rafmacalaba/datause-encoder-data huggingface dataset:
Although much has been written describing China's overall achievement in attracting foreign investment and the general pattern of Chinese FDI, little work has been done analyzing quantitatively the geographical and sectoral attributes of such investment. This paper thus attempts to assess empirically the locational and sectoral determinants of FDI within China. Shedding light on these determinants is an important policy issue for the Chinese authorities. Within the past year they have begun to rationalize the regime governing FDI flows, with an eye towards reducing various distortions, especially the system of FDI tax preferences.<sup>6</sup> In Section II, we present an overview of the recent trend in the flow and stock of Chinese FDI, placing it in the worldwide and regional contexts. Section III focuses on the geographical distribution of FDI within China and develops a simple econometric model of its locational determinants. In Section IV the sectoral pattern of Chinese FDI is analyzed and compared with that of other countries. Conclusions and policy observations are contained in the last section.

# **II.** CURRENT TRENDS IN FDI

# **World and Regional Trends**

Following rapid increases since the early 1980s, global FDI flows have increased more moderately in recent years. As shown in Tables I and 2, average annual total world inflows and outflows of FDI rose about 3.5 % and 0.4%, respectively, between 1989-94. This general increase has not been shared by all countries. Flows into and out of industrial economies have either declined or increased slightly, primarily due to recession. But both inflows and outflows of FDI in developing economies rose steadily. These countries now command an increasing portion of world FDI flows.

**Table 1: Global FDI Inflows**

$US **Millions**

||1989<br>Value<br>(%)|1990<br>Value<br>(So)|1991<br>Value<br>(%)|1992<br>Value<br>(%)|1993<br>Value<br>(%)|1994<br>Value<br>(%)|
|---|---|---|---|---|---|---|
|World Total|192,361 (100.0)|203,969 (100.0)|158,350 (100.0)|170,398 (100.0)|208,388 (100.0)|225,692 (100.0)|
|Industrial<br>Economies|166,557 (86.6)|172,524 (84.6)|118,726 (75.0)|111,223 (66.4)|129,073 (62.0)|134,984 (60.0)|
|Europe|87,985|109,348|81,345|81,655|76,387|73,660|
|United States|67,870|45,140|23,972|17,600|41,108|49,448|
|Japan|1,060|1,760|1,370|2,720|86|888|
|Developing<br>Economies|25,804 (13.4)|31,445 (15.4)|39,624 (25.0)|50,388 (33.6)|73,350 (35.2)|84,441 (37.4)|
|Africa|2,972|1,121|2,343|2,702|3,000|3,080|
|Latin America|6,191|6,937|11,508|13,973|14,980|15,930|
|Asia|13,702|18,299|20,373|27,637|49,984|60,664|
|East Asia_(sum_<br>_of below)_|7,461 (3.9)|11,662 (5.7)|12,508 (7.9)|13,871 (9.3)|16,517 (7.9)|19,600 (8.7)|
|Indonesia|682|1,093|1,482|1,774|2,004|3,000|
|Malaysia|1,668|2,332|4,073|4,118|5,206|4,500|
|Philippines|563|530|544|228|763|1,500|
|Singapore|2,773|5,263|4,395|5,635|6,829|7,900|
|Thailand|1,775|2,444|2,014|2,116|1,715|2,700|
|China|3,393 (1.8)|3,487 (1.7)|4,366 (2.8)|11,156 (7.4)|27,515 (13.2)|33,800 (15.0)|

_Source: IMF BOP Yearbook and UN World Investment Report_ 6 See Broadman (1995b).

2
need to be assessed, including its distributional effects across banks—as banks with the best information would lose in relative terms. Moreover, evidence from surveys like these could help policymakers understand where their action is needed (and where it is not needed), and what types of institutions and regulations might need revision to foster SME financing.

Finally, the question of whether SMEs are receiving “adequate” financing remains unanswered. In particular, we have not collected information on the costs of the services banks are offering. Futhermore, the data reviewed in this paper say nothing about the universe of SMEs. Hence, it may well be that the patterns documented in this paper reflect only an embryonic “bancarization” process and that, as a result, the fraction of viable SMEs being served by banks is still low. Alternatively, the SMEs that are not receiving adequate financing might simply be too risky or not have good projects for banks to finance. Put differently, the evidence presented in this paper does not dispel the commonly bifurcated perceptions that SMEs are unduly underserved, at the one extreme, and that SMEs are too opaque and/or lack bankable projects, at the other. It may be that these bipolar perceptions are outdated and that the developments documented in this paper will eventually lead to a perceptions revision. Or it may be that the perception that SMEs are underserved is dominated by the particular fact that they are not receiving long-term loans at affordable fixed interest rates. Future work is needed to have a more complete picture of how banks engage with SMEs and how that squares with the bifurcated perceptions. The type of data presented in this paper constitutes only a first step in this direction.

