rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Do labor transfers equalize farm size?

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|Regressor<br><br>|Coefcient||Ethnicity|Kinship|Kinship|Neighbor|
|(_r−_<br>_r_)<br>_NV_|_α_1|0.000<br>(0.000)|||||
|(<br>_r__~~−~~rj_)<br>_Nv_<br><br>|_α_2|0.000<br>(0.000)|||||
|(_ri−_<br>_rk_)<br>_Nv_<br>_Dij_|_α_3||0.000<br>(0.000)|0.000<br>(0.000)|||
|(<br>_rk−rj_)<br>_Nv_<br>_Dij_|_α_4||0.000<br>(0.000)|0.000<br>(0.000)|||
|(_ri−rj_)<br>2<br>_Lij_|_α_5||||0.005**<br>(0.001)|-0.000<br>(0.000)|
|Constant|_α_0|0|0|0|0|0|
|Observations||54,144|54,144|54,144|54,144|21,266|

Note: The dependent variable is the amount of household head labor transfered from _j_ to _i_ , measured in working days. Standard errors clustered by village are shown in parantheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Author’s own calculations on the Gambia Networks Data 2009.

shocks.<sup>26</sup> If labor transfers are only used as an ex post adjustment to labor shocks during peak season, cultivated land can be taken as pre-determined at the time these transfer takes place. Given this, it may be useful to reestimate model (9) using land-labor ratios calculated using cultivated land in lieu of owned land.<sup>27</sup> Results are very similar to those reported in Table 10.

# **5 Discussion**

This paper has examined whether social structure affects the equalization of factors and factor ratios across farming households. With some additional assumptions, the absence of equalization can be seen as implying a failure to allocate factors of production efficiently. How convincing is this conclusion? As noted in the conceptual section, whether or not inequality in cultivated acreage or factor ratios signals allocative inefficiency hinges on two maintained assumptions that cannot be substantiated with the data at hand: decreasing returns to scale; 31
-7-

data have been collected. The Mauritanian Living Standards Survey (Enquete Permanente sur les Conditions de Vie Des Menages) became operational in December, 1987. The Bolivian survey (Encuesta Integrada de Hogares) began in May, 1988. Finally, the Jamaican Survey of Living Conditions began operations in August, 1988.

Three more LSMS surveys are due to go into the field by late 1990. The first is the Moroccan Living Standards Survey (Enquete Sur le Niveau de Vie des Moages au Maroc) will begin field work in Lne spring of 1990. The second is a living standards survey in Pakistan (Pakistan Integrated Household Survey), which is scheduled to begin field work in late 1990. The last is the Laos Living Standards Survey, which will also begin field work in late 1990.

Several more countries have indicated interest in undertaking LSMS surveys, and discussions are now underway to see whether they will go forward. In Latin America and the Carribean discussions have been held in Brazil, Colombia, Guatemala, Venezuela and Trinidad. In Africa and the Middle East discussions have been initiated in Algeria, Burkina Faso, Egypt, Jordar and Kenya. In Asia, Nepal has shown interest in LSMS surveys.

The three African surveys (C6te d'Ivoire, Ghana and Mauritania) are now being managed by the SDA (Social Dimensions of A-justment) Project in the Africa region of the World Bank. This project aims to strengthen the capacity of African govcenments to design and monitor poverty programs and projects. It is being managed jointly by the World Bank, UNDP, and the African Development Bank.

