rafmacalaba/datause-encoder-data huggingface dataset:
excessive protection, while the other half receive negative net protection. In the process, the averag **e** x-factory price of output in this sample is higher than international prices by more than 40%.

16. Table 3 also shows that sectors producing basic intermediates have generally higher net EPRs than downstream products. For example, basic chemicals, basic steel products, synthetic yarns and miscellaneous products (which are also basic inputs such as tires, paper, etc.) have high and positive net EPRs while downstream firms in engineering, machine tools, light chemice:s have low or negative net EPRs. Again these subsectoral averages have large variations. The only exception is the electronics industry which was recently deregulated and is meeting a pent-up demand for its products. More recently the prices and EPRs in electronics have started coming down as these demands are satisfied.

17. In conclusion, the multiplicity of tariff and tax rates, special exemptions, special schemes and varying degrees of domesti **c** ompetition have created a structure of effective protection which has large variability across firms and industries. However, it is a structure that gives, on average, much lower net protection to the firms while substantially increasing the average cost of production.

# B. SIMULATING FREE TRADE

18. The EPRs sunmnarized in the previou **s** ections indicate that the production of key intermediates are relatively more inefficient and tend to have higher EPRs. This conclusion, however, is based on a sample of products and may not generalize if the whole economy is taken into account. To observe the economy-wide effects of protection, input-output tables are usually used to estimat **e** ffective protection rates. These static estimates, however, do not take into account the macroeconomic adaustments that accompany changes in the level of protection. Moving to a free trade environment (i.e., elimination of protective tariffs and QRs) will effect individual subsectors through two channels. First is the effect of changes in relative prices caused by the changes in protection. The secotid effect is caused by macroeconomic adjustments that are required to compensate for the tariff and QR changes and maintain internal and external balances. The Indian Government
the percentage of women commuting by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). This increment led the Government to increase the budget of the program from |12 million ($168 mn), allocated in the first year, to |15.2 million ($212.8 mn) in the second year (Durai, 2021; Sundaram, 2022).

# **3 Data**

## **3.1 Consumer Pyramids Household Survey**

Our main data source is the Consumer Pyramids Household Survey (CPHS). It is a household-level longitudinal survey conducted by the Centre for Monitoring the Indian Economy (CMIE). Starting with the first wave in January-April 2014, the CMIE runs surveys three rounds yearly (January-April, May-August, and September-December). Each wave covers about 160,000 households from all major Indian states, maintaining a consistently high household response rate of over 80%. A multi-stage stratified survey design is deployed. The broadest level of stratification is a homogeneous region (HR), defined as a set of neighboring districts within a state that is comparable in the following characteristics: climate, urbanization, female literacy rate, and population. In Appendix Table D1, we list the two treated states of Punjab and Tamil Nadu—which implemented free bus ride schemes for women—and the control states, i.e., the states that are adjacent to the treated states and did not distribute free bus tickets to women.<sup>7</sup> Since the CPHS data is representative at the Hr level, our analysis includes only the adjacent HRs in the control states; Non-adjacent HRs in control states are excluded.

The CPHS has four sections: Consumption Pyramids (CP), People of India (PoI), Aspiration India (AsI), and Income Pyramids (InP). This study uses the CP, InP, and PoI sections. The CP is a household-level monthly survey reporting household expenditures on various kinds of goods and services, without a breakdown of individual members of multi-person households. Specifically, it asks households about their monthly expenditure on all types of transport, including expenses on a combined category of “buses, trains, and ferries” (BTF). Our study period for the CP is from November 2020 to September 2021. The InP is a monthly survey that tracks the income of each household member. We use the same study period as in the CP data. The PoI is an individual-level survey conducted every four months. There are three waves a year: January-April, May-August,

> 7The CMIE started to collect household members’ time usage information in the wave of SeptemberDecember 2019, while the government of Delhi started the _Pink Slip_ scheme in November 2019. We do not include Delhi in the analysis since we do not have pre-period information on time usage.

5
attributed to poverty and the correlation between low per capita incomes and higher propensities for internal war is one of the most robust empirical relationships in the literature (Justino, 2006, Bannon and Collier 2003). A number of recent papers employ within-country data to explore the factors that predict violence and rebellion, and most find strong associations with local economic conditions and inequality (Barron et al 2004; Krueger and Maleckova 2003; Murshed and Gates 2005, Do and Iyer 2007, Macours 2011, Dube and Vargas 2013, Verwimp 2003, 2005, Humphreys and Weinstein 2008; Maystadt et. al 2013).