43
and (e) the fraction of fixed assets financed by banks.<sup>1</sup> From the WBES, we also gather information on different firm characteristics including firm size, age, ownership type (government or foreign owned), exporter status, and sector (manufacturing versus services).

Our second main dataset is the Doing Business data. From this source we are able to identify the countries and the year in which they introduced their credit information sharing systems (be they bureaus or registries). Also, from this source we collect information on different aspects of the functioning of these credit information systems. In particular, we gather data on the coverage of bureaus and registries and on the scope and accessibility of the credit information sharing schemes (represented in the Doing Business data as the _depth of credit information index_ ). From another module of the Doing Business data, we also obtain information on the time, cost, and number of procedures related to enforcing contracts in court.

The third database we use is the _World Development Indicators_ produced by the World Bank. From this source, we obtain macro level data such as the inflation rate, the GDP growth rate, and credit to the private sector. We also obtain an additional country-level covariate relating to the contractual environment (the Law and Order index) from the International Country Risk Guide (ICRG) published by the PRS Group.

Among countries covered by the WBES, we restrict our sample to two groups of countries: a) countries that introduced a CB/CR within the sample period with at least one survey before the introduction and at least one after, and b) countries that never introduced a CB/CR but have available at least two rounds of surveys. Our estimation strategy is akin to a difference-in-difference approach, as discussed in more detail in the next section, so we refer to the two sets of countries as the treatment/reform group and control group, respectively. Both the treatment and control sample consist of only countries that have had at least two rounds of

> 1 Appendix 1 explains how each of the measures of access to finance was constructed.

8
equilibrium model to estimate the net effects of all sectors’ distortions on agricultural markets and net farm income, to which we now turn.

# **The global LINKAGE model for assessing sectoral and welfare effects of trade distortions**

The model used for this analysis is the World Bank’s global dynamic computable general equilibrium (CGE) model, known as LINKAGE (van der Mensbrugghe 2004). It is a relatively straightforward CGE model but with some characteristics that distinguish it from standard comparative static models such as the GTAP model (described in Hertel 1997). A key difference is that it is recursive dynamic, so while it starts with 2001 as its base year it can be solved annually through to 2015. The dynamics are driven by exogenous population and labor supply growth, savings-driven capital accumulation, and labor-augmenting technological progress as assumed for the _Global Economic Prospects_ report in World Bank (2005). In any given year, factor stocks are fixed. Producers minimize costs subject to constant returns to scale production technology, consumers maximize utility, and all markets – including for labor – are cleared with flexible prices. There are three types of production structures. Crop sectors reflect the substitution possibilities between extensive and intensive farming; livestock sectors reflect the substitution possibilities between pasture and intensive feeding; and all other sectors reflect standard capital/labor substitution (with two types of labor: skilled and unskilled). There is a single representative household per modeled region, allocating income to consumption using the extended linear expenditure system. Trade is modeled using a nested Armington structure in which aggregate import demand is the outcome of allocating domestic absorption between 7
Malečková (2003) refute this notion as they find no evidence of a causal connection from poverty to terrorism. The authors claim terrorist activities are more likely to be associated with political conditions and social frustration, than with suboptimal welfare conditions. In line with this finding, Abadie (2004) shows that the risk of terrorism is relatively similar between developed and developing countries, after considering country-specific characteristics, and concludes that the level of political freedom is better at explaining terrorist incidents compared to economic and poverty conditions of the population.

Moreover, the consequences of a terrorist attack could be underestimated as they appear to affect only some parts of the economy. Becker and Murphy (2001) claimed that the attacks on the World Trade Center on September 11th in New York would barely affect economic outcomes since they only represented a loss of 0.06% of the stock of capital in the US. Yet, several studies indicate large effects on the economy. Abadie and Gardeazabal (2008) show that even if the threat of an attack only accounts for a small share of the overall risk, terrorism can have a substantial impact on the allocation of productive capital across nations. The risk of an attack increases uncertainty, reduces expected return to investments and induces a decline in net foreign direct investment (FDI).

