rafmacalaba/datause-encoder-data huggingface dataset:
networks. Finding an exogenous source of variation is challenging. A good example is Martincus and Blyde (2013) who exploit the partial destruction of the Chilean road network in the 2010 earthquake as a natural experiment. Using establishment-level export flows from geo-referenced origins to ports, they first impute distance and time components of road travel costs before and after the earthquake with a methodology similar to the one described in the previous subsection. In this imputation, they utilize a survey on the operational transport costs of land cargo services ( _Encuesta de Servicio de Transporte de Carga por Carretera_ ) conducted annually by the Chilean statistical office INE. The exogenous variation in these imputed costs induced by the earthquake provides a basis for estimating the gravity equation (2) in differences.

An alternative in addressing endogeneity concerns related to road placement is to use the historical routes as an instrument for present day investments. Under the exclusion restriction assumption that historical networks affects current trade only through the determination of current roads, this approach helps correct the bias that may plague OLS estimation. In an example from the U.S. context, Duranton et al. (2014) use exploration routes between 1528 and 1850 and railroad routes in 1898 as instruments for the Interstate Highway System. Banerjee et al. (2020) use historical transportation corridors connecting large old cities and 19th century Treaty Ports in China as instruments for modern paved roads.

To sum up, the gravity estimation relies on the presence of high quality domestic trade data which may be hard to obtain in developing countries. An alternative method is to infer trade costs from interregional gaps in prices, which is more feasible since national statistical agencies typically collect prices of various commodities with the primary intent of calculating the inflation rate. I describe this methodology next.

# **5.2 Estimation Using Price Differentials**

Spatial price gaps are informative about trade costs. Suppose the price of a good in location _o_ is _po_ , and its price in location _d_ is _pd_ . Assuming that there is a competitive trading sector, researchers invoke the following condition to estimate transport costs _t_ : This equation implies that at the prevailing observed prices, a trader who buys a good at the origin and incurs transport costs to sell it at the destination makes zero economic profits (no-arbitrage condition). Specifying _t_ as a function of distance, one can obtain an estimating equation similar to equation (2) with the dependent variable being price differentials rather than trade flows. Price differentials can be in absolute value _|po −pd|_ or a log difference ln( _po/pd_ ). The implicit benchmark in this approach is the law-of-

