rafmacalaba/datause-encoder-data huggingface dataset:
**Table B3. Simulations – FUNPI with cashew processing or subsidies to farmer inputs (** **_ε=0.75_** **<u>)</u>**

|**Policy**||**(I) Base**<br>**Scenario - no**<br>**FUNPI**|**(II) = (I) +**<br>**FUNPI**<br>**(XIV)**<br>**+**<br>**pro**<br>**t**|**= (I) + FUNPI**<br>**4 cashew**<br>**cessors (200**<br>**on each)**|**(XV) = (I)**<br>**+ 1 cas**<br>**processor**<br>**ton**|**+ FUNPI**<br>**hew**<br>**(20,000**<br>**)**|**(XVI) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in cost of farmer**<br>**services**|
|---|---|---|---|---|---|---|---|
|_International Price_|_(USD/ton)_|799.88|799.88|799.88||799.88|799.88|
|_Trade taxes (USD/_|_ton)_|93.50|199.88|199.88||199.88|199.88|
|_International Price_|_net of taxes (USD/ton)_|706.38|600.00|600.00||600.00|600.00|
|_Intermediate Price_|_(USD/ton)_|515.67|426.66|427.14||438.72|424.53|
|_Farm-gate Price (U_|_SD/ton)_|430.82|351.00|351.43||361.80|348.17|
|_Exporters + cashe_|_w processors profit (USD/ton)_|75.72|67.20|68.12||89.01|68.19|
|_of which, exporter_|_s (USD/ton)_|72.29|61.78|61.49||54.65|62.91|
|_of which, cashew_|_processors (USD/ton)_|352.13|441.14|440.66||429.08|443.27|
|_HHI exporters and_|_cashew processors_|493|510|505||584|512|
|_Traders profit (US_|_D/ton)_|41.45|34.66|34.70||35.59|34.95|
|_HHI traders_||256|256|256||256|256|
|_Quantity (tons)_||163255|140000|140129||143218|144022|
|**Table B4**|**. Simulations – Introdu**<br>**Policy**|**ction of FUN**|**PI and sensit**<br>**(I) Base**|**ivity to int**<br>**(II) = (I) +**<br>**(I**|**ernatio**<br>**II) = (II) +**|**nalprice**<br>**(IV) = (II) +**|**(****_ε=0.25_) **<br>|
||||**Scenario -**|**FUNPI**<br>**m**|**ax intn'l**|**average**||
||||**no FUNPI**||**price**|**intn'l**<br>**price**||
||_International Price (USD/ton)_||799.88|799.88|1350.00|950.00||
||_Trade taxes (USD/ton)_||93.50|199.88|199.88|199.88||
||_International Price net of taxe_|_s (USD/ton)_|706.38|600.00|1150.12|750.12||
||_Intermediate Price (USD/ton)_||523.71|426.66|935.08|563.84||
||_Farm-gate Price (USD/ton)_||441.47|351.00|826.94|478.96||
||_Exporters + cashew processors_|_profit (USD/ton)_|71.73|67.20|97.74|76.42||
||_of which, exporters (USD/ton)_||68.00|61.78|90.28|70.44||
||_of which, cashew processors (_|_USD/ton)_|344.09|441.14|737.10|523.33||
||_HHI exporters and cashew pro_|_cessors_|480|510|406|470||
||_Traders profit (USD/ton)_||40.39|34.66|63.69|42.72||
||_HHI traders_||256|256|256|256||
||_Quantity (tons)_||148261|140000|173448|151313||

