rafmacalaba/datause-encoder-data huggingface dataset:
# **II. THE EAST ASIAN CRISIS: VULNERABILITIES AND TRIGGER**

# **1. Globalization and Domestic Weaknesses**

Four factors made Thailand, Indonesia, and Korea particularly vulnerable to external shocks in the pre-crisis period: (a) a burgeoning, global flow of private capital, especially of short-term nature, in search of higher returns; **(b)** macroeconomic policies that allowed large inflows of short-term, unhedged capital to fuel a domestic credit boom; (c) newly liberalized but insufficiently regulated domestic financial markets with **highly** leveraged corporations; and **(d)** mounting political uncertainty. In essence, the push from global capital markets, often without due diligence and beyond prudence, interacted with poorly regulated domestic financial systems to exacerbate a domestic credit expansion. This combination led to over investment in domestic non-tradable sectors (manifested as property price bubbles especially in Thailand) and in selected inefficient manufacturing sectors (as was done **by** Korean chaebol firms). Availability of cheap capital that was poorly intermediated through weakly supervised and governed financial institutions allowed corporations to add debt to their already **highly** leveraged balance sheets. This vulnerability exposed many East Asian countries to the shocks of changing investor behavior.

> Patterns of indebtedness varied across countries. In Thailand, finance companies and commercial banks, availing themselves of low interest foreign-currencydenominated loans, borrowed heavily from abroad to invest in projects with low rates of return such as construction and real estate. Financial institutions' net foreign liabilities rose from **6** percent of domestic deposit liabilities in **1990** to **30** percent **by 1996** (World Bank, **1998b).** In Indonesia, corporations were the primary borrowers from foreign

**_5_**
39 Table **9. Distribution of Income** Flows **From Independent Variables in** Non-Farm **and Agricultural Income Equations in Egypt**

|Variable|Income S<br>Non-Farm<br>Income<br>Equation|hare From:<br>Agricultural<br>Income<br>Equation|I<br>Lowest|ncome Sha<br>Second|res to Qui<br>Third|ntile Group:<br>Fourth|<br>Fifth|Ratio of<br>Top<br>Quintile to<br>Bottom<br>Quintile|
|---|---|---|---|---|---|---|---|---|
|Land|||||||||
|Amount of land<br>owned by<br>household|4.7**|30.5**|10.0|10.0|15.0|20.0|45.0|4.5|
|Amountof land<br>rented in by<br>household|-6.8**|-29.1**|55.5|11.1|22.2|-|11.1|0.2|
|Labor|||||||||
|Household size|-52.3**|67.6|22.3|21.5|20.8|18.8|16.5|0.7|
|Mean age of all<br>household<br>members|271.2**|-201.8|18.4|18.5|18.9|20.3|23.8|1.3|
|Mean age of all<br>members squared|-140.4**|157.1*|16.4|17.0|17.9|20.1|28.6|1.7|
|Number of<br>household males<br>over 15 years|29.5*|71.0|20.2|20.2|20.2|19.1|20.2|1.0|
|Capital<br>Mean education<br>of household<br>males over 15<br>years|28.2|25.8|15.7|17.9|20.1|22.3|23.8|1.5|
|Mean education<br>of household<br>males squared|27.9|-64.5|16.4|17.0|17.9|20.1|28.6|1.7|
|Value of farm<br>equipment|1.3|-2.1|9.8|20.8|18.4|11.7|39.2|4.0|
|Value of<br>enterprises<br>owned|5.1*|-4.4|10.6|7.6|13.5|19.8|48.5|4.6|

Notes: Income shares calculated from tobit results in Table 7; income shares of governorate-level dummy variables are not reported.

> * Coefficient in income equation significant at .05 level.

