rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Dynamic Products in the Recent Intra-Trade of Mercosur Countries.**

||Exports to M|ercosur|RO Index|RO Index<br>Change|Factor<br>Intensity*|Growth<br>Rate|
|---|---|---|---|---|---|---|
|Commodity (SITC)|1988|1994|1988||||
|Non-Alcoholic Beverages (111)|349|26,238|2.35|46.12|162|85.4|
|Tobacco Manufactures (122)|2,032|112,681|0.95|0.91|210|77.5|
|Articles of Plastic (893)|4,225|95,535|12.45|-7.52|86|56.1|
|Alcoholic Beverages (112)|4,137|81,671|1.87|4.61|182|53.1|
|<br>Perfumnes and Cosmetics (553)|<br>4,766|<br>86,282|5.22|8.16|160|51.2|
|Fumiture (821)|3,972|66,213|1.15|-0.10|60|49.5|
|Iron and Steel Castings (697)|287|3,696|0.30|0.41|145|44.1|
|Power Machinery Non-Electric (711)|25,140|290,687|0.40|0.79|105|41.9|
|Road Vehicles Non-Motor (733)|3,118|35,854|2.23|4.65|90|41.7|
|Wood Manufactures (632)|1,472|16,689|0.43|-0.10|80|41.5|
|Machines for Special Industries (718)|10,763|120,617|0.93|0.04|113|41.2|
|Structures and Parts (691)|1,783|19,834|0.72|1.05|105|41.1|
|Prepared Meat (011 -013)|21,934|237,912|0.17|0.23|102|40.6|
|Road Motor Vehicles (732)|206,996|2,112,750|1.25|3.17|122|39.4|
|Plywood and Veneers (631)|3,707|35,630|0.20|0.16|57|38.2|
|Lace and Ribbons (654)|1,386|13,157|3.56|2.29|68|37.9|
|Special Textile Products (655)|4,945|46,919|0.88|0.59|69|37.9|
|Prepared Sugar (061-062)|11,456|102,655|0.30|0.07|140|36.8|
|Prepared Dairy (022-024)|23,495|204,019|4.31|18.17|180|36.2|
|Metal Manufactures, nes (698)|5,984|51,430|0.90|0.87|84|36.0|
|Electric Power Machinery (722)|14,278|121,717|1.18|-0.33|88|_35.8_|
|<br>Materials of Rubber (621)|<br>3,636|<br>30,780|3.13|3.13|100|35.7|
|Glassware (665)|5,381|45,017|2.21|2.88|107|_35.5_|
|Nails, Nuts and Bolts (694)|3,021|<br>24,782|2.86|0.81|88|35.1|
|Preserved Fruit (053)|4,486|36,053|0.05|0.08|116|34.7|
|Domestic Electrical Equipment (725)|12,568|97,322|2.19|3.76|92|34.0|
|<br>Base Metal Household Equipment (697)|<br>_5,592_|<br>40,452|2.72|-0.84|<br>79|32.7|
|Clothing Not of Fur (841)|19,342|138,805|0.63|0.71|64|32.5|
|Metal Tanks and Boxes (692)|3,960|28,099|2.97|-0.09|82|32.3|
|Copper (682)|3,001|21,161|0.54|0.05|120|32.2|
|ALL ABOVE PRODUCTS|417,213|4,344,658|1.49|3.06|118|41.0|

The higher the index the higher the capital intensity of the production process. Industries with a index value of 100 would have a labor/capital intensity that was average for all manufacturing activity. For information on how the index is derived see Lary (1968) or World Bank (1992).

