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4 import tax/export tax rate which, if applied uniformly to that commodity in every country that year would generate the same reduction in trade as the actual structure across countries of NRAs and CTEs for that commodity. The second of the new indexes refers to the partial equilibrium global welfare cost: it is the ad valorem trade tax rate which, if applied uniformly to that commodity in every country that year would generate the same reduction in global economic welfare as the actual structure across countries of NRAs and CTEs for that tradable commodity.

To distinguish the indexes from indexes developed previously, we give these indexes the more-precise descriptive names of the trade reduction index (TRI) and the welfare reduction index (WRI). We show that, if one is willing to assume that the domestic elasticities of supply are equal across countries for a particular commodity, and likewise for the price elasticities of demand for that commodity (as indeed many global commodity modelers do, for lack of country-specific econometric estimates), then there is no need to know the size of those elasticities in order to estimate our TRI and WRI.<sup>1</sup> Following the theoretical part of the article, we then exploit the recently compiled NRA and CTE estimates in the World Bank’s global Agricultural Distortion database to generate estimates of these new indicators for each of 28 key agricultural commodities over the past half century (whose combined share of global agricultural production was 56 percent in 2000-04), based on NRA and CTE estimates for a sample of 75 countries

> 1 The analysis follows the same steps as in Lloyd, Croser and Anderson (2009).  That paper presents estimates of the reduction in imports and the welfare loss across agricultural commodity markets for individual countries and regions. The present paper, by contrast, focuses on the reduction in imports and the welfare loss across countries for individual commodity markets.
(Eq 11) _Sct_<sup>[2]</sup> = α _Sct_ + (1‐α) _Sc,t‐1_<sup>[2]</sup> , where _Sct_<sup>[2]</sup> is the smoothed original series from _Sct_ = α _Xct_ + (1‐α) _Sc,t‐1_ , and α is the smoothing parameter estimated by minimizes the in‐sample sum‐of‐squared predicted errors; _Xct_ is the original series.

- 4) For each country _c_ , we estimated the following regression where β represents the yearly difference in OOPPC over the previous year, and ε _ct_ is a random error term:

(Eq 12) _OOPPCct_ = α + β _Year trendct_ + ε _ct_

- 5) For each country _c_ , we estimated the following regression that additionally accounts for non‐linearities in the time trend of OOP:

(Eq 13) _OOPPCct_ = α + β1 _Year trendct_ + β2( _Year trendct)_<sup>2</sup> + ε _ct_

- 6) For all countries _c_ , we estimated the following pooled regression that includes country fixed effects (γc) as well as a flexible time trend:

(Eq 14) _OOPPCct_ = αc + β1 _Year trendct_ + β2( _Year trendct_ )<sup>2</sup> + γc + ε _ct_

- 7) For all countries _c_ , we estimated the following pooled regression that includes three dummy indicators for World Bank (Wr) income group classifications as well as a flexible time trend:

- 8) The same regression for Equation 9, but correcting for AR1 serial correlation.

The estimated parameters were then applied to the testing data set to obtain predicted values for 2009 through 2013. These predicted values were then compared with actual data in 2009‐ 2013. To assess the fit of each model, we again calculate the mean square root of the squared error, displayed in Table C.1.

> � � (Eq 16) Mean error = �� ���������<sup>�</sup> �� ∑�������

37
**Table C3. Probability of a Sudden Stop: Additional Domestic Variables (probit model, marginal effects)**