This paper also contributes to the literature on the effects of transportation infrastructure on welfare. Recent papers provide suggestive evidence on how better transport infrastructure, by enabling greater access to markets, decrease trade costs and interregional price gaps (Donaldson 2013; Casaburi et al, 2013), and affect input and output prices of crops (Khandker et al., 2006, Minten and Kyle 1999). These in turn affect agricultural returns and hence land values (Jacoby 2000, Shrestha 2012, Donaldson 2013). Econometric analysis of household data on the effects of road connectivity on input use, crop output, and household incomes in Madagascar and Ethiopia (Chamberlin et al 2007, Stifel and Minten 2008) suggest that remoteness negatively affects agricultural productivity and incomes at the household level. Not surprisingly the literature also finds that access to good quality roads facilitates economic diversification (Gachassin et al 2010, Fan et. al 2000, and Mu and van de Walle 2007). Several other researchers use microeconomic data to examine transportation infrastructure’s impact on welfare variables, such as on income (Donaldson 2013, Jacoby and Minten 2009), consumption per capita (Khandker et al. 2006), and poverty reduction (Fan et al. 2000, Gibson and Rozelle 2003; Warr 2008).

As noted earlier, there is remarkably little empirical evidence on the direct causal impact of access to markets on well-being in countries with conflict, and even less evidence on the combined impact of transportation costs and conflict. For instance, Ulimwengu et al. (2009) analyze the effect of market access on agricultural production and household wealth in DRC, but do not explicitly explore the role of conflict beyond the inclusion of province level fixed effects, which does not capture variations in conflict within the large provinces of DRC. It is imperative to assess the direct impacts of conflict and transport infrastructure and the combined effect of these two in order to evaluate the effectiveness of providing transport infrastructure in areas with conflict. In contrast Martin et al (2008) investigate the links between trade and conflict. They conclude that trade openness may deter the most severe civil wars (those that destroy the largest amount of trade) but may increase the risk of lower-scale conflicts. The implication is that transportation costs can affect the level of conflict indirectly through its impact on the amount of trade that can occur.

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- 6) derive attrition-adjusted weights for all individuals, including split-off<sup>4</sup> households, then aggregate these weights to the level of the round 2 household;

- 7) trim these weights;

- 8) post-stratify the pooled weights to known population totals.

# _1) Base weights from round 1_

The panel weight calculations are based on the household weights from round 1 of the survey (W1). The round 1 weights incorporate a selection weight factor equal to the reciprocal of the probability of selection of each unit into the round 1 sample, as well as any non-response correction or post-stratification adjustments. These weights are nonzero for all round 1 respondents and form the base of the round 2 calculations.

# _2) Probability of EA selection into round 2_

In many panel surveys, it is often not possible, either for reasons of cost or logistics, to re-survey all responding round 1 households in round 2. It is therefore necessary to choose a sub-sample. The subsample can be chosen as a probability sample of all eligible round 1 households or enumeration areas, or households in certain regions can be disproportionately subsampled compared to their representation in round 1. If, for example, the capital is determined to be the area of highest variation or the most interest analytically, all responding households in round 1 can be selected into round 2 in the capital, and then only a sub-sample of enumeration areas in other regions.

The probability of selection for each household in stratum _h_ is: where _m_ is the number of households sub-sampled into round 2 in stratum _h_ and _M_ is the total number of households in the round 1 sample in stratum _h_ .<sup>5</sup> For the round 1 households in the hypothetical capital, which were retained for round 2 with certainty, p1 would equal one. (Note that if whole EAs are selected lh for round 2, then p1 = ~~,~~ where _lh_ is the number of EAs sub-sampled in stratum _h_ and _Lh_ is the total Lh number of EAs in the round 1 sample in stratum _h_ .) If all round 1 households are included in the sample for round 2, then p1 is one for all households.

# _3) Probability of household selection into tracking_

In panel surveys, some households from round 1 will evolve into more than one household in round 2. That is, the set of household members from round 1 will no longer all be residing together by round 2. For the purposes of administration in the LSMS-ISA surveys, one of the round 2 households will be

> 4 For the purposes of this note, ‘parent’ generally refers to the household found at the same location as the previous round of data collection, and ‘split-off’ refers to new households entering the sample through an individual originally resident in a parent household during a previous round. Note that this difference between ‘parent’ and ‘split’ is purely administrative.

> 5 For the simplification of notation the subscript _h_ , representing each stratum _h_ , is generally dropped in the subsequent formulas despite the fact that the LSMS-ISA surveys all have stratified designs.

3
# **1 Introduction**

There is now wide agreement that progress towards the decarbonization of the global economy will require a significant increase in the utilization of technologies that contribute to reducing CO2 emissions. While these green technologies are becoming more ubiquitous, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and the economic mechanisms underlying their diffusion. In this paper, we draw on the textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide systematic evidence on these issues.