# D. Future Survey Implementa.ion

The LSMS program began as an experimental research project but has
|**Table 7 : Impact on firm performances**|<br>(1)<br>**Total**<br>**sales in**<br>**the last**<br>**day**<sup>**αβ**</sup><br>**(CFAF)**|(2)<br>**Total**<br>**sales in**<br>**the last**<br>**week**<sup>**αβ**</sup><br>**(CFAF)**|(3)<br>**Last**<br>**month**<br>**profit**<sup>**αβ**</sup><br>**(CFAF)**|(4)<br>**Summar**<br>**y index**<br>**of sales**<br>**and**<br>**profit**<sup>**αβ**</sup>|(5)<br>**Total**<br>**number**<br>**of emplo**<br>**yees**<sup>**α**</sup>|(6)<br>**‐**<br>**Any tax**<br>**paid for**<br>**business**<br>**activity in**<br>**2015**<sup>**Β**</sup>|(7)<br> <br>**Sum of all**<br>**taxes paid**<br>**in 2015**<sup>**Β**</sup><br>**(CFAF)**|
|---|---|---|---|---|---|---|---|
|**1st stage: impact of treatment allocation:**||||||||
|Group1 X year1 (b1)|2,228|12,496|‐8,053*|0.008|‐0.22**|0.013|‐19|
||(2,754)|(14,029)|(4,798)|(0.057)|(0.10)|(0.030)|(1,747)|
|Group2 X year1 (b2)|540|‐7,376|‐3,016|‐0.052*|‐0.06|0.048***|‐51|
||(1,451)|(7,312)|(3,021)|(0.031)|(0.09)|(0.018)|(1,091)|
|Group3 X year1 (b3)|‐114|‐1,224|‐3,106|‐0.010|‐0.11|0.005|‐2,041**|
||(1,384)|(6,399)|(2,858)|(0.030)|(0.08)|(0.016)|(949)|
|Group1 X year2 (c1)|602|12,192|470|0.041|‐0.09|‐0.066**|‐3,308**|
||(2,930)|(14,243)|(5,742)|(0.060)|(0.10)|(0.030)|(1,678)|
|Group2 X year2 (c2)|1,246|‐5,235|‐874|‐0.007|0.05|‐0.055***|‐3,413***|
||(1,832)|(8,010)|(3,377)|(0.036)|(0.07)|(0.018)|(1,047)|
|Group3 X year2 (c3)|1,847|3,998|242|0.026|0.08|‐0.067***|‐5,967***|
||(1,669)|(7,911)|(3,233)|(0.035)|(0.07)|(0.017)|(869)|
|Observations|5,918|6,043|5,874|5,926|6,206|6,163|6,096|
|Mean Dep. var in control year1|17,373|99,984|53,313|‐0.02|1.14|0.507|18,856|
|Mean Dep. var in control year2|17,882|106,803|54,536|‐0.003|1.23|0.413|14,221|
|Adjusted R‐squared|0.227|0.260|0.159|0.254|0.350|0.356|0.257|
|_Test for impact constant..._||||||||
|...accross treatments, year1 (b1=b2=b3)|0.684|0.401|0.593|0.406|0.457|0.085|0.168|
|...accross treatments, year 2 (c1=c2=c3)|0.908|0.449|0.957|0.670|0.303|0.826|0.024|
|Coef. are jointly 0 (b1=b2=b3=c1=c2=c3=0)|0.873|0.796|0.438|0.447|0.111|0.000|0.000|
|**(IV) impact of Formalization:**||||||||
|Formalization instrumented by 1st|4,718|‐1,877|‐10,235|‐0.008|‐0.12|‐0.127*|‐18,789***|
|<br>stage treatment variables|(6,511)|(31,925)|(13,388)|(0.143)|(0.30)|(0.075)|(4,463)|
|P‐values|0.469|0.953|0.445|0.957|0.687|0.091|0.000|
|Sharpened two‐stageq‐values<sup>μ</sup><br>Note : Panel data from midline and endline<br>dummies (dummies for each triplet). Stand<br>truncated at the 99th percentile. Β: control<br>in Anderson (2008) using P‐values in table|0.755<br>surveys<br>ard error<br>ing for ba<br>6 and 7.|1<br>in 2015 an<br>s (in paren<br>seline val<br>***, **, * i|0.755<br>d 2016.<br>theses) a<br>ue. μ: Sha<br>ndicate s|1<br>All regress<br>re cluster<br>rpened tw<br>tatistical|1<br>ions are c<br>ed at the f<br>o‐stage q‐<br>significan|0.251<br>ontrolling<br>irm level.<br>values as<br>ce at 1, 5 a|0.001<br>for strata<br>α:<br>described<br>nd 10%|

