rafmacalaba/datause-encoder-data huggingface dataset:
_K_ where<sup>_y_</sup> _i_ represents the real per capita income; _X i_ = { _xk_ , _i_ } _k_ = 1 are the independent variables, we estimate: The wage growth during the period 1989-2004 can be decomposed as the following: log ~ _y_ 04 − log ~ _y_ 89 = β ˆ 89( _X_ 04 − _X_ 89 ) + ( β ˆ 04 − β ˆ 89 ) _X_ 04 (5) ~ ~ where _y_ 89 and _y_ 04 are geometric mean income in 1989 and 2004, respectively. ( _X_ 04 − _X_ 89 ) denotes the main effect of the independent variables, for example, that of education which signify the changing education stock of the population, that of industry which signify a structural ˆ 04 − ˆ 89 transformation or the changing industry composition, etc. ( β β ) denotes the year effect, for example, the changes in returns to education, the changes to industry-specific premiums, etc. Decomposition into main effects and year effects allows assessing the impact of structural transformation on income growth, and identifying sources of widening income gaps.

# **5. Unequal Distribution and Trends in Changes in Inequality**

Using the CHNS data, we show that the distribution between coastal and inland regions and across rural and urban areas has been unequal in both income and non-income dimensions. Over time, the inequality of income has widened while that of human capital endowment starts to narrow.

## **5.1. Income Inequality**

Overall, household per capita income increased and income inequality widened over time (Table 1). Household per capita income increased by 164% from 1044 yuan in 1989 to 2765 yuan in 2004. Inland-coastal income gap widened as income growth was faster in coastal provinces. Rural-urban income gap remained: mean household per capita income in urban areas was 40% higher than that in rural areas in 2004, a gap similar to 1989. However, rural income grew more rapidly in the three coastal provinces than in the five inland provinces. Household per capita income of coastal rural more than tripled while that of inland rural areas doubled in the same period. Income growth in coastal urban areas was also higher than that in urban inland. This is consistent with the concerted development effort to first concentrate resources in coastal provinces. As a result, coastal provinces are at a more advanced stage of development than inland provinces, and coastal rural-urban mean income gap seems to have begun to narrow while ruralurban gap is still widening in inland provinces. The sharp increase in inequality in rural and urban areas during the 15 years led to substantial increase in overall Gini coefficients. First, income 7
“blending”, as considered in sub-sections 3.2-3.4, and become the focus for more, perhaps most, of the mitigation activity in lower-income countries. The World Bank’s Transformative Carbon Asset Facility (TCAF) has been set up for such purposes; the future will show what impact this will have on the nature of offset transactions under the PA.

Regardless of this, however, problems will remain for the future of carbon markets.

First, under uncertainty about demand for mitigation quotas, offset markets tend to exhibit (perhaps severe) fluctuations in equilibrium carbon prices, as pointed out in section 3.2 above: an unwanted feature for market participants. Such fluctuations can, in principle, be partly countered by countervailing shifts in the climate finance supply to the carbon market, and by careful banking and other long-term management issues for the markets. Good such management would require mobilizing additional climate finance in global downturn situations, which may be difficult.

Secondly, global carbon markets may, at least gradually over time, lead to carbon prices at similar levels throughout the world economy. This is positive in several respects, but could still have some negative implications, at least when the carbon price in this market remains (substantially) below the “global cost of carbon”. Tying global carbon prices “too much” to the carbon market could make it more difficult to realize certain globally favorable solutions: there may be willingness to increase the carbon price in some markets and regions, which can be held back by a carbon market with a global reach.

Thirdly, while project-based offset markets could provide substantial transfers from H to L countries, these transfers are subject to constraints. First, they cannot be expected to raise revenues close to those raised through global carbon taxation.<sup>15</sup> Secondly, this revenue will go to private parties in L countries, often not those parties which need such funds the most; and nothing will be available as free revenue for governments. “Finance blending” through the offset market could worsen this problem by further diverting valuable public donor country funds away from more important uses.

> 15 Considering a “correct” global carbon tax of $40/ton CO2; a conservative estimate considering Stiglitz and Stern’s (2017) optimal range $40-$80; and a global GHG emissions rate of 40 billion tons CO2 equivalents, this would correspond to a global annual GHG emissions tax revenue of $1.6 trillion; perhaps an order of magnitude higher than a realistic transaction level for a global carbon market.

