rafmacalaba/datause-encoder-data huggingface dataset:
Both Fuji Heavy Industries and Nissan Diesel have their assembly plants in Ohta. In Gumma prefecture, where Ohta is located, Daihatsu and Hina also have assembly plants. Nissan, Honda, and Isuzu operate assembly plants in neighboring prefectures. These near-by assembly plants offer abundant business opportunities for the auto parts producers in Ohta," -ilthough Fuji heavy industries dominates. (A sample survey conducted by the Ohta city office in 1992 revealed that 50 percent of 36 subcontracting parts suppliers listed Fuji Heavy Industries as their major sales partner).

Despite the dependence of local Ohta auto parts suppliers on Fuji Heavy Industries, they also have extensive business relationships with assemblers outside of Gumma. Through diversification, parts suppliers attempt to minimize the costs of business fluctuations associated with a particular assembler, while assemblers seek to procure parts from the most efficient suppliers. Overall the transactional relationship between subcontractors and their parents in transport machinery production is not as rigid as the relationships observed in the electronics industry, which also follows a pyramid-style subcontracting pattern.'<sup>2</sup> The average number of parents per subcontractor has risen from 4.8 in 1990 to 5.3 in 1992. Simiiarly, over the same period, the share of subcontractors whose sales-dependence on.their primary parent firm exceeded 80 percent, dropped from 36 percent of subcontractors to 30 percent.'<sup>3</sup> In particular, dependence on Fuji Heavy Industries is dropping rapidly, as the share of Ohta subcontractors that transact with Fuji Heavy Industries -declined from 64.4 percent in 1990 to 49.5 percent in 1992.14 The degree of dependence on single parent firm differs between the first- and secondtier subcontractors. First-tier subcontractors, generally with higher technological capabilities, produce high quality parts that could be sold on their own. Thus, a survey of subcontractors

**'The** size **ot** their potential market may be indicated by the fact that the number of workers at thase assembling plants in Gumma and its three neighboring prefectures amounted to over 30,000 in 1987, of approximately 20 percent of overall employment at assembly plants in Japan.

"The following statistics attest to this observation. The average number of parents per subcontractor for transport machinery Is higher at 5.3 than that for electric machinery at 4.4, implying that subcontractors in transport machinery rely less en one particular parent (Ohta City Office [1992]). This point may also be observad in the degree of dependence of subcontractors on parma firms for their sales in these two sectors. In transport machinery, those subcontractors whose dependence on their main parent firm (ie the firm taking in the largest sales) exceeds 80 percent. account for 30 parcent of the total, while the corresponding ratio in electric machinery is significantly higher at 50 percent.

**13 Ohta** City Office [1990 and 1992].

**4 Ohta** City office. **Intemal** document.
this region, Guatemala has the unfortunate distinction of having the highest sibling correlation in schooling: 0.71, and the country with the lowest sibling correlation is the Dominican Republic with an estimate of 0.57.

Intergenerational educational mobility is also low (comparable to Latin America) in East Asia (average estimate 0.64) and South Asia (average estimate 0.62).<sup>31</sup> Among the East Asian countries, Cambodia and Vietnam have the lowest intergenerational educational mobility, with a sibling correlation estimate of 0.66 in both countries. The sibling correlation estimate for Philippines is the lowest in this region (0.60). In South Asia, the estimates are very close in four out of ve countries, ranging from 0.62 (Nepal) to 0.64 (Bangladesh). Afghanistan enjoys the highest intergenerational educational mobility with an estimate of 0.56.

We have two countries from the Middle-East and North Africa region for which the required DHS data were available: the Arab Republic of Egypt and Jordan.<sup>32</sup> The estimate suggests that sibling correlation is much lower compared to the three regions discussed above (Latin America and Caribbean, South Asia, and East Asia and Pacic). Sibling correlation in schooling is 0.48 in Jordan and 0.54 in Egypt which are smaller than, for example, the estimate for the most mobile country in South Asia, Afghanistan (0.56).

For Sub-Saharan Africa, we have 30 countries (please see Table 1 for the list of the countries), with an average sibling correlation of 0.59. On average, Sub-Saharan Africa is more mobile than Latin America, South Asia and East Asia, but the mean estimate hides substantial heterogeneity across countries. The highest estimate is 0.77 for Madagascar which is also the highest among our 53 countries. There are three other countries with estimates of 0.70 or higher: Chad (0.74), Nigeria (0.70), and Ethiopia (0.70). The lowest estimate is 0.49, for South Africa.