> ** Coefficient in income equation significant at .01 level.
**Table 26. Storage Days on Last Transaction by Firm Assets**

|||Beni|n||||Malaw|i||
|---|---|---|---|---|---|---|---|---|---|
||Mean|S.d.|Median|N||Mean|S.d. M|edian|N|
|**Working Capital**||||||||||
|<br><$167|17.36|28.85|7.00|169|< $78|3.54|4.90|2.00|245|
|$167-$667|23.64|41.10|8.00|242|$78 - $244|7.08|12.09|3.00|245|
|>= $667|23.61|32.69|10.00|210|>= $244|13.29|21.69|6.00|246|
|**Human Capital**||||||||||
|**<-1**employee|24.43|40.32|8.00|349|0-1 employee|6.40|13.79|2.00|523|
|<br>1 - 2 employees|13.35|19.62|7.00|147|>1 employee|12.03|17.81|6.00|204|
|> 2 employees|23.25|31.46|10.00|140||||||
|**Social Capital**||||||||||
|**<**10|29.97|33.00|20.00|179|_< 15_|6.84|14.31|2.00|219|
|10 - 33|17.79|34.96|7.00|242|15 - 36|8.60|14.63|3.00|269|
|>= 33|19.18|35.69|7.00|219|>= 36|8.30|16.38|3.00|248|
|**Gender**||||||||||
|Male|26.98|41.89|7.00|121|Male|9.02|15.82|3.00|468|
|Female|20.39|33.08|8.00|521|Female|6.15|13.73|2.00|268|
|**Total**|21.63|34.97|8.00|642|Total|7.98|_15.15_|3.00|736|

# **_S.4 Arbitrage over Transacdon Size_**

Evidence from the last transaction reveals that, on average, the load is purchased from 5 different suppliers in Benin -- 15 in Malawi. The load is then sold to an average of 10 (Benin) to 50 (Malawi) different clients. In two third of the cases, the trader himself or herself traveled<sup>to the supply market or</sup> markets to oversee the purchase. In nearly all cases, the respondent supplies<sup>his or her own bags or</sup> containers at the time of purchase, i.e., agricultural products are transferred from<sup>the seller's to the buyer's</sup> bags.

The ratio of the number of clients to the number of suppliers indicates whether<sup>the trader is engaged</sup> in arbitrage across form: buying in large quantities from a few suppliers and selling<sup>in small quantities to a</sup> large number of buyers. The data reveal that traders in Malawi appear to be more engaged<sup>in</sup> transformation to retail quantities than traders in Benin. Viewed by asset<sup>category, traders with larger</sup> financial endowments carry out less arbitrage over transaction size than smaller<sup>traders. The same is true</sup> for traders with higher social capital. In terms of human capital, given that transforming<sup>goods from larger</sup> 45
WPS3974

# **Substitution in Markusen’s Classic Trade and Factor Movement Complementarity Models***

# **Maurice Schiff World Bank and IZA**

## **Abstract**

Two classic papers on the relationship between trade and factor movement are Mundell (1957) and Markusen (1983). Mundell showed that substitution holds in the HeckscherOhlin model. Markusen challenged the substitution result and showed in five different models that removing barriers to factor movement results in complementarity under free trade, identical factor endowments and a change in any one of the other assumptions underlying the Heckscher-Ohlin model. This paper generalizes Markusen’s analysis by considering: i) the elimination of barriers to factor movement under _any_ protection level; and ii) a change in trade barriers under free factor movement. I show that: a) substitution holds under high protection; b) complementarity holds under low protection; and c) either substitution or complementarity hold for large increases (reductions) of low (high) protection rates.

### World Bank Policy Research Working Paper 3974, August 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* I would like to thank Mohammad Amin, Arye Hillman, James Markusen, Hillel Rapoport and participants at a seminar at Bar-Ilan University for their useful comments.
25 both regional and local goverrnents throughout the whole period. Between 1994 and 1996, the ratio of regional transfers to total expenditures of local governments fluctuated in a narrow band of 35%. At the same time, with the exception of 1995 the ratio of federal transfers to total regional expenditures stayed around 42.5%. In 1995 federal transfers to regional governments were substantially reduced. The seven percentage points drop in the average value of the above ratio coincided with a drastic increase in regional disparities - in one year the coefficient of variation jumped by 43%. This apparent shock to the system of federal transfers quickly disseminated and by 1996 both the mean of the ratio of federal transfers to total regional expenditures and its coefficient of variation returned to their pre-1995 values.

