rafmacalaba/data-use-mentions-extended huggingface dataset:
# **9.  Summary and Conclusions**

In this paper, we have developed, estimated and applied a spatially-explicit model that links road upgrading to forest clearing and biodiversity losses in moist tropical forest areas. We have chosen one World Bank IDA country from each major tropical forest region (Latin America (Bolivia), Africa (Cameroon), and Asia (Myanmar)) for illustrative purposes. However, our approach is applicable to every moist tropical forest country because we employ standard global databases:<sup>14</sup> High resolution forest-clearing information from Hansen et al. (2013); information on biodiversity and protected areas from IUCN and WWF International; digital road maps from Delorme, Inc.; digital elevation maps from DIVA-GIS; and information on agricultural opportunity values from Deveny et al. (2009). Using the 30 m Hansen data, we estimate forest clearing rates for all 250 m cells in the moist tropical forests of Bolivia, Cameroon and Myanmar. For each 250 m cell, we use GIS to measure the distance to the closest point on the nearest road; the distance-minimizing route to the nearest urban market, with explicit measurement of distances traveled on secondary and primary roads; mean elevation; terrain slope (the standard deviation of within-cell elevation measures); the mean agricultural opportunity value of the land; and legal protection status.

We use an appropriately-specified econometric model to estimate the effects of these variables on the cumulative percent of forest cleared in 2014. We take explicit account of spatial autocorrelation, as well as simultaneity in the relationship between forest clearing and road location. Our parameter estimates for the three countries are extremely robust, with the expected signs and very high statistical significance in all cases. The estimated elasticities exhibit little

> 14 The Delorme digital road maps are available for a fee; all other data sources are in the public domain.

57
<mark>is the country in the region with the lowest spending in social assistance relative to GDP (1.6 percent), its resources are very well targeted to the poorest with almost half of the benefits being means-tested but there is still a significant share of social assistance benefits that go the richest quintile (around a quarter). Similar to social assistance, unemployment benefits are among the least generous in the region, although with a very</mark> long average duration (18 months).

<mark>Poland has also made significant reforms in the education sector. As part of its 1999 structural reform of the education system, the Government of Poland introduced a new type of school—the “gymnasium”—to provide lower secondary education. In the course of their evaluation of Poland’s reform, Jakubowski, et al. (2010) found that delaying specialization and exposing students to general education for one additional year was shown to correspond to an average increase of one-quarter of a standard deviation in the math and reading PISA scores. This, in fact, may explain the improvement over time in Poland’s PISA scores. In addition, Poland has introduced per capita financing in tertiary education to better align financing incentives</mark> with outcomes and results.

<mark>Innovation has also played an important role in Poland’s story so far. A transition towards a low-emissions economy is highly likely to benefit Poland in the near future. The demand for products and processes with lower greenhouse gas emissions will accelerate as more countries adopt abatement targets. Innovation will be critical in this growing market for clean technology—the expertise and equipment related to new developments in areas such as renewable energy (in particular, wind power, solar power, biomass, hydropower, and biofuels), electric motors and low emission transportation, energy efficient lighting and appliances, and green buildings. The energy sector, the dominant source of today’s emissions, is also central in the adoption of cleaner technology.
High|-1.7|10.2|Centralized|
|Spain|38.2|High|-1.6|22.4|Centralized|

**a** Payroll taxes are firm donations plus obligatory personal contributions. The values for France, Spain, Italy and Japan correspond to 1994, those for Malaysia to 1995, and those for Argentina and Chile to 1996.

b Severance payments based on OECD indexes.

Employment growth is measured as annual average percentage growth.

d Argentina amended its labor laws in 1996, and payroll taxes now average 41.0.

Source: Guasch (1997)
# **3.1.Measuring Vulnerability**<sup>**16**</sup>

# **_Asset Based Approach_**

There are several arguments in favour of an asset-based approach to vulnerability. Firstly, since vulnerability is a dynamic concept, we can consider that consumption-poverty or income-poverty measurements, because they are static, are of limited use in capturing complex external factors affecting the poor as well as their response to economic difficulty (Moser, 1998). Secondly, owning assets reduces the risk for households to fall into poverty as a result of macroeconomic volatility (de Ferranti et al., 2000). Hence, accumulating assets―be they liquid or not (e.g., durable goods and housing), material or not (by fostering education, health, family and social networks)―helps people to insure themselves against falling into poverty and to cope with risks and shocks. Asset accumulation should thus be considered as a major factor in risk management.