Empirical research has documented the effect of terrorism on consumption and gross domestic product (GDP) due to increased uncertainty and disruption to the markets. Eckstein and Tsiddon (2004) use a long time series data and find a decrease in annual consumption per capita of around 5% after a year from a terrorist incident. Fielding (2003) describes how violence in Israel explains reductions on aggregate consumption and savings between 1987 and 1999. Using a synthetic control method, Abadie and Gardeazabal (2003) estimate a decline of 10% in GDP per capita over two decades as a result of terrorism in the Basque Country. Some authors have analyzed the consequences of terrorism on other outcomes. Nitsch and Schumacher (2004) associate terrorist actions with a reduced volume of trade across various countries, while Enders and Sandler (1991) report an annual reduction of FDI inflows by 13.5% and a loss of 140,000 tourists between 1970 and 1988 in Spain. All these negative effects can ultimately limit economic growth of a country. Gaibulloev and Sandler (2009) use panel data for Asian countries and find that <mark>terrorist incidents reduce private sector investment and increase government spending, which leads to a decline in GDP per capita growth by 1.5%. Similarly, R</mark> uiz Estrada, Park, and Khan (2018) conclude that terrorist attacks have slowed economic growth in Turkey between 1990 and 2016.<sup>9</sup>

> 9 See Frey, Luechinger, and Stutzer (2007) for a summary of the literature on the economic impact of terrorism.

5
reunification, skilled migrants and business investors.<sup>5</sup> Almost ten times as many applications are received than the quota allows, so a ballot is used by the New Zealand Department of Labour (DoL) to randomly select from amongst the applicants. Once their ballot is selected in the lottery, applicants must then provide a valid job offer in New Zealand within six months in order to have their residence application approved and be allowed to immigrate.<sup>6</sup> The survey design and enumeration, which was overseen by the authors in 2005, covered three sub-samples of applicants to the 2002/03 and 2003/04 PAC ballots: (a) individuals who had their names drawn in the ballot, whose residence applications were approved, and who had emigrated to New Zealand (ballot winners) (b) individuals who applied for the PAC, but whose names were not drawn in the ballot and so they were still residing in Tonga (ballot losers); and (c) individuals who had their names drawn in the ballot, but who had not emigrated to New Zealand at the time of the survey (typically because their residence application was still being processed).<sup>7</sup> McKenzie, Gibson and Stillman (2006) show that there is little selection among ballot winners, and so we focus on a comparison of migrants and ballot losers here. We use the sample of ballot winners still in Tonga in part of the paper, and also employ a sample of individuals who did not apply to the PAC, collected as part of the same survey.

While the survey obtains labor market information from all adults the detailed questions on expectations are directed only at the Principal Applicants. We have 65 Principal Applicants in the immigrant sub-sample and 78 in the sample whose ballot application was unsuccessful. Table 1 reports selected characteristics for these two groups.

> 5 Specifically, any Tongan citizen aged between 18 and 45, who meets certain English, health and character requirements, can register to migrate to New Zealand. The person who registers is a Principal Applicant. If they are successful, their immediate family (spouse and children under age 18) can also apply to migrate as Secondary Applicants. The quota of 250 applies to the total of Primary and Secondary Applicants, and corresponds to about 70 migrant households.

> 6 The spouse of an applicant can also be used to meet the job offer condition if they are included on the initial application. Applicants with dependent children must also meet a minimum income requirement. See McKenzie, Gibson and Stillman (2006) for a more detailed description of the PAC.

> 7 A detailed description of the sampling rules used to ensure that each sample is representative is provided by McKenzie, Gibson and Stillman (2006).

