rafmacalaba/datause-encoder-data huggingface dataset:
evaluated for both imports and exports, addressing such issues as trade creation and trade diversion consequences and gains resulting from access to partners’ markets.

The objective of this paper is to provide a preliminary assessment of the potential economic impacts of an FTA between India and the United States. For this, we use the Global Trade Analysis Project (GTAP) model (Hertel 1996) (Version 8).<sup>2</sup> GTAP is a relatively standard applied Computable General Equilibrium (CGE) model which is used for a variety of applications, including for studies to evaluate, _ex ante_ , the welfare impacts of FTAs (Hertel, Hummels, Ivanic and Roman, 2007). To ensure maximum clarity and transparency, the analysis is intentionally simple and “static” in order to address key issues such as the nature and extent of trade creation and diversion from an agreement, and the sensitivity of these effects to the presence of other preferential trade agreements.

There are two major limitations to this study. Because of our focus on simplicity and transparency, the paper does not take into account “dynamic” impacts of an FTA such as the impacts of the increased foreign direct investment (FDI) inflow, positive impacts on productivity growth resulting from access to foreign knowledge, and accelerated domestic reforms (Fukase and Winters, 2003). For the same reason, we use standard trade-weighted averages of protection, rather than the more sophisticated optimal aggregation approach outlined in Laborde, Martin and Van der Mensbrugghe (2011). Since the nature of liberalization to be undertaken is not clear at this stage, the simulation scenarios are based on a uniform assumption, _i.e._ 100 percent Ad Valorem Equivalent (AVE) tariff cuts for goods and 50 percent cuts for services. We use the smaller reduction in barriers on services trade because many of these barriers are qualitative and difficult to distinguish from non-discriminatory liberalization. In sum, the main focus of the

> 2 The base year for GTAP 8 is 2007.

3
received DAU.<sup>13</sup> In order to avoid spurious changes in per capita growth rates resulting from the splitting of districts, we have used GRDP data as our reference point. GRDP data for the predecentralization era are only for 292 districts. We therefore collapsed the post-decentralization data in order to provide data for exactly the same set of 292 districts throughout.<sup>14</sup> We have also compiled a wide range of variables that can be used to explain district level economic growth. Specifically our data include three types of variables: _Factor endowments_ – We follow the neoclassical Solow growth model (Barro and Sala-i-Martin, 1994) by interpreting capital in a broad sense including measures of population and human capital as well as the availability of physical capital or land. Following Barro (1991), we use education as a proxy for human capital under the assumption that for a given starting value of GRDP per capita, a country’s subsequent growth rate is positively related to this human capital. Good measures of physical capital are difficult to obtain at the district level. However, we do have data on the share of households with telephone connections, which we use as an indication of the development of physical capital.

_Geography_ – Geographical location and topography may be as important for district growth as natural resource endowments. For example, Krugman (2007) emphasizes that transaction costs across distance play a crucial role in shaping a pattern of trade that can induce growth. We therefore include some proxies for remoteness and topography in the data. Specifically, we measure distance by calculating the straight line distance from the main town of the district to the provincial capital and to the largest five cities in the country.<sup>15</sup> We also incorporate dummy variables for whether these destinations are separated from the district town by sea.

A district’s growth performance is also affected by the growth performance of its neighbors. We calculate the weighted growth of neighboring districts using the following formula (Catin et al, 2005): where _GAWi,t_ is the weighted average growth performance of the districts that are neighbors to district _i_ at time _t_ . We use GDP weights for importance i.e. _w,q,t_ = GDPq,t /<sup>mi</sup> Σq=1 GDPq,t. Growth

> is recorded differently by different institutions. To be consistent, we have used the definition of the Ministry of Finance

> — an autonomous province/district is the one that receives DAU in the beginning of fiscal year.

> 13 Since 2001, the number of districts in Indonesia (excluding six non-autonomous district level governments in Jakarta) is: 2001 = 336, 2002 = 348, 2003 = 370, 2004 = 410, 2005-07 = 434, 2008 = 459.