**Table 15. Measures of Fiscal Subordination of Subnational Governments in the Period 1994 - 1996, %**

|**Summary Statistics/ Year**|**1994**|**1995**|**1996**|
|---|---|---|---|
|1. Ratio between regional transfers to local<br>governments and local governments total<br>expenditures||||
|1.1. Mean|36.87|33.40|34.85|
||(1.71)|(1.65)|(1.78)|
|1.2. Standard deviation|15.48|15.18|16.39|
|1.3. Coefficient of variation|0.42|0.45|0.47|
|1.4. Minimum value|0|3.01|0|
|1.5. Maximum value|91.73|74.77|82.45|
|1.6. Valid number of observations|82|85|85|
|2. Ratio between federal transfers to<br>regional governments<br>and total regional<br>expenditures||||
|2.1. Mean|42.35|35.36|42.58|
||(2.38)|(2.8)|(2.46)|
|2.2. Standard deviation|21.41|25.51|22.24|
|2.3. Coefficient of variation|0.51|0.72|0.52|
|2.4. Minimum value|1.61|0|0.04|
|2.5. Maximum value|96.05|99.71|88.95|
|2.6. Valid number of observations|81|83|82|

Standard error of the mean in parentheses
# **Table A1. Key indicators across quarters**

|_Demographic Indicators_|**Quarter 1**<br>**(Fall)**|**Quarter 2**<br>**(Winter)**|**Quarter 3**<br>**(Spring)**|**Quarter 4**<br>**(Summer)**|**All**|
|---|---|---|---|---|---|
|Average household size*|7.4|7.5|7.1|7.2|7.3|
|Average age(years)|20.6|20.4|20.7|20.5|20.6|
|Household members %,age <15)|47.9|48.7|48.4|48.7|48.5|
|Age dependencyratio|131.6|134.2|133.6|134.0|133.4|
|_Education and Health Indictors_||||||
|Full Immunization(%,age 12-23 months)*|33.0|41.1|34.8|37.6|36.7|
|Literate household head(%)*|34.4|28.8|28.4|29.5|30.1|
|Ever attended school(%,age >18)*|21.7|21.3|18.9|21.6|20.9|
|Education level ofpersons(age >18)|2.0|1.9|1.6|1.9|1.9|
|<br>_Access to Services and Infrastructure Indi_|_cators_|||||
|Safe drinkingwater(% hh)*|30.4|27.7|24.2|25.3|26.8|
|Sanitarytoilet(% hh)|5.9|5.6|4.5|4.0|4.9|
|Electricity (% hh)|40.9|42.2|41.5|39.8|41.1|

Note: Estimated population weighted means. * denotes estimates that are statistically different at 10% across quarters. _Source:_ NRVA 2007/08 33
convergence is weaker using means from consumption surveys only (Column 2) or national accounts (Column 3), though conditional convergence is still evident.

The data also suggest that higher growth rates are associated with higher (proportionate) rates of poverty reduction. The regression coefficient of _gi_ (<sup>_H_</sup> _it_ ) on<sup>_g_</sup> _i_ (<sup></sup> _it_ ) is -1.085 (t=-4.128) with R<sup>2</sup> =0.377. The (absolute) elasticity is lower using the growth rate of consumption per capita from national accounts (an elasticity of -0.797, t=-3.089) while it is higher using a lower poverty line; for the $1.25 line the elasticity with respect to growth in the survey mean is -1.385 (t=3.735).

However, as we saw in Figure 1, there is no sign of convergence for the $2 a day poverty rate, with proportionate rates of poverty reduction roughly orthogonal to initial levels.<sup>26</sup> This appears to be robust to the choice of poverty line.<sup>27</sup> It is also robust to including the same set of controls, although then there are slightly stronger signs of conditional convergence at lower poverty lines; for the $1.25 a day line the conditional convergence parameter for the poverty rate is -0.025, but this is still not significantly different from zero at the 10% level (t=-1.575).

In summary, while there is mean convergence and growth tends to reduce poverty, there are no signs of poverty convergence. The rest of this paper will try to explain why. It will be argued that the initial poverty rate matters to the subsequent rate of poverty reduction through two distinct channels, namely the growth rate in mean consumption and the elasticity of poverty to the mean. First, it will be shown in the following section that the parameter  _i_ (in equation 6) is a decreasing function of the initial poverty rate. Second, section 7 will be shown that the elasticity of poverty to the mean,  _i_ (equation 7), is a decreasing function of the initial level of poverty. Section 8 will bring these two elements together to answer the question posed in the title to this paper.

