rafmacalaba/data-use-mentions-extended huggingface dataset:
for a 10% increase. Earlier research has indicated that economic impacts of air pollution stem mainly from health effects that decrease length and quality of life, increase health expenditures, and reduce labor supply and productivity. In 2013, one-tenth of deaths worldwide were attributable to air pollution, resulting in about $225 billion annually in lost labor income (World Bank and Institute for Health Metrics and Evaluation, 2016). For those facing negative health impacts, personal wealth can suffer immensely, between $240 billion and $630 billion are spent each year on health care costs related to pollution (Preker et al., 2016). Quality of life is affected too, Levinson (2012) found individual average willingness to pay up to $42 per day for a one-standard-deviation improvement in air quality in the United States. Reduced air pollution has also been shown to increase short-run labor supply due to a healthier population, in one case by 3.5 percent, translating to increased income for individual workers (Hanna and Oliva, 2015). Worker productivity in certain sectors also relies on health from clean air, with a 10 ppb change in average ozone exposure, for instance, resulting in a 5.5 percent change in a study of agricultural worker productivity (Zivin and Neidell, 2012). In that case, reducing ozone pollution could result in annual cost savings in labor expenditure—totaling approximately $700 million in the United States if a 10 ppb reduction was implemented.

While many of the results point toward an economic failure, we also see potentials for enhanced growth. Policy targeted at exogenous pollution can have positive growth effects by reducing the harmful effects of pollution. Positive economic effects, specifically on the poor, in turn help combat air pollution. Bottom-up growth spills over positively to average growth while reducing pollution, and can therefore be seen both as an effective component in pollution reduction strategies as well as in general economic growth programs. Health policies for the poor that reduce the economic impact on these households, may similarly have
# Table A4: Robustness checks

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||_Ou_|_tcome: Sur_|_vey comple_|_ted_||
|Phone survey|0.427***<br>(0.038)|0.423***<br>(0.038)|0.425***<br>(0.039)|0.427***<br>(0.038)<br>[0.000]|0.417***<br>(0.038)|0.347***<br>(0.053)|
|Mean of Dep. Var (WhatsApp)|0.299|0.299|0.299|0.299|0.299|0.386|
|R-squared|0.212|0.234|0.212|0.212|0.242|0.217|
|Observations|597|597|597|597|597|358|
|Controls (Age, Sex)|Yes|Yes|Yes|Yes|Yes|Yes|
|Controls (Education)|No|No|No|No|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Controlling for baseline completion||✓|||||
|Controlling for mode of initial recruitment|||✓||||
|Computing randomized inference p-values<br>Imputing missing values||||✓|✓||

_Notes:_ This table shows the estimated impacts of the phone-based survey relative to WhatsApp-based survey on completion rate. “Phone survey” is a dummy
# _Catching Up on Enrollment_

The strong rise in exports over the last decade suggests that lack of skilled labor may well become the binding constraint to growth. Indeed, about 30 percent of manufacturing firms consider this as severe or very severe obstacle according to the 2004/5 Investment Climate Survey (World Bank 2005a). The argument goes as follows. With economic development and globalization, Thailand’s comparative advantage, as that of other middle income countries, is shifting up the skill ladder towards more skill-intensive products. This trend is expected to accelerate over time, increasing demand for skilled workers. Unless the supply of skills is improved, it is likely to fall short of what is needed to sustain a more skill-intensive growth path. However, data on Thailand’s manufacturing sector suggested that in spite of the sharp rise in exports since the mid1990s, the contribution of heavy and high-skill labor-intensive industries is still below the levels of prior to the Asian crisis (Figure 7.C). This section will look at education and wages for evidence on increasing returns to skills.

Thailand’s primary education rate was high already in 1960s, and access to education increased continuously over the last 20 years.<sup>16</sup> Figure 21.A shows that attendance to educational institutions was substantially higher in 2002 than in 1986 across all age groups. While less than half of the 15 year-old were in school in 1986, it is more than four in five in 2002. Higher school attendance came together with more students reaching higher education levels (Figure 21.B). Among the 6 to 25 year-old, the share attending lower secondary or higher education levels more than doubled to around one third between 1988 and 2002.<sup>17</sup> Among the age group of the 15 to 21 year-old, one in ten attended university in 2002, compared to one in 25 in 1988, and close to one in five upper secondary education, compared to one in 16 in 1988 (Figure 21
the link with crime and violence, and the very emergence of the term “ _ninis_ ” may explain the increased media attention to the phenomenon in recent years.<sup>7</sup> Not all countries show the same trend, of course. The change in the national percentage of ninis between 1992 and 2010 ranges from a decrease of 12.3 percentage points in República Bolivariana de Venezuela to an increase of 1.6 percentage points in Argentina. For each country, Annex 4 provides graphs showing how the total number and percentage of ninis aged 15 to 24 have evolved over that period, while Figure 3 and Annex 3 summarize these national levels and trends using maps. Using data for 2010, Figure 3 shows that Peru and República Bolivariana de Venezuela have the lowest shares of ninis (at 10.9 and 13.0 percent, respectively), while the highest shares are concentrated around Central America. The highest numbers of ninis are found in Brazil, Colombia,

