rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Effects of fiscal policy volatility on GDP growth volatility**

|<br>|<br>O<br>|GDPgro<br>LS<br>|wth volatili<br>|ty<br>IV<br>||
|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|
|Fiscal policy volatility|0.341***|0.359***|0.363***|0.499***|0.141***|
||(0.072)|(0.0734)|(0.104)|(0.175)|(0.0523)|
|GDP per capita||0.837*||0.886|0.318|
|||(0.454)||(0.564)|(0.281)|
|EMDE||1.073*||0.886|0.950*|
|||(0.624)||(0.679)|(0.489)|
|Commodity exporters||0.156||-0.715|-0.339|
|||(0.412)||(0.446)|(0.388)|
|Constant|1.358**|-7.421|1.309**|-8.426|-0.762|
||(0.527)|(5.097)|(0.649)|(6.548)|(3.037)|
|Observations|178|177|133|132|77|
|R-squared|0.300|0.338|0.23|0.254|0.238|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0. Fiscal policy volatility in all specifications is measured using primary expenditures. Instruments for (3) and (4): Political constraints; control of corruption. Instruments for (5): Political constraints; control of corruption; resource rents; fiscal rules dummy; capital account openness; and exchange rate regime.

36
Table 6: Impact of migration on labor force characteristics

|||Agric-|Participat|ion in non-a|griculture|emplo-|
|---|---|---|---|---|---|---|
||LFP|ulture|All|Wage|self|yer|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||**Working ag**|**e (15-64)**||||
|Village migration rate|||||||
|x migrant in HH|0.192**|0.400***|-0.207***|-0.126**|-0.081**|0.076**|
||(0.095)|(0.087)|(0.075)|(0.058)|(0.032)|(0.039)|
|x no migrant in HH|0.353***|0.112|0.241***|0.144***|0.097***|0.134***|
||(0.085)|(0.100)|(0.065)|(0.053)|(0.028)|(0.043)|
|Observations|3881384|3881384|3881384|3881384|3881384|2270376|
|Adj R-squared|0.053|0.199|0.087|0.054|0.036|0.024|
|||**Male sub**|**-sample**||||
|Village migration rate<br>x migrant in HH|-0.060|0.641***|-0.701***|-0.532***|-0.168***|0.117***|
||(0.095)|(0.101)|(0.115)|(0.097)|(0.047)|(0.043)|
|x no migrant in HH|0.443***|0.369***|0.074|0.010|0.065|0.162***|
||(0.068)|(0.113)|(0.104)|(0.086)|(0.043)|(0.046)|
|Observations|1847306|1847306|1847306|1847306|1847306|1344491|
|Adj R-squared|0.028|0.194|0.134|0.075|0.054|0.022|
|||**Female su**|**b-sample**||||
|Village migration rate|||||||
|x migrant in HH|0.138|0.079|0.059|0.113***|-0.055**|0.089**|
||(0.117)|(0.109)|(0.049)|(0.038)|(0.026)|(0.044)|
|x no migrant in HH|0.214*|-0.110|0.323***|0.221***|0.102***|0.134***|
||(0.129)|(0.120)|(0.052)|(0.041)|(0.024)|(0.050)|
|Observations|2034078|2034078|2034078|2034078|2034078|925885|
|Adj R-squared|0.160|0.249|0.057|0.042|0.025|0.035|

Source: Author’s estimates using the Census 2001 and 2011 data. Note: This table shows the impact of village migration rates on labor force participation for the working age population (15-64) from households with and without a current migrant. Reported coefficients are the estimates of _β_ from Equation (2). The column headings indicate the participation in various types of activities. Dependent variable is the migration rates in the village. Standard errors, clustered at the village level, are reported in parentheses. _∗∗∗_ : _p <_ 0 _._ 01; _∗∗_ : _p <_ 0 _._ 05; _∗_ : _p <_ 0 _._ 1 26
- McDonald, S., Robinson, S. and Thierfelder, K. (2007) “Asian Growth and Trade Poles: India, China and East and Southeast Asia” Paper presented at the 10<sup>th</sup> Annual Conference on Global Economic Analysis, Purdue University, <u>www.gtap.org.</u>

- Mitra, D. and Ural, B. (2006), Indian Manufacturing: a Slow Sector in a Rapidly Growing Economy, Mimeo, World Bank.