> 14 Our database has data for 292 districts. However, regression results do not always show this number of districts because data are missing for some observations.

> 15 Ideally actual road distance would be a better measure of remoteness than straight line distance but unfortunately these data were not available.

5
# **Table 6a – Regressions with Different Estimators – Balanced Panel**

|**Column**|**`1`**|**`2`**|**`3`**|**`4`**|**`5`**|**`6`**|
|---|---|---|---|---|---|---|
|**Dep. Var.**|**y**|**y**|**y**|**poor**|**poor**|**poor**|
|**Model**|**fe**|**re**|**be**|**fe**|**re**|**be**|
|`b/sbd`|`0.136`|`0.138`|`0.173`|`-0.056`|`-0.033`|`0.001`|
||`-1.3`|`-1.44`|`-0.7`|`-1.95`|`-1.39`|`-0.03`|
|`hfem`|`0.179`|`0.134`|`0.017`|`-0.062`|`-0.03`|`0.012`|
||`(3.05)**`|`(3.07)**`|`-0.25`|`(2.05)*`|`-1.51`|`-0.45`|
|`hage30_45`|`0.019`|`-0.116`|`-0.345`|`-0.104`|`-0.012`|`0.092`|
||`-0.19`|`-1.42`|`(2.45)*`|`(2.00)*`|`-0.31`|`-1.59`|
|`hage46_60`|`0.065`|`-0.119`|`-0.413`|`-0.138`|`-0.017`|`0.086`|
||`-0.57`|`-1.4`|`(3.07)**`|`(2.35)*`|`-0.44`|`-1.55`|
|`hage60plus`|`0.028`|`-0.232`|`-0.616`|`-0.117`|`0.036`|`0.178`|
||`-0.23`|`(2.40)*`|`(3.66)**`|`-1.8`|`-0.78`|`(2.56)*`|
|`hprim`|`0.181`|`0.17`|`0.15`|`-0.06`|`-0.084`|`-0.089`|
||`-0.99`|`-1.47`|`-1.01`|`-0.63`|`-1.64`|`-1.46`|
|`hsec`|`0.168`|`0.18`|`0.12`|`-0.144`|`-0.091`|`-0.044`|
||`-0.98`|`-1.67`|`-0.86`|`-1.63`|`-1.91`|`-0.77`|
|`htert`|`0.529`|`0.833`|`1.027`|`-0.25`|`-0.26`|`-0.265`|
||`(2.60)**`|`(6.50)**`|`(6.21)**`|`(2.37)*`|`(4.56)**`|`(3.89)**`|
|`nchild`|`-0.213`|`-0.252`|`-0.353`|`0.13`|`0.131`|`0.138`|
||`(5.50)**`|`(8.59)**`|`(6.89)**`|`(6.49)**`|`(9.49)**`|`(6.41)**`|
|`dep3_4`|`-0.152`|`-0.098`|`0.194`|`0.061`|`0.042`|`0`|
||`(2.05)*`|`-1.46`|`-1.2`|`-1.61`|`-1.28`|`-0.01`|
|`dep5plus`|`0.069`|`0.18`|`0.63`|`-0.056`|`-0.127`|`-0.303`|
||`-0.6`|`-1.81`|`(3.04)**`|`-0.95`|`(2.64)**`|`(3.52)**`|
|`terremprate`|`0.217`|`0.36`|`0.799`|`-0.051`|`-0.141`|`-0.304`|
||`-1.85`|`(3.49)**`|`(3.58)**`|`-0.84`|`(2.78)**`|`(3.23)**`|
|`growth`|`4.953`|`5.049`|`26.627`|`-2.977`|`-3.095`|`1.122`|
||`(12.39)**`|`(12.67)**`|`-0.26`|`(14.12)**`|`(14.88)**`|`-0.03`|
|`Constant`|`-4.116`|`-4.153`|`-26.924`|`3.782`|`3.817`|`-0.638`|
||`(8.86)**`|`(9.30)**`|`-0.25`|`(15.55)**`|`(16.65)**`|`-0.01`|
|`Observations`|`3463`|`3463`|`3463`|`3463`|`3463`|`3463`|
|`Number of hhid`|`866`|`866`|`866`|`866`|`866`|`866`|
|`R-squared`|`0.09`||`0.18`|`0.11`||`0.14`|
|`Hausman test (fe/re)`|||||||
|Chi2|36.74|||26.71|||
|Prob>chi2|0.0005|||0.0136|||

z-stat or t-stat under coefficients 26
7 Table 1: Regression estimates of the impact of population growth on the growth rate of per capita output.