> 7 According to an analysis using Google Trends, searches from Mexico using the term “ _ninis_ ” surged in late 2010, after having been non-existent before that year. Moreover, this was not simply due to economic circumstances: searches using “desempleado” (unemployed) had already risen sharply some two years earlier, just after the global financial crisis hit.

7
# **Findings and Policy Responses**

# **_Findings_**

# Themes arising from the PPAs

Very specific findings vary from site to site, but there are a number of common themes that can be identified:

- A near-unanimous recognition that economic wellbeing in rural areas has improved over recent years;

- A strong demand for a greater range of opportunities to develop sustainable livelihoods, particularly those which reduce the dependence on landholdings of reducing size;

- A strong sense of vulnerability to both household-level and community level shoclk s, with ill health being the single most significant shock which poor households enduire;

- Identification of a range of coping strategies which households use to deal with hardship and shock, many of which can be destabilizing for poor households in the longer term;

- A voiced concern about the lack of information about initiatives, plans and programs which affect their livelihoods and a sense of alienation from decision-making processes;

- A number of issues related to intra-household inequity which highlight the vulnerability of children and women;

- A range of gender-related dimensions of illbeing;

- A number of specific policy issues: the status of migrants; the role of the off-farm private sector in rural areas; the way in local fees and contributions are levied; the costs of healthcare and education; the limitations of current mechanisms to consult poor households; and, the limited outreach of formal safety nets.

# Links between the PPA findings and household survey data

The PPAs were analyzed and synthesized at the same time as the quantitative household survey data from the second round of the VLSS were being analyzed. There were strong links between these exercises, with much of the analysis for both of these exercises taking place within the World Bank.

Much of the value of the PPAs lies in covering research territory that is more difficult to probe with household survey data. This is demonstrated in the themes identified above. many of which are issues that would not have emerged from the quantitative analysis alone. There is also a useful overlap, though, in the
enterprises. Latvian policy makers should utilize this audit tool to assess the strengths and weaknesses of a large representative sample of Latvian enterprises. Based on the weaknesses revealed by the audit and international lessons of experience, they should develop specific targeted strategies that would help enterprises improve in areas where they are deficient and achieve higher levels of sophistication.

**RECOMMENDATION 11.  Develop matching grant programs to align government R&D spending more closely to the innovation and competitiveness needs of domestic industry.  At present, Government R&D spending priorities bear little relationship to the innovation or competitiveness needs of Latvian enterprises.  Nor are Government  R&D spending programs designed to catalyze private innovation spending.  To a certain extent, this is a classic chicken and egg dilemma. As the preceding discussion of the Latvian innovation survey indicated, Latvian enterprises are not particularly prone to innovate and, when they do, R&D is not the favored mode of innovation.  Not surprisingly, therefore, Government R&D spending priorities have not responded to the low or non-existent demand from industry for more targeted R&D spending.  At the same time, it is also important to recognize that more targeted Government R&D spending, by itself, will not be sufficient to stimulate greater enterprise spending on or interest in innovation.  Targeted programs, in other words, are not a panacea or magic bullet.  Nevertheless, if coupled with other complementary policies and programs, World Bank projects in India, Turkey, and Chile among others, suggest that carefully crafted matching grant programs can be one part of a comprehensive approach to stimulating greater private sector innovation.**

For example, a 1999 World Bank Industrial Technology project in Turkey<sup>55</sup> , provided matching grants to co-finance up to 50% of the cost of product and process R&D among private enterprises. The project places special emphasis on promoting linkages between the R&D institutions and Turkish industry. A project with a similar component is currently under development in Croatia.
# I. Introduction

The paucity of economically relevant city-level data can be a significant hindrance to research on urbanization, especially in the context of less developed economies. Even in the circumstances where the desired data are available, users of such data (researchers and policy makers) may have to rely on small-scale surveys and coarse statistics. Such data constraints make it difficult to measure important urban phenomena. They also reduce the scope for economic analyses and policy design.