- Nikomborirak, D. (2006), A Comparative Study of the Role of the Service Sector in the Economic Development of China and India, Mimeo, World Bank.

- Puga, Diego, and Anthony Venables 1999. “Agglomeration and economic development: import substitution vs trade liberalization” _The Economic Journal_ 109:292-311

Rodrik, D. What’s so special about China’s exports? Mimeo, Harvard University.

- Schott, P. (2007) “The Relativive Sophistication of Chinese Exports,“ paper prepared for Global Implications of China’s Trade, Investment and Growth Conference, IMF Research Department, April 6 2007.

- Srinivasan, T.N. (2004), ‘China and India: Economic Performance, Competition and Cooperation’ Mimeo, Yale University.

- Steinfeld, E. (2002), Chinese enterprise development and the challenge of global integration. Cambridge: Department of Political Science, MIT.

- Walmsley, T., Dimaranan, B. and McDougall, R. (2002), “A Base Case Scenario for the Dynamic GTAP Model.” Paper prepared for the GTAP Advisory Board Meeting, June 2 - 3, Taipei.

- Winters, L. Alan and Yusuf, S. (2007), _Dancing with Giants: China, India, and the Global Economy_ , World Bank, Washington DC.

World Bank (1994), _China: Foreign Trade Reform._ World Bank Country Study, World Bank, Washington DC.

- World Bank (2003), _Global Economic Prospects and the Developing Countries 2004._ World Bank, Washington DC.

- World Bank (2004), _Trade Policies in South Asia: an Overview_ . Report No 29949, World Bank, Washington DC.

- World Bank Office, Beijing (2006), ‘GDP revisions- what has changed and what has remained the same?”, in _Quarterly Update: February 2006,_ World Bank Office, Beijing, <u>http://siteresources.worldbank.org/INTCHINA/Resources/318862-1121421293578/cqu_feb06.pdf,</u> downloaded April 3, 2006.

WTO (2002), _Trade Policy Review: India 2002._ World Trade Organization, Geneva.

29
# **III. Data and Model**

The second round of GDS was conducted from May to September 2006, to assess the state of governance and local public service delivery in Indonesia by collecting data on quality and satisfaction from households, communities, and facilities. The GDS-2 household sample is nationally representative, selected using a stratified random sampling approach. A total sample of 8544 households were distributed equally among 1068 hamlets (dusun) that served as the primary sampling unit (PSU), which were in turn distributed between 89 districts (kabupaten/kota), 267 sub-districts (kecamatan) and 534 villages.<sup>7</sup> The sample of health and education facilities was not selected at random, and was instead guided by which facilities were reported as most frequently used by households.

For health services, the 6 community health centers (puskesmas) that were most frequently mentioned by surveyed households were selected for secondary data collection within each district included in the survey. Excluded from the facility survey sample were private facilities, public general hospitals and the ancillary facilities (known as the pustu, polindes, and pusling) that form the extended network supporting the main puskesmas.<sup>8</sup> Education facilities were selected for secondary data collection (through a facility survey) in a way similar to that for puskesmas. The most frequently used public elementary school in a 7 89 districts (kabupaten/kota) were randomly selected from the 408 that remained after excluding all districts in Aceh and Jakarta, and the 3 districts used for pre-testing. Three sub-districts (kecamatan) were randomly selected from each district using probability proportionate to size sampling; the same method was used to randomly select 2 villages (desa) within each sub-district; and 2 hamlets (dusun) were randomly selected within each village. Latest population lists provided by the hamlet heads were used to randomly select 8 households in each hamlet.

8 Out of 8,544 households, 4,358 use puskesmas and its related networks. Of these, facility/secondary data are available for facilities used by 2,269 households (see Appendix A, Table A-1), since the less frequented puskesmas and most of the affiliated facilities were excluded from the facility sample.

8
# **6. Conclusions**

This paper extends the analysis of Robertson et al. (2021) to determine why Egypt’s trade liberalization and the resulting boost in trade flows did not directly increase employment. It would be misconstrued to conclude from the lack of labor market response that “trade does not work” because the story is much more nuanced.