|Study|Estimate|t-statistic-|Period.|
|---|---|---|---|
|Levine and Renelt (1992)<br>.__________|-.15<br>___________________|.79|1960-85<br>(average)|
|Brander and Dowrick (1994)|-.27|1.45|1960-85<br>(five year<br>averages)|
|Kelley and Schmidt (1994)a|.052|.63|1960-70|
||-.21|.16|1970-80|
||-1.32|3.2|1980-90|
|Barro (1991)|-.14|2.15|1960-85|
|Mazikw, Romer, Weil (1992)|-.14|1.75|1960-85|
|Landau(1986)<br>.|-.19|.93|1960-80|
|Ram (1986)|-.306|.87|1970-80|
|Kormendi and Meguire (1985)|-.42 -|2.47|1950-77|

Notes: The details of each study (e.g., definitions of dependent and independent variable, estimation method, sample) are included in Appendix table Al.l A Coefficients from the original smdies have been transformed so the reported coefficient represents the impact on the per capita growth rate of a 1 percentage point acceleration in population growth.

a) Estimates are inclusive of interaction effects with level of income per capita, while t- statistics are for slope term alone.

The small and insignificant effect of population in regression estimates are often dismissed entirely by saying that the results are "difficult to interpret" or that "not much is leamed" from country level aggregate studies which show a lack of correlation. While the studies may not have empirically confirmed the common sense view, and certainly have some deficiencies, these studies do establish two facts. First, on average, across countries and time periods, the higher population growth is not associated with significantly less rapid growth rates
Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008b. “Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.” AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

IBNET (The International Benchmarking Network for Water and Sanitation Utilities). 2009. www.ib-net.org/.

- WHO (World Health Organization) Joint Monitoring Program (JMP). 2010. “Uganda Estimates for the Use of Improved Drinking-Water Sources.” www.wssinfo.org/resources/documents.html.

- ———. 2010b. “Uganda Estimates for the Use of Improved Sanitation Facilities.” www.wssinfo.org/resources/documents.html.

- Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. “Provision of Water to the Poor in Africa: Experience with Water Standposts and the Informal Water Sector.” AICD Working Paper 13, World Bank, Washington, DC.

Marin, Philippe, William Muhairwe, Silver Mugisha, and Josses Mugabi 2010. “Internal Delegation Contracts for Water in Uganda.” Gridlines Series, PPIAF, Note No. 55, World Bank, Washington, DC, June 2010.

- Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. “Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.” AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

# Other

Nathan Associates. 2010. “Definition and Investment Strategy for a Core Strategic Transport Network for Eastern and Southern Africa.” Preliminary draft report prepared for the World Bank, Washington, DC.

- Teravaninthorn, Supee, and Raballand Gael. 2009. _Transport Prices and Costs in Africa: A Review of the Main International Corridors_ . Washington, DC: World Bank.

- World Bank. 2008. _Doing Business 2009: Comparing Regulation in 181 Economies_ . Washington, DC: World Bank.

- ———. 2010b. “Agricultural Technology and Agribusiness Advisory Services.” Project Appraisal Document, World Bank, Washington, DC, May 26, 2010.

World Bank 2011a. Uganda’s Transition to an Oil Economy: Fiscal Decentralization and Local Development in the Producing Areas. Mimeo. Washington DC

- ———. 2011b. “Uganda Demography and Economic Growth in Uganda.” Draft version, World Bank, Washington, DC.