Over the past decade, cities have increasingly become a reservoir for billions of digital footprints that can be used to measure and assess mobility patterns with high time frequency and high spatial resolution. Combined with new methods of analysis, including artificial intelligence approaches (such as machine learning), this has led to a “big data” revolution in transportation. Researchers, experts and policy makers are increasingly exploring the wealth of mobility and city data from various sources—including digital repositories from the public and private sectors, remote and in-situ sensors, mobile phones, among others—to examine traffic flow, urban congestion and its economic, social, and environmental externalities (e.g., pollution, health hazards, or accidents). In the present review, we seek to examine how new data sources have stimulated innovative approaches to measure and predict road traffic flows, and to assess the economic Our review of this emerging literature indicates that big data has a strong potential to more accurately measure social costs in a variety of contexts, including in developing countries where traditional transport data are lacking and where data “leapfrogging” is occurring.<sup>1</sup> In fact, unlike traditional household travel surveys alone, new sources of data such as Google Maps, GPS traces and sensor networks can be used to track individual travel demand patterns and estimate private and social trip costs, two important ingredients of a welfare analysis. They also make it possible to design policies

> 1 For a survey of big data uses in an urban context more broadly, see Glaeser et al
of roads that are in poor condition, water that never reaches its final customers, and electricity that is lost through transmission and distribution.

Our second contribution is to document how the quantity and quality of public capital vary across countries with different levels of development, and how this affects the impact of new public and private investment on growth (Section 6). This analysis is conducted through the lens of the LTGM‐PC and utilizes the cross‐country data on the IEI and public capital stocks collected for the Excel‐based tool.

Surprisingly, we find that the effect of an extra 1ppt of GDP of public investment on growth is roughly constant across different levels of development.<sup>5</sup> This puts us at odds with optimistic commentators claiming that sizable “infrastructure gaps” mean a larger growth dividend from public investment in low‐income countries. But it also puts us at odds with pessimistic commentators who claim that the low efficiency of public investment in developing countries ‐ due to corruption and mismanagement for example ‐ means that such projects have little effect on growth.<sup>6</sup> Overall a 1ppt increase in public investment as a share of GDP increases growth by 0.1‐0.2ppts in our model, depending on the calibration. As public investment is typically around 5% of GDP and usually less than 10% of GDP, higher public investment alone cannot turn a slow‐growing country into a tiger economy.

Instead, developing countries are short of _private_ capital, both relative to GDP and in absolute terms. Private capital as a share of GDP in low‐income countries is only 2/3 of that in middle‐income countries, and almost half that in high‐income countries. By our calculations, this means the return to

> 4 Other indices like the World Economic Forum’s infrastructure quality index or the IMF Public Investment Efficiency Index (PIE‐ X) include survey‐based scores or distance to the frontier analysis, which means
other loan term regressions are at the level of facility (in the case of Maturity, Spread and Collateral) or package (in the case of Covenant). Since a loan may have several lenders, we aggregate lender characteristics by taking their unweighted average, including the characteristics of the countries where lenders reside.<sup>3</sup> We matched Dealscan with monetary policy rates from the International Financial Statistics (IFS) database of the IMF. Our main dependent variable, IR, is the lender-country central bank policy rate (replaced by the discount rate at which commercial banks can borrow from the central bank against eligible securities in case of a few countries where the central bank policy rate data was missing). Similarly, IR (Borrower) is the borrower-country central bank policy rate or the discount rate. In some specifications we use deviations from an estimated Taylor rule type monetary policy rate for the lender country as an alternative measure of the stance of monetary policy in this country. To calculate this variable, called Taylor residual, we regress the monetary policy rate, IR, on real GDP growth and the inflation rate separately for each country, and then take the errors from these regressions. According to Table 1, the average monetary policy rate, IR, over the whole sample period was 2.48%, while Taylor residuals averaged -0.005. An additional lender-country monetary policy variable is QE, which indicates that a quantitative easing program was in place in a lender country in a given month. This variable reflects that the Fed, the European Central Bank, Bank of England, and the Bank of Japan implemented various quantitative easing programs at different points in time (see the appendix for the exact dates<sup>4</sup> ) in efforts to

> 3 <mark>By taking the unweighted average of lender characteristics we avoid the problem of missing loan volume contributions. For example, using non-missing loan shares as weights would reduce the number of loans from 92,191 to 18
||7.6%||9.2%||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

40
The statistical model-based predictions we develop have the potential to improve on this baseline in several ways. We explore this using Table 4, which provides cross-validation results for 4-, 8- and 12-month ahead forecasts. Note that the 4- and 8-month forecast horizons are roughly comparable to the near- and medium-term outlooks considered by FEWS NET.