39
# Table 2.  The “Worst of the Worst” Financial Crises

|% change<br> <br>Crisis Severity: Percent Decline in Per Capita GDP,<br>Years to Full Recovery in 25 of the Worst Systemic|<br>Number<br>Duration<br>Banking|<br>of years<br>of Contra<br>Crises, 18|ction and<br>57-2018|
|---|---|---|---|
|Peak to<br>Year<br>Country<br>trough|Peak to<br>trough|Peak to<br>recovery|Severity<br>index|
|1<br>1926 Chile<br>-46.6|3|16|62.6|
|2<br>1931 Spain<br>-34.6|9|26|60.6|
|3<br>1983 Peru<br>-32.0|11|25|57.0|
|4<br>1931 Uruguay<br>-36.1|3|17|53.1|
|5<br>1893 Australia<br>-28.0|8|20|48.0|
|6<br>1929 Mexico<br>-31.1|6|16|47.1|
|7<br>1921 Italy<br>-25.5|3|21|46.5|
|8<br>1890 Brazil<br>-21.7|4|21|42.7|
|9<br>_2008 Greece_<br>_26.3_|_6_|_16_|_42.3_|
|10<br>1890 Uruguay<br>-21.0|2|19|40.0|
|11<br>1981 Philippines<br>-18.8|3|21|39.8|
|121980/1985 Argentina<br>-21.8|11|18|39.8|
|13<br>1929 India<br>-8.2|9|31|39.2|
|141929/1933 US<br>-28.6|4|10|38.6|
|15<br>1994 Venezuela<br>-24.2|11|14|38.2|
|16<br>1939 Netherlands<br>-16.0|6|21|37.0|
|171931/1934 Argentina<br>-19.4|3|15|34.4|
|18<br>1931 Poland<br>-24.9|4|9|33.9|
|191929/1931 Austria<br>-23.4|4|10|33.4|
|20<br>1981 Mexico<br>-14.1|7|17|31.1|
|21<br>1920 UK<br>-18.7|3|11|29.7|
|22<br>2001 Argentina<br>-20.9|4|8|28.9|
|23<br>_2008 Italy_<br>_-11.9_|_6_|_16_|_27.9_|
|24<br>1980 Chile<br>-18.9|2|8|26.9|
|25<br>2002 Uruguay<br>-18.9|4|8|26.9|

_Sources:_ Reinhart and Rogoff (2014) and Reinhart (2018).

# **III. Recessions, Depressions, and Recovery**

The preceding narrative suggests that: (i) the incidence of sovereign debt/fiscal problems is on the rise; (ii) debt crises are often accompanied by currency, banking, and inflation crises of varying severity; and (iii) sudden stops are another risk. Thus, many countries may be facing the risk of a _conglomerate_ crisis at time when they are attempting to recover from the ravages of 14
- 7 -

# Table 2

# DISTRIBUTION OF INSTITUTION ISSUES IN PROJECT BRIEFS AND SARS

||Frequency|in|
|---|---|---|
|Institutional issues<br>Project<br>identified/discussedl<br>(FY8|Briefs (%)<br>4&FY85)|SARs(%)<br>(FY83-FY87)|
|1. Manpower inadequacies (including<br>skills)|56|56.3|
|2. Coordination<br>problems|53|28.5|
|3. Gaps in planning, monitoring and<br>evaluation systems|50|31|
|4. Weak local capacity|40|10|
|5. Need to restructure or decentralize<br>organizations|34|*|
|6. Problems of financial management (cost recovery<br>& support systems, budgetary controls)|32|17.5|
|7. Constraints on compensation<br>and incentive<br>structures|18|*|
|8. Lack of attention to demand for project<br>services|15|20|
|9. Autonomy issue|12|1.3|

l In both sets of documents, SARs and project briefs, the categories of issues reported sometimes overlap. Adjustments have been made to minimize double counting.

- This category does not exist as such in SARs.
hold that export processing activities (most likely to include those by foreign firms) may provide women with higher earning opportunities relative to an alternative job in the informal sector (e.g., Glick and Roubaud, 2006; Madani, 1999). However, the previous studies usually did not differentiate the impacts of foreign ownership on wages by gender.<sup>29</sup> Columns 1-2 in Table 5 show the regression results run separately by gender comparing the foreign wage levels to those in the informal wage sector. Foreign firms appear to pay higher wages for both male and female workers, as the coefficients for the foreign dummy turn out to be positively significant at the one percent level. The wage differentials between the foreign sector and the informal wage sector are found to be larger for females at around 58 percent relative to 43 percent for males. In fact, women seem to experience a generally higher “formal wage premium” as the coefficients for foreign, state and private dummies all turn out to be larger for women than for men. This suggests that the lower wages in the informal sector for women are likely to be the source of larger foreign-wage premiums for women relative to men.