As we discussed, while increasing trade often increases employment, there are at least four reasons why it may not. In the case of Egypt, we demonstrate that exporting firms are too small of a segment of the local labor market to significantly affect aggregate employment. Second, in the presence of hiring and firing costs, firms may hold on to workers, creating a relatively idle “reserve” group of workers that fill the demand from rising exports. Our microanalysis of firms demonstrates a positive employment response to export expansion, inconsistent with the idea of idle excess worker capacity. Third, we demonstrate that, as is the case for most exporters, Egyptian exporters tend to be in capital-intensive sectors and so may expand production by using either new machinery or extending the run-time of existing machinery. Finally, while some firms shift production from the domestic to the foreign market without increasing employment, these firms account for only a small share and thus cannot explain the disconnect between export flows and the labor market.

In sum, the lackluster response of Egypt’s labor market following its trade liberalization is in large part because its export market remains a very small share of its economy. Our firm microanalysis demonstrates a positive employment response to export expansion, but not at large enough scale to be felt at the macro level. This calls for expanding Egypt’s export market and its integration into global value chains.

23
McKinsey report (Farrell et al. (2006)) and as suggested in Allen et al. (2006).<sup>15</sup> However, the use of funds from Family and Friends and Informal lending sources such as money lender or informal banks seems to be comparable with its use in other countries.

China also looks unique in the large usage of Equity financing (12.39%) compared to the other developing countries (all below 5%). However, when we look across other geographic regions, the East Asia and Pacific region (excluding China) uses the largest amount of Equity financing (21.38%) compared to other regions (below 5%). Our data on China allow for a more detailed breakdown of the equity issuance into sale of stock to employees, public issue of marketable shares to outside investors and sale of stock to legal persons. The sale of stock to employees and public issuance of marketable shares to outside investors is quite low at 2.89% and 1.26% respectively and most of the equity issuance is really sale of stock to legal persons (8.24%). Legal Person shareholders are unique to China and are analogous to institutional shareholders in western economies except that they tend to have strong state linkages<sup>16</sup> and are not widely held as in western economies.

In panel B, we look at the aggregate financing patterns. When we look at Internal Financing, which includes Retained Earnings, loans from Family/Friends and Other sources, Chinese firms look comparable to firms in other countries. Focusing on the

> 15 It could be that the Other Sources in China are actually Internal Funds/Retained Earnings that are misclassified. While we don’t have a detailed breakdown of what these Other sources could be for all firms, the responses from the 100 firms that do report the exact type of Other Financing suggest that there could be some mis-classification.

> 16 The legal person identity was created to aid the transition of China's companies from state-owned to private-owned since the private sector did not have enough capital to acquire large tranches of state shareholdings. As described in Xu and Wang (1997) and Delios and Wu (2005), the legal person shareholder category comprises private companies, state-owned enterprises and non-bank financial institutions such as investment funds and security companies. The legal person shareholders differ from pure state shareholders in that they are profit seeking, and have relatively more freedom than state shareholders in deciding how to allocate profits and formulate and implement firm strategy.

14
# LIST OF APPENDIX TABLES

# APPENDIX A

- Table A.1 Changes In Population, Workforce, and Nonfarm Employment In India,

- Table A.2: Income Distribution by Farm Size, Rural India Table A.3: Income Distrlbution by Farm Slze, Rural India 1970/71 and 1980/81 Table A.4: Locational Changes In Population, Workforce and Nontarm Employinent In India, 1961-81

- Table A.5: Measured Female Employment Density by Activity, India 1961-81

- Table A.6: Urban Population Distrlbution Across States with Differing Agricultural Incomes: India 1981

- Table A.7: Rurai-Urban Differences In Nonfarm Activity in States with Differing Agricultural Incomes, India 1981

- Table A.8: Changing Composition of Nonfarm Activity Across States with Differing Agricultural Incomes, India 1971-81

- Table A.9: Employment Density by Activity In Six Indian States, 1971-81 Table A. 1 0: Varlations In Male Agricultural Wages a/ Across Regions, India 1970/71-1984/85

