rafmacalaba/datause-encoder-data huggingface dataset:
10 Note that in the consumption equation, the growth rate of consumption per _adult equivalent_ is implicitly approximated as being equal to the growth rate of GDP per _capita._<sup>_5_</sup>

# _Inequality adjustment_

The assumption of distribution-neutral growth can be relaxed by adjusting consumption for each household within each sector year-by-year. This adjustment is made after the growth projection for each year. Take the percentage change in the Gini coefficient in sector _Si_ in year _t_ as ∆ _GtS_ . The adjusted level of per capita consumption for household _i_ is then This produces a proportional shift in the sectoral Lorenz curve by adjusting consumption for each household relative to its deviation from the sector-specific mean. Note that the mean here is necessarily the weighted mean, calculated with the weights given by{<sup>_w_</sup> _i_ , _t_<sup>}. This adjustment</sup> effectively redistributes consumption from households below the mean to those above the mean (for an increase in the Gini), while leaving mean consumption by sector constant. It would be possible to undertake alternative redistribution procedures that would achieve the same outcome in terms of changes to the Gini and the mean.

# **4.2 Two Survey Approach**

The Datt and Walker approach outlined above was designed to project changes in poverty based on national growth data and a single household survey, in the absence of data from multiple household surveys. For the analysis constructed in this paper, the problem is somewhat different, as the goal is to understand changes in the distribution during the period between two household surveys. While in the Datt and Walker general case the distribution of consumption is known only at the beginning of the simulated period, for Tanzania in the 1990s the full distribution is known both at the beginning and the end of the period.<sup>6</sup> An extension of the Datt and Walker method can be used to force the distribution at the end of the simulation to closely match that of the survey data. Specifically, extend the notation from above to reference each household’s quantile, with n the number of fractions in which the distribution is broken down. For instance if n equals five, the distribution would broken down in quintiles.

These quantiles are defined over individuals by household consumption per adult equivalent and can be in terms of the full national distribution, or in terms of each sector’s distribution. The simulation is carried out using these growth rates:

> 5 In fact, the 2002 Census report shows that children made up a slightly smaller percentage of the total population in 2002 as they did in 1988. This means that the growth rate of consumption per adult equivalent was slightly larger than the growth rate of consumption per capita.

> 6 More precisely, _estimates_ of the distribution are known from the household surveys.
we obtained GDP per capita from the World Bank’s World Development Indicators,<sup>3</sup> and cultural dimensions data from Geert Hofstede’s website.<sup>4</sup> We capture the environmental, social, and governance dimensions that represent ethical practices by constructing three binary variables from the ES. Specifically, the environmental dimension is captured by a binary variable that takes the value of one if the firm has monitored its CO2 emissions over the past three years (“ _mon_emi”_ ), and zero otherwise.<sup>5</sup> The social dimension is captured by a binary variable that takes the value of one if the firm offered formal training programs for permanent, full-time employees (“ _training_ ”) in the last fiscal year, and zero otherwise. The governance dimension, related to the employment of female top managers (a gender issue) is captured by a binary variable that takes the value of one if a company employs female top managers (“ _top_man_fem_ ”), and zero otherwise.

To identify digital-tech-oriented firms, we build two binary variables. First, we exploit the R&D intensity classification at the two-digit level by Galindo-Rueda and Verger (2016). We construct a first binary variable that takes the value of one for firms in sectors with at least medium technology orientation as implied by their R&D intensity classification, and zero otherwise (“ _tech orientation”_ ).<sup>6</sup> Second, we compute a binary variable that takes the value of one if the establishment has its own website or a social media page, and zero otherwise (“ _digital orientation”_ ). The product of tech

> 3 Data can be retrieved from the following web-link: https://databank.worldbank.org/source/world-development-indicators (last accessed: November 2024).

> 4 See: Dimension data matrix, version 2015 12 08 0-100, available at the following link: <u>https://geerthofstede.com/research-andvsm/dimension-data-matrix/ (last accessed: June 2024). In our database, cultural data begin in 2015.</u>

> 5 CO2 emissions monitoring data is available for the years 2020-2023 for 49 countries.

> 6 See: Galindo-Rueda and Verger (2016).