Nevertheless, though an asset index can be a good proxy for living standards in order to measure poverty<sup>17</sup> , two problems arise when using household wealth as an indicator of well-being in order to measure vulnerability to poverty. On the one hand, if assets are used for consumption-smoothing, then an asset-based approach overestimates vulnerability since assets can fluctuate whereas consumption does not. On the other hand, if assets are not used to smooth consumption, the approach would underestimate vulnerability. So, knowing whether an asset-based approach deviates from a more standard consumption-based approach is mainly an empirical question.<sup>18</sup> Besides, we could ask whether, in some circumstances, an asset-based approach is not preferable when it comes to measuring vulnerability. Indeed, let us consider the most interesting and realistic case where productive assets contribute towards the income generation process and can also serve as buffer-stock in order to face a non-anticipated drop in income (Deaton, 1991, Carroll, 1992). Empirically
explain the global inflation synchronization. Trade linkages increase the exposure of domestic inflation to foreign shocks and may lower import prices. Auer et al. (2017a) find that the expansion of GVCs has not only been an important contributor to the change in inflation dynamics, and it has become a key channel through which global economic slack influences domestic inflation, by making domestic inflation more sensitive to the global output gap.

There is strong evidence of the existence of international inflation spillovers that generates inflation synchronization through trade linkages. Auer and Mehrotra (2014), in the analysis most closely related to ours, find that both headline inflation rates and producer prices move more closely together between Asian-Pacific region economies that trade more with one another.<sup>3</sup> They show that in closely integrated supply chains, any shock to domestic production costs or exchange rates could be easily passed through to economies in the supply chain, affecting intermediate prices in other economies. More recently, Auer et al. (2017b) show that input-output linkages account for half of the global component of producer price inflation and that this synchronization across countries is driven primarily by common sectoral shocks and input-output linkages amplify the co-movement by propagating those shocks. Andrews et al. (2018) use data for 22 OECD countries over the period 1995 to 2014 to provide evidence that rising participation in GVCs has placed downward pressure on producer price inflation, by increasing the ability of firms to substitute domestic inputs with cheaper foreign equivalents. The mechanism at work is a downward pressures on unit labour costs (raising productivity and reducing wages) in the importing country.

Regarding the the policy relevance of inflation synchronization across countries, monetary policy is the most concerned. As supply chains represent a direct link between foreign marginal costs and domestic production costs, their prevalence has a significant effect on the extent to which optimal monetary policy is inward-looking (Auer et al., 2017b). In other words, this rise the question of to what extent central banks control domestic inflation dynamics (
Figure 3: Share of digital services trade of country-pairs sharing the same data models (2015) <!-- Start of picture text --> Cross-border data transfers rules<br>47.0%<br>53.0%<br>Trade between country-pairs sharing the same data model<br><!-- End of picture text --> Trade between country-pairs sharing the same data model Trade between country-pairs not sharing the same data model Domestic processing of data rules <!-- Start of picture text --> 46.8%<br>53.2%<br>Trade between country-pairs sharing the same data model<br>Trade between country-pairs not sharing the same data model<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data. Note: Digital services trade covers ISIC Rev. 4 codes 45-82.

13
than 660,000 people migrate irregularly to Thailand (Schloenhardt, 2014). Another example is Mexico, known for being a major migrant sending country to the United States, for its unauthorized emigration, and for its substantial return migration flows. According to the Mexican National Survey of Demographic Dynamics (ENADID), between 2009 and 2014, about half (48%) of the Mexican immigrant population in the United States was unauthorized, and one million Mexicans returned from the United States, leaving of their own accord (Gonzalez-Barrera, 2015).<sup>1</sup> Many other developing countries are likewise experiencing high return migration rates, as well as an increase in unauthorized emigration.

This paper is the first to examine the impact of undocumented migration status on migrants upon return to their home country. It investigates an unexplored question, namely the impact of the legal status of migrants on their wages upon return. We study the impact of return migration on wages by disentangling the effects of overseas legal versus illegal status and ask whether undocumented temporary migration has any impact on human capital accumulation that persists after return. From a conceptual

> 1 See Massey and Singer (1995) for an estimation of the probability of border apprehension and undocumented Mexican migration in the United States between 1965 and 1989.

2
context, we turn to the Indonesian national standard for ECED as an alternative way to look at quality. Using textual analysis, we compared ECERS-R to Indonesia’s ECED Standard and found 28 out of 43 ECERS-R items discussed in the national standard (see Table 9).<sup>18</sup> Based on this crosswalk of ECERS-R items to Indonesia’s own standards, we conducted further analyses. First, we calculated the mean ECERS-R score using only the 28 items that were found to be common between ECERS-R and the national standard—we call this alternative measure of quality, the “Indonesia standard.” The mean and standard deviation of this alternative measure is presented in Table 10.

As shown in Figure 1, many of the items that were retained in the Indonesia standard are those that pick up variation in quality, such as items 16, 17, and 22. Meanwhile, the Indonesia standard also lacks quite a few items that showed no variation such as items 10, 11, and 27. Thus, we believe that this measure of observed classroom quality may be more suitable than the original ECERS-R total score for our analysis of preschool quality and child development.