The region with the highest intergenerational educational mobility is Europe and central Asia; the average sibling correlation is 0.48.<sup>33</sup> Among the 5 countries from this region in

> 31Our East Asia sample does not include Japan because DHS surveys do not cover Japan. The countries included are: Cambodia, Indonesia, Philippines, and Vietnam. The South Asia sample includes Afghanistan, Bangladesh, India, Nepal, and Pakistan.

> 32As discussed in the data section, we excluded the countries with DHS survey if the sample size is less than 1000 observations.

> 33This average estimate is slightly higher than the average of 0.44 for 6 developed countries reported by Grtz et al. (2021). The developed countries are: Finland, Norway, Germany, United States, United Kingdom, and Sweden. The countries in our sample are: Albania, Armenia, Kyrgyz Republic, Tajikistan, and Turkiye.

18
|**Table 1: Descriptive**|**Statistics Fir**<br>**Non-Prog**<br>**Construc**|**ms**<br>**ram**<br>**tor**|**Progra**<br>**Construc**|**m**<br>**tor**|**Non-Prog**<br>**Contrac**|**ram**<br>**tor**|**Progr**<br>**Contra**|**am**<br>**ctor**|
|---|---|---|---|---|---|---|---|---|
||**mean**|**sd**|**mean**|**sd**|**mean**|**sd**|**mean**|**sd**|
|Firm-age|3.78|4.33|2.27|1.80|6.95|4.90|6.65|6.00|
|**_Revenue_**|||||||||
|Revenue (log)|11.56|1.39|11.94|1.26|13.92|1.59|14.30|1.59|
|**_Factors_**|||||||||
|Workers (log)|1.86|0.68|2.56|0.62|3.09|1.48|3.33|1.37|
|Capital (log)|9.97|2.26|10.71|1.51|12.39|2.06|12.97|1.80|
|Inputs(log)|10.94|1.64|11.37|1.65|13.30|1.44|13.95|1.48|
|Capital per worker|8.11|2.14|8.15|1.46|9.30|1.99|9.65|1.87|
|(log)|||||||||
|**_Labor intensity_**|||||||||
|<br>Inputs per|9.09|1.62|8.81|1.50|10.20|1.33|10.62|1.25|
|worker(log)|||||||||
|<br>Education of the|7.65|2.86|9.51|2.76|8.77|2.92|9.27|2.75|
|workforce (years)|||||||||
|<br>**_Characteristics of the_**|**_workforce_**||||||||
|Age of the|29.68|7.70|28.52|6.49|27.37|6.68|26.96|6.36|
|workforce|||||||||
|Labor force at|1.49|0.87|2.40|0.84|1.92|1.60|2.83|1.25|
|startup (log)|||||||||
|**_Price Deflators_**|||||||||
|<br>Output price deflator|1.11|0.46|0.97|0.36|1.06|0.41|1.08|0.34|
|Input price deflator|1.01|0.45|1.03|0.40|0.93|0.41|0.91|0.33|
|<br>Complete|78||70||9||13||
|observations|||||||||

Note: All monetary measures in Ethiopian Birr (ETB). 1 USD dollar is worth approximately 9.1 Ethiopian Birr (ETB)

-12-
With all the promise, the paper identified several open questions that need to be addressed to better assess the potential, limitations, and optimal features of a survey system that complements in-person surveys with phone-based data collection. Rigorous survey methods research will have to provide the basis to inform several of these open questions and trade-offs, but one should be realistic and pragmatic in acknowledging that pending conclusive evidence from methodological studies, survey design choices will have to be made based on incomplete evidence, and informed choices made based on reasonable priors, accumulated experience, and the effective boundaries provided by available human, financial and technical resources.

26
across occupations and levels of education, as wage-earners and salaried workers (most of who live in urban areas) tend to have higher rates of financial literacy than farmers in rural areas.

There are also often other regional differences in financial literacy and financial access within countries. In Malawi, for instance, the Northern and Central regions tend to have better access to financial services than the Southern region. Analogous patterns are found in high income countries as well, for instance in Italy (where the Centre-North region has a higher financial literacy rate than the South) and Germany (where the West does better than the East). Sometimes these patterns are more idiosyncratic: in the U.S., Bumcrot et al. (2011) find that South Dakota and Idaho have higher levels of financial literacy than their demographic make-up would predict, while the opposite is apparently true for Pennsylvania, New Jersey, New York, and Connecticut.

With regard to disparities by ethnicity and race, the FinScope survey in South Africa indicates that blacks (57 percent) are less likely to be banked than whites (91 percent), with coloureds (68 percent) and Asians (85 percent) falling in between. Issues of race may not be as stark in other countries, but it may be important to pay attention to similar ethnic disparities in targeting programs. Unfortunately, many of the other FinScope surveys do not provide breakdowns in this regard.