7
# **<<A>>** Acronyms and Abbreviations

|AIDS|Acquired immunodeficiency syndrome|
|---|---|
|ANC|Antenatal care|
|BulkSMS|Web-based bulk text-messaging platform|
|CAS|Country assistance strategy|
|CPIs|Client-provider interactions|
|DHS|Demographic and Health Survey|
|GPOBA|Global Partnership on Output-Based Aid|
|GPS|Global positioning system|
|HFA|Health facility assessment|
|HNP|Health, nutrition and population|
|IMF|International Monetary Fund|
|IVEA|Independent verification and evaluation agent|
|KfW|Kreditanstalt für Wiederaufbau, German Development Bank|
|MDG|Millennium development goal|
|MSI|Marie Stopes International|
|NGO|Nongovernmental organization|
|OBA|Output-based aid|
|PNC|Postnatal care|
|PPH|Postpartum hemorrhage|
|RHAP|Reproductive health action plan|
|RHVP|Reproductive health voucher program|
|SD|Safe delivery|
|SP|Service provider|
|STD|Sexually transmitted diseases|
|STI|Sexually transmitted infection|
|UBOS|Uganda Bureau of Statistics|
|UHC|Universal health coverage|
|UNDP|United Nations Development Program|
|USAID|United States Agency for International Development|
|VCBD|voucher community-based distributor|

3
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# **Table A10: Real expenditure on consumption (services), % yoy**

||**Educati**|**on and hea**|**lthcare**|**Utilitie**|**s and tran**|**sport**|**Ot**|**her servic**|**es**|
|---|---|---|---|---|---|---|---|---|---|
||**2015**|**2016**|**2017**|**2015**|**2016**|**2017**|**2015**|**2016**|**2017**|
|Average|5.5|‐1.2|5.0|‐4.4|‐7.3|‐1.4|‐3.2|‐3.1|8.6|
|||Poor and|vulnerable|populatio|n|||||
|Vulnerable|12.4|1.3|6.8|‐0.8|‐4.6|2.3|‐3.2|3.7|8.6|
|Official poverty|9.0|10.0|‐0.2|‐1.7|‐3.4|6.2|5.7|15.2|‐2.0|
|Moderatepoverty|11.3|14.4|13.4|0.7|‐0.9|6.5|6.3|11.2|6.9|
|||Pl|ace of resid|ence||||||
|Minsk|4.9|‐1.6|7.4|0.0|‐5.8|‐5.4|0.4|‐10.9|7.1|
|Large cities|7.2|0.5|3.1|‐4.4|‐5.5|‐3.2|‐2.8|‐2.4|8.8|
|Small cities|0.7|0.7|5.9|‐6.4|‐10.2|‐0.5|‐9.7|3.4|6.4|
|Rural area|2.9|‐6.2|3.8|‐11.7|‐10.6|6.2|‐8.4|3.7|11.4|
||||Age|||||||
|Child|9.0|‐0.3|6.7|0.0|‐6.4|‐2.9|‐1.9|‐2.1|6.4|
|Working age|1.5|‐1.6|4.8|‐4.2|‐8.4|‐2.5|‐4.8|‐3.2|8.3|
|Retiree|9.3|‐2.0|3.0|‐7.5|‐6.2|0.1|0.6|‐2.6|12.9|
||Emplo<br>|yment stat<br>|us for wor|kingagep<br>|opulation<br>|||||
|Employed|3.1|‐2.4|5.4|‐6.1|‐7.6|‐2.3|‐4.4|‐5.1|9.6|
|Partly employed|‐10.8|12.8|6.4|‐4.6|‐6.8|‐7.6|‐17.6|5.5|1.4|
|Non‐employed|6.1|‐2.8|2.5|‐0.2|‐9.0|‐1.0|‐1.7|‐1.9|6.8|
|<br>|<br>Edu|<br>cation leve|<br>l for workin|<br> gagepop|<br>ulation|||||
|High education|2.7|6.1|3.7|‐4.1|‐4.6|‐10.0|‐7.9|‐3.2|10.0|
|Secondary specialized|‐5.7|‐6.3|4.0|‐3.5|‐13.9|2.4|‐4.3|‐6.1|6.3|
|Other|4.8|‐6.8|4.7|‐6.8|‐7.1|0.1|‐4.0|‐2.0|6.4|
|||Type|of househ|old(HH)||||||
|HH without children|4.1|‐1.0|2.4|‐7.0|‐7.8|0.3|‐3.6|‐4.2|10.1|
|Retiree HH|11.8|‐5.1|5.0|‐8.3|‐6.6|0.7|‐3.3|2.3|15.9|
|HH with children|7.1|‐1.6|8.8|‐0.6|‐6.8|‐3.4|‐2.8|‐2.0|7.0|
|1 child|4.3|‐7.3|12.0|‐2.0|‐6.5|‐6.3|‐5.2|‐2.3|7.4|
|2 children|7.2|8.7|2.0|‐3.0|‐7.6|2.2|‐1.2|‐2.7|7.0|
|3 and more children|10.9|14.1|13.3|0.9|‐5.5|5.0|‐8.3|6.0|0.7|
|singleparent|12.8|‐12.0|6.9|0.8|‐3.2|‐2.1|2.9|‐2.4|1.9|

Source: Authors’ estimates based on HSS data.