40
||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Increased<br>use of<br>digital<br>platforms|New<br>investment<br>in digital<br>solutions|More<br>work from<br>home|More<br>online<br>sales|Increased<br>use of<br>digital<br>tech|
|2 quantile|0.19***|0.12***|0.22***|0.14***|0.25***|
||(0.05)|(0.04)|(0.04)|(0.05)|(0.05)|
|3 quantile|0.32***|0.13***|0.22***|0.11**|0.35***|
||(0.06)|(0.05)|(0.05)|(0.05)|(0.06)|
|4 quantile|0.29***|0.15***|0.24***|0.09|0.34***|
||(0.06)|(0.05)|(0.05)|(0.06)|(0.07)|
|5 quantile|0.31***|0.17***|0.26***|0.13**|0.37***|
||(0.07)|(0.05)|(0.05)|(0.06)|(0.07)|
|Medium|0.03|0.10***|0.04*|0.02|0.04|
||(0.04)|(0.03)|(0.02)|(0.03)|(0.04)|
|Large|0.10**|0.13***|0.06**|0.03|0.09*|
||(0.05)|(0.04)|(0.03)|(0.04)|(0.05)|
|Age 6 to 10|-0.04|0.00|-0.00|-0.06*|0.05|
||(0.05)|(0.03)|(0.03)|(0.03)|(0.05)|
|Age 11 to 15|-0.03|-0.03|-0.03|-0.04|-0.00|
||(0.05)|(0.03)|(0.03)|(0.04)|(0.05)|
|Age 16+|-0.00|0.01|-0.02|0.02|-0.02|
||(0.05)|(0.04)|(0.03)|(0.05)|(0.05)|
|Export|-0.00|0.01|0.04|0.04|0.06|
||(0.04)|(0.03)|(0.03)|(0.04)|(0.05)|
|Foreign owned|-0.01|-0.04|-0.01|0.05|-0.04|
||(0.07)|(0.06)|(0.04)|(0.06)|(0.07)|
|Change in mobility|-0.03|-0.02|-0.03*|-0.01|-0.02|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Work experience|-0.00|-0.00|-0.00|-0.00***|-0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Post-graduate degree|0.16***|0.07|0.02|-0.01|0.14***|
||(0.05)|(0.05)|(0.04)|(0.04)|(0.05)|
|Studying abroad|0.09*|0.06|0.02|0.05|0.07|
||(0.05)|(0.05)|(0.04)|(0.05)|(0.05)|
|Experience in large business|0.05|0.03|0.04|0.01|0.06|
||(0.04)|(0.03)|(0.03)|(0.03)|(0.04)|
|Management Z-score|0.02|0.01|0.02|-0.00|0.01|
||(0.03)|(0.02)|(0.02)|(0.02)|(0.03)|
|Country-sector fxed effects|YES|YES|YES|YES|YES|
|Observations|885|891|853|605|892|
|R-squared|0.18|0.12|0.23|0.20|0.14|

Table B2: Regressions of digitalization on quantiles of tech sophistication. _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses. The average marginal effects of independent variables are reported.

31
accounts (GDP, GNI, or Household Final Consumption Expenditure (HFCE)) differs from growth in average household consumption measured in household surveys (Ravallion, 2003; Deaton, 2005; Pinkovskiy & Sala-i-Martin, 2016, Lakner et a. 2021). To account for the difference between growth rates in per capita household consumption expenditures in national accounts and the per capita household consumption expenditures recorded in surveys, we construct different estimates for the pass-through using global data.

Specifically, we estimate different values of the pass-through rate using survey data on monthly household consumption available in PovcalNet (http://iresearch.worldbank.org/PovcalNet/home.aspx) matched to national accounts data from the WDIs to estimate the difference in growth rates between the two. To do this, we first pool all of the available data on household welfare (measured as either income or consumption) and national accounts for country-years in PovcalNet, obtaining a data set of 1,751 country-year spells when using GDP data and 1,521 spells when using GNI data (Table _10_ in the Appendix).<sup>20</sup> Then, we construct separate samples and run separate regressions using data for: all countries except high-income economies (column 2, Table _10_ ); all lower-middle-income countries (column 3, Table _10_ ); all countries with survey-estimates based on consumption as a measure of household welfare (column 4, Table _10_ ); and for countries in Sub-Saharan Africa (column 5, Table _10_ ). Each regression estimates a different value of the pass-through rate using the growth rate of either GDP per capita or GNI per capita as a predictor. The bottom panel of Table _10_ shows the results when running the regressions on the same groups of countries but using only “comparable surveys” as defined in PovcalNet’s comparability data set.<sup>21</sup> Results are reported in Figure 4 for pass-through rates between 0.42 (pass-through estimate for countries in fragile-conflict affected situations, see Corral et al. 2020) and 0.87. Poverty estimates for 2009 at the US$1.90 poverty line range between 42.2 (assuming a pass-through of 0.42) and 45.3 percent (assuming a pass-through of 0.87), and between 73.5 and 75.8 percent at the US$3.20 poverty line (assuming a passthrough of 0.42 and 0.87 respectively). These result show that changing the assumption about the passthrough rates has little effect on the backcasted series. Indeed, if anything, using lower pass-through rates

> 20  We focus on GDP and GNI pass-through rates as HFCE is not currently used in PovcalNet’s lining-up exercise to extrapolate household consumption estimates from surveys collected in Sub-Saharan African countries (see Prydz et al. 2019). Pass-through rates estimates using HFCE remain within the range reported in this note, for comparable consumption-based surveys we estimate a pass-through of 0.66 in line with what reported in World Bank (2020, box 2).

