-wage areas where foreign enterprises are typically located. Therefore, the coefficient for the foreign dummy might be reduced by attributing higher wages to locations rather than to foreign ownership. Using regression (3) as a basic model, foreign firms are found to pay about 46 percent<sup>28</sup> higher wages relative to similar workers in the informal wage sector. The wage premiums are highest for foreign firms followed by state and private organizations.

Columns 4-6 in Table 3 report the regression results for a subset of workers employed in the formal sector so that the sample is roughly comparable to the firm-level analysis (e.g., Ramstetter and Ngoc, 2007a). The domestic private sector is the omitted ownership category. Restricting the sample to workers in the formal sector, the foreign wage premium relative to the domestic “private” sector is estimated to be 30 percent.

Columns 1-5 in Table 4 report the regression results by subsets of workers with different educational attainments, namely, college/university graduates, workers who have diplomas for upper-secondary, lower-secondary and primary education, and those without any school degree.

> 27 Krueger and Summers (1988) use cross-sectional and longitudinal data and demonstrate that industry wage differentials are substantial after controlling for detailed individual characteristics. 28 0.38
Yes<br>1.|Yes|Yes|

To ascertain the robustness of our findings, we use two different approaches.

First, we utilize data for 15 countries that have an additional survey conducted between the initial and final years of the survey period. For these nations, we estimate the Poverty Space (and the corresponding Poverty Centrality) for all three survey years and compute the change in headcount ratio between the first and second, as well as the second and third survey years. This enables us to construct a new unbalanced panel sample which is then used to re-estimate our model. To maintain the restrictiveness of the model we also include year dummies in this analysis. The results, presented in Table 2, affirm that Poverty Centrality remains a significant and negative predictor of changes in headcount ratios over time even within this more homogeneous sample.

22
6 to separate how many of these households received internal versus international remittances, these 88 households are counted in both columns of remittance-receivers. A smaller number of households (43) received remittances from other countries (such as Central American or “other”), and are not counted as remittance-receiving households in this study.

The data in Table 1 reveal several interesting contrasts between the three groups of households, that is, those receiving no remittances, those receiving internal remittances (from Guatemala) and those receiving international remittances (from USA). On average, when compared to non-remittance households, households receiving remittances (internal or international) have more education, fewer children under age 5 and are more likely to live in urban areas. In a broad sense these findings tend to accord with human capital theory, which suggests that educated people are more likely to migrate because educated people enjoy greater employment and income opportunities in destination areas.

The Guatemala survey also collected detailed information on a wide variety of household expenditures. As shown in Table 2, data were collected on six major categories of expenditure, and on several subdivisions within each category. While the time base over which these expenditure outlays were measured varied (from last 7 days for most food items, to last year for most durable goods), all expenditures were aggregated to obtain yearly values. For household durables (stove, refrigerator, automobile, etc), annual use values were calculated to obtain an estimate of the cost of one year’s use of that good. Annual use values were also calculated to obtain an estimate of the one year use value of housing (rented or owned).