- Table A.1 1: Composition of Change In Rural Income for a Panel of Households Indla 1970/71 - 1981/82

- Table A. 1 2: Rural Consumption Profile by Expenditure Class, India 1983 Table A. 1 3: Regression Coefficients for Estimating Income Multipliers District Data

- Table A.14: Data, Income and Wage Multipliers for Regions of Varying Agricultural Productivity

- Table A.15: Regression CoeffIclents for Estkiating Income Multipliers State Data

- Table A.16: Regression Coefficlents for EsthnatUng Empioyment Multipliers Table A. 17: Changes In Rural Consumption Patterns, India 1967/68 - 1983
# **1 Introduction**

Green transition is becoming more pressing than ever, both in the context of climate change and in the energy crisis scenario caused by the Russian invasion of Ukraine. To face the climate change challenge, the European Green Deal (see EU 2020), adopted in 2020, sets the path to make the EU's climate, energy, transport, and taxation policies fit for efficiently facing the environmental degradation threat. As part of the European Green Deal, with the European Climate Law, EU Member States agreed on the ambitious target of achieving climate neutrality by 2050, under the so-called “Fit for 55” package. In accordance with this set of measures, as an intermediate step, Member States are pledged to reduce emissions by at least 55% by 2030, compared to 1990 levels. The significant commitment thereof comes as an opportunity for developing new policy evaluation tools, especially in the new energy crisis scenario that seems to undermine the achievement of the ambitious “Fit for 55” goals. The increase in energy prices and the worsening of international relations with the Russian Federation are already influencing economic activity, employment, and household living conditions so that new policy strategies are needed. Developing and testing new economic models featuring detailed energy structure can play an important role in designing quick policy responses and assessing options for the energy crisis.

This paper has two main research objectives, which are novel contributions to the existing literature. From a methodological perspective, we present the Italian Regional and Environmental Computable General Equilibrium of the Department of Finance (IRENCGE-DF, henceforth), which seeks to answer to the new current policy needs. As we shall see, IRENCGE-DF’s structure makes it very topical and extremely useful for analyzing different policy scenarios coming either from the EU legislation or from the international political scenario. The IRENCGE-DF is a single-country CGE model with regional and environmental modules, tailored to the specific Social Accounting Matrix (SAM) built for Italy. It is a multi-sector, multi-household CGE model, based on a set of equations which aim to capture the structure of the economy and behavioral response of agents such as firms, households, government, and the rest of the world. This provides a very rich framework to simulate the policy changes and trace the impact on the key economic variables, including income and expenditure flows. The model incorporates an environmental module which features a detailed energy specification that allows for capital/labor/energy substitution in production, intra-fuel energy substitution across all the demand agents, a multi-output multi-input production structure, an energy system extended with 11 different types of technologies including renewable and clean energy, and an extended environmental policy that can allow simulation of the effects of carbon tax and carbon emission on different sectors.

2
- Rapoport, Hillel and Frederic Docquier (2006): The Economics of Migrants’ Remittances, in S.-C. Kolm and J. Mercier Ythier, eds.: _Handbook of the Economics of Reciprocity, Giving and Altruism_ , Amsterdam: North Holland, forthcoming.

- Rivera-Batiz, Francisco (1999) “Undocumented workers in the labor market: An analysis of the earnings of legal and illegal Mexican immigrants in the United States”, _Journal of Population Economics_ 12:91-116.

- Rivers, Douglas and Quang H. Vuong (1988) “Limited Information Estimators and Exogeneity Tests for Simultaneous Probit Models”, _Journal of Econometrics_ 39: 347-66.

- Secretaría de Educación Pública (SEP) (1999) _Profile of Education in Mexico._ Ministry of Public Education, http://www.sep.gob.mx.

- Taylor, J. Edward and T.J. Wyatt (1996) “The Shadow Value of Migrant Remittances, Income and Inequality in a Household-farm Economy”, _Journal of Development Studies_ 32(6): 899-912.

- Woodruff, Christopher, and Rene M. Zenteno (2001): Remittances and micro-enterprises in Mexico, Mimeo, UC San Diego.

- World Bank (2005) _Global Economic Prospects 2006: Economic Implications of Remittances and Migration_ , The World Bank, Washington D.C.