||(1)<br>1991-2002|(2)<br>2003-2014|(3)<br>1991-2002|(4)<br>2003-2014|(5)<br>1991-2002|(6)<br>2003-2014|(7)<br>1991-2002|(8)<br>2003-2014|(9)<br>1991-2002|(10)<br>2003-2014|(11)<br>1991-2002|(12)<br>2003-2014|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VIX, log|0.0089*|0.0109***|0.0051|0.0111***|0.0091*|0.0115***|0.0125**|0.0122***|0.0087*|0.0113***|0.0088*|0.0091***|
||[1.93]|[6.34]|[1.49]|[6.22]|[1.86]|[6.53]|[2.46]|[5.82]|[1.89]|[6.60]|[1.70]|[6.43]|
|US Policy Rate|0.0101***|0.0038|0.0056***|0.0040|0.0092***|0.0052*|0.0080***|0.0049|0.0099***|0.0050*|0.0089***|0.0052***|
||[4.39]|[1.54]|[2.96]|[1.31]|[4.22]|[1.78]|[3.25]|[1.37]|[4.88]|[1.72]|[5.45]|[2.68]|
|Capital Flows/GDP|0.0123***|0.0009|0.0088***|0.0012|0.0133***|0.0016|0.0054***|0.0020|0.0131***|0.0012|0.0124***|0.0006|
||[5.96]|[0.72]|[5.43]|[1.04]|[5.14]|[1.37]|[3.50]|[1.36]|[6.52]|[1.00]|[6.00]|[0.67]|
|Domestic Credit/GDP|-0.0027|0.0038***|-0.0020|0.0030**|-0.0023|0.0034***|-0.0022|0.0028**|-0.0030|0.0031***|-0.0006|0.0037**|
||[1.28]|[3.83]|[1.21]|[2.56]|[0.98]|[3.05]|[1.55]|[2.49]|[1.31]|[3.08]|[0.26]|[2.13]|
|GDP Growth|0.0020|0.0026|||||||||||
||[0.65]|[1.06]|||||||||||
|Fiscal deficit/GDP|||-0.0029|-0.0028*|||||||||
||||[1.05]|[1.65]|||||||||
|Debt/GDP|||||-0.0007|0.0007|||||||
||||||[0.43]|[0.32]|||||||
|Capital Controls|||||||0.0011|-0.0001|||||
||||||||[0.76]|[0.08]|||||
|Political Risk|||||||||0.0005|0.0010|||
||||||||||[0.30]|[0.58]|||
|Foreign currency position|||||||||||-0.0091***|-0.0004|
||||||||||||[3.42]|[0.22]|
|Observations|861|1307|660|1286|777|1306|454|1073|846|1316|603|875|
|Pseudo R-squared|0.124|0.269|0.156|0.283|0.132|0.277|0.205|0.265|0.130|0.278|0.162|0.363|

Dependent variable is a binary variable which is equal to 1 if a sudden stop occurs and 0 otherwise. The first quarter of sudden stop is included in the regressions, and all subsequent quarters dropped. Domestic variables are averages of previous eight quarters. All variables have been standardized around zero mean and standard deviation equal to 1. Regressions are estimated with robust standard errors, and observations clustered by countries. Z statistics reported in parentheses. ***,** and * indicate significance at 1, 5, and 10% levels, respectively.

.

35
exports. The income elasticity of arm’s-length exports is about one-fifth higher than that of intra-firm exports. Similarly, the real exchange rate elasticity of arm’s-length exports is one-tenth stronger. As a result, the post-crisis global growth weakness has weighed more on arm’s-length than on intra-firm trade growth.

Several other factors may have also constrained trade among unaffiliated firms. In particular, unaffiliated firms may have been hindered more than multinational firms by constrained access to finance during the crisis, heightened policy uncertainty, and their typical firm-level characteristics.

The rest of the paper is organized as follows: Section 2 examines the characteristics of U.S. arm’s-length exports and imports focusing on sectoral and regional composition; Section 3 explores the evolution of pre- and post-crisis arm’s-length and intra-firm trade flows; Section 4 discusses the factors that contributed to the sharp post-crisis slowdown of arm’s-length trade; Section 5 uses an econometric framework of the determinants of U.S. exports to demonstrate differentials in the income and real exchange rate elasticity between arm’s-length and intrafirm trade; and finally, Section 6 concludes.

# **2 Characteristics of arm’s-length and intra-firm trade**

**Data.** There is only one publicly available dataset on international intra-firm trade with a com-prehensive set of partner economies. This unique U.S. trade dataset from the U.S. Census Bureau uses customs declarations to distinguish arm’s-length trade from intra-firm transactions.<sup>4</sup> At the most detailed level, the data contain exports and imports at the 6- digit North American Industry Classification System (NAICS) level as well as information on countries of origin and destination, covering annual bilateral trade flows with 234 partner economies for 2002-14. Similar data are unavailable at the global level; hence, the analysis here relies on this U.S. trade dataset.