37
hardest those localities most dependent on these taxes. Data in Table 25 below show that across provinces, the weight of agricultural taxes varied from 17 percent of total revenues in Anhui to nearly zero in Tibet. In this table we provide an illustrative ranking of provinces according to the revenue impact of the RTFR.

The rankings presented in Table 25 are approximate because the 2003 data already reflect some reductions in agricultural tax revenues from the RTFR, and therefore understate the effect of the reform on provincial revenues.<sup>57</sup> Excluding Yunnan, Table 25 shows the most affected provinces to be Anhui, Henan, and Hubei, where the reforms will reduce revenues by some 10 percent or more. At the bottom, there are nine provinces and cities where the impact will be less than 1 percent of total revenue, and another eight where the impact will less than 4 percent of revenue.

**Table 25.  Ranking of Estimated Revenue Impact by Province (% of total revenue), 2003**

|Province and<br>national<br>ranking|Taxes to be<br>eliminat<br>ed*|Five agric.<br>Taxes|Province and<br>national<br>ranking|Taxes to be<br>eliminate<br>d*|Five<br>agric.<br>taxes|
|---|---|---|---|---|---|
|1. Anhui|12.09|17.12|18. Chongqing|4.72|8.34|
|2. Henan|11.96|14.25|20. Jiangsu|3.33|10.17|
|3. Yunnan|10.86|13.57|23. Liaoning|2.31|6.18|
|4. Hubei|9.96|12.79|28. Guangdong|0.71|4.90|
|6. Jiangxi|9.52|10.92|34. Shanghai|0.01|7.54|
|15. Sichuan|6.83|8.32|35. Tibet|0.00|0.03|
||_ave_|_rage_||_4.75_|_8.52_|
||_standard_|_deviation_||_3.76_|_3.46_|
||_coefficient_|_of variation_||_0.79_|_0.41_|

Notes: Taxes to be eliminated include the agricultural tax, animal husbandry tax, and agricultural special products tax. Shares are as a percentage of total provincial revenues. “Provinces” include some major cities such as Dalian and Xiamen. Yunnan is excluded because of the large portion of tax revenues accruing to tobacco, which will not be affected by ATA.

Source: Based on Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, 2003.

> 57 For the agricultural special products tax, for example, the RFR had reduced the scope of what could be taxed and reduced the rates by as much as one-half on many products. In Henan, for example, revenues from the agricultural special products tax had fallen from RMB 937 million in 2001 to only RMB 271 million in 2003.

53
A major criticism of early Ricardian method applications is that it does not address irrigation water (Cline 1996). Mendelsohn and Dinar (2003) use surface withdrawal data in their re-estimation of the Ricardian model but actual withdrawals are endogenous. Mendelsohn and Dinar (2003), Schlenker et al. (2005), and Kurukulasuriya et al. (2006) also address the irrigation issue by estimating Ricardian models for dryland separately from irrigated land. In Israel, water supplies are determined exogenously by administrative and historic mechanisms. This specific situation allows us to explore what difference exogenous flows of irrigation water have on farm performance and on its climate sensitivity.

Due to the fact that Israeli agriculture depends heavily on water, there have been significant efforts of the public sector to provide incentives to farmers for efficient water use. Using diversified water availability levels across the country, the paper offers a unique opportunity to investigate the role of irrigation water as an adaptation strategy of farmers to climate change in Israel.

Israeli agriculture is also unique in its investment in capital to substitute for water and land. Farmers use combinations of advanced irrigation technologies, such as drip irrigation and cover technology in order to adapt specialized farming techniques to local climate. Israeli farmers have consequently been able to shape their agricultural system to the climate of their country and take advantage of heat rather than be a victim of it. We consequently see that Israeli agriculture is relatively more heat tolerant than, for example, the United States (Mendelsohn and Dinar 2003). Having observed that, an investigation of the impact of climate change on the Israeli agriculture is still needed to address the question in the title of the paper.

The next section will provide the background information on climate and water quotas in Israel—two essential resources that shape the nature of the Israeli agriculture. Then section three of the paper spells out the model applied, followed in section four by the data sources and data preparation procedures. The results of the sensitivity surface estimates are presented and discussed in section five. A set of forecasts of impacts is detailed in section six, followed by a conclusion section. The paper ends with a section on possible extensions and policy implications.

