**<u>TABLE 6: REGRESSIONS</u>**

||**{1}**|**{2}**|**{3}**|**{4}**|**{5}**|**{6}**|**{7}**|**{8}**|
|---|---|---|---|---|---|---|---|---|
|**...Dependent**<br>**Variable**|**_Raw_**<br>**_Materials_**|**_Raw_**<br>**_Materials_**|**_Raw_**<br>**_Materials_**|**_Upstream_**<br>**_Inventories_**|**_Upstream_**<br>**_Inventories_**|**_Upstream_**<br>**_Inventories_**|**_Raw as % of_**<br>**_Raw +_**<br>**_Upstream_**|**_Raw as % of_**<br>**_Raw +_**<br>**_Upstream_**|
|**Log real PPP**<br>**GDP/Capita**|-0.0229<br>(0.0186)|0.0010<br>(0.0285)|-0.0304*<br>(0.0171)|-0.0328***<br>(0.0950)|-0.0193**<br>(0.0103)|-0.0320***<br>(0.0077)|0.0444*<br>(0.0227)|0.0523*<br>(0.0274)|
|**Telephone**<br>**mainlines per**<br>**person**|-<br>0.2934***<br>(0.0948)||-0.1968**<br>(0.0928)|0.0950*<br>(0.0539)||-0.0926**<br>(0.0549)|-0.5417***<br>(0.1695)||
|**Infrastructure**<br>**Quality**||-0.0300***<br>(0.0086)|||0.00021<br>(0.0044)|||-0.0374***<br>(0.0076)|
|**Transfers &**<br>**Subsidies/GPD**|0.7427***<br>(0.2226)|0.4105**<br>(0.1947)|0.6453**<br>(0.3128)|0.2136*<br>(0.1202)|0.3098**<br>(0.1235)|0.6608***<br>(0.1238)|0.4385<br>(0.4809)|<br>-0.3475<br>(0.4063)|
|**Imports/GDP**|0.0290*<br>(0.0166)|0.0372***<br>(0.0124)|0.0449<br>(0.0296)||||-0.1765<br>(0.1798)|<br>0.1615<br>(0.1596)|
|||||-0.0157|-0.0151|0.0158*|0.2721|<br>-0.0767|
|**Exports/GDP**||||(0.0108)|(0.0111)|(0.0110)|(0.1798)|(0.1856)|
|**Lending**<br>**interest Rate**<br>**(real)**|||-0.0317<br>(0.0368)|||-0.0442***<br>(0.0149)|||
|**GDP Growth**|||-0.0113<br>(0.0073)|||-0.0038**<br>(0.0016)|||
|**GPD Growth**<br>**Standard**<br>**Deviation **|||0.0108<br>(0.0075)|||-0.0066**<br>(0.0019)|||
|**(24 industry**<br>**dummy**<br>**variables)**|Included|Included|Included|Included|Included|Included|Included|Included|
|**# of clusters**<br>**(countries)**|42|29|31|44|30|32|41|29|
|**R-Squared**|0.2528|0.2897|0.2846|0.3893|0.4291|0.4549|0.3234|0.3518|
|**# of**|||||||||
|**Observations**|2086|1627|1408|1962|1642|1271|1554|1307|

Robust standard errors corrected for clustering at the country level are in parenthesis. *Included significance at the 10% level; **Indicates significant at the 5% level; ***Indicates significant at the 1% level. Inventories greater than .5 have been dropped for these regressions. Coefficients in regressions (1)-(6) represent the effect of an absolute change in the explanatory variable on inventory level expressed as fraction of a year, For example, if telephone mainlines per person increased from .5 to .6 in regression (1), inventories would fall by o.2934 of a year or about 11 days.

Coefficients in regressions (7)-(8) represent the effect of an absolute change in the explanatory variable on the percentage of inventories held as raw materials. For example, if telephone increased from .5 to .6 in regression (7), 5417% of inventories more are held as raw materials. In the U.S., the median industry holds 57% of inventories as raw materials so that the .1 in telephone mainlines leads to a 9% change in holdings.

# **5.2 Conclusions (ii)**

The work done by Guasch and Kogan in 2001 introduced a new cross-country dataset on inventories at the industry level into the literature documenting the determinants of inventory levels in developing countries. Given the high costs of capital in developing countries, usually in the 15 percent to 30 percent rate, the impact on unit costs of holding inventories is enormous. The analysis explores some broad causes of high raw materials inventory levels across countries 22