**Definition of arm’s-length and intra-firm trade.** Intra-firm trade consists of crossborder transactions between firms linked by a degree of control and ownership whereas arm’slength trade is defined as cross-border transactions between unrelated firms. The U.S. Census Bureau records transactions between related-parties. Related-party imports are defined as shipments between “any person directly or indirectly, owning, controlling or holding power to

> 4The U.S. Bureau of Economic Analysis collects similar data with a confidential dataset on intra-firm trade data based on firm surveys. The Organisation for Economic Co-Operation and Development (OECD) database on the Activities of Multinational Enterprises covers trade between OECD countries.

5
on the YWI, and ranks first in connectedness to institutions, it ranks 17<sup>th</sup> (of 27) in health. Conversely, Amazonas ranks 23<sup>rd</sup> overall, but it ranks fifth in the health indicators. The correlation between the ranking for the YWI and categories of indicators is very low for health and behaviors – 0.3 and 0.22 respectively – and very high for school performance (0.81). The correlation between the YWI and socioeconomic indicators, which are exogenous to the YWI is also fairly high, at 0.68 (last row of Table 3).

Both the indicators that comprise the health category have high values for the Southern states and low values for the Northern states (Annex 1). The incidence of AIDS is reported to be consistently higher in the Southern states (14 per 100,000 youth) and lower in the Northeast (3 per 100,000 youth) (Figure 2). For example, nine states, all in the North and Northeast, report zero AIDS incidence among 10-17 year olds in 2002. This may be linked due to the fact that the epidemic started in the Southeast and is still concentrated, while incidence is lower, but increasing, in the North and Northeastern Regions (NAH/MoH, 2005). Related to this is the significant under-reporting in the North and Northeast, partly due to fewer for well-trained professionals to identify and report AIDS incidence as the epidemic grows in these areas (NAH/MoH, 2005).

The behavior category, with eight indicators, is not as consistently skewed, but a few indicators stand out, particularly in the Southeast (Figure 3). Homicide rates in the Southeast, the home of Rio de Janeiro and São Paulo, are particularly elevated. While fewer than 50 of every 100,000 youth die of homicide in the other four regions, nearly 84 do in the Southeast. Alcohol and tobacco use is also much higher in the South and Southeast than in the rest of the country (Annex 2).

Further disaggregating the indicator categories, a few specific behaviors are worth noting due to their importance in determining the future of Brazil’s youth and their high variance across the country. In the Northern and Northeastern states, adolescent pregnancy rates were higher. They varied from 26.8 percent in Tocatins to 16.5 percent in the Federal District to (national average of 21.8 percent). This is a concern since early pregnancy increases mortality rates for women and their children (Pinto e Silva, 1998, <u>www.ibge.gov.br).</u> In terms of participation in educational and labor institutions (Annex 3 & 4), youth connectedness to the former breaks earlier and connectedness to the latter begins 10
cover a greater variety of products and more markets. Hence, Slovakia’s exports are less reliant on few key industries, which enables firms to take advantages of changes in their external price competitiveness. Table 5 below presents developments in export concentration in Macedonia and the Slovak Republic. Although Slovakia increased concentration of its exports by products, it has diversified exports across more destinations. On the other hand, Macedonia exports become less diversified in both product and destinations.

**<u>Table 5: Developments in export concentration, in percent of total exports</u>**

|**Slovakia**|1996|2006|**Macedonia**|1996|2006|
|---|---|---|---|---|---|
|top 5 countries|70.2|56.0|top 5 countries|62.4|69.2|
|top10 countries|85.3|77.5|top10 countries|80.7|85.1|
|top 5 products|19.6|37.6|top 5 products|36.9|45.3|
|top10 products|30.6|46.2|top10 products|54.7|61.0|