First, when we set a conservative value of w=<sup><u>1</u></sup> 3<sup>toimposeaheavierpenaltyonfalse</sup> alarms, the FPR increases slightly relative to the performance baseline produced with binarized FEWS NET outlooks, to around 7% for the 4- and 8-month forecast horizon. However, this comes with a very large reduction in the FNR to below 20% which is substantially down from the baseline rates of 74% and 79%. Taken together, the slightly higher FPR and much lower FNR results in an improvement in _LA_ from the baseline value of around 26% using binarized FEWS NET outlooks, to around 11% for the model predictions.

Second, the model-based approach allow us to fine-tune predictions for different use cases corresponding to different weights in the prediction loss function. As we have discussed, the severe costs of food crises suggest that for some policy purposes, a greater penalty should be imposed on failure to predict crises, i.e. that a higher value of w would be warranted. The FEWS NET outlooks offer just one fixed set of forward-looking ratings, and increasing the weight on FNR in the prediction loss function in Table 3 sharply increases _LA_ , indicating a sharp decline in predictive performance with regard to the less conservative forecasting objectives. For example, for the medium-term outlook, _LA_ increases from 28% to 54% when w increases from<sup><u>1</u></sup> 3<sup>to</sup><sup><u>2</u></sup> 3<sup>.Incontrast,themodelpredictionsmaintaincomparablepredictive<
Policy Research Working Paper 10039

# **Abstract**

Stringent employment protection laws are argued to be a cause of reduced employment flexibility, slower growth and increased reliance on temporary employment contracts in many countries, including India. In 2014, the Indian state of Rajasthan amended labor laws to increase employment flexibility in firms. The most discussed of the amendments lifted the requirement for government approval for retrenching regular workers in medium-size factories. This paper first conducts a synthetic control analysis of the policy change using state-level panel data from 1980 to 2018, finding no evidence of an impact on aggregate manufacturing employment and output. The paper then uses firm-level panel data to conduct a difference-in-differences analysis of the main amendment, exploiting its size-dependent feature for identification. This analysis finds that the amendment reduced the implicit regulatory cost of labor in firms, but there is no discernible impact on their total employment and output. The amendment also led to firms substituting temporary (“contract”) workers for permanent workers. This collateral impact is contrary to the expectation that easing the flexibility of permanent employment arrangements would make them more attractive to firms.

This paper is a product of the Office of the Chief Economist, South 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://www.worldbank.org/prwp. The authors may be contacted at ssharma1@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
have also emphasized the importance of objective area measurement in land registration and redistribution. In its support to the Zimbabwe Ministry of Lands and Rural Resettlement 2014-2016 Action Plan (UNDP, 2014), the United Nations Development Programme explicitly mentions GPS and remote sensing as requirements for the successful tracking of the redistributed, post-2000 Land Reform Program parcels through the creation of a national land information database.

The methodological menu for collecting land area measurements is diverse and selection of the appropriate method depends on several factors. This paper focuses on the methods that hold relevance for agricultural and household surveys.<sup>2</sup> Readers interested in a broader approach to the measurement of agricultural land are referred to FAO (1982) and Sud et al. (2015). This is an important distinction because several measures of agricultural land that are important for agricultural statistics can be collected separately from information about the holding or the household (e.g. when the goal is to estimate crop

> 2 The focus will also be on low income countries with little or no cadastral or administrative information to integrate this type of data collection.

4
and temperature ( _Annual_tempi_ ): They both capture climatic properties. While it may be preferred to use measures reflecting agroclimatic zones, considerable heterogeneity within large countries makes it difficult to assign specific zones to a country.

- Share of agricultural land ( _agricultural_landi_ ): Agriculture is a main driver of deforestation.

Variants of the specification have also considered urban density, education, real effective exchange rates, and the share of services as a proxy for structural change. Yet, these variables were statistically insignificant and did not change R<sup>2</sup> considerably. Hence, they are excluded from the final specifications. All data have been obtained from the World Bank’s World Development Indicators.

The regression model above seeks to explain the forest coverage by various explanatory variables. Yet, if countries are rich today, have a high population density, or have a high share of agriculture, they could have reduced their forest shares many decades, even centuries ago. While it may be useful to consider natural propensity for a country to have forest cover, such historic data are challenging to collect for a large sample of countries. Further, since much of the work in the paper 16