rafmacalaba/data-use-mentions-extended huggingface dataset:
is ambiguous.

Counter to our hypothesis, there is a positive correlation between the outmigration of high-skilled males and renting from relatives.

These correlations are sensitive to the specification, and in particular, the control variables included. Table 4 adds our main set of control variables to the OLS following specification 21: the vector of population controls, bank branches, road length, and measures of adverse weather events. The correlations between the out-migration of high-skilled men and the prevalence of fixed rent in money terms or sharecropping are no longer statistically significant. Expanding the list of control variables to measures of the agro-climatic zone (soils, length of growing season and water stress levels through evapotranspiration) yields similar patterns (Table A6). The latter specification has a smaller sample due the missing data in the ICRISAT dataset. Table 4 is our preferred specification as it controls for weather effects.

# **Main results: Standard IV specification**

Table 5 presents the results using our standard instrumental variable specification. The set of dependent variables is the same as in Table 4. The results indicate that migration has been a driving force in the shift from sharecropping to fixed rent: the coefficient on high-skilled male out-migrants is positive and significant for Column (1) and Column (2), both for fixed rent and fixed rent in monetary terms. The negative relationship with sharecropping is also statistically significant at the 5% level. A one percentage point increase in high-skilled (male) out-migrants increases fixed rent arrangements with 2.7 percentage points and reduces sharecropping with 1.9 percentage points in sharecropping arrangements. This corresponds to an effect size of, respectively, 6% and 5%. The sign on relatives has changed direction and is now the expected negative sign, but we note no statistically significant relationship with either contract length or written contracts in Columns (6) and (7).

The results with a more extensive set of controls in Appendix Table A8 are very similar, while the results without the control variables (Appendix table 31
population. Global settlement patterns show that with the exception of the Western countries, this phenomeon has been slow to emerge in most other countries. In most developing countries, until the 1960s, at least 70 percent of the population resided in the rural areas. By the 1990s, in Brazil, Russia and the Asian Tigers, the share of the rural population had declined to about 30 percent. However, as late as 2010, at 70 percent in India, it indicated the enormous challenges to structural transformation when the industrial sectors are not competitive.

Like other indicators of structural transformation, Table 1.3 shows a persistence in the share of the rural population in Sub-Saharan Africa.

**Table 1.3: Kuznets fact 3 – structural change can be measured by the decline in the share of the rural population in total population  (%)**

||1980|1990|2010|
|---|---|---|---|
|Burundi|98|92|89|
|Uganda|98|88|87|
|Niger|96|84|83|
|Ethiopia|94|85|82|
|Rwanda|98|86|81|
|Malawi|96|85|80|
|Burkina Faso|95|83|80|
|Eritrea|90|82|78|
|Kenya|93|83|78|
|_Source:WDI_||||

Another characteristic of structural transformation, called the fourth Kuznets fact, is that manufacturing expands because of productivity growth powered by an increasing capital-labor ratio in the sector. Productivity growth prompts labor to shift out of agriculture and into manufacturing. Until the mid-1990s, with the highest capital-labor ratio, the U.S. was leading by a huge margin countries like Germany, Korea and Malaysia. In the last decade, China‘s capitallabor ratio has begun picking up as it strives to catch up with the East Asian countries (Figure 1.7).

11
Importantly, however, switching the latter for the former would not significantly address what we argue is the core problem: students learn too little from primary schooling.

# **8. To what extent does teacher quality matter?**

So far we have focused on using the data to establish descriptive facts. The linked studentteacher data, however, also allow us to go one step further and to examine the extent to which teacher quality matters for student achievement. In other words, we ask whether, and to what extent, students’ academic performance would increase if they were taught by better teachers, in particular those with sufficient content and pedagogical knowledge.