Source: Derived from United Nations Comtrade Statistics.
**Table8: Firm and Owner Characteristics by Cluster**

|(as a percent of the cluster)|1|2|Cluster<br>3|4|_5_|6|
|---|---|---|---|---|---|---|
|_Site_|||||||
|automobile|3.1|41.8|2.8|37.9|4.0|_1.5_|
|imnpermanent|52.3|50.8|75.7|27.0|76.6|75.8|
|permanent|44.5|27.8|21.5|_35.1_|19.5|22.7|
|_Clients_|||||||
|big frms|6.2|_5.1_|2.5|7.8|3.0|3.1|
|small firms|11.9|9.6|8.3|13.2|6.8|7.0|
|public|81.8|85.3|89.2|78.9|90.2|89.9|
|_Financing to start firm_|||||||
|bank|2.2|2.1|1.0|2.2|0.8|0.6|
|private loans|16.9|23.1|17.9|22.5|12.4|11.6|
|personal savings|60.9|61.5|52.5|51.1|60.4|51.4|
|credit (clients/suppliers)|4.7|3.8|2.3|4.8|3.4|3.7|
|donotneed financing|15.4|9.5|26.3|19.4|23.0|32.7|
|_Additionalfinancing_|||||||
|do not need|67.1|63.4|73.9|56.3|74.3|75.4|
|too costly|15.0|13.7|13.1|17.2|13.6|11.0|
|don't know how to apply<br>|1.6<br>|2.9<br>|4.5<br>|0.6<br>|4.5|3.6|
|applied, did not receive|1.4|3.9|1.2|2.3|1.2|1.4|
|received financing from:|||||||
|bank|5.94|5.83|1.49|10.34|1.56|2.87|
|private loan|4.22|6.80|3.34|5.75|2.56|2.74|
|<br>credit(suppliers/clients)|4.53|2.91|2.34|7.47|2.20|2.49|
|<br>_Maquila_|1.7|1.0|_1.5_|0.6|1.6|0.9|
|_Plans_|||||||
|no change in firm|63.4|57.8|62.6|75.58|79.7|85.4|
|<br>expandthefirm|29.9|29.9|19.9|19.2|11.1|10.2|
|shutdown the firm|6.2|11.2|16.5|5.2|9.0|4.3|
|_Registrationltax_|||||||
|state treasury|31.4|30.1|18.0|36.8|19.1|18.8|
|Secretary of Treasury|53.8|56.0|23.2|64.9|24.7|27.6|
|_Size of lastfirm:_|||||||
|self-employed|7.7|7.8|8.3|8.6|13.7|10.2|
|<br>2-5|29.1|22.0|28.7|25.9|30.9|29.6|
|6-10|11.7|6.5|8.2|6.9|9.2|9.4|
|11-15|5.3|3.9|4.2|4.6|4.4|4.1|
|16-50|9.2|8.4|10.7|6.3|9.2|8.0|
|50+|27.3|4.3|25.8|25.9|27.1|19.3|
|not previously employed|9.5|8.4|14.0|21.3|5.2|19.2|
|PayIMSS(asa%offirms|27.31|40.8|12.33|43.75|10.94|16.92|
|with salaried workers)|||||||

30
used and it is clear that while sharecropping matters to explain transfer rights, identification will not exclusively depend on this more obvious source of absence of transfer rights.

**Table 4: Coffee: Panel Tobit Regression (t-value in brackets)**

||(1)<br>Share<br>allocated<br>to coffee<br>Fixed<br>effects|(2)<br>Share<br>allocated<br>to coffee<br>Fixed<br>effects|(3)<br>Share<br>allocated<br>to coffee<br>Fixed<br>effects<br>IV<sup>*</sup>|(4)<br>Share<br>allocated<br>to coffee<br>Fixed<br>Effects<br>IV**|(5)<br>Share<br>allocated<br>to coffee<br>Random<br>effects<br>IV<sup>*</sup>|(6)<br>Share<br>allocated<br>to coffee<br>Random<br>Effects<br>IV**|
|---|---|---|---|---|---|---|
|Inherited plot?|0.087<br>(2.53)||||||
|Purchased plot?|0.029<br>(0.63)||||||
|Sharecropped<br>plot?|-0.493<br>(7.88)|||-0.411<br>(4.92)||-0.326<br>(3.95)|
|Transfer right?||0.129<br>(3.32)|0.696<br>(9.96)|0.315<br>(3.05)|0.588<br>(8.31)|0.315<br>(3.22)|