> 21 <u>https://development-data-hub-s3-public.s3.amazonaws.com/ddhfiles/506801/povcalnet_comparability.csv</u>

16
as Bankscope coverage improves over time, while the sampling across countries—which is equivalent to the red line divided by the dark blue line—averages 44 percent over the sample. Therefore, about half of the gap is coming from a lack of complete country coverage. This is partly due to the absence of the United States and Japan in the maturity data.

For credit data, the sampling is better. This is because Bankscope has much better coverage of purpose of credit data than maturity of credit data. The within-country sampling is 99 percent, and the across-country sampling is 60 percent. Therefore, for the purpose data, most of the gap is from countries that are absent from the reporting sources, rather than countries in the sample reporting less credit in the sources used than in IFS.

# **4. Credit Allocation in LAC and around the World**

Figure 3 compares the mean value of credit by different maturity groupings (maturity triples and doubles) as share of GDP at two points in time for different regions in the world according to the World Bank regional classification. Given few country observations for SubSaharan African countries, MNA countries, and South Asian (SAR) countries, observations for countries in those regions have been grouped in the ‘other’ region category. Outliers are addressed by dropping the top and bottom one percentile of credit measures across countries and within years.

The data reveal that in all regions, the average share of long-term loans (as measured by loans over one year) is higher than the share of short-term lending. Credit over GDP has grown over time in all regions except in high-income countries reflecting the effects of the global financial crisis and the great recession. Short-term credit was particularly affected in high-income countries. When splitting maturity into triplets (second panel of the graph), we see that the share of midtermcredit tends to be greater than the share of long-term lending except for LAC and high-income countries. The share of credit beyond five years has increased in all regions except for high-income countries. Overall, LAC compares favorably to other developing regions in terms of the share of long-term credit (particularly beyond five-year maturity) but seems to have less short-term credit to GDP than other regions. This result holds if median values (by region) are used instead of means.

Analysis of the share of credit by purpose of credit (Figure 4) reveals that in all regions, commercial credit has the largest share followed by consumer credit and mortgages. In recent years, despite the mortgage crisis in the United States and some European countries, overall 14
- 121 -

- Jimenez, Emmanuel and Bernardo Kugler, with Robin Horn. An Economic Evaluation of a National Job Training System: Colombia's SENA, Education and Training Department Discussion Paper No. 24, The World Bank, Washington D.C., 1986.

- Knight, J.B. and R.H. Sabot. Why the Returns to Experience Increase with Education, mimeo, Development Research Department, The World Bank, Washington D.C., 1983

- Maddala, G.S. Limited Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge, 1983.

- Mincer, Jacob. Schooling, Experience and Earnings, New York, National Bureau of Economic Research, 1974.

- ONFP. (Office National de Formation Professionnelle). Bilan de 1' Enseignement Technique et de la Formation Professionelle, Abidjan, 1984.

- ONFP. Les Besoins Prioritaires de la Formation Professionnelle Continue en M8te d'Ivoire, Abidjan, 1985.

- ONFP. Repertoire des Moyens de Formation en Republique de C8te d'Ivoire, Abidjan, 1986.

- ftaai;.-, C-e;ge .Rt-- s t u'_t' n: .^ivvt4rv A Interntional<sup>4</sup> Update and Implications, Journal of Human Resources, Vol. XX(4), 1985.

- Vandergaag, J. and W. Vijverberg. gage Determinants in C8te d'Ivoire. Living Standards Measurement Study Working Paper, No. 33, The World Bank, Washington D.C., 1987

- World Bank. World Development Report 1986, Oxford University Press, New York, 1986.
classifications in economic census data.

First, industrial classifications in censuses are based on points of production rather than consumption, and so may include or exclude goods and services that are not in the relevant product market. Even so, for purposes of data management, the Commission indicates a high-level industrial classification of the relevant market in each case and then provides further detail in a written description because the relevant markets in question are more specific than those covered by the high-level classification. We use this information to assign each relevant market a 6-digit industry classification from the North American Industry Classification System (NAICS), the lowest level of aggregation possible.<sup>10</sup> We define this as the _treated industry_ . We then work forward and backward from the treatment date to define a consistent industry classification that captures the same set of activities from the first to last census in our dataset, including new activities that emerge during that time period. These classifications are generally more inclusive than a 6-digit classification, but there are many multiples within 3-digit classifications.