_Source_ : COMTRADE Third, Macedonian exports had a more restricted market access due to lower trade integration into the European region, low levels of FDI, and inability to meet quality standards. Compared to Slovakia, which gained duty free access to the EU market under the Europe Agreement in 1995, Macedonia’s access to the EU market occurred much later with the signing of the Stabilization and Association Agreement in 2001, and more intensive integration with the immediate neighborhood started only with the 2006 CEFTA agreement. The FDI inflow, typically associated with increased market access and transfer of know-how,<sup>13</sup> has been much lower for Macedonia compared to Slovakia. Over the 1997-2007 decade, FDI averaged 4.6% of GDP in Macedonia, compared to 6.4% of GDP in the Slovak Republic; as a result, cumulative inflows of FDI over this period in Macedonia reached USD2.3 billion, or only 10% of the Slovak USD23.7 billion inflow. Also, greater compatibility with quality standards can explain the greater responsiveness of Slovak exports to changing price competitiveness. According to the International Organization for Standardization, only 217 Macedonian firms were ISO 9001-certified at the end of 2006 compared to 2195 of Slovak firms. Similar

> 13 This link has been well-demonstrated especially by the Volkswagen FDI in the Slovak Republic.

19
sample. This decline is offset by a rise in the share of contract workers and the informal salaried while the rate of transition into contract work from formal sector work now exceeds the rate of reverse transition. It is clear from figure 3 that contract earnings are falling at a more rapid rate than any other sector, suggesting that workers are not better off as a result of this trend. On the other hand, contract earnings are always the most volatile of the four series so it is not obvious that they are losing relatively more than would be the case in any previous downturn.

This evolution appears correlated with a restructuring ofthe manufacturing sector that is very likely related to increased external competition. Tables 2 & 3 show that while from 1990 to 1994 manufacturing grew 7.8%, employment in the sector actuallyfell 8.5%. The competitive cushion offered by the high real exchange rate and soft labor market in the early part of the period may have delayed the bite of reduced protection until 1990 and several manufacturing sectors sharply reversed their expansions of the late 1980's during this period.<sup>20</sup> It is in 1990 that the trade balance becomes negative for the first time since 1982 due both to a rise in imports as a share of GDP from 13.3% in 1987 to 18.9% in 1993 and a fall in exports from 17% in 1990 to 14% in 1993. Thus, the pressure to cut costs and restructure arguably appeared only at the end of phase II, the period when contract work begins its secular expansion. Though only micro-analysis of individual firm behavior can confirm whether the sub-contracting is in fact related to global competitive pressures, the evidence here suggests that this is the case.

20 Textiles **fell** 15.7% from 1990-1994, wood products fell 18% from 1991-94, paper products **fell** 7.9% **from** 1990 to 1994. Sexto Informe de Gobiemo, 1994.

19
**Table 9. Largest Decreases in Russian Exports to Belarus and Kazakhstan, by Product at the HS 2-digit Level, 2012–20**

|**Product**|**Product description**|**Change in Value of**<br>**Exports (US$,**<br>**millions)**|**Percentage**<br>**Change in Value**|
|---|---|---|---|
|85|Electrical machinery and equipment and parts thereof; sound recorders and reproducers,<br>television image and sound recorders and reproducers, and parts and accessories of such<br>articles|-37|-2.7|
|73|Articles of iron or steel|-26|-1.8|
|39|Plastics and articles thereof|-23|-2.5|
|87|Vehicles other than railwayor tramwayrollingstock, andparts and accessories thereof|-16|-1.6|
|94|Furniture; bedding, mattresses, mattress supports, cushions and similar stuffed<br>furnishings; lamps and lighting fittings, not elsewhere specified or included; illuminated<br>signs, illuminated nameplates and the like;prefabricated buildings|-14|-5.5|
|84|Nuclear reactors, boilers, machineryand mechanical appliances;parts thereof|-13|-0.6|
|33|Essential oils and resinoids;perfumery, cosmetic or toiletpreparations|-11|-6.4|
|40|Rubber and articles thereof|-10|-1.7|
|30|Pharmaceuticalproducts|-9|-5.8|
|44|Wood and articles of wood; wood charcoal|-8|-1.8|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 36
# **2. The challenge of assessing the causal impact of migration**<sup>**1**</sup>