In order to investigate further the source of wage differentials, columns 3-4 and columns 5-6 in Table 5 report the results of gender-specific regressions run by subsets of skilled workers (with at least an upper-secondary education diploma) and unskilled workers (without upper-secondary education) respectively. The gender difference in the foreign wage premium is particularly large for the subset of unskilled workers (57 percent for women vs. 31 percent for men), suggesting that foreign employment provides higher earnings opportunities for unskilled women relative to unskilled men. The results of the Wald Tests in Table 5 indicate that the equality of the

> 29 One exception is a study by Braunstein and Brenner (2007) which compares the foreign wage premiums by gender in urban China in 1995 and 2002. Using province-level macro data and household survey data for 1995 and 2002, Braunstein and Brenner find that women received a higher foreign wage premium than men did in 1995, perhaps because FDI in export-oriented and female-dominated industries raised labor demand for women. However, after China committed to opening domestic markets in preparation for WTO accession in 2001, gender-based advantages reversed, as foreign investment in more capital- or technology-intensive sectors grew by 2002 to more greatly advantage workers in male-dominated than in female-dominated industries (Braunstein and Brenner, 2007).

15
ential and its components. Effects are somewhat smaller, and with a more marked difference between host and non-host effects, than generally found above, but remain quite strongly significant.

# **5.4 Assessment**

These empirics lead to several reasonably clear-cut conclusions. One is that the _I_ - _METR_ , while theoretically appealing, does not serve well as a sufficient statistic for cross-border tax effects on tangible investment, at least as it has been operationalized here. This reflects very different impacts from its main components: the cross-border rate differential, in turn decomposed into host and non-host effects, and the host _KF_ - _METR_ .

The former, reflecting the difference between the statutory rate in the host country and a weighted average of those available elsewhere to the parent investor, has a highly significant and very large effect, especially but not only where markets are more highly integrated. While the point estimates above vary quite widely, between the extremes ofaround 1.6 to 10.1, they tend to be notably higher than the 2.5 or so reported as benchmark in De Mooij and Ederveen (2003) and as median estimate in Feld and Heckemeyer (2011). Our preferred estimate would be around the 3.6 in the baseline results in column (2) of Table 2. We return shortly to the question of why our results point to stronger tax responsiveness than does the bulk of previous work.

Importantly, the semi-elasticities with respect to host and non-host statutory rates that emerge are much the same as those for the difference between them. There are thus strong signs of spillover effects at work through this route. (The meta-analyses, recall, look only at the effects of the host country rate.) The impact of the host country _KF_ - _METR_ , in contrast, appears close to zero and insignificant. This is somewhat more in line with, for example, the conclusion that Feld and Heckemeyer (2011) draw from their analysis, though we are not aware of any other study comparable to that here in relating the _KF_ - _METR_ to a measure approximating cross-border tangible investment.

There also emerges a significant interaction between the rate differential and the host _KF_ - _METR_ , tending to diminish the impact of the former while making more likely a 33
Proponents of the first class of methods do not claim that the surveys are accurate, but argue that there is no justification for assuming that the discrepancy between the survey mean and NAS consumption per capita is solely due to underestimation in the surveys or that the survey measurement errors are distribution neutral. The discrepancy between the two data sources reflects a number of factors, including differences in what is being included,<sup>33</sup> and selective compliance in surveys. Arguments can be made for and against both methods. For example, there is also likely to be under-reporting or selective compliance with the randomized assignment in a survey, but it would seem unlikely that this would only affect the mean and not the measure of inequality; more plausibly, the underestimation of the mean by surveys due to selective compliance comes with an underestimation of the extent of inequality.<sup>34</sup> To give a counter-example, suppose that the surveys exclude imputed rent for owner-occupied housing (practices are uneven in how this is treated), and that this is a constant proportion of expenditure. Then the surveys get inequality right and the mean wrong.

In our case, it is also important to note that the underlying national poverty lines were calibrated to the surveys. By the most common methods of setting poverty lines, underestimation of non-food spending in the surveys will lead to an under-estimation of the poverty line, which is anchored to the spending (as measured in the surveys) of sampled households living near the food poverty line (or with food-energy intakes near the recommended norms). Correcting for under-estimation of non-food spending in surveys would then require higher poverty lines. This provides a further justification for testing robustness to a range of poverty lines.

