rafmacalaba/datause-encoder-data huggingface dataset:
previous period’s patenting activity to consider the path dependence that would be present if there were hysteresis in patenting. Using this lagged variable provides us with three periods per country in the sample.

# **_Inside-the-Frontier Innovation_**

Performance in inside-the-frontier innovation is measured by discovery counts as described in Klinger and Lederman (2006), available for a cross-section of countries during the period 1997-2002. The unit of observation is the Leamer (1984) commodity group in each country, and therefore the performance measure is discovery counts in the country/commodity cluster. Discovery counts are first aggregated to the SITC 3-digit level, and the Gini coefficient for each country/Leamer commodity cluster was calculated as described above across the SITC 3-digit clusters comprising the commodity group.

The controls comprising the _X_ vector in this case follow from Klinger and Lederman (2006). First, as above, we control for the historical level of discovery activity in the country/commodity group to control for country/commodity group fixed effects. Unfortunately, discovery data is only available in a cross-section, rather than in multiple panels as is the case with our patent data. As such, we are unable to include a lagged variable that allows us to distinguish between fixed country effects and persistence that from path-dependent innovation. In the case of discoveries and export booms, both of these effects will be captured in the historical variable.

It is also necessary to control for GDP per capita (in quadratic form) to capture the discovery curve identified in Klinger and Lederman (2006) and capture distance to the global technological frontier. Finally, we include commodity cluster dummy variables 10
# **Higher firing costs are associated with smaller employment loss and higher earnings loss**

<!-- Start of picture text --> East Asia (omitted)<br>Europe ***<br>Latin America ***<br>Middle East **<br>Low middle income (omitted)<br>Middle middle income **<br>Upper middle income<br>Small manufacturing sector ...<br>Large manufacturing sector<br>Less dependent (omitted)<br>**<br>More dependent **<br>Fixed or Peg (omitted)<br>Managed float<br>Full float<br>Low difficulty of redundancy<br>High difficulty of redundancy<br>Low redundancy cost<br>High redundancy cost *<br>Firm survey<br>Household survey (omitted)<br>Change in employment growth -4 -2 0 2 4 6<br>Change in earnings growth<br>Note: Bars indicate coefficients from fourteen separate cross-sectional regressions<br>of changes in employment and earnings growth on change in GDP growth, data<br>source, and each country characteristic. Stars indicate statistical significance at the<br>10 , 5, and 1 percent level. There are 35 or 36 observations in the employment<br>regressions and 30 or 31 in the earnings regressions.<br>Region<br>Income<br>e<br>Structur<br>Exports<br>rate<br>Exchange<br>Regulation<br>Data source<br><!-- End of picture text --> _Countries with larger export sectors, and less stringent regulation better maintained previous trends in earnings without incurring large employment costs._ Figure 9 shows how earnings and employment are associated with each set of country characteristics after accounting for the declines in countries’ GDP growth.<sup>22</sup> The most striking results are those associated with exports and firing regulations. High export countries were much better able to maintain earnings, at negligible cost to employment. Countries with higher redundancy costs were better able to protect employment, on the order of 1 to 2 percentage points. However, this may have partially come at the expense of greater earnings reductions. Countries with more costly and cumbersome firing regulations had two to three percentage point greater declines in earnings. The relationship between firing regulations and earnings declines is only suggestive and not statistically significant, however, due to the imprecision of the earnings measures.

**Figure 9**

> 22 Figure 9 reports coefficient estimates from 7 regressions. Each bar represents the coefficient from a regression of employment or earnings growth on the change in GDP growth, its square, cube, and quartic, and one set of dummy variables.Stars indicate significance level. For earnings and employment equations, the regression also controlled for whether the data was derived from a firm or a household survey.