34
Table 2: Baseline Summary Statistics and Take-Up Analysis (continued) Mean and Standard Deviations

|Panel C: Other variables|Treatment|Control|Orthogonality<br>Verification<br>(1)-(2)<br>Difference<br>(p-value)|Took-up<br>Treatment|Did Not<br>Take-up<br>Treatment|(4)-(5)<br>Difference<br>(std. err.)|
|---|---|---|---|---|---|---|
|- Business outcomes|(1)|(2)|(3)|(4)|(5)|(6)|
|Avg. sales Jul, Aug, and Sep 2007|79.163|55.258|23.905|105.916|48.260|57.656|
|(1000s USD)|(288.679)|(140.493)|(0.286)|(349.912)|(194.184)|(0.267)|
|Avg. sales Jul, Aug, and Sep 2007|66.025|54.923|11.102|84.114|45.129|38.985|
|(1000s USD), 1% winsorized|(188.367)|(137.581)|(0.520)|(200.838)|(172.209)|(0.250)|
|Sep 2007 costs (1000s USD)|44.565|56.216|-11.651|58.736|26.974|31.762|
||(120.341)|(263.955)|(0.633)|(145.907)|(75.425)|(0.135)|
|Sep 2007 costs (1000s USD),|44.275|40.049|4.226|58.212|26.974|31.238|
||(118.714)|(95.965)|(0.709)|(143.516)|(75.425)|(0.136)|
|Profits (Sep 2007 sales minus|13.281|-3.797|17.078|8.375|19.365|-10.991|
|costs, 1000s USD)|(112.277)|(204.743)|(0.411)|(87.198)|(137.923)|(0.609)|
|Profits (Sep 2007 sales minus|10.220|10.156|0.064|11.144|9.075|2.069|
|costs, 1000s USD), 1% winsorized|(74.720)|(75.010)|(0.994)|(72.355)|(78.279)|(0.885)|
|Business assets (1000s USD),|296.964|945.842|-648.879|341.570|246.574|94.996|
|1% winsorized|(767.969)|(7822.005)|(0.376)|(779.399)|(758.949)|(0.510)|
|Business assets (1000s USD),|295.337|331.749|-36.412|338.505|246.574|91.931|
|1% winsorized|(756.984)|(770.078)|(0.685)|(758.886)|(758.949)|(0.518)|
|Productivity residual|0.028|-0.016|0.045|0.439|-0.437|0.876***|
||(1.349)|(1.253)|(0.787)|(1.477)|(1.017)|(0.001)|
|Productivity residual,|0.031|-0.015|0.046|0.444|-0.437|0.881***|
|1% winsorized|(1.317)|(1.239)|(0.776)|(1.419)|(1.017)|(0.001)|
|Return on assets (ROA)|-0.026|0.152|-0.178|0.160|-0.254|0.414**|
||(0.956)|(0.817)|(0.120)|(0.366)|(1.342)|(0.042)|
|Return on assets (ROA),|0.033|0.118|-0.085|0.160|-0.121|0.281**|
|1% winsorized|(0.596)|(0.640)|(0.305)|(0.366)|(0.770)|(0.026)|
|N|150|282|432|80|70|150|

Note: Columns 1, 2, 4 and 5 present means and standard deviations (in parentheses). Column 3 shows the difference in means across the treatment and control group with the corresponding p-value in parentheses. Column 6 shows the difference in means across treatment enterprises that did and did not take-up the program with the corresponding p-value in parentheses. For the 1% winsorized variables, sales, costs, and assets are only winsorized at the top 1% since they are bounded below by zero. All other variables are winsorized at the top and bottom 1%. Significance levels: *10 percent, **5 percent, ***1 percent.

24
**_Governance 1: The accountability hypothesis._** _SP is equally important for people from poor and rich countries, but the governments of poorer countries tend to be less responsive to their citizens’ demands._ An autocratic dictator likely faces fewer incentives to respond to citizens' demands (at least once the local elite is protected) than a government in a robust democracy. Even among democracies, some have better institutions (such as through greater freedom for the media) to help assure accountability.

The literature has provided mixed evidence related to Governance 1. Lindert (1994) argued that the rise of democracy, alongside economic development, was an important factor in the increase in SP spending in today’s rich world when it was not nearly so rich. However, <u>Mulligan et al. (2004)</u> find little sign that democracies tend to pursue different social policies, once one controls for other economic and demographic differences. Dincecco (2009) argues that more absolutist (and presumably less accountable) political regimes in pre-World War 1 Europe tended to raise less revenue generally, which would have constrained SP spending. We shall explore this hypothesis further, as well as its companion:

**_Governance 2: The government effectiveness hypothesis._** _Poorer countries have less capable governments for delivering public spending, whether on SP or something else._ The literature on institutions and development has often pointed to ways in which being a richer country can promote better institutions and polices.<sup>7</sup> The cost of delivering a unit of social protection is presumably lower in countries with better institutions. That can explain Ravallion’s <u>(2017)</u> finding that the coverage of social protection is positively correlated with national income. Government effectiveness is likely to be correlated with GDP, but it also has its own independent variation, in that there are poor countries with relatively effective governments, and some rich countries for which one could not reasonably say that. This is illustrated by Figure 2, which plots the “government effectiveness (GE) index” from the World Bank’s _<u>Worldwide Governance Indicators</u>_ (WGI) against GDP per capita. The GE index is highly correlated with GDP per capita. However, there are low-income and middle-income countries with relatively high GE values and high-income countries with low values. (For example, Saudi Arabia’s GE index is about the same as Rwanda’s.)

> 7 Thus, this strand of the literature gives much attention to the likely endogeneity of institutions in explaining differences in rates of economic growth; see, for example, Acemoglu et al. (2005).

7
government price controls. However, out of the nine that are under price control, only one molecule was subjected to a product patent implying that, at least within this therapeutic segment, the likely effect of price controls on curbing price increases is negligible.

# **IV. Conclusion**

This paper explores the effects of product patent protection on drug prices and utilization using data on one particular therapeutic segment relating to central nervous system treatments in the Indian pharmaceuticals market. The study highlights the importance of the distinction between different types of product patents and their differential impact on average prices and total utilization of drugs. The analysis finds a statistically significant effect of a sub-class of product patents. In particular, product patents that were granted on the active ingredient instead of incremental innovations of a drug, led to a significant increase in drug prices, and an increase in the fraction of total quantity sold to the innovator with a corresponding withdrawal of generics from the pharmaceutical market in India. The purpose of this paper is to provoke discussion on some of the early estimates emerging from preliminary analysis using this data. In forthcoming work we plan to extend the analysis to all therapeutic segments of the Indian pharmaceutical market, and explore in greater detail heterogeneous effects by sub-therapeutic segments, welfare implications of stringent patent protection using demand and supply parameters estimated earlier in the literature, and whether there are any substitution effects between patents and price controls as alternative policy approaches.

12
# **<u>Table A1.S: Construction of Matched Sons-Fathers in India</u>**

|||**IHDS Wave 1**<br>**Azam (2015)**|**IHDS Wave 2**|
|---|---|---|---|
|Total Num of Individuals<br>Surveyed in IHDS||215,784|204,569|
|Total Num of Men in 20-65<br>Age Group||58,194|56,883|
|Education Information<br>Missing (dropped)||325|232|
|Identification through<br>coresidence only|Father identified if coresidence is<br>used|19,556|19,629|
||percentage of male aged 20-65 who<br>can be potentially matched using<br>coresidence|33.60%|34.508%|
|Panel A. Total Number of<br>Men (20-65 age group) with<br>Education Information|Identification of Father|||
||a) Individual is head of household|34,069|31,780|
||b) Individual who are not household<br>heads, however, whose father is<br>living in the household|18,056|18,325|
||c) Individual is neither head of the<br>household, nor his father is living in<br>the household (no father<br>identification is provided)|4,029|1,905|
||d1) Individual is head's father<br>(dropped, father cannot be<br>identified)|591||
||d2) Individual's father cannot be<br>identified (dropped)|1,124||
|Total number of men (20-65<br>age group) whose father is<br>identified: a) + b) + c)||56,154|52,010|
|Percentage of men (20-65 age<br>group, panel A) whose fathers<br>are identified||96.494%|91.433%|

Notes: Column 1-3 are directly obtained from Table 8 in Azam (2015) using IHDS 2005 while column 4 is based on authors’ own calculation using IHDS 2012.
# **THE CRISIS-RESILIENCE OF SERVICES TRADE**

Ingo Borchert and Aaditya Mattoo* _Keywords_ : Services, trade, crisis _JEL Classification_ : F13, F14