- Yang, Dean (2004) “International Migration, Human Capital, and Entrepreneurship: Evidence from Philippine Migrants’ Exchange Rate Shocks”, Mimeo. Ford School of Public Policy, University of Michigan.

29
between 2005 and 2009 (but not between 2001 and 2005), we code migration according to step (iii) above.

Notes: the MxFLS website does not provide a specific module for migrants to the US in the second follow-up.

# **B.6 Nigeria Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA)**

Notes: the survey collects information on occupation of migrants in section 1 of each wave (Household Roster). However, it does not specify whether the migrant is selfemployed or a wage worker.

Baseline – Post Planting Wave 1 (August - October 2010):

- status: we record an individual as not if she answers no

- Employment working to question 4 (“During the past 7 days, have you worked for someone who is not a member of your household, for example, an enterprise, company, the government or any other individual?”), question 5 (“During the past 7 days, have you worked on a farm owned or rented by a member of your household, either in cultivating crops or in other farming tasks, or have you cared for livestock belonging to yourself or a member of your household?”), and question 6 (“During the past 7 days, have you worked on your own account or in a business enterprise belonging to you or someone in your household, for example, as a trader, shop `[U+2010]` keeper, barber, dressmaker, carpenter or taxi driver?”) of Section 3 (Labour).

- Self-employed: individuals who report being self-employed in question 15 (“Who is the employer in this job?”). We remove people self-employed in agricultural activities using the answer to question 13 (“What was your primary activity in your main job?”) and question 14 (“In what sector is this main activity?”).

- Wage worker: individuals who report not being self-employed in question 15, and that are not unpaid family workers or in an unpaid traineeship (question 20, “What is the main reason you received no payment for this work?”). We remove people employed in agricultural activities as above.

- Agricultural worker: all individuals employed in agricultural activities.

- Income: we compute monthly income as sum of wages and non-farm business sales. Wages and salaries are reported in question 21 of Section 3. We compute

54
3-

- (vi) The problems of the econometric approach are reviewed in some detail. For studies pertalnlng to a given country four issues appear to be important in assesolng the reliabillty of the eotlmatest

- (a) The cholce of functi ial form: thli has been the toplc of greatest concern. DLfferent flexLble cost functions have been estlmated. A common feature is the property of non-constant elastLiLties of substitutLon. If such functions are used then the elasticLtLes vary with the fuel shares at whlch they are evaluated - there is no unlque elasticlty of substitution; simple comparisons between industries, time periods or countrles are not meaningful unless allowance li made for thls factor. Recent work suggests that, at a given fuel share, the elasticitLes are fairly robust wlth respect to the choice of flexible functlonal form, although the work of Conidine, using a completely new functlonal form, does not fully support this view.

- (b) The dynamlcs of the fuel choice response: early studies showed that pure tlme serLes data tended to yield lower prlce elasticities than cross-section data. Thls has been lnterpreted as suggesting that the period of adjustment li lengthy so that static models may well underestimate the total responsiveness of fuel cholce to price changes. Little systematic investigatlon of thLs issue has been undertaken, but recent models which allow for a crude dynamic adjustment through an autoregressive error process do yield values substantially higher than some earlier studies.

- (c) The level of aggregation of the lndustrial sector: the shifts in industrial composltlon over lengthy time periods could imply aggregation bias for elasticitles if the values for indivldual industrieo are substantially different. The recent work on Canadian manufacturing suggests that the valueos are very similar when evaluated at their fuel shares for the same year, so that estimates which were based on the aggregate sector may not be strongly dependent on the estimation perLod for reasons of changLng aggregation.

- (d) The stability of estimates: much of the econometrlc estimation of fuel price elasticities has been based on data which finished in the 1970s or early 1980s. Given the very large oil price rise at the end of this period, and the likelihood that full adjustment is a lengthy process, it seems that were the structural cost parameters to have changed then this would not have been captured by such estimates. There has been apparently no check on structural stability and, only in the most recent studies has an attempt been made to allow for shifts in the cost function induced by technical progress. Even if there is insuffLicent data to test for structural stability, it would be desirable to base eotimation on data including as much of the recent past as possible.