- WEF (World Economic Forum). 2007. “Competitiveness and Information and Communications Technologies in Africa.” In _Africa Competitiveness Report 2007_ , ed. World Economic Forum, Geneva

55
|beverages,<br>tobacco,<br>wood, paper<br>(HS 11, 15-24,<br>44-48)|||||||||
|---|---|---|---|---|---|---|---|---|
|Chemicals,|42172|0.58|394995|2.27|20.32|11.49|0.08|0.20|
|plastics,<br>rubber<br>(HS 28-36,<br>38-40)|||||||||
|Machinery,<br>electronics,<br>transportation<br>equipment<br>(HS 84-89)|61309|0.85|227942|1.31|11.37|14.10|0.04|0.06|

Source: Authors’ calculations based on data from Comtrade.

Top Pakistan’s exports products are the ones in which the country has a revealed comparative advantage (RCA)<sup>6</sup> , like textiles, apparel, leather, footwear, agriculture products, meat and dairy and seafood. For rising products such as mineral, metals and machineries and electrics, the country has no comparative advantage in relation to the rest of the world. The export growth of these products is reflected though in increasing values of RCA. In section 3.3 there is a further analysis relating sectors and revealed comparative advantage.

# 2.2 **Diversification**

Diversifying exports across markets and products is important because it reduces the vulnerability of the country’s export portfolio to partner-specific shocks and to extreme volatility in export prices. The recent crisis has highlighted the critical importance of diversification in reducing the risks of volatility in global demand. Evidence suggests it is not natural resource dependence per se that increases risk, but rather the concentration of exports (Lederman and Maloney, 2007).<sup>7</sup> Overall, Pakistan’s export bundle is relatively diversified in terms of products, but the emphasis on industrialized countries export markets makes the country too vulnerable to economic developments in these countries. As it already emerged from the analysis of Pakistan’s market shares, in terms of geographical specialization, Pakistan’s exports are relatively more concentrated than peer countries.

> 6 The RCA index is a measure for calculating the relative advantage or disadvantage of a certain country in a certain industry as evidenced by trade flows. An index above the unit indicates that a country’s share of exports in that sector exceeds the global export share of the same sector. If this is the case we infer that the country has a comparative advantage in that sector. However, because high export volumes can results from subsidies or other incentives provided, included under-valued exchange rates, RCA have been argued to be a misnomer in the sense that they are a better measure of competitiveness than comparative advantage (Siggel, 2006).

> 7 Increasingly, services trade has also become an important source of export diversification. This dimension is not analyzed in this paper, but the reader can find some information in the policy note on reinvigorating the trade agenda in Pakistan (World Bank, forthcoming).

7
respectively 20 percent and 60 percent higher in NPS than in HBS. While one could suspect underreporting by the non‐poor in HBS 2011/12, this seems implausible when comparing the households’ characteristics (household size and number of dependents) between the HBS and NPS last rounds. However, the drop in household size and number of dependents in NPS 2012/13 compared to the previous rounds and the significant increase of the consumption levels of the richest quintiles point to the possibility of sample design/weighting and/or overestimation of consumption by the non‐poor in the last NPS round.

v) To summarize, the divergence between HBS and NPS in the levels and distribution of consumption could be explained by: i) underestimation of consumption by the poor in HBS 2007. This might have led to an overestimation of the rate of poverty reduction in the HBS survey. But the overestimation is not expected to affect the poverty trend and the pro‐poor growth pattern given that the different methods to address the data comparability problems confirmed the decline in poverty and the emerging signs of pro‐poor growth; ii) changes in sample design or sample weighting in the last NPS round or overestimation of consumption by the non‐poor.

The following sections will explore other potential sources of the mismatch between the two surveys.