Second, relevant markets sometimes have specific geographic boundaries. In the majority of investigations, the relevant market is national (e.g., the national market for soft drinks). In the remaining investigations, we use relevant market definitions in the disclosures to define geographic boundaries for each market as a 6-digit industry within a set of municipalities or states. Within these boundaries, we aggregate establishment-level micro-data to construct series for the treated industry (e.g., all real estate brokers in the municipalities surrounding Lake Chapala in Jalisco state).

We define a pool of _control industries_ to be all other 6-digit industries in the same 3- digit sub-sector as the treated industry. The motivation for this is that other industries in the treated industry should share common (unobserved) trends in demand and costs, while still being outside the relevant market defined by investigators. This approach to identifying controls in similar industries has been used in other studies of antitrust (Sproul, 1993; Kwoka, 2014). We successfully identify treatment industries for 130 of 261 investigations. Some cases are excluded because the decision of the Commission occurs after 2018, which means we do not observe treated outcomes, or because the decision occurs prior to 1998, which means we would not observe trends in prior outcomes, as required by the difference-in-differences estimator described in Section 3. Of these investigations, we are able to find control industries in the same sub-sector for 90 investigations. For some cases, control industries could not be identified in the same 2-digit or 3-digit sub-sector. These are industries with unique economic

> 10NAICS is a joint classification developed by the Statistical Offices of the United States, Mexico, and Canada. It offers high-level comparability across the three countries in terms of the conceptual framework but still leaves room for country-level specific codes.

12
-32-

no claim to a complete or systematic analysis47. Two, I focus attention<sup>in</sup> those factors that have characterized the Mexican economy<sup>over the last</sup> decades, since the current structure of poverty<sup>is the cumulative result of</sup> past policies. As I point out below, progress has been made in<sup>reforming</sup> rural regulations, in changing the pattern of subsidies, and in reducing macroeconomic uncertainty. But these changes have only<sup>occurred recently, so</sup> that their effects on poverty will only be felt in the future.

# V.1 Rural and Agricultural Develo2ment.

There is a certain paradox in the argument that the root cause<sup>of poverty</sup> in Mexico lies in the rural areas. The paradox lies in the fact that<sup>for a</sup> substantial period of time Mexico's agriculture was a success story.<sup>Indeed,</sup> using the language of Timmer (1988), during the period of 'the agricultural transformation' the agricultural sector in Mexico helped to: ".. (i) increase the supply of food for domestic consumption, (ii) release labor<sup>for industrial</sup> employment, (iii) enlarge the size of the market for industrial<sup>output, (iv)</sup> increase the supply of domestic savings, and (v) earn foreign exchange"<sup>(op.</sup> cit., p. 290). As argued by Yates (1981, p. 7-8), between 1940 and<sup>1965</sup> agricultural output in Mexico increased at an average annual rate<sup>of 5.71; and</sup> while the population was growing substantially, output per capita increased over 2X annually during this time.