Note: All regression controls for plot size, total land owned per adult, livestock owned per adult, plot quality (high quality, medium quality and low quality), slope (flat, sloping, steep), number of male adults, number of female adults, sex of head, age of head and age head squared, village times time dummies. Base group in (1) for plot mode of acquisition: land allocated by government. * Full IV, transfer rights endogenous with modes of acquisition (inherited, purchased, allocated, sharecropped) and number of years plot used by this household – see table 5.

** Full IV, but second stage controls for sharecropped plots.

Columns (3) and (4) give the fixed effects tobit regression explaining land allocated to coffee, using the instrumented transfer rights. In column (3), all the modes of acquisition restrictions have been dropped and used as identifying instruments. However, it could well be argued that in the case of sharecropped plots, this may not be an appropriate exclusion restriction, since investment decision may well be mediated by the contractual issues surrounding sharecropping, in quite a different way from land that is inherited or given by the government. In this way, excluding the sharecropped plot variable may not be correct. Column (4) gives the results, including the sharecropped plot variable. Both in (3) and (4), the coefficient on perceived transfer rights is strongly significant and higher than in (2), suggesting that measurement error 19
time for the holders of ruble treasury bills to exit—as they were demoted to junior status. The second was to encourage a swap out of ruble treasury bills carrying nominal interest rates of 50 percent into long-term dollar Eurobonds at 12 percent to lower interest payments and catalyze a virtuous cycle. It did not work because the interest differential was not an arbitrage opportunity but a measure of default and devaluation risks. Besides, the swap increased the volume of dollardenominated Russian bonds on offer, lowering their price and triggering margin calls from Russian banks which had borrowed against such bonds, setting off a downward spiral. Third, based on signals from the market as early as mid-May 1998 (the meltdown occurred some three months later) it was evident that the ruble was hugely overvalued and that the market was pricing in both a big devaluation and a large default; it was also evident by then that government debt was on an explosive path.<sup>23</sup> Now consider the following quote from another former IMF chief economist, Mussa (2002), in the context of Argentina 2001: ―...the Fund did make at least two important mistakes....(ii) in extending substantial additional financial support ...after it had become abundantly clear that the Argentine government‘s efforts to avoid default and maintain the exchange rate peg had no reasonable chance of success‖. He also emphasized its systemic importance since Argentina‘s debt obligations accounted for 20 percent of the entire EM market asset class by late 2000. In its post-mortem of Argentina 2001, the IMF‘s Independent Evaluation Office noted that official rescue packages are unlikely to be catalytic in insolvency situations, that financial engineering in the form of voluntary debt swaps are ineffective and that procrastination is costly.<sup>24</sup> These were very much the lessons from Russia 1998. The first two, on why rescue packages may backfire instead of being catalytic and the inefficacy of sovereign debt swaps, are discussed briefly below. The third on procrastination is discussed separately as it is an integral reason why ODRs tend to be elusive.

> 23 Details in Kharas, Pinto and Ulatov (2001). 24 IMF (2004, lessons 7-9).

14
# **2. Findings**

# **2.1. Labor Market Regulation**

Labor market regulations like minimum wages and employment protection legislation (EPL) set the legal parameters of employment and imply costs to employers and workers. Minimum wages, for instance, are widely used to correct labor market imperfections that can result in an unfair distribution of gains in productivity. However, when set incorrectly, minimum wage regulation can result in the loss of jobs and decreased firm competitiveness.