Among higher income countries, Lusardi and Mitchell (2011b) find that financial literacy scores in the U.S. are lower among Hispanics and African-Americans than among whites and Asians. Relatedly, Lusardi (2005) finds that African-American and Hispanic households in the U.S. are less likely to have checking accounts and hold high-return assets (such as stocks and business equity), and incur higher information and planning costs than the rest of the population. Crossan et al. (2011) find that financial knowledge among Maori is lower than that of the general population of New Zealand, excluding a certain Maori tribe who receive financial education courses and have a long-term savings scheme. In the Netherlands, Alessie et al. (2011) furthermore find that members of religious minorities, such as Muslims, also display lower levels of financial literacy.

14
Miller, Douglas, and Anna Paulson. 2007. Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand. Federal Reserve Bank of Chicago Working Paper 2007–01.

Miranda, M. J.. 1991. Area-Yield Crop Insurance Reconsidered. _American Journal of Agricultural Economics_ 73: 233–242.

Morduch, Jonathan. 1995. Income Smoothing and Consumption Smoothing. _Journal of Economic Perspectives_ 9 (3): 103–114.

Nair, R. 2010. Crop Insurance in India: Changes and Challenges. _Economic and Political Weekly_ , February 6, 19–22.

Parchure, R. 2002. Varsha Bonds and Options: Capital Market Solutions for Crop Insurance Problems. National Insurance Academy Working Paper, Balewadi, India..

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_Economic and Political Weekly_ , May 2007, 1905–1908.

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1

# 1 Introduction

Over the past decade, many developing countries have taken a fresh look at their policies towards foreign direct investment. Since 1982, foreign capital inflows to developing countries have declined and world real interest rates have increased. It is against this background that foreign direct investment (FDI) has been viewed by some as a panacea for declining domestic investment and higher costs of borrowing abroad. The empirical evidence provides no support for such euphoria.

The vast literature on foreign direct investment **(FDI)** falls roughly into four categories: (a) macroeconomic studies of the determinants of aggregate **FDI** flows, some of which use measures of various government incentives or disincentives as well as the degree of protection/distortion in the domestic economy as explanatory variables; (b) macroeconomic analysis of the effects of **FDI** and other capital inflows on the rate of economic growth, invariably igno-ing the extent of government incentive/disincentives packages (hereafter referred to simply as packages) and the extent of other distortions in the economy; (c) microeconomic studies of the effects of **FD!** in specific industries which generally ignore packages but may examine the productivity or efficiency of **FDI** compared to dormestically financed investment; (d) descriptive studies in which long lists of government incentives and disincentives are compiled and discussed.<sup>1</sup> Virtually all the empirical investigations into the causes and consequences of **rDI** use single-equation models.<sup>2</sup> Here, I examine the effects of **FDI** in a four-equation macroeconomic model containing investment, saving, growth and current account equations. I apply this framework to a sample of 16 developing countries (Argentina, Brazil, Chile, Egypt, India, Indonesia, Korea, Malaysia, Mexico, Nigeria, Pakistan, the Philippines, Sri Lanka, Thailand, Turkey, and Venezuela). The results provide some new information on the direct and indirect effects of FDI inflows to this sample of developing countries.

In undertaking any analysis of FDI, one must recognize that **FDI** data record financial flows which may or may not correspond to changes in capital formation. Whether or not they do depends on the extent of substitutability of this type of financial flow for other types of financial flows. In some of the literature, the view prevails that FDI can serve two 'Guisinger (1986) may hold the record with a list of 64 types of government incentives and disincentives affecting **FDI.** The Organisation for Economic Co-operation and Development (1989, pp. 21-25) also lists and classifies numerous incentives and disincentives to **FDI.** 2The exceptions include the simultaneous two-equation models used by Lee, Rana, and Iwasaki (1986) and Husain and Jun (1992).
# **What Determines the Extent of Fiscal Decentralization? The Russian Paradox**

# **I. Introduction**

Why are some countries highly decentralized while others are not? And in particular, what determines the degree of fiscal decentralization, i.e., the decentralization of government spending? The benefits and perils of decentralization have been actively studied both theoretically and empirically; however, few studies have attempted to construct and empirically test the positive theory of fiscal decentralization. A general consensus that emerged from several cross-country empirical studies (most notably Oates, 1972, Pommerehne, 1977, and Panizza, 1999) pointed toward four main determinants of fiscal decentralization: larger size, higher income per capita, higher ethnolinguistic fractionalization, and higher level of democracy.