10
# **Appendix Table 1. Average Monthly Expenditures on Energy**

(By expenditure quintile and location in lei)

|||Energ|yProduct||
|---|---|---|---|---|
|Quintile|Central Gas|LPG|Electricity|Central Heat|
||(1)|(2)|(3)|(4)|
|**_A. All households _**<sup>**_1_**</sup>|||||
|Poorest|6.3|2.7|27.9|0.6|
|Second|12.1|3.2|33.0|3.1|
|Third|14.2|4.0|38.9|8.6|
|Fourth|21.7|5.0|44.8|16.1|
|Richest|30.1|6.2|57.3|26.4|
|**_B. Connected households_**|**_with positive expendi_**<br>|**_tures _**<sup>**_2_**</sup><br>|||
|Poorest|38.4|76.0|33.2|...|
|Second|47.8|70.3|37.7|101.8|
|Third|55.3|66.5|43.6|138.2|
|Fourth|65.9|65.5|49.8|168.4|
|Richest|72.3|70.9|67.9|165.8|
|Of which:|||||
|**_B1. Large Cities_**|||||
|Poorest|28.2|...|48.7|...|
|Second|24.3|...|57.5|101.9|
|Third|23.3|...|69.1|138.7|
|Fourth|27.0|...|73.3|168.4|
|Richest|38.2|...|92.8|165.4|
|**_B2. Small towns_**|||||
|Poorest|42.7|79.4|41.5|...|
|Second|51.5|80.3|41.4|...|
|Third|68.2|77.0|46.0|...|
|Fourth|101.6|70.3|50.4|...|
|Richest|131.6|76.5|65.0|...|
|**_B3. Rural areas_**|||||
|Poorest|34.7|73.7|30.6|...|
|Second|62.5|65.2|34.3|...|
|Third|84.2|64.4|38.0|...|
|Fourth|124.8|64.4|40.9|...|
|Richest|139.3|70.3|48.4|...|

Source: HBS and Bank staff calculations. Note: "..." indicate insufficient observations.

1Unconnected households as well as households that are connected but have missing expenditure data are assumed to have zero expenditures on energy.

2Includes only households that are connected and are reporting expenditure information. Implicitly, the calculations include households that are connected but have missing expenditure data, and these households are assumed to have expenditure levels equal to _average_ expenditures by quintile and location.