# **Israeli Climate Conditions and Water Quotas**

Israel's total area is about 22,000 square kilometers. The northern part is characterized by a Mediterranean climate while the southern part is a hot dessert. In between there is a 3
**<u>Table 4: Distributional changes in global poverty profiles with square-root allocation rule</u>**

|Region|Per<br>capita<br>poverty<br>rate (%)<br>at $1.90|Square-<br>root<br>poverty<br>rate (%)<br>at $4.47|Change<br>in<br>poverty<br>(pp)|Per-<br>capita<br>poor<br>(millions)|Square-<br>root poor<br>(millions)|Change<br>in<br>millions<br>of poor|Absolute<br>deviations<br>in<br>millions<br>of poor|
|---|---|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Sub-Saharan<br>Africa|41.2|38.8|-2.3|431|406|-24.5|35|
|Middle East &<br>North Africa|7.1|5.3|-1.8|23|17|-5.8|6|
|Europe and<br>Central Asia|1.3|1.3|0.0|6|6|-0.2|1|
|**World**|11.6|11.6|0.0|683|683|0.0|87|
|Other High<br>Income|0.7|0.8|0.1|7|8|1.0|1|
|Latin America &<br>Caribbean|3.8|4.4|0.6|22|26|3.7|5|
|South Asia|9.7|10.6|1.0|170|187|16.9|27|
|East Asia &<br>Pacific|3.7|5.1|1.4|24|33|8.9|11|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table reports poverty estimates for the 2017 reference year. For countries with no surveys conducted</u> exactly in 2017, the poverty estimates are extrapolated from the latest survey if conducted before 2017, otherwise extrapolated or interpolated from the closest surveys before and after 2017. The extrapolation and interpolation rules applied are the same ones the World Bank applies when “lining-up” global poverty estimates for every year (see the - Poverty and Inequality Platform (PIP) Methodological Handbook via <u>https://worldbank.github.io/PIP Methodology/lineupestimates.html). The table is arranged in ascending order of change in poverty (column 4). These</u> results are based on the full sample of 162 countries covered in the paper.

33
shocks. Finally, equation (6) can be estimated using ordinary least squares to get consistent estimates of β _er_ .<sup>3</sup> In the **<u>fourth step</u>** <u>, the coefficients</u> α _ey_ and α _ry_ in equation (2) can be determined using the structural uncorrelated fiscal shocks<sup>_v_</sup> _te_<sup>and</sup><sup>_v_</sup> _tr_<sup>as instruments for</sup><sup>_u_</sup> _te_<sup>and</sup><sup>_u_</sup> _tr_<sup>,</sup> respectively.

**Combined, the four steps** allow us to estimate the A and B matrices of the standard relation between the reduced-form innovations and the structural shocks _Aut_ = _Bvt_ , where:

With this, impulse responses are computed using Monte Carlo simulations based on 10,000 replications.

Given the non-stationary properties of the underlying series, we test for co-integrated relationships among variables. If no co-integrated relationships are found, a Vector Auto Regressive (VAR) model, specified in first log differences, is estimated. If there are cointegrated relationships, a vector error correction (VEC) model is estimated, without imposing additional restrictions in order to avoid over-identification. In its basic form, a VEC model can be represented as: Where Π is the long-run matrix which contains the equilibrium correction terms. As usual, the _number_ of co-integrating vectors is given by the number of linearly independent rows of Π , namely the rank of Π .

> 3 β _er_ is estimated to be 0.7541 for Argentina.

6
Table A1: Proportion of Labor Market Fines in the City <u>(2002)</u>

||Obs|Average|SD|
|---|---|---|---|
||(1)|(2)|(3)|
|Worker's Formal Registration|1,453|0.22|0.31|
|Mandatoty Work Period|1,453|0.10|0.20|
|Mandatory Work Pause Period|1,453|0.09|0.17|
|Wage|1,453|0.09|0.18|
|FGTS Contributions|1,453|0.26|0.32|
|Other (incl. Health, Security Restrictions)|1,453|0.23|0.29|

Source: Brazilian Ministry of Labor (2002)

# <u>Table A2: City Employment Composition</u>

||Obs.|Share Total<br>Population|
|---|---|---|
||(1)|(2)|
|Domestic worker with formal work permit|5,507|0.008|
|Domestic worker without formal work permit|5,507|0.025|
|Employee with work permit|5,507|0.137|
|Employee without work permit|5,507|0.163|
|Employer|5,507|0.015|
|Self-Employed|5,507|0.196|
|Unpaid apprentice|5,507|0.001|
|Unpaid employee|5,507|0.036|
|Worker self-consumption|5,507|0.046|
|No employment status|5,507|0.373|