A large part of the development literature in migration attempts to answer questions of the form “what is the effect of migration or remittances on outcome Y?”. One branch of this focuses on the migrants themselves, and is interested in how migration changes their incomes, health, stress levels, and life opportunities. A second branch focuses on remaining household members and communities in the sending areas, and is interested in the impact of having a household member migrate or of receiving remittances on the education and health of children, on levels of entrepreneurship and labor supply of adult members, and on poverty and inequality levels in the village. A common approach to answering such questions is to use survey data on migrants and non-migrants, and attempt to control for differences between them in a linear regression framework. We will set this out for the case of estimating the impact of migration, but the challenge is analogous for estimating the impact of remittances. For example, researchers may attempt to estimate an equation of the form: Where _Migranti_ is a dummy variable which takes the value 1 if the individual is a migrant, and zero if he or she is not, Yi is the outcome of interest (e.g. individual income), and Xi are a set of observed characteristics of the individual which are presumed not to have changed with migration (e.g. age, sex and education level, location of birth, ethnicity, religion, parental education, etc.).<sup>2</sup> Then in order for the linear regression estimate of β to give the causal impact of migration on the outcome of interest, we require that: That is, we require the unobserved determinants of the outcome of interest (income in our example) to be uncorrelated with whether or not an individual migrates once we have conditioned on the observable characteristics of these individuals. But in the absence of

> 1 See McKenzie (2005) and Gibson et al. (2010) for more detail on these challenges.

> 2 For simplicity we consider only the case of an individual-level outcome here, assuming that all migrants and nonmigrants are observed. When the comparison involves households with and without migrants, a second form of selectivity is involved, since households can also choose whether all members migrate, or only some. Return migration also introduces a third form of selectivity. See Gibson et al. (2010) for discussion of this more complicated case.

2
Table 7: Cultivated Area as Percent of Farm Area, By Size of Farm.

|Size of Farm<br>(ha)|Cultivated Area<br>/Farm Area (%)||
|---|---|---|
|under 0.5||92|
|O.5to1.0||92|
|1.Oto2.0||91|
|2.0 to 3.0||91|
|3.0 to 5.0||90|
|5.0 to 10.0||86|
|10.0 to 20.0||79|
|20.0 to 60.0||7|
|above 60.0||54|

Source: Census of Agriculture, 1990 Controlling for intensity of cultivation, three important effects of size on productivity can be distinguished. First, small farmers have an advantage in labor supervision over larger farmers. Therefore the quality of labor input is likely to be higher on small farms. Second, however, small farmers can have restricted access to modem inputs, and this can hold back their productivity. Third, small and large farners may differ in their attitudes to risk and uncertainty -- large farmers may be more willing and able to carry greater risks. What is the evidence on each of three expected effects of fim size on productivity?

The labor supervision issue has a number of facets. Large farmers who chose to selfcultivate will have to hire-in labor. The inability to completely supervise this labor will give rise to a principal-agent problem, more severe than that faced by small farmers, who rely more on family labor (Singh 1988). Frisvold (1994) finds significant supervision effects for plots in semiarid India, and finds output losses from supervision constraints exceeding 10 percent on over 40 percent of plots in his sample. The alternative for large farmers is to rent out land to fixed-rent tenants or sharecroppers. However, Mahnood (1994) argues that the threat of land-to-the-tiller reforms has inhibited renting-out in Pakistan. This, coupled with cheap machinery may have favored self-cultivation, and the benefits of optimal labor supervision are likely not being - realized. Landlords who rent out may also face supervision problems discussed this in greater detail in the section on tenancy.

As for use of modem inputs, the 1980 agricultural census showed no clear differences between small and large farmers in adoption of fertilizer and plant protection (Mahmood et al). The Green Revolution package of fertilizer and seed was scale neutral, but other yield-increasing technology may involve indivisible investments. Institutional constraints could impede access to inputs or credit for small farmers. However, tractor use and tubewell use was much higher on medium and large farms in both Punjab and Sindh in 1980. While the productivity benefits of 15