# **6. The relevance of initial poverty to the growth rate in the mean**

The section begins with ―benchmark regressions‖ for growth, and then tests its robustness to various changes in specification.

> 26 Recall that poverty convergence is defined in proportionate rather than absolute terms. The absence of poverty convergence by this definition implies that poorer countries tend to see larger absolute reductions in their poverty rate.

> 27 This was also true for the $13 line, for which the convergence parameter was -0.009 (t=-0.480). Similarly, for a lower line of $1.25 a day the parameter was -0.005 (t=-0.393).

14
investment in the automotive sector and have ordered localities not to use government expenditures or loans to invest in the auto industry. Though the orders produced little immediate effect, the sharp decline in consumer demand following the imposition of macroeconomic tightening in spring 2004 caused numerous firms to scale back investment plans.

GM and VW, the leading auto firms in North America and Europe, have particularly strong incentives to continue expanding in China. Both are under pressure from the Japanese in America and Europe, but aggressive early investments have brought them big leads in China. China will soon surpass Germany as VW’s largest market, and for both GM and VW profitability is reportedly much higher in China than elsewhere, despite shrinking margins ( _Wall Street Journal_ 2003.10.17; _Reuters_ 2003.07.03, 2003.10.20; _Dow Jones Business News_ 2003.11.06; Asia Pulse October 21, 2003). As a member of the board of VW explained in announcing a new plant to build advanced, low-emissions engines in Shanghai, “China is the most important strategic market for Volkswagen, that's why we need to keep bringing the technical know-how into this country to realize localized manufacturing of either vehicles or auto components" (AsiaPulse, October 21, 2003).

Similarly, while the leading parts producer Delphi is hurting elsewhere, it has recorded strong increases in Chinese sales, estimated at one billion dollars for 2003, up 40 percent from 2002. The company announced that it would spend 50 million dollars to build a 500-person R&D center in Shanghai. Since it already exports about one-fourth of the output of its Chinese plants, if a serious capacity glut did appear in China, in all likelihood it would expand exports rather than cut production (Reuters December 17, 2003; AutoAsia December 4, 2003). Delphi’s rival Visteon announced that it would move its Asia-Pacific headquarters to Shanghai from its long-time base in Yokohama, while Honeywell’s Allied-Signal moved its regional headquarters to Shanghai to Singapore (AutoAsia July 3, August 29, 2003).

Some of the dynamics already underway will contribute to enhancing exports. The 51
<!-- Start of picture text --> Road<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> <!-- Start of picture text --> Road - Prices<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> <!-- Start of picture text --> Road - Entry<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> 63
21 cn interest rates or, more directly, by changing the set of fiscal and finanrial incentives available to investors. Second the level of InN estment in the economy will also depend on the availability oF credit. The joint presence of both the cost of capltal and oF credit avallability mak appear redundant (or even contradictory), until we recall that investment in this model is not fully determined by the stock of credit that financial intermediaries are willing to extend to firms. Even if Firms are credit constrained, a change In the interest rate r wlll still affect investment through its impact on the discount rate i and thus on the choice cf retained earnings. By varying their retention behavlour firms can relax somewhat the credit constraint. Fiscal policy therefore will aFfect investment by influencing either the Interest rate or credit availability. This latter channel, as we shall see later in the paper, can play a crucial role in determining the macroeconomic outcome oF different fiscal Folicies. It is also plausible of course to interpret ths joint significance of credit and Interest rates in eq. 10 as implying that only a subset of firms is credit constrained. Finally, by putting together the estimation results for eqs. 10 and 11 we can argue that public investment bears a complementarity relationship with private investment. Indeed an increase in public investment will lead to higher capacity output (eq. 11 and, through this channel, to higher private investment. By severely cutting public investment, fiscal policy may have contributed in the past to the stagnation of private investment.