To assess the effect of teacher quality on student achievement we face several challenges. First, the four dimensions of teacher quality (all linked to individual student measures of test 17
# **5.5 Robustness**

In this section we conduct a series of robustness tests of the baseline results. We first explore a continuous treatment measure that takes into account the size of the investment incentive available to each firm. In Table 7 we replace the dummy treatment variable with a continuous version that measures the number of pounds a firm’s average 2005/06 investment is below the AIA threshold in that year. By so doing we attempt to capture differences in how strongly a firm was treated by the AIA - firms with a larger unused investment allowance are likely to have a stronger incentive to invest more.<sup>40</sup> As the estimation method of Callaway and Sant’Anna (2021) is applicable only for binary treatment variables, in the investment regression we take advantage of the recent development by Gardner (2022) that allows for continuous treatment in two-way fixed effects settings with a staggered treatment design.<sup>41</sup> For the cloud regression we continue to use a long-difference specification estimated using OLS. These results provide further evidence that firms with the greatest scope for increasing investment responded to the AIA by increasing their investment and reducing the likelihood of adopting cloud services. Each additional £1,000 of AIA allowance reduced cloud diffusion by 0.3 percentage points.<sup>42</sup> Given the focus of the paper, we concentrate the remaining tests of robustness on the adoption of cloud. Investment data in the UK is, as in most other countries, highly skewed. A small number of firms make very large investments, whereas most firms invest a more modest amount each year. The concern is therefore whether our results are driven by the presence of these largest firms. In column 1 of Table 8 we exclude firms with the largest 5 percent of investment, using investment defined consistent with the treatment status of the firm i.e. the average across 2005/06, while in column 2 we exclude the largest 10
Policy Research Working Paper 5910

# **Abstract**

This paper presents the results of an attempt to use the combined results of the dedicated survey performed by CASE in 2007 and Polish LFS data in order to: (a) analyze the development of the shadow employment in Poland in years 2003–2008 and, (b) analyze the transition probabilities in and out of shadow employment. The estimated share of shadow workers in total employment in Poland in years 2003–2008 was increasing until 2006 and then started to decrease in the years 2007 and 2008. Other results are in line with one of the main conclusions of the CASE study from 2007 suggesting that shadow employment is more a way of coping with lack of other employment opportunities than an equivalent or even superior alternative to any legal employment contracts. On the other hand those who enter shadow employment are more active part of the group having problems with finding full time/open term employment. They are much more inclined to cope with their situation by entering some form of self-employment than to stay passive and depend on social assistance.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. 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://econ.worldbank.org. The author may be contacted at Mateusz.Walewski@case-research.eu.

_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
-> _(Average, in thousands of 2009 dollars) (Median, in thousands of 2009 dollars)_ <!-- Start of picture text --> 40 14<br>35 12<br>30<br>10<br>25<br>8<br>20<br>6<br>15<br>10 4<br>5 2<br>0 0<br>Pioneering Other firms Pioneering Other firm Pioneering Other firms Pioneering Other firm<br>firms firms firms firms<br>FCS Non-FCS FCS Non-FCS<br><!-- End of picture text --> _Source:_ Calculations using World Bank Enterprise Survey data. _Note:_ Labor productivity is the ratio of value added by the number of workers.

These conditions are reflected in lower productivity premia for pioneers in FCS: higher returns are offset by important costs and uncertainty. Estimates from the World Bank Enterprise Survey suggest that the productivity of pioneering firms in FCS is lower than that of their peers in nonFCS. As illustrated in Figure 4, the median value added per worker in pioneering firms in FCS ($6,986) is lower than that of pioneering firms in non-FCS ($11,651).

15
The structure of the paper is as follows. Section 2 describes the survey. Section 3 presents summary statistics. Section 4 describes the main characteristics of Venezuelan migrants, using natives in the same local area as benchmark. Section 5 analyzes the occupations of Venezuelans in Ecuador and in the home country. Section 6 collects our policy analysis and Section 7 concludes.