18
||||**An**|**nex Table**|**1 ‐ Operatio**|**nal Balan**|**ce and Grow**|**th(contin**|**ued)**||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Gh**|**ana**|**Ke**|**nya**|**Mozam**|**bique**|**Ke**|**nya**|**Mozam**|**bique**|**Nig**|**eria**|
||Ope. Bal.<br>(share of<br>GDP)|Growth|Ope. Bal.<br>(share of<br>GDP)|Growth|Ope. Bal.<br>(share of<br>GDP)|Growth|Ope. Bal.<br>(share of<br>GDP)|Growth|Ope. Bal.<br>(share of<br>GDP)|Growth|Ope. Bal.<br>(share of<br>GDP)|<br> <br>Growth|
|1981|‐0.084|‐0.085|‐0.054|0.004|‐0.074|0.020|‐0.054|0.004|‐0.074|0.020|‐0.120|0.161|
|1982|‐0.044|‐0.123|‐0.043|0.014|‐0.036|‐0.099|‐0.043|0.014|‐0.036|‐0.099|‐0.158|‐0.038|
|1983|‐0.025|‐0.098|‐0.031|‐0.019|‐0.158|‐0.195|‐0.031|‐0.019|‐0.158|‐0.195|‐0.129|‐0.078|
|1984|‐0.022|0.032|‐0.036|‐0.019|‐0.134|‐0.088|‐0.036|‐0.019|‐0.134|‐0.088|‐0.066|‐0.047|
|1985|‐0.029|0.024|‐0.043|0.005|‐0.097|‐0.006|‐0.043|0.005|‐0.097|‐0.006|‐0.016|0.052|
|1986|‐0.031|0.025|‐0.042|0.032|‐0.121|‐0.035|‐0.042|0.032|‐0.121|‐0.035|0.038|‐0.120|
|1987|‐0.023|0.022|‐0.032|0.022|‐0.072|0.128|‐0.032|0.022|‐0.072|0.128|‐0.054|‐0.143|
|1988|‐0.026|0.051|‐0.028|0.025|‐0.065|0.071|‐0.028|0.025|‐0.065|0.071|‐0.047|0.044|
|1989|‐0.020|0.003|‐0.034|0.011|‐0.042|0.056|‐0.034|0.011|‐0.042|0.056|‐0.027|0.034|
|1990|‐0.021|0.007|‐0.048|0.008|‐0.061|0.002|‐0.048|0.008|‐0.061|0.002|0.030|0.091|
|1991|‐0.013|0.027|‐0.082|‐0.017|‐0.028|0.045|‐0.082|‐0.017|‐0.028|0.045|0.007|‐0.035|
|1992|‐0.089|0.035|‐0.106|‐0.039|‐0.026|‐0.072|‐0.106|‐0.039|‐0.026|‐0.072|0.021|‐0.023|
|1993|‐0.099|0.023|‐0.115|‐0.028|‐0.035|0.063|‐0.115|‐0.028|‐0.035|0.063|‐0.068|‐0.006|
|1994|‐0.089|0.007|‐0.055|0.000|‐0.054|0.032|‐0.055|0.000|‐0.054|0.032|‐0.063|‐0.018|
|1995|‐0.064|0.014|‐0.005|0.019|‐0.033|‐0.003|‐0.005|0.019|‐0.033|‐0.003|0.034|‐0.030|
|1996|‐0.095|0.020|‐0.010|0.017|‐0.026|0.112|‐0.010|0.017|‐0.026|0.112|0.026|0.022|
|1997|‐0.103|0.016|‐0.016|‐0.020|‐0.028|0.081|‐0.016|‐0.020|‐0.028|0.081|‐0.010|0.000|
|1998|‐0.081|0.021|‐0.006|0.012|‐0.019|0.092|‐0.006|0.012|‐0.019|0.092|‐0.091|0.000|
|1999|‐0.082|0.018|0.003|0.001|‐0.009|0.061|0.003|0.001|‐0.009|0.061|‐0.052|‐0.022|
|2000|‐0.079|0.011|‐0.006|‐0.017|‐0.052|‐0.007|‐0.006|‐0.017|‐0.052|‐0.007|0.059|0.025|
|2001|‐0.059|0.016|‐0.021|0.021|‐0.054|0.094|‐0.021|0.021|‐0.054|0.094|‐0.053|0.051|
|2002|‐0.044|0.019|‐0.032|‐0.018|‐0.070|0.068|‐0.032|‐0.018|‐0.070|0.068|0.008|0.165|
|2003|‐0.033|0.026|‐0.017|0.007|‐0.042|0.044|‐0.017|0.007|‐0.042|0.044|‐0.011|0.071|
|2004|‐0.031|0.029|‐0.001|0.025|‐0.044|0.058|‐0.001|0.025|‐0.044|0.058|0.063|0.073|
|2005|‐0.017|0.032|‐0.017|0.037|‐0.022|0.057|‐0.017|0.037|‐0.022|0.057|0.081|0.025|
|2006|‐0.071|0.036|‐0.025|0.042|‐0.014|0.054|‐0.025|0.042|‐0.014|0.054|0.077|0.033|
|2007|‐0.069|0.037|‐0.032|0.050|‐0.056|0.045|‐0.032|0.050|‐0.056|0.045|0.009|0.034|

27
correlation between inequality and mobility (i.e. more income inequality is correlated with less social mobility). In other words, while in principle there may be examples of countries with relatively high social mobility and high income inequality (e.g. Chile), on the whole it seems that lack of mobility and inequality tend to move together.

# _II.5 Inequality and demographics: Could things become worse before getting better?_

The introduction of mobility and time horizons in the analysis of income inequality enriches significantly the analysis. For example, as argued by Deaton and Paxton (1994) the observed cross sectional inequality in a given period of time, is just the average distribution of the income of successive cohorts, which in principle should show very different distributions of income. For example, if per capita income _y_ evolves according to the following simple law of motion: where _v_ is a random shock independent of _yt-1_ that has variance σ 2 , then if follows that the variance of the income of a person of age T is given by _T_ σ 2 . In other words, inequality should increase with the age of the cohort. As a result, countries with older populations will have a tendency to have higher levels of income inequality.

**<u>Figure 7. Inequality and demographics</u>**

<!-- Start of picture text --> Panel A. S.D. of incomes by age cohort Panel B. Inequality vs. age of population<br>Costa Rica 2004<br>20<br>Costa Rica 2004 y = -0.2687x - 0.0331<br>1.1<br>15<br>1 10<br>0.9 5<br>0<br>0.8<br>-15 -10 -5 0 5 10<br>-5<br>0.7<br>-10<br>0.6<br>20s 30s 40s 50s >60 -15<br>Adjusted Share of Young<br>Adjusted<br><!-- End of picture text --> Source: Perry et al. (2006).