15
75 _Social Capital, Household Welfare and Poverty in Indonesia_

# **Table A9: Means and Standard Deviations of Regression Variables**

|**Variable List**|**Ind**|**onesia**|**Ja**|**mbi**|**Jawa**|**Tengah**|**Nusa**<br>**T**|**Tenggara**<br>**imur**<br>|
|---|---|---|---|---|---|---|---|---|
||**Mean**|**Standard**<br>_**Deviation**|**Mean**|**Standard**<br>**Deviation**|**Mean**<br>|**Standard**<br>**Deviation**|**Mean**<br>|]**Standard**<br>**|Deviation**|
|Dependent Variables|||||||||
|Ln(Household Expenditure Per Capita)|13.0100|0.632|13.1277|0.528|13.1209|0.619|12.7956|0.673|
|<br>Fraction of Households below Poverty Line|0.1970|0.398|0.0888|0.285|0.1578|0.365|0.3316|0.471|
|<br>Durable Goods Score (principal component weights)|0.5600|0.569|0.7983|0.668|0.6645|0.515|0.2454|0.341|
|Fraction of Households Who Increased Savings|0.1653|0.372|0.0516|0.221|0.3130|0.464|0.1190|0.324|
|Fraction of Households Who Obtained Credit|0.6121|0.487|0.6075|0.489|0.7735|0.419|0.4557|0.499|
|Ln(Amount of Credit Received+1)|7.0281|5.789|7.0457|5.854|8.8251|5.117|5.2245|5.816|
|Number of Times of Participation in Collective Action|25.0704|29.825|13.5931|20.995|30.8728|31.598|29.4380|31.824|
|Social Capital Variables|||||||||
|Aggregate Social Capital Index|17.2271|16.975|7.9560|7.731|17.7074|16.125|24.9407|19.578|
|<br>Number of Active Memberships|5.4617|3.263|3.6476|2.260|6.0025|2.988|6.5266|3.600|
|Heterogeneity Index|53.2725|19.897|38.8849|15.302|57.6389|18.083|61.6403|18.350|
|Number of Meeting Attendance Per Membershin|5.950]|6.515|6.7899|_°.°95_|_5.9727_|5.490|5.i855|4.6i5|
|IndexofParticipationinDecisionMaking|63.4931|31.734|63.4909|28.126|55.5768|31.149|71.3713|33.389|
|Cash Contribution Score|0.6199|1.844|0.6428|1.425|0.65020|1.664|0.5694|2.293|
|Work Contribution Score|2.8473|5.877|0.12261|0.864|1.4660|5.571|6.6290|6.724|
|% of Membership in Community-Initiated Associations<br>Control Variables|52.8911|25.329|62.2211|28.654|48.0128|25.774|49.5014|18.743|
|Household Size|5.0035|2.192|4.7278|1.940|4.3562|1.680|5.8911|2.540|
|Years of Education Per Adult Household Member|4.8460|2.442|4.8295|2.557|4.6753|2.549|5.0304|2.211|
|Female Head of Household|0.0800|0.271|0.0860|0.281|0.0712|0.258|0.0835|0.277|
|Age of Head of Household|44.7845|13.659|41.4871|14.428|48.3003|13.084|44.2000|12.705|
|AgeofHeadofHouseholdSquared|2192.05|1319.75|1928.74|1357.89|2503.67|1315.35|2114.66|1227.87|
|<br>Farmer Household|0.7696|0.422|0.7106|0.454|0.6743|0.469|0.9165|0.277|
|Land Ownership (Hectares)|1.8549|9.821|2.8979|16.990|0.7266|1.651|2.0561|4.259|
|Number of Large Animals Owned|3.8593|7.032|1.3266|2.856|2.3079|3.002|7.6405|10.198|
|<br>Number of Farming Equipment Owned|0.6517|0.552|0.9656|0.225|0.0738|0.271|0.9494|0.501|
|Jatnbi|0.3069|0.461|1.0000|0|0|0|0|0|
|Jawa Tengah|0.3456|0.476|0|0|1.0000|0|0|0|
|Nusa Tenggara Timur|0.3474|0.476|0|1<br>0<br>|1<br>0|0|1.0000|0|
is especially likely to be affected. Second, IDPs may have cultural and gender norms that differ from host-community members’ norms, and, in doing so, potentially influence the evolution of the latter norms and discrimination against women (Bredtmann _et al._ , 2020). Third, when host-community members make contact with IDPs and learn from them about the nature of the attacks and the underlying ideology, they may change their beliefs (e.g., about the role of women in society) and behaviors to distance themselves from the ideology of the aggressors.<sup>2</sup> In contrast, the other prominent theoretical channel of worsening economic conditions as a result of interrupted trade and mobility restrictions (e.g., Blomberg and Hess, 2006) arguably applies to the two forms of attacks (by the jihadist group and by pastoralist militias) in a similar way. Hence, this channel is unlikely to lead to pronounced differential spatial effects on women across attack type. Also, the direction of the effect on women’s economic conditions is arguably ambiguous. On the one hand, economic disruptions may affect men more than women given their greater economic integration as observed in survey data. On the other hand, households may diversify sources of income as a risk-mitigation strategy by adjusting women’s labor supply (Attanasio _et al._ , 2005).

Our primary contribution to the literature is to provide the first estimates of the impact of conflict on relatively safe, geographically proximate areas within the same country on a range of measures relevant for economic development. The thin body of available empirical research examines the spillover effects of conflict from one country to another (Murdoch and Todd, 2002, 2004; Blomberg and Hess, 2006) but not within countries. Yet, most forcibly displaced people stay within their home country (UNHCR, 2022). Further, to the best of our knowledge, empirical evidence on the gender dimension of the spillover effects of conflict is currently missing altogether. By adding to the evidence base on the determinants of gender dynamics within societies (e.g., Akerlof and Kranton, 2000; Bisin and Verdier, 2000; Bertrand _et al._ , 2015), we aim to inform policies of national governments and international organizations targeting gender imbalances and promoting

> 2For instance, US bombing intensity reduced noncommunist civic engagement during the Vietnam War (Dell and Querubin, 2017). Similarly, the 9/11 attacks affected the assimilation rate of Muslims in the US (Gould and Klor, 2016).