* World Bank, 1818 H Street NW, Washington DC 20433. <u>iborchert@worldbank.org, amattoo@worldbank.org.  This paper is part of a World Bank research project on trade in services supported in</u> part by the governments of Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development.  We are grateful to Bernard Hoekman for asking the interesting question and for providing valuable direction.  We would like to thank Pritam Banerjee, Olivier Cattaneo, Nora Dihel, Ana Fernandes, Caroline Freund, Leo Iacovone, Justin Yifu Lin, Will Martin, Richard Newfarmer, Çaglar Özden, Sebastian Saez, Arvind Subramanian and Randeep Sudan for valuable comments.  We also thank Raju Bhatnagar, NASSCOM’s Vice President BPO and Government Relations, Margrith Lütschg-Emmenegger, president of FIMBank plc, T.V. Mohandas Pai, Member of the Board and Director of Human Resources, Infosys, and Ganesh Natarajan, Chairman of NASSCOM, and, for discussing with us ongoing developments in services trade. The views contained in this paper are those of the authors and not necessarily those of the World Bank or any of the aforementioned individuals or institutions.
**Figure 1: Illustration of the rapid consumption survey methodology (using illustrative data only). The consumption module is partitioned into core and optional modules, which in turn are assigned to households. Consumption is imputed utilizing the sub‐sample information of the optional modules either by single or multiple imputation methods.**

|**Consumption**<br>**Module**<br>Item 1<br>Item 2<br>...<br>Item N<br>Questionnaire|**Core Module**<br>Item C1<br>Item C2<br>...<br>Item CX||**Opt Module 1**<br>Item D1<br>Item D2<br>...<br>Item DY||**Opt Module 2**<br>Item E1<br>Item E2<br>...<br>Item EZ||
|---|---|---|---|---|---|---|
|Survey||**Household**<br>**Group 1**<br>Core Module<br>Opt Module 1||**Household**<br>**Group 2**<br>Core Module<br>Opt Module 2|||
|Imputation||**Household**<br>**Group 1**<br>Core Module<br>Opt Module 1<br>_Opt Module 2_|Imputation|**Household**<br>**Group 2**<br>Core Module<br>_Opt Module 1_<br>Opt Module 2||**Income**<br>Estimated<br>Real|
|||**Household**<br>**Group 1'**||**Household**<br>**Group 2'**|||
|Multiple Imputation||Core Module<br>Opt Module 1<br>_Opt Module 2_<br>...<br>**Household**<br>**Group 1''**<br>Core Module<br>Opt Module 1<br>_Opt Module 2_|Imputation<br>Imputation|Core Module<br>_Opt Module 1_<br>Opt Module 2<br>...<br>**Household**<br>**Group 2''**<br>Core Module<br>_Opt Module 1_<br>Opt Module 2||**Income**<br>Imputation 1<br>Imputation 2<br>Imputation 3<br>Imputation 4<br>Real|

First, core consumption items are selected. Consumption in a country bears some variability but usually a small number of a few dozen items captures the majority of consumption. These items are assigned to the core module, which will be administered to all households. Important items can be identified by the average food share per household or across households. Previous consumption surveys in the same country or consumption shares of neighboring / similar countries can be used to estimate food shares.<sup>5</sup>

> 5 As shown later, the assignment of items to modules is very robust and, thus, even rough estimates of consumption shares are sufficient to inform the assignment without requiring a baseline survey.

4
- 21 -

return or, to put it differently, was the growth rate that vas achieved<sup>'satisfactory' relative to the</sup> level of investment that toak place.

Flgure 8 <!-- Start of picture text --> GDI AS RATIO OF GDP<br>CAn,a *V r ^p)<br>30<br>22~~~~~~~~~~~~~2.<br>21 3a als . g~~~2.<br>26<br>22<br>20<br>J1414 . .<br>12<br>10<br>a<br>6<br>4<br>2<br>0<br>OW*na Maawi *WV Taruanla Ugrd<br>1g81-as 3 198690<br><!-- End of picture text --> Source: IMF and World Bank data, and authors' estimates.

# V.3.2 Investment

To answer these questions, we look at two related indicators: the ratio of investmen **t** o GDP, and a measure of the 'rate of return' or the efficiency of investment, i.e.,<sup>the GDP growth rate as</sup> a ratio of a unit of investment (defined as the ratio of gross domestic investment to GDP).