# **2 Data: The EPEC Survey**

The goal of the EPEC survey was to collect household-level information on the population of recent Venezuelan migrants to Ecuador along with the native population in the receiving communities. The survey focuses on Venezuela-born individuals that arrived in Ecuador after January 2016 and over-samples this population.<sup>2</sup> In June and July 2019, almost 1,900 households were interviewed (in person) and provided information on 6,425 individuals. The survey is representative of the population of recent Venezuelan migrants in Ecuador and of the native population residing in the areas where Venezuelan migrants are found. Besides the typical socio-demographic and labor market information of labor force surveys, the questionnaire also includes questions regarding pre-migration employment, the gateways of entry into the country, health, children’s schooling, access to internet and remittances, among other topics.<sup>3</sup> In the final sample (of 6,425 individuals), 10% of respondents resided in sectors with low density of Venezuelan migrants (below 5% of the sector’s population), 43% in medium-density sectors (with a density ranging between 5% and 15%), and 47% from high-density sectors (with a density of Venezuelan migrants above 15% of the sector’s population). The final sample contained 6,425 individuals. Among these, 1,715 were born in Venezuela and considered recent migrants.

> 2EPEC stands for _Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador_ , which can be translated as Survey of Migrants and Receiving Communities in Ecuador.

> 3To be specific, Ecuador is
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2 different incomes pay different shares of their income in health care payments and cases where households at the same income pay different amounts for health care. The index is also sensitive to the overall average share of income spent on health care. Section V outlines an alternative approach developed in the income redistribution literature and used in the health economics literature in the late 1 990s. This approach explicitly approaches the fairness of health financing in terms of its impact on the distribution of income, and allows one to show the separate redistributive effects of the average proportion of income spent on health care, the progressivity or regressiveness of the payment structure, the horizontal inequities in the financing system, and the extent of reranking generated by it (well-off households becoming poor, and vice versa). The paper ends in section VI with an empirical illustration of this method using data on out-of-pocket payments from Vietnam for two years-1993 and 1998. Though this is intended simply as an illustration of the two approaches, the analysis is of some interest from a policy perspective. A recent assessment of the Vietnamese health sector [2] highlighted Vietnam's heavy reliance on out-of-pocket payments to finance health care-81% of health spending in Vietnam was financed privately in 1997, and this was almost entirely paid out-of-pocket. The assessment also highlighted, however, two important changes over the course of the 1990s: rising user fees (even at supposedly free commune health centers), offset-at least in part-by reductions in the cost of medicines.

# **II. Health Care Payments and Ability to Pay**

WHO calls its index an index of fairness of financial contribution (FFC). The index aims to capture empirically the financial protection issue highlighted in chapter _5_ of the WHR. This is evidently only one aspect of fa&rness in health financing, since the way a country finances its health care-and in particular the balance it strikes between pre-payments and out-of-pocket payments-has implications not just for how people pay for health care (the focus of the FFC index) but also for whc
component, and imposing a direct price tag on carbon-equivalent GHG emissions (i.e., a carbon tax).<sup>5</sup> This will allow for gradual substitution across energy types and provide an incentive for producers to transition to low-carbon technologies.

# **2.2 South Asia’s climate mitigation commitments**

All South Asian economies have committed to reducing GHGs as specified in their Nationally Determined Contributions (NDCs) towards climate mitigation announced in the 26<sup>th</sup> United Nations Climate Change Conference of the Parties (COP26). Table 1 summarizes the NDCs of the economies in the region that have specified mitigation pledges.<sup>6</sup> Appendix 1 provides details on the pledges under COP26, and policies undertaken so far. Each of these NDCs comes with a set of proposed actions, detailed in the official UNFCCC NDC Registry.<sup>7</sup>