4
Policy ReseaRch WoRking PaPeR 4429

# **Abstract**

This paper discusses a set of statistics for examining and comparing labor market dynamics based on the estimation of continuous time Markov transition processes. It then uses these to establish stylized facts about dynamic patterns of movement using panel data from Argentina, Brazil and Mexico. The estimates suggest broad commonalities among the three countries, and establish numerous common patterns of worker mobility among sectors of work and inactivity. As such, we offer some of the first comparative work on labor dynamics. The paper then particularly focuses on the role of the informal sector, both for its intrinsic interest, and as a case study illustrating the strengths and limits of the tools. The results suggest that a substantial part of the informal sector, particularly the self-employed, corresponds to voluntary entry although informal salaried work may correspond more closely to the standard queuing view, especially for younger workers.

This paper— a product of the Chief Economist Office for Latin America and the Caribbean Region—is part of a larger effort in the department to understand the dynamics of developing country labor markets and the role of informality. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmaloney@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
|P145231|2014|2016|10/30/2015|1 Closed|Serbia|
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|P145231|2014|2017|6/28/2017|2 Closed|Serbia|
|P145231|2014|2018|8/14/2017|3 Closed|Serbia|
|P145231|2014|2018|1/22/2018|4 Closed|Serbia|
|P145234|2015|2015|12/19/2014|1 Active|Kenya|
|P145234|2015|2015|6/26/2015|2 Active|Kenya|
|P145234|2015|2016|12/23/2015|3 Active|Kenya|
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|P145234|2015|2017|4/14/2017|5 Active|Kenya|
|P145234|2015|2018|1/20/2018|6 Active|Kenya|
|P145234|2015|2018|6/18/2018|7 Active|Kenya|
|P145234|2015|2019|2/19/2019|8 Active|Kenya|
|P145234|2015|2020|9/25/2019|9 Active|Kenya|
|P145234|2015|2020|4/24/2020|10 Active|Kenya|
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|P145237|2015|2020|2/10/2020|8 Active|Philippines|
|P145241|2014|2014|3/27/2014|1 Closed|Jordan|
|P145241|2014|2015|8/31/2014|2 Closed|Jordan|
|P145241|2014|2015|3/10/2015|3 Closed|Jordan|
|P145241|2014|2016|7/20/2015|4 Closed|Jordan|
|P145241|2014|2016|2/23/2016|5 Closed|Jordan|
|P145241|2014|2017|7/28/2016|6 Closed|Jordan|
|P145241|2014|2017|3/7/2017|7 Closed|Jordan|
|P145251|2014|2016|2/3/2016|1 Closed|Niger|
|P145254|2014|2016|8/13/2015|1 Closed|Tanzania|
|P145261|2014|2015|11/4/2014|1 Active|Niger|
|P145261|2014|2015|6/15/2015|2 Active|Niger|
|P145261|2014|2016|1/27/2016|3 Active|Niger|
|P145261|2014|2017|9/13/2016|4 Active|Niger|
|P145261|2014|2017|6/30/2017|5 Active|Niger|
|P145261|2014|2018|3/6/2018|6 Active|Niger|
|P145261|2014|2019|10/31/2018|7 Active|Niger|
|P145261|2014|2019|5/24/2019|8 Active|Niger|
|P145261|2014|2020|12/26/2019|9 Active|Niger|
|P145261|2014|2020|6/30/2020|10 Active|Niger|
|P145263|2014|2014|6/14/2014|1 Active|Congo,Republic of|
|P145263|2014|2015|11/20/2014|2 Active|Congo,Republic of|
|P145263|2014|2015|6/10/2015|3 Active|Congo,Republic of|
|P145263|2014|2016|1/7/2016|4 Active|Congo,Republic of|
|P145263|2014|2017|8/1/2016|5 Active|Congo,Republic of|
|P145263|2014|2017|5/10/2017|6 Active|Congo,Republic of|
Figure 9. Policies to Stem Declining Potential Growth A. Global potential growth under reform scenarios B. EMDE potential growth under reform scenarios <!-- Start of picture text --> Percent Pension reforms<br>3.5 Labor market reforms<br>Education and health improvements<br>3.0 Fill investment needs<br>Baseline<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2013-17 2018-27 Reform impact<br>C. EMDE potential growth under reform<br>scenarios<br>Percent Pension reforms<br>12 Labor market reforms<br>10 Education and health improvements<br>Fill investment needs<br>8 Baseline<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>2013-17 2018-27 Reform impact 2013-17 2018-27 Reform impact 2013-17 2018-27 Reform impact<br><!-- End of picture text --> <!-- Start of picture text --> Percent Pension reforms<br>5.5 Labor market reforms<br>Education and health improvements<br>5.0 Fill investment needs<br>Baseline<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>2013-17 2018-27 Reform impact<br><!-- End of picture text --> D. EMDE potential growth under reform scenarios <!-- Start of picture text --> Percent Pension reforms<br>10 Labor market reforms<br>Education and health improvements<br>8 Fill investment needs<br>Baseline<br>6<br>4<br>2<br>0<br>MNA SAR SSA<br>2013-17 2018-27 Reform impact 2013-17 2018-27 Reform impact 2013-17 2018-27 Reform impact<br><!-- End of picture text --> Source: World Bank estimates. Note: GDP-weighted averages.