Figure 8 presents data on the ratio of gross domestic investment (GDI) to GDP for the five countries. It reveals that in all countries except Malawi, reform was accompanied by<sup>an increase in</sup> the share of GDP being invested. In the case of Tanzania, there<sup>was a dramatic increase, with</sup> investment's share of GDP increasing from an already high average of<sup>18.7 percent in the pre-reform</sup>
<!-- Start of picture text --> Exporters per capita − GDP per capita Entry Rate − Total Number of Exporters<br>EST NZL SWE NOR<br>MKDLBNBGRMUS PRTESPBEL KWT YEMMWI KWT<br>MWI MLIUGABFATZABGDKENSENKHMLAOCMRYEMPAK NICMARGTMJOREGYSLVECUALBDOMPERCOLZAFBRACRIIRNTURBWAMEXCHL NER MLIBFALAOKHMSENCMRUGANICBWATZAALBJORMUSSLVDOMCRIMKDECUGTMESTLBNKENMARBGDPERCHLCOLIRNNZLBGRPAKPRTNORZAFBELSWEMEXTUR ESP<br>EGY<br>NER R2=0.83 BRA R2=0.35<br>6 7 8 9 10 11 4 6 8 10 12<br>Ln GDP per capita Ln Total Exporters<br>.6<br>−6<br>.5<br>−8<br>.4<br>Entry Rate<br>−10<br>.3<br>Ln Exporting firms per capita<br>−12 .2<br><!-- End of picture text --> Figure 1: Exporters per capita and export entry rate in Brazil Source: World Bank Export Dynamics Database (Cebeci et al., 2012) In this regard, an important question that comes from analyzing a specific policy in a single country is related to external validity. Does this experience in Brazil reflect the expected outcome from similar policies in other countries? Even though policy outcomes can vary over different institutional environments among different countries, it is important to highlight that the export behavior in Brazil is very similar to that in leading export countries such as France and the United States, as described by Arkolakis (2010). Also, I find many similarities with the patterns of Colombian exporting firms (Eaton et al., 2010). However, the comparison of EPAs’ impact should incorporate the heterogeneity among the agencies and countries’ institutions. Hence, accumulating further knowledge on the effect of these programs in different contexts is critical to fully understand how and in which circumstances they work.

# **3 Data and Descriptive Statistics**

This paper is based on a unique firm-level dataset that resulted from merging three different sources of information in Brazil: 1) the _Rela ̧c ̃oes Anual de Informa ̧c ̃oes Sociais_ (RAIS), a matched employer-employee dataset from the Ministry of Labor;<sup>13</sup> 2) the SECEX/MDIC dataset, which provides firms’ exporting status; and 3) the APEX data, identifying the firms which received the treatment from Apex-Brasil.<sup>14</sup> The data was merged using a unique identifier at the firm level.<sup>15</sup> The data from Apex cover the period between 2007 and 2010. I used RAIS and SECEX/MDIC from 2005 to 2010 in order to capture the previous characteristics of exporting firms and the

> 13RAIS is a register of all formal firms in Brazil. It provides information about the size of the establishment, geographic distribution and workers, such as wage, education, age and gender.

> 14 _Secretaria de Com ́ercio Exterior_ (SECEX) from the Ministry of Development, Industry and Trade (MIDC). A list of exporting firms by year is publicly available on www.mdic.gov.br.

> 15RAIS provides information at the plant level with a 14-digit identifier that can be converted to the firm level using 8 digits, which was used in this paper. The location of the firm is defined as the location of the larger plant.

6
10 Equivalently, it is _one minus the share of total U.S. imports from products that are_ not _exported by country c._ Note that this measure depends on the _set of exports_ by country c,<sup>I</sup> ct<sup>, but not on its</sup> value of exports, except insofar as they affect the value of worldwide exports.

# **4.  GDP Function with Export Variety**

To study the effects of export variety on productivity, we need to model the allocation of factors among the production of goods in all industries. The effects of export varieties on productivity depend on the elasticities of substitution in production between different variety within the industries – for a two good case, the elasticity of substitution in production captures the curvature of the PPF of an economy given fixed endowments, as in Figure 1. In this section, we develop a general equilibrium based GDP function approach which links export variety to country productivity. Such a model will allow us to develop an empirical model for the estimation of the elasticities of substitution in production of various industries, and will also allow us to infer the contribution of export variety in explaining country productivity.

Suppose there are M kinds of factor endowments in the economy, denoted by the c c c endowment vector Vt = (vt1 ,..., v Mt ) > .0 There are N differentiated traded good sectors in the c c economy, with output denoted by (Qt1 ,..., Q Nt ) , each of which is a CES aggregate as in (1) with σ n < 0.<sup>4</sup> In addition to the N traded goods, each country has one homogeneous nontraded good sector produces output Q cN + t1<sup>. The aggregate output vector of the economy is denoted by</sup> c c c c c c c c Q t = ( Qt1 ,..., Q Nt , Q N + t1 ) > 0 . Likewise, the aggregate price vector is Pt = (Pt1 ,..., PNt , PN + t1 ) > 0 , which consists of N traded good prices as defined by the CES unit-costs in (3) , and one

> 4   In the case that some of these sectors are imported rather than exported, we would denote Q<sup>c</sup> nt<sup><</sup> 0 as the negative of the CES aggregate (1), with σ n > 1.