Panel A of Figure 7 lends support to the hypothesis that inequality increases with the age of the cohort. More specifically, this panel presents the standard deviation for five Costa Rican population cohorts in 2004. Inspection of this figure indicates that the standard deviation of income increases steadily with age and the cohort of those in their 60s have a standard deviation of income that is almost twice as large as that of the cohort in their 20s. Moreover, these effects are not small.

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Policy Research Working Paper 6068

# **Abstract**

Water is increasingly becoming a limiting factor for sustainable economic growth and development in many countries. Its allocation has significant impacts on overall economic efficiency, particularly with growing physical scarcity in certain regions. Greater water supply variability further increases vulnerability in affected regions. Water also has become a strategic resource involving conflicts among those who may be affected differently by various policies. This paper analyzes various policy interventions aimed at improving water allocation decisions, using a novel approach that incorporates macro and micro level considerations in a unified analytical framework. The framework facilitates assessment of various linkages among policies and their impacts within individual sectors and economy-wide. Drawing on country based studies in Morocco, South Africa, Turkey, and Mexico, the analysis reveals difficult tradeoffs among various policy objectives, including priorities placed on different sectors, regional advantages, and general economic efficiency gains versus broader social impacts. The comparison of policy impacts demonstrates the usefulness of the framework in information that policy makers can use to rank the policy interventions according to the emphasis placed on different policy objectives. The paper also compares approaches used in other studies that apply computable general equilibrium models in various contexts of water, environment and agriculture.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at adinar@ucr.edu. Further information about the research underlying the paper can be obtained by contacting Mike Toman, Environment and Energy Research Manager, at mtoman@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/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
- 6 -

reasonable to assume that residential energy demand would be more or less the same in a certain region.

The current paper concentrates on investigating the industrial demand for energy, partially because of data availability but mostly because it is expected to yield important policy implications for the SEE countries, where the supply of energy is not always secured and several energy-intensive industries agglomerate in the region to take advantage of cheaper energy inputs. The paper mainly uses firm-level data from the 2005 Business Environment and Enterprise Performance Survey (BEEPS) and estimates the price elasticities of industrial energy demand.<sup>5</sup> Unlike some of the earlier literature (e.g., Kamerschen and Porter, 2004; Filippini and Hunt, 2009), the analysis focuses on investigating how individual enterprises would likely respond to any possible supply shock of energy. The firm behavior may differ across countries, from sector to sector and depending on individual firms. The paper also quantifies technical inefficiency in firm production by applying a stochastic frontier technique.

The remainder of the paper is organized as follows: Section II discusses the importance of the price elasticity for energy in the real economy. Section III provides a brief overview of energy demand and supply in SEE countries. Section IV establishes an empirical model and describes our data uses. Section V summarizes the main estimation results, and Section VI discusses some policy implications.

# **II. PRICE ELASTICITY FOR ENERGY AND ITS POLICY IMPLICATIONS**

When implementing industrial and energy policies, policymakers should pay more attention to the industrial energy demand. There are two particularly important parameters when

> 5 The 2005 BEEPS data originally cover about 4,000 firms in 26 ECA countries (e.g., World Bank, 2007a). But the current paper relies on data for only seven SEE states.
# **Are Carbon Taxes Good for South Asia?**

Valerie Mercer-Blackman<sup>1</sup> Lazar Milivojevic<sup>2</sup> Victor Mylonas<sup>3</sup>

**Keywords:** carbon tax, climate change, decarbonization, energy transition, South Asia, fiscal incidence, revenue recycling, fossil fuel subsidy reform