**Table 6: Value of spatial price deflators by survey** **<u>quarter and strata, HBS and NPS (Fisher price indices)</u>**

|||Oct–Dec 2008|Jan–Mar 2009|Apr–Jun 2009|Jul–Sep 2009|
|---|---|---|---|---|---|
||Dar es Salaam|1.08|1.18|1.20|1.15|
|NPS 200809|Other urban|1.00|1.04|1.04|1.04|
|/|Rural|0.92|0.86|0.92|0.96|
|||Oct–Dec 2010|Jan–Mar 2011|Apr–Jun 2011|Jul–Sep 2011|
||Dar es Salaam|1.05|1.11|1.17|1.18|
|NPS 201011|Other urban|0.90|0.97|1.06|1.08|
|/|Rural|0.87|0.86|0.98|1.02|
|||Oct–Dec 2012|Jan–Mar 2013|Apr–Jun 2013|Jul–Sep 2013|
||Dar es Salaam|1.12|1.17|1.13|1.07|
||Other urban|0.99|1.04|1.02|0.93|
|NPS 2012/13|Rural|0.95|0.94|1.00|0.93|
|||Jan–Mar 2007|Apr–Jun 2007|Jul–Sep 2007|Oct–Dec 2007|
||Dar es Salaam|1.19|1.18|1.25|1.34|
|HBS 2007|Other urban|1.04|1.06|1.09|1.10|
||Rural|0.94|0.96|0.98|1.04|
|||Oct–Dec 2011|Jan–Mar 2012|Apr–Jun 2012|Jul–Sep 2012|
||Dar es Salaam|1.09|1.18|1.22|1.22|
|HBS 201112|Other urban|1.00|1.03|1.02|1.06|
|/|Rural|0.93|0.94|0.98|0.97|

27
# References

- [1] Acemoglu, D., James A. Robinson and Simon Johnson, 2001, "The Colonial Origins of Comparative Development: An Empirical Investigation", American Economic Review, 91:1369-1401.

- [2] Aghion P. and P. Bolton,1997, "A Theory of Trickle-Down Growth and Development", Review of Economic Studies, 64:151-172

- [3] Alesina A. and G. Tabellini, 1989, "External Debt, Capital Flight and Political Risk," Journal of International Economics, 27:199-220

- [4] Barro, R., 1979, "On the Determination of Public Debt," Journal of Political Economy, 87: 94-122

- [5] Barro, R. and X. Sala-I-Martin, 1995, Economic Growth, MIT Press.

- [6] Clements, B., R. Bhattacharya and T.Q. Nguyen, 2003, "External Debt, Public Investment and Growth in Low Income Countries", IMF Working Paper 03/249.

- [7] Cohen, D, 1993, "Low investment and Large LDC Debt in the 1980’s", American Economic Review, 83:437:449

- [8] Cohen D., 1995, "Large External debt and (slow) Domestic Growth: a Theoretical Analysis," Journal of Economic Dynamics and Control 19:1141-1163

- [9] Cohen, D., and J. Sachs, 1986, "Growth and External Debt Under Risk of Debt Repudiation", European Economic Review, June, 30:529-550

- [10] Dikhanov, Y., 2004, "Historical Present Value of Debt in Developing Economies: 19802002", manuscript, The World Bank.

- [11] Doppelhofer, G., R. Miller and X. Sala-I-Martin, 2004, "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach", American Economic Review, 94(4): 813-35.

- [12] Easterly, W., 2001, "Growth Implosions, Debt Explosions and My Aunt Marylin: Do Growth Slowdowns Cause Public Debt Crises?", World Bank Policy Research Working Paper No.2531.

- [13] Hauk, W., and R. Wacziarg, 2004, "A Monte Carlo Study of Growth Regressions", NBER Technical Working Paper #T0296.

- [14] Henry, P. and S. Arslanalp, forthcoming, "Is Debt Relief Efficient?", Journal of Finance.

- [15] Kaufmann, D. A. Kraay and M. Mastruzzi, 2004, "Governance Matters III: Governance Indicators for 1996-2002", World Bank Economic Review.

- [16] Krugman, P, 1988, "Financing vs. Forgiving Debt Overhang",Journal of Development Economics,29, 253-268

- [17] Levine, R., and D. Renelt, 1992, "A Sensitivity Analysis of Cross-Country Growth Regressions," American Economic Review, 82(4):942-63.