Various factors account for this magnificent performance, but two need to be singled out: one, there was an increase in the extensive margin,<sup>with</sup> harvested area growing at about 31 annually from 1940 to 1960. Two,<sup>technical</sup> change generated an increase along the intensive margin with yields<sup>growing at</sup> about 2X annually. Much of the growth in irrigated land occurred<sup>in the orth</sup> 4<sup>7</sup> Major research challenges lie in these areas. At the<sup>theoretical level</sup> it is necessary to develop models that capture the incentive and efficiency effects of the ejido/private land dichotomy. At the<sup>empirical level more</sup> evidence is needed on the determinants of crop choice,<sup>tenancy arrangements</sup> and the demand for rural labor. Unfortunately, the data for such analysis is particularly scarce in Mexico. In addition, because some tenancy<sup>arrangements</sup> are illegal (although apparently common), the quality<sup>of the official data is</sup> suspect. ("However, given the clandestine renting<sup>out of ejido land to</sup> private farmers, the census data probably fail to<sup>reflect the real</sup> distribution of inputs and outputs between the two<sup>sectors; this places in</sup> doubt any conclusions about relative productivity that are<sup>derived from the</sup> census data." Heath, 1990, p. v.)
Another reason for restricting focus to only these pollutants is that while _NO_ 2 , _CO_ , and _PM_ 2.5 are mainly generated from transportation sources, _SO_ 2 and _O_ 3 are not. In one of the first pollution inventory studies for Delhi, Gurjar et al. (2004) infer that during their study period (1990–2000), transport sector contributed about 82 percent of nitrogen oxides ( _NOx_ ),<sup>7</sup> and 86 percent of _CO_ . In another study for Delhi conducted in 2007, NEERI (2010) reports that the contribution of vehicles towards _NOx_ , _CO_ , and particulate matter ( _PM_ 2.5 and _PM_ 10 ) was 18, 58, and 59 percent, respectively. For Delhi in 2010, Guttikunda and Calorie (2012) estimate that 67, 28, and 35 percent of _NOx_ , _CO_ , and _PM_ 2.5 , respectively, can be attributed to vehicles.<sup>8</sup> While there is variation across studies in the exact share of transportation sources in generating these pollutants, all of them report substantial shares. On the other hand, NEERI, and Guttikunda and Calorie, report that vehicular emissions were responsible for 0.3 percent and 3 percent, respectively, of _SO_ 2 .<sup>9</sup> None of these studies look at _O_ 3 . However, it is known that _O_ 3 is not directly emitted by motor vehicles, but is created through a complicated nonlinear process wherein oxides of nitrogen and volatile organic compounds react together in the presence of sunlight (Sillman, 1999). Thus, of the five pollutants for which we have data, motor vehicles constitute a major and direct source of only three, namely, _NO_ 2 , _CO_ , and _PM_ 2.5 . To the extent that one of the main channels through which the DM is likely to affect air pollution is through its impact on overall levels of vehicular emissions, we focus our attention on these three pollutants. Moreover, Delhi is a heavily motorized city,<sup>10</sup> and the consequent vehicular emissions are a

> 7. _NOx_ refers to both nitrogen monoxide ( _NO_ ) and nitrogen dioxide ( _NO_ 2 ). 8. The contribution of vehicles towards particulate matter reported in NEERI (2010) and in Guttikunda and Calorie (2012) includes the contribution of road dust as well.

> 9. Gurjar et al. (2004) do not report this figure for _SO_ 2 . 10. Among the 44 reported million-plus cities in India, Delhi had the largest number of registered motor vehicles during 2011–12 with 7.4 million vehicles (TRW 2013). Goel et al. (2015) estimate that of all registered cars and

5
effects or even the direct second-round effects of trade liberalization operating through the labor market. We adopt a combination of ex-ante and ex-post approaches. In the exante simulations we assess the extent to which first-round direct effects have contributed to poverty reduction across heterogeneous household categories. In ex-post analysis we describe the income growth trend of the same groups of households and provide some possible explanations for the observed income change.

# **3. Background and data**

The data in this paper are stemming from the 1995-96 and 2000 Household Income Expenditure Surveys (HIES) of Bangladesh. We use the 1995-96 survey in the ex-ante simulations and both surveys in the ex-post analysis. The same communities, or _thana_ as they are called in Bangladesh, are included in both rounds of this repeated cross-section survey, enabling us to combine the two data sets in a single community-level panel data set. Although it would be preferable to have an individual-level panel data set, _thana_ - level panel allows us to control for unobserved heterogeneity by community when we examine the trends in producer and consumer prices of rice, production volume, and rice yields.

Although the sampling frame has not changed, significant modifications in the questionnaire design result in poor comparability of households according to some dimensions. That is particularly true of the questions about the total area of land cultivated and about agricultural production, which are of crucial importance in our analysis. In particular, the calculated change in yields is highly sensitive to changes in the size of cultivated area and should be interpreted with caution. Thus, average yields calculated from the _thana_ -level panel are sensitive to the presence of outliers.<sup>1</sup> These data comparability issues and our approach to resolving some of them are described in Appendix 2. We resolved some of these issues and developed a consistent definition of farming household in both surveys. Due to the remaining unresolved survey comparability issues, such as the absence of a consistent definition of agricultural wage earners, we are unable to conduct a detailed analysis of the welfare effect of price and productivity changes on this category of households.

> 1 This effect persists even after cleaning the data by dropping observations over 5 standard deviations from the mean of the data.

9