Arguably the more important concern is the heterogeneity of surveys given that the level of the poverty line is always somewhat arbitrary. In an interesting variation on the re-scaling method, Karshenas (2003, 2004) replaces the survey mean by its predicted value from a regression on consumption per capita from the NAS. So instead of using NAS consumption, Karshenas uses a stable linear function of NAS consumption, with mean equal to the overall mean of the survey means. As in Bhalla’s method, this assumes that national accounts

> 33 For example, NAS private consumption includes imputed rents for owner-occupied housing, imputed services from financial intermediaries and the expenditures of non-profit organizations; none of these are included in consumption aggregates from standard household surveys. Surveys, on the other hand, are undoubtedly better at picking up consumption from informal-sector activities. For further discussion see Ravallion (2003) and Deaton (2005).

> 34 Korinek et al. (2006) examine the implications of selective compliance and Korinek et al (2007) provide an econometric method for correcting survey data for this problem.

20
# **The Evolving Importance of Banks and Securities Markets**

Asli Demirguc-Kunt, Erik Feyen, and Ross Levine<sup>1</sup>

## **JEL Classification** : G1, G2, O16

**Keywords** : Financial development, economic development, banks, capital markets, financial structure

> 1Demirguc-Kunt: World Bank; Feyen: World Bank; Levine: Brown University and the NBER. We received very helpful comments from Thorsten Beck, Norman Loayza, Yona Rubinstein, and seminar participants at the World Bank’s June 16, 2011 conference, “Financial Structure and Economic Development.”  Mauricio Pinzon Latorre provided outstanding research assistance.
# **Tables**

Table 1: Summary of key variables used for the analysis

|||All|||Urban|||Rura|l|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Obs|Mean|Std. Dev.|Obs|Mean|Std. Dev.|Min|Max|
|Electricity in the community|47937|0.660|0.474|18769|0.935|0.247|28568|0.472|0.499|0|1|
|Piped water in the community|47937|0.625|0.484|18769|0.866|0.341|28568|0.460|0.499|0|1|
|Paved road in the community|47937|0.578|0.494|18769|0.765|0.424|28568|0.447|0.497|0|1|
|School in the community|47937|0.890|0.313|18769|0.9129|0.284|28568|0.874|0.332|0|1|
|<br>Hospital in the community|47937|0.644|0.479|18769|0.730|0.444|28568|0.580|0.494|0|1|
|Frequencyof electricityavailability|47924|1.871|1.617|18765|2.986|1.313|28559|1.122|1.360|0|5|
|Tax Index|47937|0.622|0.278|18769|0.632|0.269|28568|0.615|0.287|0|1|
|Agree must pay taxes|47937|0.641|0.480|18769|0.687|0.464|28568|0.611|0.487|0|1|
|Legitimate to refuse to pay taxes|47937|0.683|0.465|18769|0.689|0.463|28568|0.681|0.466|0|1|
|<br>Government right to collect taxes|47937|0.719|0.450|18769|0.745|0.436|28568|0.699|0.459|0|1|
|<br>Trust tax ofcials/administration|47937|0.444|0.497|18769|0.405|0.491|28568|0.468|0.499|0|1|
|<br>Respondent’s Gender|47937|0.497|0.50|18769|0.50|0.50|28568|0.496|0.50|0|1|
|<br>Respondent’s Age Group|47650|2.121|0.805|18688|2.04|0.771|28363|2.172|0.821|1|5|
|<br>Respondent’s Education Level|47806|3.393|2.166|18732|4.290|2.082|28475|2.788|2.007|0|9|
|Employment Status|47709|0.379|0.485|18687|0.451|0.498|28424|0.333|0.471|0|1|
|Household Size|47865|3.884|2.372|18732|3.80|2.292|28535|3.945|2.428|1|10|

Table 2: Summary Statistics of data used in Simulations

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|Tax Revenue % GDP|17.03|8.65|1.55|50.7|
|Proft Tax rate|41.09|13.83|13.6|73.3|
|Discount rate (%)|10|0|10|10|
|CIT loss from existing frms(%)|11.18|8.61|3.24|45.85|
|CIT loss from new frms(%)|13|0|13|13|