Second, we re-estimated Model 5 from our regression analyses above by using the Indonesia standard instead of the ECERS-R. We call this Model 6. It is our preferred model specification as it corrects for measurement error using instrumental variable and also uses a measure of observed classroom quality that is more locally relevant. The results of Model 6 are presented in Table 11.<sup>19</sup> The results of Model 6 show that utilizing the Indonesia standard yields fairly consistent significant relationships between observed classroom quality and child development outcomes. For the social competence domain of the EDI, a one unit increase in the Indonesia standard is associated with a 0.121 unit increase in child development. Similarly, the size of the coefficient is 0.167 units for the language and cognitive development domain – the largest association for this model.
and section 5 concludes.

# **2. Data Sources and Summary Statistics**

The study merged information from three rounds of EPAs’ surveys. The first survey was conducted in the fall of 2005 by the World Bank and was the base for the study in Lederman, Payton and Olarreaga (2010). The second round was conducted in the fall of 2010, also by the World Bank, and the final round was conducted in the fall of 2014 by the International Trade Center (ITC).

The initial survey contacted all EPAs in the ITC’s contact information database available on the ITC’s web page in 2005. The list was complemented with the help of World Bank country economists who provided contact information on national EPAs that were not listed in the ITC database. A total of 116 EPAs were contacted by email; 92 answered of which only 4 percent declined. In 2010, the same 116
having spatial information only at the level of administrative units or strata (strata is defined as a combination of _marz_ and settlement types–urban or rural status–following other official and major surveys) like in several other easily accessible datasets. The primary and more serious disadvantage of this sampling frame, however, is the large-sized settlements, especially in Yerevan. Although the identification information is unavailable, the Census frame from the Committee of the Republic of Armenia (ArmStat) has about 12,000 enumeration areas. The settlements in this frame are thus 12 times larger than the Census enumeration areas on average. Using a few large PSUs would also conceptually undermine the two-stage sampling design, converging to a one-stage design. Large “settlements” thus must be segmented into smaller areas to make workable PSUs.<sup>11</sup> For example, large-sized PSUs were manually segmented into smaller PSUs in the past, such as in Nepal (Central Bureau of Statistics of Nepal, 1996); however, this traditional or manual method is costly and time-consuming in practice. Thus, an innovative technique has been proposed in this paper to divide these large areas into smaller areas.

Another problem with this potential sampling frame is that the population based on the 2011 Census in this sample frame is outdated and misallocated. The outdated data is generally not a severe concern for national sampling frames because any survey fielded before the 2022 Armenia Census could be based on the 2011 Census frame. However, this problem can be significant in Armenia, given that it has been experiencing substantial household displacement and domestic migration due to territorial conflicts in recent years, i.e., the current spatial distribution of the population might be significantly different from that during the

> 11 If these large census settlements were divided into grids, challenges related to the traditional gridded sampling approach would be raised, such as building-cutting boundaries.

6
that, if the households have a higher participation rate (in particular in casual wage activity) than rich households, non-farm income can reduce rural inequality.<sup>3</sup> China is a good candidate for a country case study that examines the impacts of non-farm activity on rural income inequality. The objective of this paper is to examine whether and why non-farm activity reduces overall household income inequality in rural areas of two Chinese provinces, Hebei and Liaoning, in 1995/1997.<sup>4</sup> The oversupply of agriculture labor, the relatively limited quantity of arable land, and the obsolete agricultural technology in China result in its low level of farm income per capita. The economic reforms, in particular the implementation of the Household Responsibility System (HRS), in the late 1970s, not only stimulated the incentive of the farmers and contributed to the increase of the agricultural productivities, but also legitimized the rural redundant labor to leave land and participate in non-farm activity. Since then, the non-farm sector has played an increasingly important role in absorbing the surplus agricultural labor and reducing rural poverty.<sup>5</sup> Based on the data from the Living Standards Measurement Study (LSMS), we study rural household income determination. We examine the impacts of participation in non-farm activity on rural income inequality in two ways: First, we assume non-farm income as an exogenous transfer to total income to decompose the Gini index; second, we assume non-farm income as a potential substitute for farm income to take household choices into account and simulate household income. The results suggest that non-farm activity reduces rural inequality. The poor households would have suffered a relatively larger income loss than the rich households if they were not allowed to participate in non-farm activity.