Vietnam first set a statutory minimum wage in 1992 that exclusively applied to foreign companies. Over time, it also established a minimum wage for domestic firms. The country’s current minimum wage system was introduced in 2006. Under this system, minimum wages vary according to location and sector of employment. Prior to 2012, separate rates were set for general government employees (i.e. staff of the government and of public service units or PSUs like schools and hospitals), for private domestic firms (including state-owned enterprises) and for foreign companies as well as for four broadly defined regions. Today, minimum wages continue to be differentiated by region, but Vietnam’s WTO accession agreement stipulated a gradual alignment of minimum wage levels between domestic firms and foreign-owned companies. This alignment was completed in 2012. Since then, minimum wages outside the general government sector have only been differentiated across the four regions. A separate rate still applies to workers in the general government sector nationwide.

Vietnam’s Labor Code states in Article 91 that minimum wages are to be determined for the month, day or hour. However, in practice stakeholders almost exclusively focus on monthly minimum wages. These are often seen as a tool for assuring living standards. In fact, it has been the Government of Vietnam’s stated ambition to raise minimum wages to a level that allows households to reach the “minimum monthly living standard” in 2018. The consumption necessary to obtain the minimum monthly living standard was measured in a study by the Institute for Workers and Trade Unions (2014) which is part of the Vietnam General Confederation of Labour (VGCL). As detailed in the study, current minimum wage levels outside the general government sector fall far short of the minimum monthly living standard. However, this does not imply that households with minimum wage earners necessarily fall below the poverty line. Figure 1 shows the ratios of labor earnings to the poverty line – as defined by the General Statistics Office (GSO) and the World Bank (cf. Demombynes and Vu, 2015) – for different household compositions that vary according to two dimensions. First, the number of household members varies from one to six. Second, a varying number of household

- 5 -
eliminating such barriers and providing farmers with better access to inputs must be an integral part of today‘s agriculture for development agenda.

_Farm Size and Land Tenure_ Many farmers in developing countries operate on a small scale. Increasing farm size is key for improved incomes in agriculture because it allows for the use of mechanization that has indivisibilities (with differences in access to credit by small and large farms favoring the latter), implying increasing returns to scale and higher profitability per hectare. In a detailed empirical paper, Foster and Rosenzweig (2010b) show that this is the case today in India, where most farms are too small to exploit the productivity and cost-saving advantages from mechanization. But land consolidation in larger farms with continued population growth and limited longdistance migration requires vigorous employment creation in the rural non-farm economy, basically in secondary towns and cities. Agriculture-for-development strategies need to focus on the smallholder sector, understand the challenges they face and find ways to make them more productive (World Bank 2007).

Much of the early discussion on smallholders revolved around the issue of efficiency of farm scale. Whereas there are potential economies of scale on large farms,<sup>9</sup> small farms have often been seen by family-farm theories as more efficient because they do not bear the cost of labor supervision and there are no moral hazard issues. Many developing country studies have found an inverse relation between farm size and land productivity even after controlling for other productivity determinants, such as land quality (Eastwood et al. 2010). However, in the presence of market failures, large farms can have an advantage over small farms. For example, they might be more able to take out loans and hence face lower capital costs. This would suggest a U-shaped relation between farm size and productivity (Chavas 2001). Moreover, even if economies of scale in production are outweighed by the labor costs borne by large firms, economies of scale in

> 9  In the past there were no inherent economies of scale in agriculture other than for plantation crops but new sources of economies of scale have emerged. They originate in technological change (new technologies such as zero tillage and genetically modified organisms, new management techniques, precision farming), new markets (product certification due to demanding sanitary and phyto-sanitary standards, contracts with supermarket chains for continuous and uniform deliveries), institutional changes (access to international finance and re-insurance) and public-private partnerships in the provision of public goods (infrastructure for access to low populated areas and opening of new lands).  See Byerlee, de Janvry and Sadoulet (2010).