We begin by establishing new stylized facts about the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. This approach allows us to identify in a systematic way green technologies. The analysis of patent data for the period 2000-2021 reveals that green innovation was primarily driven by new technologies from the construction, energy, and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones, and hybrid/electric vehicles. We then provide evidence on patterns of adoption and diffusion of these green technologies across firms and countries, drawing on the textual analysis of earnings conference calls transcripts (ECTs). We follow a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that publicly listed firms host with financial analysts. This approach has been increasingly used by researchers to measure firm uncertainty (Hassan, Hollander, van Lent, and Tahoun, 2019), firm sentiment (Hassan, Hollander, van Lent, and Tahoun, 2020), and the diffusion of disruptive technologies (Bloom, Hassan, Kalyani, Lerner, and Tahoun, 2021). The rationale behind it is that earnings calls are a venue in which senior management responds directly to questions from market participants about the firm’s prospects. They consist of a management presentation followed by a Q&A session, which requires management to comment on subjects they might not otherwise have voluntarily proffered. Thus, the extent to which the adoption of green technologies is discussed in earnings conference calls serves as a proxy of their importance for both the firm’s management and market participants.

Using unique firm identifiers, we were able to merge these firm-level textual-based indicators 1
The Local Economic Impacts of Resource Abundance:

# What Have We Learned?*

Fernando M. Arag ́on<sup>a</sup> Punam Chuhan-Pole<sup>b</sup> Bryan Christopher Land<sup>b</sup> JEL Classification: H70, I30, O11, O33 Keywords: Local Impact, Natural Resources, Resource Curse, Welfare

> ---------------------------------------------------------------

> * This paper is part of the Socioeconomic Impact of Mining on Local Communities in Africa (P148422) research study, which is supported by the Africa Regional Studies Program of the World Bank. We thank Stuti Khemani for valuable comments on an earlier draft. We acknowledge the contributions of Francisco H. G. Ferreira and other participants at the Mid-Term Review Workshop of the study in May 2014. The findings and views expressed in this paper are entirely those of the authors.

> a Department of Economics, Simon Fraser University.

> b World Bank Group.
<!-- Start of picture text --> 70<br>60 -=-<br>-<br>Pal<br>50 “<br>-<br>40<br>oF<br>30<br>20 — = = a<br>0<br>1955 2000 2005 2010 2015 2020 2025 2030<br>eee SSA RR ee oA MONRR<br><!-- End of picture text -->
**Figure 3: Exports, imports and average job incomes**

<!-- Start of picture text --> Exports Imports<br>Total labor income Total labor income<br>15,000,000<br>15,000,000<br>10,780,209 12,001,126<br>10,000,000<br>10,000,000<br>High exports High imports<br>5,000,000 4,099,168 Low exports Low imports<br>5,000,000<br>2,878,250<br>0 0<br>Average labor income Average labor income<br>6,000 6,000<br>5,196<br>4,888<br>5,000 5,000<br>3,996<br>3,688<br>4,000 4,000<br>High exports High imports<br>3,000 3,000<br>Low exports Low imports<br>2,000 2,000<br>1,000 1,000<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median. (3) Total and average labor incomes are expressed in 2015 Mexican pesos.

Fourth, it is also possible that trade induces a shift from informal to more formal work which is not reflected in higher employment rates. We therefore inspect the informality channel and find a strong difference in informality, measured as the percentage of employees without pension or retirement benefits at work for municipalities with high trade per worker relative to those with low trade per worker (Figure 5). In particular, the average informality rate in municipalities with low exports per worker is 13 percentage points higher than in municipalities with high exports per worker (left panel). The difference ascends to almost 19 percentage points for imports (right panel). The scatterplot across all Mexican municipalities suggests a slightly negative correlation between exports and imports per worker and informality (Appendix 4).

6
1 Introduction Some of the poverty at any one date is bound to be transient, in that it is due to a shortlived drop in individual levels of living, as distinct from chronic poverty arising from low longterm welfare. This paper addresses three questions that often arise in policy discussions: , i) How much of the poverty observed at one date is transient? As a rule, policy makers tend to attach more concern to chronic poverty. This is understandable. But it is still important to know about transient poverty, because of its bearing on our ability to reach the chronically poor through informationally feasible interventions; this leads us to pose a second question: ii) How much does the existence of transient poverty constrain the scope for reaching people who are typically poor? We know very little about the extent of leakage to the transiently poor from the commonly used modes of targeting transfers (in cash or kind) on the basis of various types of essentially cross-sectional data.

iii) ‘Are different processes at work in determining transient versus chronic poverty? It is often claimed that different policies are called for. Transient poverty has been identified as a motive for certain anti-poverty policies, such as various social safety nets (Lipton and Ravallion, 1995, section 6). Insurance and income-stabilization schemes are seen to be more important when poverty is more transient. Increasing the human and physical assets of poor people, or the returns to those assets, are thought to be more appropriate to chronic poverty. Such tailoring of policies to objectives presumes that different processes are at work determining the two types of poverty. But it may well be that the things one does to reduce chronic poverty also reduce transient poverty and vice versa.

These can be difficult questions to answer. Most household surveys are essentially static in that the living standards data refer to a relatively short period, based on a single interview.