## **JEL Classification:** E62, H22, H23, Q43, Q54

_________________________________________________________________________________ 1/ The World Bank, email: <u>vmercerblackman@worldbank.org; 2/ The World Bank, email: lmilivojevic@worldbank.org;</u> 3/ International Monetary Fund (IMF) and The World Bank, email: <u>vmylonas@worldbank.org. The authors thank Yi</u> (Claire) Li for excellent research assistance, and Paolo Agnolucci (The World Bank), Defne Gencer (The World Bank), Dirk Heine (The World Bank), Jules Hugot (Asian Development Bank), Tom Moerenhout (The World Bank), Ian Parry (International Monetary Fund), Margaret Triyana (The World Bank), and Hans Timmer (The World Bank) for helpful comments. The authors also thank participants at: i) the ADBI Conference on the Energy Market, Energy Pricing, and Low-Carbon Transition; ii) the Development Economics Prospects Group (The World Bank) internal seminar; and iii) the South Asia Chief Economist Office internal seminar (The World Bank) for helpful discussions. This paper builds on work initiated in the context of the Spring 2022 _South Asia Economic Focus_ (World Bank, 2022a). All remaining errors are our own _._
the decline of the sugar industry, the government initiated a contract with Booker Tate, a private management company, in October 1990 to manage day-to-day operations and prepare to rehabilitate the industry. Booker Tate raised wages of field and factory workers by 200 percent and installed a new more qualified management team. Production responded to better management and good weather and increased to 247,000 tons in 1992. The country filled its export quotas for the first time in four years (World Bank 1993). Production has continued to increase and has nearly returned to the levels prior to nationalization.

**Figure 6. Guyana’s sugar production, 1970-2003**

<!-- Start of picture text --> Sugar Production<br>500<br>Industry Professional<br>400 Nationalized Management Hired<br>300<br>200<br>100<br>0<br>1970 1975 1980 1985 1990 1995 2000<br>Thousand Tons<br><!-- End of picture text --> Source: FAOSTAT and Davis et al. (2004).

# **_Add Value_**

Nearly all sugar producers are looking for ways to add value to their sugar production activities. In a survey done in 2001, LMC International found that sugar refining, ethanol production from molasses, and power cogeneration from bagasse were the most common activities undertaken to add value to sugar production. They also found that few sugar companies were successful in exploiting a brand name because of the homogeneity of sugar and the reluctance of consumers and industrial users to pay a premium for branded sugar. Less common ways to add value were to produce paper or particle board from cane residue.

One of the most promising ways to add value is to produce energy as a byproduct. This is currently viewed as attractive because of high energy prices, but it may be viable even without recent increases in energy prices because of the combined economic and environmental benefits. Sugar cane produces very large volumes of biomass (60-80 tons per hectare) and that biomass can be used as a clean burning fuel. It has been used to power the sugar factories for decades, but it can also be used to produce surplus electricity to meet a portion of the national electricity demand. This is being done in some countries (such as India and Mauritius), but not extensively in the Caribbean sugar producing countries (Belize and Guyana are reported to be considering such activities). The technique is called cogeneration, and the objective is to obtain surplus energy from 16
The amendment of the Act represents an important step towards greater flexibility in the marketing of agricultural produce in the state.

The 45 districts can be divided into two main regions: the high soy producing region in the west central part of the state and the low soy producing region in the east. The abundance of deep and medium black soil in the western region of the state makes it very conducive to soy production, while the eastern and northern regions are primarily a rice growing belt. Soy constitutes more than 65 percent of total volume of agricultural production in the monsoon season in this group of districts as opposed to approximately 11 percent in the east (see Table 1). The kiosks and hubs were built in the 23 high soy producing districts in the west central region of the state.<sup>19</sup> An average of 74 kiosks and 2 hubs per district were built and the majority of the hubs were established in the same town as a main _mandi_ .

[Insert Table 1 about here] The next section outlines a model that captures some of the key features of the rural marketing environment. In particular, the presence of physical transport costs and information frictions, which tend to limit farmers’ choices of where to sell their produce are considered. An important motive for ITC to start buying directly from the farmers was to be able to control the quality of soybeans that it purchases. The endogeneity of supply in a simultaneous game gives us predictions about the determination of prices of differentiated products. There are other significant features of the intervention, such as the offer of a guaranteed price by ITC Limited and the repeated interactions between the traders and farmers, that are not explicitly modelled. However, even this simple and tractable model provides an understanding of the types of effects that can be expected from the intervention and the underlying mechanisms of change.

# **II. Conceptual Framework**

Consider a variant of the Harold Hotelling (1929) model in which farmers are uniformly

> the _mandis_ . Since the website was accessible to anyone, including the government, to cross-check posted prices - it facilitated the government’s acceptance of the initiative.”

> 19According to Census 2001, there were 45 districts in MP. Three new districts were created in 2003 making a total of 48 districts in 2005. The total number of districts in census 2001 are considered to avoid complications.

11