26
O s Table 18: EARNINGS FUNCTION (lnW2) - ALL F.MPLOYEES

||(|1)|(|2)|(|3)|(4|)|(5)||(|6)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Intercept|9.644|(109.7)|9.741|(107.0)|9.665|(102.6)|9.649|(98.6)|9.659|(98.7)|9.955|(82.4)|
|Years of Schooling|.139|(21.1)||-||-|-|-||-|-|-|
|Years of Primary Schooling|-|_|.087|(5.3)|.091|(5.6)|.082|(5.0)|.056|(2.2)|.042|(1.6)|
|Years of Secondary Schooling|_|_|.183|(9.9)|.188|(10.2)|.166|(8.1)|.141|(5.6)|.126|(5.1)|
|Years of Higher Schooling|-|_|.150|(5.1)|.142|(4.8)|.162|(5.2)|.127|(3.0)|.123|(3.0)|
|Years of Current Job Experience<br>|.103|(7.6)|.106|(7.8)|.104|(7.7)|.101|(7.6)|.099|(7.4)|.092|(7.0)|
|(Years of Current Job Experience)<sup>2</sup>|-.0020|<br>(4.2)|-.0020|<br>(4.3)|-.0019|<br>(4.1)|-.0019|(4.0)|-.0018|(3.9)|-.0017|(3.8)|
|Years of Previous Job Experience<br>|-|-|-|-|.054|(2.2)|<br>.046|(1.9)|.044|(1.8)|.036|(1.4)|
|(Years of Previous Job Experience)<sup>2</sup>|-|-|-|-|-.0020|<br>(1.2)|-.0016|<br>(0.9)|-.0014|(0.8)|-.0008|(0.5)|
|Years of Apprenticeship|-|-|-|-|-|-|.031|(1.5)|<br>.034|(1.6)|<br>.018|(0.9)|
|Years of Secondary VTE|-|-|-|-|-|-|.083|(3.3)|.066|(2.2)|.069|(2.3)|
|Years of Post-Secondary VTE|-|-|-|-|-|-|.163|(1.8)|<br>-.023|(0.2)|-.004|(0.0)|
|Years of Teacher Training|-|-|-|-|-|-|.129|(1.9)|<br>.109|(1.2)|<br>.112|(1.2)|
|CEPE|-|-|-|-|-|-|-|-|.192|(1.2)|.134|<br>(0.8)|
|BEPC|-|-|-|-|-|-|-|-|.119|(0.5)|.150|(0.6)|
|Secondary VTE Diploma|-|-|-|-|-|-|-|-|.346|(1.7)|.281|(1.4)|
|Post-Secondary VTE Diploma|-|-|-|-|-|-|-|-|.964|(2.5)|.876|(2.3)|
|Teacher Diploma|-|-|-|-|-|-|-|-|.413|(1.4)|<br>.420|(1.4)|
|Other Diploma|-|-|-|-|-|-|-|-|.516|(1.8)|<br>.508|(1.8)|
|Sex (1-female)|-|-|-|-|-|-|-|-|-|-|-.204|(2.3)|
|Non-Ivorian|-|-|-|-|-|-|-|-|-|-|-.275|(2.7)|
|Seasonal Job|-|-|-|-|-|-|-|-|-|-|-.622|(2.9)|
|Abidjan|-|-|-|-|-|-|-|-|-|-|.039|(0.5)|
|Rural Areas|-|-|-|-|-|-|-|-|-|-|-.283|(2.3)|
|R<sup>2</sup>|.52||.53||.54||.55||.56||.59||
|R2|.51||.52||.53||.54||.54||.57||
|F|176.2||110.6||81.2||54.2||35.8||30.7||