**Table 1. South Asia: NDCs and NDC-consistent carbon taxes by economy, 2030**

|**Economy**|**NDC description**|**2030 NDC-consistent carbon**<br>**tax(US$/ton CO2e)**|**NDC achievable**<br>**in BAU?**|**Income**<br>**group**|
|---|---|---|---|---|
|**Afghanistan**|13.6 percent reduction in emissions against the baseline<br>(conditional on external financing).|50.0|No|IDA|
|**Bangladesh**|Limit of 381.85 mt CO2e including LULUCF.|0.0|Yes|IDA|
|**Bhutan**|Remain carbon neutral.|Unquantifiable NDC|n.a.|IDA|
|**India**|45 percent reduction
Table 4: Descriptives on Access to Demand and Location Choice

||Share (%)|
|---|---|
|_Panel A: Access to demand and customers_||
|_(a) Marketing strategies_||
|Owner spends money on marketing|6.6|
|Owner talks directly to customers|59.6|
|Owner does nothing|21.5|
|_(b) Sales characteristics_||
|Orders by phone|17.2|
|Orders from walk-in consumers|79.5|
|Sales to fnal customers|92.8|
|Shipping to fnal customers|16|
|_Panel B: Reasons for location choice_||
|Closeness to customers / market|52.5|
|Afordable rent / land price|40|
|Closeness to a good transportation network|32.4|
|Low exposure to air pollution|9.6|
|Low exposure to water pollution|2.2|
|Low exposure to solid waste pollution|1.5|
|_Panel C: Production location_||
|Firm produces only outside|39.7|
|Firm produces mostly outside|24.4|
|Firm produces sometimes outside|20.1|
|Firm produces only inside|15.7|

Notes: Panel A describes the average firm’s marketing strategies and business practices, using the full sample of firms in our survey. Firms that had relocated (or considered to relocate) their premises in the previous year (138 firms) were asked the reasons for their location choice. Panel B gives the share selected as top 3 out of 18 potential reasons for location choice asked in the survey. The three top rows correspond to the most common reasons. The three bottom rows correspond to the environmental-related reasons. Panel C gives details on production location, again using the full sample of firms in our survey.

44
, which is largely informed by the “Voluntary Guidelines for the Responsible Governance of Tenure of Land, Forests and Fisheries in the Context of National Food Security”.<sup>9</sup> The United Nations World Committee on World Food Security endorsed these guidelines in 2012, and thus they can be viewed as an internationally accepted standard (FAO, 2012). The sub-indicators for 1.4.2 are computed as follows: Sub-indicator 1.4.2 (b) is made up of two components, the perception of tenure security and the right to bequeath, where both are necessary conditions in order for an individual to be considered as having secure rights under the sub-indicator. In the ALTA study, perception of tenure security is asked on a scale from 1 to 5, ranging from “not at all likely [to involuntarily lose land]” to ‘extremely likely [to involuntarily lose land]”, in line with the joint module by FAO, World Bank, and UN-Habitat (2019). The appropriate threshold for what constitutes a secure perception on this scale is not identified in the metadata, leaving room for various interpretations. As this is a subjective dimension, we avoid any assumption about what the appropriate threshold for the secure perception of land would

> 7 The agricultural population is defined in the SDG 5.a.1 metadata as: “adult individuals living in agricultural households, i.e. households that operated land for agricultural purposes and / or raised livestock over the past 12 months, regardless of the final purpose of the production.”

> 8 SDG indicator 5.a.1 metadata is available here: https://unstats.un.org/sdgs/metadata?Text=&Goal=&Target=5.a.

> 9 SDG indicator 1.4.2 metadata is available here: https://unstats.un.org/sdgs/metadata/files/Metadata-01-04-02.pdf

9
_t+2_ while the TTB is in effect. While expected, this result is important as it confirms that the HS-06 import data is sufficiently disaggregated for this context so as to capture the trade-impacts of the antidumping policy which itself is sometimes applied at an even more disaggregated level.