# _b) the saving decision_

A steady supply oF domestic saving will ensure that a sustained rate of investment would not be incompatible with existing constraints on the external payments front. It is essential thereFore to gather an adequate understanding oF the determinants oF saving behaviour. Unfortunately the measurement of saving is beset with difficulties, to the extent that consumption is computed residually in Morocco's national accounts. We can rely on two alternative measures of private seving, one derived as the difference between private disposable income and private consumption, the other based on the saving-investment identity for the economy. Because of statistical inconsistencies, the two procedures do not yield the same result. In what follows we use the first measure. We find that, after peaking during the phosphate price boom and declining aFterward, the average propensity to save has been steadily increasing during the 1980's.
prioritize construction of new or upgraded roads over preservation of existing roads, spending around six times as much on investment as maintenance. Some countries appear to neglect road maintenance almost completely (such as Gabon, The Gambia, Jordan, Mexico and Paraguay). Even countries with Road Funds, while they may spend more on maintenance than their peers, still seem to dedicate more resources to investment than maintenance overall (such as Cameroon, Kenya, Senegal, Mozambique and Namibia). Mozambique and Ethiopia are among the few countries reporting both high and balanced levels of expenditure between road investment and road maintenance.<sup>13</sup> Only a minority of countries are dedicating adequate resources to either investment in or maintenance of roads. An important question is whether spending levels are adequate to meet development targets. High level estimates of spending needs for roads suggest that countries may need to spend similar amounts, of the order of 1 percent of GDP, on both capital and operating expenditure for roads (Rozenberg and Fay, 2019). These spending adequacy thresholds are represented by the green box in Figure 18, meaning that countries appearing in the green box are spending adequate amounts on both investment and maintenance. Strikingly, Ethiopia and Mozambique are the only two countries that fall into this category. Additionally, the red box represents the area where countries are neither spending enough on investment nor maintenance, and the majority of countries appear in this area. Namibia is the only country that spends adequately on maintenance, but not on investment. A larger group of countries, including some with Road Funds, spend adequately on investment but not on maintenance.

Countries with PPP programs for toll roads (represented as black triangles in Figure 18 tend to exhibit a particularly high level of capital bias in road spending allocation decisions. Countries with Road Funds (represented by blue circles in Figure 18) are more likely to allocate adequate resources to the road sector, either on maintenance and/or investment, than those without. Nevertheless, the presence of a Road Fund is not of itself a guarantee that maintenance expenditure will be _enough_ . Countries like Lesotho and Sierra Leone, for instance, do not appear to capture adequate resources through their Road Funds to support full maintenance requirements.

> 13 The above estimates are based on the BOOST, Road Fund and OECD-DAC data and do not include any maintenance expenditure undertaken by toll roads as this data is not available. However, based on the earlier analysis of Road Funds, this is likely to be relatively small overall.

20
# **Table 4. Participation in societal institutions**

|**Dependent variable**|**Log revenue**|**Years in business**|**Pseudo R**<sup>**2**</sup>|**# obs**|
|---|---|---|---|---|
|Registered with federal treasury|.661<br>(37.7)|.006<br>(3.95)|0.200|8870|
|Registered with local treasury|.371<br>(26.9)|.006<br>(3.57)|0.083|8870|
|Pays any taxes|.556<br>(35.6)|.008<br>(5.35)|0.158|8870|
|Any paid workers registered with<br>IMSS|.634<br>(17.2)|.010<br>(2.95)|0.161|1697|
|Enumerated in 1989 census|.538<br>(26.5)|.013<br>(6.26)|0.154|5121|
|Registered with business<br>guild/organization|.383<br>(26.8)|.006<br>(3.87)|0.090|8870|
|Pays any dues|.312<br>(21.6)|.007<br>(4.02)|0.066|8870|

Probit regressions. Absolute values of z statistics in parentheses. The standard errors have been corrected for arbitrary forms of heteroskedasticity.

**Table 4.A. Participation in societal institutions** (difference in probabilities from a one unit change in the regressor)

|**Dependent variable**|**Log revenue**|**Years in business**|
|---|---|---|
|Registered with federal treasury|.254|.002|
|Registered with local treasury|.113|.002|
|Pays any taxes|.199|.003|
|Any paid workers registered with<br>IMSS|.226|.004|
|Enumerated in 1989 census|.188|.004|
|Registered with business<br>guild/organization|.108|.002|
|Pays any dues|.070|.002|

29