Note: Absolute t - values in parentheses.
USAID 2009. Indoor Residual Spraying (IRS) for Malaria Control Indefinite Quantity Contract (IQC) Task Order 1. IRS Training Guide for Spray Operations http://www.pmi.gov/technical/irs/irs_training.pdf van den Berg, H. (2009). Global Status of DDT and its Alternatives for Use in vector Control to Prevent Disease. Environmental Health Perspectives, **117** (11):1656-63.

Venners SA, Korrick S, Xu X, Chen C, Guang W, Huang A, Altshul L, Perry M, Fu L & Wang X (2005) Preconception serum DDT and pregnancy loss: a prospective study using a biomarker of pregnancy. Am J Epidemiol, **162** : 709–716.

WHO, 2011a. World Malaria Report, 2011 <u>http://www.who.int/malaria/world_malaria_report_2011/9789241564403_eng.pdf</u> WHO, 2011b. The use of DDT in malaria vector control <u>http://whqlibdoc.who.int/hq/2011/WHO_HTM_GMP_2011_eng.pdf</u> WHO 2011c. Environmental Health Criteria 241- DDT in Indoor Residual Spraying: Human Health Aspects http://whqlibdoc.who.int/publications/2011/9789241572415_eng.pdf WHO, 2006. Global Burden of Disease and Risk factors Woods JS, Polissar L, Severson RK, Heuser LS, & Kulander BG (1987) Soft tissue sarcoma and nonHodgkin's lymphoma in relation to phenoxyherbicide and chlorinated phenol exposure in western Washington. Journal of the National Cancer Institute, **78** (5): 899-910.

Yanez L, Ortiz-Pérez D, Batres LE, Borja-Aburto VH & Díaz-Barriga F (2002) Levels of dichlorodiphenyltrichloroethane and deltamethrin in humans and environmental samples in malarious areas of Mexico. Environ Res, **88** : 174–181.

19
The revenue impact of fall in oil production owing to technical problems in 2005 has been more than offset by oil prices that are about twice as high as were projected<sup>11</sup> . Under the implied well-head price per barrel assumed in early projections and given high initial investments, oil income taxes were not expected until well after 2009. However, in early 2006 oil companies confirmed that the high well-head price which reached US$33 a barrel in 2005, allowed them to recover part of the upstream investment. As a result, in 2006 oil companies are expected to pay income tax equivalent of about 13 percent of the GDP on 2005 profits, part of which was already paid on March 31, plus equal advance on 2006 income tax, paid in equal quarterly installments.

<!-- Start of picture text --> Chad Oil Sector 2003 - 2009<br>(In US$)<br>70<br>60<br>50<br>40 Reference Spot price<br>US$ Discount of Doba crude (per barrel)<br>30<br>Chadian oil price (per Barrel)<br>20 Transportation cost<br>Implied w ellhead oil price (per Barrel)<br>10<br>0<br>2003 2004 2005 2006 2007 2008 2009<br>Years<br><!-- End of picture text --> Sources: Chadian authorities Bank’s staff and IMF projections.

Although Chad’s basic economic challenge in the next few years is to well manage its oil-rent. However, Cotton and Cattle sectors if well reformed and managed could generate much more revenues per year for rural poor. Therefore, the rural sector development and agriculture and breeding sectors reforms should also be among the highest priorities for Chad development process. Chad's temporary oil windfall should be used to promote the development of the non-oil sector to promote its development process as it has been the case of Malaysia and Indonesia or Botswana therefore ensuring continuation of the implementation of poverty reduction strategy even after Chad’s oil is depleted.