Large federations (such as the United States, Brazil, India or Russia) provide a valuable opportunity to analyze the determinants of fiscal decentralization at the regional level<sup>4</sup> . Such analysis is in many ways similar to a cross-country analysis since regions in large federations are often as large as independent states; they enjoy sufficiently high fiscal autonomy and arguably are under the influence of the same decentralization drivers as independent states.

This paper tests the hypotheses about the determinants of fiscal decentralization using data on regions in Russia from the 1990s to the early 2000s. Russian regions constitute an interesting sample for studying the determinants of decentralization for four main reasons. Firstly, cross-country studies of the determinants of decentralization inevitably run into difficulties when trying to control for unobserved heterogeneity. Moreover, the choice of a specific sample of countries often influences the results and data are not necessarily consistent across countries since the methods and standards of data collection vary. In the case of Russia, though present, unobserved heterogeneity is arguably less of a problem, the sample choice is well defined and the data are collected and reported using the same methods and standards. Secondly, Russian regions

> 4 Fiscal decentralization at the regional level is conventionally measured by the ratio of municipal government expenditure to general regional government expenditure.
# **References**

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# Table 15

Primary government spending by function, Argentina and Chile (% GDP)

||Argentina<br>2002|Chile<br>2000|
|---|---|---|
|Government functions|5.6|4.1|
|Education|4.3|4.2|
|Health|4.4|2.9|
|Social security|7.0|8.5|
|Other social|3.8|1.0|
|Economic services|1.4|2.8|
|Total|26.5|23.5|

Source: MECON (2003b); IMF (2001b) The comparisons in table 15 suggest that Argentina’s spending on administration, justice, and defense (“government functions”) is relatively high, and could be cut by about as one fourth to approximate relative levels in Chile. The estimates also suggest that spending on education and health in Argentina is not low, although the same may not be true for the quality of this spending. Social security spending is only moderately lower than in Chile, at about 7 percent of GDP compared to 8.5 percent. “Other social” spending is considerably higher in Argentina, however (3.8 percent of GDP versus 1.0 percent). A closer look at this grouping indicates that the difference does not arise in provision of water or housing services, but in “social assistance” (1.15 percent of GDP in Argentina) and “labor” (1.52 percent of GDP, of which 0.53 percent was in family allowances). The “labor” category comprised only 1.0 percent of GDP on employment programs and unemployment insurance, up from 0.37 percent in 2001 given the new spending in the Heads of Households program.

Overall the primary spending picture that emerges for Argentina is one of potential room for slimming down government expenditures in the areas of bureaucracy and some of the general social programs (outside of education and health). It could make sense to focus the latter more heavily on a genuine public works employment program requiring actual performance of public service work.

The broader picture, however, is that a combination of closer control of primary expenditures, a close attention to provincial expenses, and especially a concerted effort at collecting the taxes that are on the books should provide ample room for Argentina to increase its primary surplus. It is crucial to recognize, moreover, that without spending discipline, the benefits of greater tax enforcement could be squandered by induced increases in government spending. This risk is especially present in provincial spending, because of the automatic revenue sharing provided by the central government. Tax enforcement efforts thus need to be accompanied by structural changes in provincial spending arrangements to ensure that potential improvement in fiscal performance from
Table 9: Effect of GDP over-reporting on promotion

||<br>|Linear Prob<br>|.<br>|Or<br>|dered Probi<br>|t<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reported growth|0.003<br>(0.004)|||0.005<br>(0.002)<sup>∗</sup>|||
|Over-reporting|||0.017<br>(0.010)<sup>∗</sup><br>[0.011]<sup>∗</sup>|||0.018<br>(0.006)<sup>∗∗∗</sup>|
|Predicted true growth||0.001<br>(0.006)<br>[0.006]|−0.005<br>(0.007)<br>[0.008]||0.003<br>(0.004)|−0.003<br>(0.004)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|2460|2460|2460|2486|2486|2486|
|# of Cities|279|279|279|279|279|279|
|Adj_R_<sup>2</sup>|0.07|0.08|0.07||||

_Notes_ : This table shows the marginal effects of reported GDP growth, predicted true growth, and estimated over-reporting on promotion using the linear and ordered Probit model. Reported growth, predicted true growth, and estimated over-reporting are averaged annual rates of the city since the PS assumed office until the current year. City controls include log population, the log level of GDP per capita, and log government expenditures, all measured in the year before the PS assumed office. A full set of year-in-office dummies is also included. Parametric and bootstrapped standard errors clustered at the city level are reported in parentheses and brackets, respectively.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

47