23
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# **1 Civil War and Country Size**

The most robust empirical finding in country-level studies of civil war is that large countries more frequently have civil war than small countries (Fearon & Laitin, 2003; Collier & Hoeffler, 2004; Hegre & Sambanis, 2006). A country with a population of 10 million inhabitants has an estimated risk that is at least three times higher than one of 1 million inhabitants.<sup>1</sup> As Sambanis (2003) points out, however, there is little agreement on why large countries have more civil war than small ones (2003:26). The conflict literature has suggested several competing explanations for this relationship. Are large countries more conflict-prone because of the difficulty of projecting power over long distances? Has it to do with governance problems due to the multiple layers of authority necessary in large countries? Or do conflicts stem from the cultural heterogeneity typical of large countries? Or is it simply because there are so many people that might start a bloody quarrel?

Country size, in all these studies, is measured in terms of the size of the population. But the sample of explanations above show that there are several relevant manifestations of size that are closely related at the national level: Most fundamentally, countries may be large in terms of population or in terms of territory. But countries may also be large in terms of the number of distinct cultural or ethnic groups living in the territory, or in terms of the distances over which a government must be able to deploy forces, or the length of the border. It is difficult to distinguish between these variables in empirical studies at the national level, since they are highly correlated when aggregated to this level. Previous studies have largely tested them using state-level population measures – the most notable exception is the study of Buhaug & Rød (2006). The reliance on national-level studies presents an ecological inference problem as the nature of populations and population density in particular areas is assumed to be homogeneous across a state. By disaggregating both the dependent variable of conflict occurrence and the measure of

> 1Calculated on the basis of a parameter estimate for ln( population) of .22 , as in Hegre (2003). Other studies obtain larger estimates for this variable.

2
|Rest in Distri* % Foreign Own.|||||||(0.494)<br>-0.0114<br>(0.0145)|||
|---|---|---|---|---|---|---|---|---|---|
|Rest in EGW||||||||-11.79**<br>(4.838)||
|Rest in EGW* % Foreign Own.||||||||-0.0644<br>(0.0835)||
|Rest in Transport|||||||||-2.259**<br>(0.915)|
|Rest in Transport * % Foreign|||||||||0.00103|
|Own.|||||||||(0.0246)|
|Constant|0.389***|0.380***|0.383***|0.387***|0.317***|0.322***|0.317***|0.357***|0.384***|
||(0.0471)|(0.0483)|(0.0471)|(0.0471)|(0.0315)|(0.0342)|(0.0326)|(0.0353)|(0.0405)|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Standard Errors|Clustered|Clustered|Clustered|Clustered|Clustered|Clustered|Clustered|Clustered|Clustered|
||ISIC2/Year|ISIC2/Year|ISIC2/Year|ISIC2/Year|ISIC2/Year|ISIC2/Yea<br>r|ISIC2/Yea<br>r|ISIC2/Yea<br>r|ISIC2/Yea<br>r|
|Observations|40,208|37,387|40,208|40,208|40,208|40,208|40,208|40,208|40,208|
|R-squared|0.029|0.032|0.030|0.029|0.029|0.029|0.029|0.030|0.030|
|Number of id|25,425|23,888|25,425|25,425|25,425|25,425|25,425|25,425|25,425|
|Robust standard errors in<br>parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||||||||||