> 11 In late 2005 the discount also narrowed to about US$9 per barrel compared to early projections, in the expectation of rising demand for heating fuel during the peak-demand in northern hemisphere winter. In early projections, the discount and transportation costs were projected to stay at about US$10 per barrel over the medium term. In 2004 markets forces pushed the discount up to US$14 per barrel, as refineries tended to choose light crude over heavy like Chad’s oil.

9
|Skills Obstacle (0-4)|0.597***|0.666**|0.636**|0.638**|0.636**|
|---|---|---|---|---|---|
||(0.228)|(0.269)|(0.269)|(0.269)|(0.269)|
|How Much Of An Obstacle:|-0.035|0.017|-0.011|-0.008|-0.016|
|Transport?||||||
||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|-0.920*|-1.058*|-1.031*|-1.018*|-0.994*|
|N:0||||||
||(0.524)|(0.558)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)||-0.171**|-0.154**|-0.156**|-0.155**|
|||(0.070)|(0.068)|(0.068)|(0.068)|
|Courts Obstacle (cell average)||-0.005|-0.454|-0.383|-0.422|
|||(0.776)|(0.739)|(0.734)|(0.737)|
|Crime Losses Y:1 N:0||-0.571|-0.633|-0.651|-0.672|
|||(0.667)|(0.669)|(0.672)|(0.674)|
|Firm Inspected Y:1 N:0||-0.814|-0.847|-0.854|-0.822|
|||(0.604)|(0.603)|(0.604)|(0.604)|
|How Much Of An Obstacle: Tax||-0.224|-0.253|-0.250|-0.264|
|Rates||||||
|||(0.230)|(0.229)|(0.230)|(0.230)|
|How Much Of An Obstacle: Labor||0.029|0.036|0.046|0.051|
|Regulations?||||||
|||(0.301)|(0.301)|(0.301)|(0.300)|
|Population (logs)|||-35.364***|-35.332***|-35.099***|
||||(10.960)|(10.933)|(11.115)|
|Urbanization (%)|||1.673***|1.692***|1.642***|
||||(0.415)|(0.419)|(0.405)|
|WBL Index|||17.580**|17.641**|29.339**|
||||(8.651)|(8.608)|(11.496)|
|Women can work in the same|||-2.120|-1.756|-1.808|
|industries as men Y:1 N:0||||||

74
6 of the transparency of the microdistortions (particularly the trLde distortions that consist in a uniform tariff rate) and the availability of good time series data. Moreover, we show that the structure of distortions in Chile, in particuar &e combination of protecting capital intensive activities and wage distortions, is quite representative of what prevails in the majority of LDCs although the distortio.ns are substantially more moderate and fewer than in most other countries. Therefore, one would reasonably expect that the results for Chile are relevant to many other countries as well.

The remainder of this paper is organized as follows: in section I we present a conceptual framework followed by section 1I that introduces the empirical model, econometric results and an evaluation of the static welfare losses caused by the coexistence of tariff and labor distortions. Section III is devoted to the evaluation of the investment efficiency effects of distortions and corresponding dynamic social income losses. In section IV we discuss the applicability of the results for Chile to other countries and the last section summarizes the main conclusions.

## 1. The Conceptual mode!

We assume that the import substitution sector includes two industries, one producing final goods and the other producing intermediate goods. The rest of the economy is comprised of an export sector. We define social income as aggregate GDP evaluated at world prices. If the trade distortion consists of a uniform tariff, social income is equal to GDP at domestic prices less the rents accruing to domestic producers of import substitutes due to the tariff,

# (1) **_R'_** = **_R[(1+)p P2 ,(1_** _+?)p;;_ **_4LK,_** _K_ - ?[p;RI **_*1_** +p3R _R3]_

where R* is the GDP at world prices, R, is GDP at domestic prices, _r_ is the tariff rate affecting final import substitutes and (imported) intermediate inputs; p, and p3 are their -espective world prices, P2 is the price of exportables, L is actual employment, K is the stock of capital and t a vector that may include