Our main results begin by comparing what happens to annual exports once the TTB is _removed_ in year _T_ . In specification (1), average exports to both high-income markets and emerging markets

> 12 First, we transform the data by adding 1 to real exports so we do not lose observations when taking logs. Application of antidumping could result in product-level exports going to zero, thus dropping observations for which trade flows disappeared would introduce sample bias. Second, we take logs because the distribution of export data is bounded below by zero but includes a handful of outliers at the high end that severely distort the mean, relative to the median. In light of these two issues, Appendix Table A2 presents summary statistics which confirm the basic pattern of results when focusing on medians as opposed to means. Third, we use the US consumer price index to deflate the product-level bilateral trade flows from UN Comtrade reported in current US dollars. Table 4 (discussed below) addresses this potential limitation by reporting estimates from changes in product-level export market shares for which no price deflators are required.

13
Figure 4.0: Average HLO derived from EGRA and PIRLS, 2000-2017 <!-- Start of picture text --> 330.5<br>Egypt<br>364.6<br>416.6<br>Indonesia<br>411.8<br>442.0<br>Macedonia<br>414.6<br>449.6<br>Honduras<br>432.2<br>0 100 200 300 400 500<br>HLO - PIRLS HLO - EGRA<br><!-- End of picture text -->

# 2.2. PASEC (2014)

The most recent PASEC in 2014 uses Item Response Theory (IRT) and is a high-quality RSAT. Ten countries participated, including Benin, Burkina Faso, Burundi, Cameroon, Chad, Congo, Cote d’Ivoire, Niger, Senegal and Togo. We include these countries using available microdata. Madagascar also participated in 2015 and was scaled to the PASEC 2014 round. We include Madagascar in our database using estimates from reports. To this end, inclusion of the recent PASEC data adds countries as well as enhances the quality of data from sub-Saharan Africa. This marks a significant improvement over past datasets. To provide a link to past PASEC rounds, which use classical test theory and have substantially different test items, we create an inter-temporal comparison using Togo, which participated in all rounds of PASEC. However, given that PASEC did not conduct an intertemporal scaling calibration as is typical for IRT ISATs and RSATs, priority should be given to analyzing the most recent PASEC 2014 data. Any intertemporal comparisons should be conducted with caution.

# 2.3. SACMEQ (2013)

SACMEQ collected its fourth round of data in fifteen countries in East and Southern Africa from 2012-2014. These include: Botswana, Kenya, Lesotho, Mauritius, Malawi, Mozambique, Namibia, Seychelles, South Africa, Tanzania, Uganda, Zambia, Zanzibar and Zimbabwe. SACMEQ was designed and
to _p_ ( _θa_ ) once we account for informal firms? Figure C1 suggest that a 10 percentage point increase in formality corresponds with a 0.5 percentage point increase in _p_ ( _θ_ )- _p_ ( _θa_ ) (0.35 standard deviations). As expected, _p_ ( _θa_ ) is lower than _p_ ( _θ_ ) for sectors and counties with a high degree of formality, indicating that their importance for the overall economy is overstated in the administrative data. To recap, our proposed revised network accounts for informal firms being born into the network based on their sector-region profile. Rather than thinking of the exercise as adding new firms, we adjust the weights of each sector-region-size type.

# **5.2 Assumptions about Linking Probabilities of Informal Firms**

Another challenge for integrating informal firms into the network of formal firms arises due to the lack of granular data on the sectoral and geographic composition of how informal firms link

> 24If big formal firms employ informal casual workers not captured in the administrative data, we understate their size and hence overestimate the probability of big formal firms in the network. As a result, our revised network becomes biased towards the baseline network that only covers the formal sector. This is also illustrated in Figure 8 where we compare the spatial inequality in county-level outdegrees in the baseline network to several scenarios that account for informal firms. Comparing the two scenarios where in one case we use the simple employment shares to compute _p_ ( _θa_ ) and in the other case further adjust for differences in firm size across sectors and counties, we find that in line with the intuition on the implications of informal workers in formal firms, the former scenario is closer to the original network with only formal firms.

24