25
Table 1: Summary Statistics

||Pre-|FPE (201|0-2011)|Post-|FPE (20|12-2014)<br>lfdif|
|---|---|---|---|---|---|---|
|Variable|N|Mean|Std.Dev|N|mean|_p−_vaue or .<br>Std.Dev|
|OWM|5654|59.88|9.605|7795|59.25|9.094<br>0.0001|
|Standardized OWM|5654|0.0268|1.034|7795|-0.0204|0.970<br>0.0068|
|Female|5776|0.563|0.496|8016|0.601|0.490<br>0.0000|
|Age|5764|21.10|3.915|8004|20.89|3.588<br>0.0016|
|_COSC pass_|||||||
|First class|4765|0.300|0.458|7713|0.314|0.464<br>0.0991|
|Second class|4765|0.551|0.497|7713|0.571|0.495<br>0.0245|
|Third class|4765|0.149|0.356|7713|0.115|0.319<br>0.0000|
|APS|5221|14.92|5.242|7343|15.43|5.413<br>0.0000|
|_Distribution of Students_|_by (S_|_chool) Dist_|_rict (%)_||||
|Berea|5776|0.0734|0.261|8016|0.109|0.312<br>0.0000|
|Botha-Bothe|5776|0.0682|0.252|8016|0.0828|0.276<br>0.0015|
|Leribe|5776|0.199|0.399|8016|0.178|0.382<br>0.0017|
|Mafeteng|5776|0.101|0.301|8016|0.112|0.316<br>0.0321|
|Maseru|5776|0.352|0.478|8016|0.340|0.474<br>0.1401|
|Mohale’s Hoek|5776|0.0829|0.276|8016|0.0639|0.245<br>0.0000|
|Mokhotlong|5776|0.0303|0.171|8016|0.0308|0.173<br>0.8623|
|Qacha’s Nek|5776|0.0156|0.124|8016|0.0147|0.120<br>0.6823|
|Quthing|5776|0.0298|0.170|8016|0.0250|0.156<br>0.0842|
|Thaba-Tseka|5776|0.00606|0.0776|8016|0.0126|0.112<br>0.0001|
|Foreign|5776|0.0424|0.202|8016|0.0314|0.175<br>0.0006|
|_Distribution of Students_|_by Fa_|_culty (%)_|||||
|Agriculture|5776|0.0571|0.232|8016|0.0586|0.235<br>0.7101|
|Education|5776|0.254|0.436|8016|0.246|0.431<br>0.2653|
|Health Sciences|5776|0.0703|0.256|8016|0.0750|0.263<br>0.2969|
|Humanities|5776|0.115|0.319|8016|0.130|0.337<br>0.0052|
|Law|5776|0.0452|0.208|8016|0.0485|0.215<br>0.3610|
|Science & Technology|5776|0.144|0.351|8016|0.124|0.329<br>0.0006|
|Social Sciences|5776|0.315|0.465|8016|0.318|0.466<br>0.7142|

_Source_ : Own calculations using NUL students’ records data for 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean, calculated as the weighted sum of final scores of core, pre-requisite, and/or elective subject courses that make a maximum of 36 credit hours, where the weights are each course’s credit hours divided by 36. The N for some variables is smaller due to missing values.

11
world real GDP growth. We find that the elasticity of trade to income has increased over time, from under 2 in the 1960s to over 31⁄2. These results are very similar to Irwin (2002). Using a slightly different methodology, he estimates that that the elasticity of world trade to income has grown over time, and was also about 3.4 in the 1990s. Overall, the results imply that trade should fall (in percent) about 31⁄2 times as much as GDP, assuming crises are not special.

The significant increase in the elasticity of trade to income may be attributed to the fragmentation of production and/or lean retailing. Because many new goods use small inputs that are nearly costless to trade (eg cell phones and digital cameras), the production process of these goods has become fragmented across countries. Many traditional goods such as shoes and cars are also increasingly incorporating imported inputs. The elasticity of trade to GDP will rise if there is more incentive to outsource part of the production chain when demand is high. This is because GDP is measured in value added while trade is a gross measure. So an increase in GDP may lead to more outsourcing and much more measured trade, as an increasing number of parts travel around the globe to be assembled, and again to their final consumer.

The expansion of lean retailing in recent years implies that supply responds almost immediately to changes in demand. Rapid technological advances make computers and other electronics almost perishable, and many companies have started selling directly to the consumer and producing for demand. For example, Dell sells directly to its customers and builds a made-to-order computer as orders arrive.<sup>3</sup> This type of retailing implies that a drop in demand will show up immediately in trade statistics.

> 3 Arthur, J. (2006), Chapter 2 p. 25.

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