21
-11 -

Project-Financed PHN Research

- In countries where the institutional capability exists, using a national institution to ister research grants c;un be quite effective. Experience indicates some sort of research.

- There probably is room for more "best practices" workshops where PHN staff sary to retain experienced consultants to assist with the design of substantial research components.

- More systematic collection and dissemination of project-financed research is of money and effort which is devoted to it.

# Other Sector Project-Financed Research

Two other recent studies of World Bank project-financed research have been published, one each in the education and agriculture sectors.

# _Education_

The education study is _World Bank Lending for Education Research, 1982-89_ by Marlaine E. between fiscal year 1982 and fiscal year 1989. Of these, 116 included research components. The authors found that 2.2 percent of the loans and credits of these 116 projects was allocated **to** research, an outcome not much different from the however, that research as a percentage of total loan commitment declined sharply from 1982 to 1989, an outcome not demonstrated by this study for the PHN sector. Of the 436 planned studies were completed and of these only 84 were available through Regional Information Centers. not yield anything close to such a precise estimate of the number of studies included in projects, but the general impression from talking to task managers is that in the PHN sector, too, many planned critical of the fact that only 5.6 percent of the completed studies "had anything to do with assessing educational outcomes." The authors recommended that more attention be paid to the design and implementation of conclusion which many would probably feel valid for PHN projects. To facilitate this, they recommend that the Bank develop a program to train operational staff to design studios with appropriate methodologies and which would develop domestic research capacity. In the PHN sector, it would appear more appropriate to assure access to suitable consulting services for the design of research components rather than to undertake a training program substantial enough to teach operational staff how to design research components. They recommended free-standing educational research projects in larger countries. Except for the Brazil Health Policy Studies Project that was part of the Second Health Project, no PHN project reviewed substantial measure of support for free-standing PHN research projects. Finally, the authors recommend much more attention be paid to dissemination of research reports within the Bank and within the borrowing country, and that project studies completed under a project, both suggestions which would be applicable to the PHN sector.
For example, cargo traffic and rail freight declined, oil demand collapsed, and India’s Purchase Manager Index dropped to an all-time low in April. An excellent discussion of the economic impact of COVID-19 on India’s economy is provided by Dev and Sengupta (2020).

# **3 Data**

## **3.1 Daily Electricity Consumption**

We observe daily electricity consumption from April 1, 2013 to May 31, 2020. The data is measured and collected from the Power System Operation Corporation Limited (POSOCO), which is a government-owned enterprise under the Ministry of Power. It is responsible for ensuring the integrated and reliable operation of India’s grid. POSOCO makes available daily reports of electricity consumption with one-day delay. We download the daily documents and scrap the electricity information to build our electricity consumption database for India. While the total electricity consumption in these documents does not differentiate between different uses (residential, commercial, etc.), it does breakdown the electricity consumption of the different states. This will later allow us to track the specific impact of the lockdown on the different states.

Figure 2 shows India’s daily electricity consumption from April 1, 2013 to May 31, 2020.<sup>9</sup> A couple of features are clearly noticeable. First, until the end of 2019, there is a clear upward trend with electricity consumption on average growing 4.3 percent each year. Second, there is a clear seasonality in the data with electricity consumption being higher between May and September than at the beginning and end of the year. Third, there was a noticeable decline already at the end of 2019, long before the COVID-19 pandemic disrupted economic activity in India.<sup>10</sup> Fourth, around the national lockdown announced on March 24, electricity consumption dropped strongly. Fifth, at the end of May electricity consumption first recovered before falling again.

## **3.2 Nighttime light intensity**

The nighttime light data are extracted from the VIIRS-DNB Cloud Free Monthly Composites (version 1) made available by the Earth Observation Group at the National Geophysical Data

> 9The figure is in mega units. A mega unit is one million units of electricity, where one unit is equal to one kilowatt hour.

> 10This decline at the end of 2019 has been linked to weakening GDP growth.

6