Source: Census (2000) Table A3: Distribution of City Wages by Employment Status

||Percentile 10|Percentile 50|Percentile 90|
|---|---|---|---|
||(1)|(2)|(3)|
|Formal Wage Earners|5.03|5.60|6.51|
|Informal Wage Earners|4.52|5.34|6.37|
|Self-employed|4.35|5.40|6.69|

This table shows percentiles of the wage distribution for the formal wage earners, informal wage earners and self-employed, respectivley.
<!-- Start of picture text --> Chart 1<br>Trade openness for different regions, 1975-1997<br>40<br>30<br>4 25<br>0<br>20<br>) )10 lMM<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997<br>-iSub-Saharan Africa --- High income: OECD A Latin America & Caribbean -Europe & Central Asia<br><!-- End of picture text --> _Source:_ World Bankl999b,c.

however, any adverse impact on African exports is likely to be slight. A more significant effect could arise from progress in reforming the EU Common Agricultural Policy. The common policy was discussed recently, but none of the issues under discussion is likely to have any substantial effects for Sub-Saharan Africa countries.

# _2.3. Trade effects through changes in the euro_ **_real_** _exchange_ **_rate_**

The real exchange rate is another channel through which the euro could affect African exports to Europe. Again, the composition of exports from Sub-Saharan African countries is a central variable, but the lack of estimates for the elasticity of exports to changes in the real exchange rate makes it hard to estimate the sensitivity of exports to such changes.

The effect of changes in the real exchange rate depends on the exchange rate regime in each country (table 3) and on geographical trade patterns. Countries that peg their exchange rate to the euro would expect a loss of competitiveness relative to third parties if 7
Policy ReseaRch WoRking PaPeR 4723

# **Abstract**

Recent developments in trade theory, especially research on multi-product firms, have not been matched by similar progress on the empirical front. This paper aims to fill this gap by presenting a novel set of stylized facts on firm-product dynamics observed during an export boom. This exercise is possible thanks to a unique firm-product level dataset covering about 85 percent of Mexican industrial output for the period 1994–2003. The main findings are as follows. First, there is a substantial degree of product turnover at the firmproduct level in response to declining trade costs. Second, "core competencies"—the fact that firms have a cost advantage or greater expertise at manufacturing some of their products—are the main driver of firms' decision to introduce or drop export products. Third, new exporters tend to "start small" in terms of both values and number of exported products. Fourth, even if the expansion in the number of exported products played a role in stimulating Mexican exports, the growth in volume of pre-existing products was the main driver of the export boom. Finally, the introduction of new export products is preceded by a surge in investment. These findings are in line with many, but not all, predictions of recent theoretical work.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand how export drivers of exports grow. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at Liacovone@worldbank.org and Beata.Javorcik@economics.ox.ac.uk.

_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 and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
are usually available for firms listed in stock exchanges across a wide set of economies, but not for unlisted firms that conduct issuances and constitute about 60 percent of all corporate bond issuers (Table 1).<sup>6</sup> As an alternative measure of size that is comparable across firms and covers the whole universe of issuers, we proxy firm size by the average amount raised per issuance, measured over all issuances per firm during 1990-2016.

To make sure that the average amount raised is a reasonable proxy for issuer size, we plot the average amount raised and the average assets (also from SDC) for listed firms. The scatter plot shows a high correlation between the two size variables (Appendix Figure 1, Panel A). Regressions of the log of average assets on the log of the average amount raised per issuance yield a point estimate of 0.99, not statistically different from one but statistically different from zero. To the extent that a similar correlation exists for unlisted firms, the average amount raised should be a reasonable proxy for the analysis of issuer size. A comparison of the firm size distribution (FSD) of listed and unlisted bond issuers illustrates the importance of using a comprehensive and complete measure of firm size. Focusing on the bottom quantiles, the FSD of unlisted bond issuers lies to the left of the FSD of listed issuers, indicating that the smallest firms tapping bond markets are unlisted (Appendix Figure 1, Panel B). If we were to use assets from only listed firms, we would be disregarding the smallest bond issuers.

To link capital raising issuances with firms’ balance sheet and income statement data, we merge the transaction‐level data from SDC with financial statement information for publicly listed firms from Worldscope. Using these data, we could also check that our results on firm size are not specific to the proxy we use. We explored two alternative measures of firm size from the Worldscope data: total assets and net sales, taking the end-of-year values reported by firms. Both sets of results with the alternative measures are qualitatively and quantitatively similar to the ones reported in the paper.

> 6 This statement also holds when considering as “listed firms” subsidiaries owned by listed parent companies.

11