The paper is structured as follows: section 2 briefly reviews the development of non-farm activity in rural China; section 3 describes the income composition and characteristics of households in the provinces of Hebei and Liaoning, based on the LSMS data; section 4
the end of the consultation. For each method discussed during the counseling session and not chosen by the client, the _app_ records the reason why she did not want to adopt it. The result is a rich database containing adoption decisions, along with relevant client characteristics and preferences.<sup>12</sup>

## **3.2 Outcomes**

The primary outcomes considered in this study are the shares of clients who adopted (i) a LARC, (ii) a SARC, or (iii) neither. We denote the last group as having _adopted no modern method_ although it includes clients who adopted LAM, condoms, or other traditional methods. We mainly focus our attention on the adoption of LARCs due to low demand for SARCs among our study population (Table A4 provides a detailed breakdown of the method mix). The primary outcomes are constructed using data collected on the tablets, which are cross-checked with hospital administrative records that are further verified by a third-party independent auditor. A non-negligible number of clients do not adopt a method during their initial visit but return to HGOPY to adopt a method later – e.g., after taking some time to think about their decision; having discussed it with their partners, spouses, or parents; needing to collect the money necessary to adopt the method; etc. For these clients, the data across consultations are linked so that the most up to date outcome is considered.<sup>13</sup> Similarly, some clients may return to the hospital to switch or discontinue their adopted method, which are reflected in the primary outcomes if they happen within the study period.<sup>14</sup> Please see Appendix D for ethical considerations, which include IRB approvals, study registration, and protocols.

# **4 Results**

## **4.1 Estimation strategy**

We begin by presenting the impact of offering discounts for LARCs using: where _Yi_ denotes the outcome for client _i_ . _Li_
Lagakos, D. and M. Waught, 2013. “Selection, Agriculture, and Cross-Country Productivity Differences”, _American Economic Review_ . 103 (2): p. 948-980 <mark>Ledesma, R.D., and P. Valero-Mora, 2007. "Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis."</mark> _<mark>Practical assessment, research & evaluation</mark>_ <mark>12.2: 1-11.</mark>

- Leight, J., P. Glewwe, and A. Park, 2015. “The Impact of Early Childhood Shocks on the Evolution of Cognitive and Non-Cognitive skills”, _Gansu Survey of Children and Families Papers_ . Paper 51

- Lipnevich, A. A., C. MacCann, and R.D. Roberts, 2013. “Assessing noncognitive constructs in education: A review of traditional and innovative approaches. In D. Sklofske and V. Schwean (eds.), _Oxford Handbook of Psychological Assessment of Children and Adolescents_ . Cambridge, MA: Oxford University Press.

Manski, C. F., 2004. “Measuring Expectations”, _Econometrica_ , 72 (5): 1329–76.

- McKenzie, D., 2012. “Beyond baseline and follow-up: The case for more T in experiments”, _Journal of Development Economics_ , 99(2): 210-221.

Murnane, R. J., J. B. Willett, and F. Levy, 1995. “The growing importance of cognitive skills in wage determination.” _Review of Economics and Statistics_ , 77 (2), 251-266.

- Pierre, G., ML. Sanchez Puerta, A. Valerio, and T. Rajadel, 2014. “STEP Skills Measurement Surveys - Innovative Tools for Assessing Skills." _<mark>Social protection and labor discussion paper</mark>_ <mark>1421. World Bank Group, Washington, DC.
financial institutions, leaving them no option but to rely on their own funds or borrow from informal networks (Shinozaki, 2022).

Similarly, many informal workers were adversely impacted by the pandemic. Workers whose jobs require face-to-face interactions – such as street vendors, domestic helpers, and drivers – experienced large reductions in working hours and incomes (Octavia, 2020). Reports from motorcycle drivers, for example, revealed that their incomes had fallen by as much as 70 to 80 percent since the start of the pandemic (Octavia, 2021). The need to travel to earn a daily wage also put informal workers at a greater risk of contracting the virus, especially women who are more likely to use public transportation than men (Setyonaluri & Samudra, 2020). Although the government’s social assistance program for the poorest Indonesians benefited some informal workers, many did not qualify for the in-kind and cash transfers because they either earned incomes above the eligibility threshold or had assets to their name (Octavia, 2020). Notably, there was no specific program designed to assist informal workers partly due to the lack of a universal database that could be used to identify and target recipients (Octavia, 2020; Pitoyo et al., 2021).

Nevertheless, not all informal enterprises and workers suffered because of the pandemic. A series of small MSME surveys conducted between March 2020 and May 2021 by the ADB revealed two distinct groups: those who were disrupted by the pandemic on one hand, and those who benefited from the pandemic on the other hand (Shinozaki, 2022). Several factors influenced outcomes. First, informal enterprises and workers in sectors that were considered ‘essential services’ – such as ICT, finance, and health and social services – fared much better than enterprises and workers in other sectors (Shinozaki, 2022). Second, informal enterprises and workers who were able to leverage the digital economy were shielded from the worst effects of the pandemic (Shinozaki, 2022). Informal enterprises that were primarily engaged in