23
# I INTRODUCTION

## Appendix (continued)

|Country,<br>ecorlnoy'<br>or|Last wubli|shed census<br>Population|||
|---|---|---|---|---|
|territory|Date|(1000)<br>*|Total population,<br>mid-1985|Total fertility,<br>1985-90|
|Tuvalu|May 79|7 F|Based on UNPVSR 7/89 (official<br>est.)|--|
|Uganda|Aug 69|9549 F|U.N. 1989 (prelim.<br>version)|U.N. 1989 (prelim.<br>version)|
|United Arab<br>Emirates|Dec 80|1043 F|UNPVSR 4/87 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|United Kingdom|Apr 81|55678 F|UNPVSR 7/86 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|United States<br>of America|Apr 80|226546 F|UNPVSR 7/86 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|Uruguay|Oct 85|2955 F|Official<br>est. from 1985 census|U.N. 1989 (prelim.<br>version)|
|U.S.S.R.|Jan 89|286717 F|U.N. 1989 (prelim.<br>version)|Based on Monnier 1988|
|Vanuatu|Jan 79|111 F|Based on official<br>1987 est.|USBOC 1987|
|Venezuela|Oct 81|14517 F|UNPVSR 1/87 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|Viet Nam|Apr 89|64411 F|Bank projection<br>from 1977 census|Bank est.|
|Virgin Islards<br>|Apr 80|97 J|USBOC 1985|Based on USBOC 1987|
|(U.S.)|||||
|Wake Island|Apr 70|2 F|Based on UNPVSR 7/89 (official<br>est.)||
|waltis and|Har 76||Based on UNPVSR 7/89 (official<br>est.)|-|
|Futuna|||||
|West Bank||---|Bank est.|Bank est.|
|Western Sahara|Dec 70|76425 F|UNPVSR 1/87|--|
|Western<br>Samoa|Nov 81|156 F|U.N. 1989 (prelim.<br>version)|Bank est.|
|Yemen Arab Rep.|Feb 86|9274 J|Bank assessment|Bank est.|
|Yemen, People's|Mar 88|2345 F|U.N. 1989 (prelim.<br>version)|U.N. 1989 (prelim.<br>version)|
|Dem. Rep. of|||||
|Yugoslavia|Mar 81|22425 J|UNPVSR 4/87 (official<br>est.)|UNPVSR 10/88 (official<br>est.)|
|Zaire|Jul 84|29441 F|U.N. 1989 (prelim.<br>version)|U.N. 1989 (prelim.<br>version)|
|Zambia<br>|Aug 80|5662 F|Bank projection<br>from census|Basedon Bank sector report|
|Zimbabwe|Aug82|7546 F|Bank projection<br>from census|DHS 1988|

Notes: When a source for an estimate is directly cited (e.g., UNPVSR 7/86). the estimate is taken as is from that publication. An estimate "based on" a source is not reported in that publication but derived from data contained in it. When a source is given for a fertitity or mortatity estimate prior to 1985-90, the corresponding 1985-90 estimate is a projection from the eartier estimate. Citations for the main documents referred to here (usually by abbreviations) are in the list of references.

- Nature of poputation estimate: F = de facto, J * de jure.

- -- Not appticabte. No census has been taken, or no estimates uere used because no projections were made for these small countries.

- a Census was for entire peninsula.

- b 55 people.
obstacles to their operations<sup>28</sup> . This is the case of the MENA region, where progress is still needed in order for the region to catch up with South East Asia and Latin America<sup>29</sup> . To meet this challenge and as pointed out by the World Bank (2004), MENA countries have to gear up their educational system both to improve basic education and to equip the labor force with skills appropriate for enterprises to invest efficiently.

# **4. Some Considerations on the Data Used**

# **4.1. A New Data Set on Private Investment**

Data on the breakdown of investments as either private or public are scarce. The best available data set on private investment was provided by the International Finance Corporation ( _IFC_ ) of the World Bank<sup>30</sup> . However, this data set covers only the period from 1970 to 1999. It also has some limitations for certain countries in terms of quality of the breakdown between public and private investment. This is most often due to the status of the state- owned enterprises, for which investment data are not always available, and which are in this case included in the private investment series.

Considering these pitfalls of existing data, we have reexamined the IFC data set and updated the private investment series for the available years after 1999. More importantly, we have carefully checked the IFC private investment series and compared them to the national sources where available, as well as to the World Bank and International Monetary Fund ( _IMF_ ) series. This has been done in close collaboration with the country economists of these two institutions. We have thus been able to generate high quality data for the majority of our sample countries, as well as for other countries which can be considered as relatively accurate. This private investment series which covers 63 17