6
# Table 1: Production functions using B&E data _(continued)._

|**1982**Cluster mean for|-0.009|-0.022|-0.032|
|---|---|---|---|
|access to roads|(0.027)|(0.030)|(0.031)|
|**1982**Cluster mean for|0.011|0.029|0.015|
|access to transport|(0.039)|(0.044)|(0.046)|
|**1982**Cluster mean for|-0.144**|-0.146**|-0.103*|
|cropped area|(0.040)|(0.043)|(0.045)|
|1982 Cluster mean for|0.091**|0.026|0.016|
|farm size|(0.020)|(0.020)|(0.020)|
|**1982**Cluster mean for|0.023|0.336|0.144|
|livestock value (000 Ksh)|(0.168)|(0.182)|(0.197)|
|**1982**Cluster mean for|-0.029|0.015|0.014|
|cash crop value (000 Ksh)|(0.019)|(0.019)|(0.020)|
|**1982**Cluster mean for|-0.011|0.018|0.020|
|other crop value (000 Ksh)|(0.015)|(0.015)|_(0.015)_|
|**1982**Cluster mean for|-0.127|-0.015|-0.017|
|non-farm income (000 Ksh)|(0.092)|(0.088)|(0.086)|
|Medium Potential Zone|-0.181|0.026|-0.108|
|Dummy variable|(0.117)|(0.109)|(0.113)|
|Low Potential Zone|-0.372*|0.248|-0.016|
|Dummy variable|(0.153)|(0.158)|(0.170)|
|Hilly area|-0.506**|0.068|-0.027|
|Dummy variable|(0.160)|(0.174)|(0.174)|
|Undulating area|0.070|0.202|0.068|
|Dummy variable|(0.120)|(0.139)|(0.153)|
|AEZ (range)|-0.94 to 0.55|-0.65 to 0.25|-0.99 to_-0. 15_|
|Jt. Signif. (F value)|(7.374)**|(3.033)**|(5.195)**|
|**_N_**|418|418|418|
|Adj. R<sup>2</sup>|0.646|0.713|0.735|
|Test for Joint significance<br>of District effects<br>(F value)||15.843**|4.121**|
|Test for the sum of Sfratio x<br>district effects<br>(F value)|||0.003|
|Test for the weighted sum<br>of sfratio x district effects<br>(F value)|||2.609|

## Notes:

Dependent variable is log of the reconstructed 1990 value of farm production. The extension variable is the revised variable used by B&E, 1997. Standard errors reported in parentheses.

* and ** indicate significance at 5 and 1% levels, respectively. District effects, with Machakos as the base case, range from -0.23 to 1.48 for model II and from -1.63 to -0. 19 for model III.

14
affecting equilibrium wages.<sup>8</sup> In the literature, these responses are captured by “wage-price” elasticities, which measure the proportional response of wages to price changes. These elasticities may vary across members of a household when they are endowed with different skills (unskilled, semi-skilled or skilled labor) or if they work in different sectors (industry premia). Clearly, if countries differ in technologies, endowments, or labor regulations, the responses of equilibrium wages to prices can be heterogeneous across countries as well.

The response of wages can generate first-order effects on household welfare. To account for these responses, the standard net-consumer/net-producer proposition needs to be modified. To see this, consider the extreme case where a farm-household consumes a product but does not produce it at all. Instead, the household earns income from selling labor in neighboring farms. By omitting wages, this household would be mistakenly classified as a net consumer and could thus be presumably hurt by a price increase. But if wages respond positively to prices, the final welfare effect may not necessarily entail a loss.

This type of wage responses were studied in Ravallion (1990), who explored the conditions under which net-consumers of food products in Bangladesh lose or gain in the face of increased food prices when rural wages adjust. Ravallion estimates low elasticities of agricultural wages to food prices and concludes that responses are unlikely to be strong enough to offset the short-run adverse distributional effects of higher food prices. The long-run estimates appear to be more favorable to the poor, but it would nevertheless take around four years for any gains to materialize. Boyce and Ravallion (1991) take another look at this issue using newer data for Bangladesh. They set up a dynamic econometric model of agricultural wages and rice prices. They find that increases in rice prices relative to the prices of manufactured goods have adverse effects on the real wages (measured per quantity of rice) in both the short and long runs.

In a study of informal export barriers and poverty in Moldova, Porto (2005) amends the first order approximation used by Deaton (1989a) to allow wages to adjust. In this setup, exporters produce agro-manufactures (i.e., wines or apple juice) for international markets. They purchase primary products from the farmers (grapes, apples) and manufacture exports

> 8Notice that we are referring here to prices of tradable goods. For non-traded goods, the issues are more complex since they are endogenous.

12
18 29) NETCAP - NETGAP + LTCAPF + OTHCAP 30) NETGAP - GAPFIL - AMTGAP Net long-term capital flows (LTCAPF) include net direct foreign investment (NETDFI), official capital transfers or grants (DBTGRT), net long-term borrowing (NETLT) and other long-term flows (OTHLTF). Other capital flows (OTHCAP) include net short-term capital (SHTERM), capital not elsewhere included (CAPNEI) plus errors and omissions (ERROMS).

# Exogenous Variables

33. The following items are determined exogenously: other factor services (OTHFSY), net current transfers (NETCTR), net long-term flows (LTCAPF) and net other capital flows (OTHCAP). Note that net long-term borrowing (NETLT) is made up of both expected and pipeline flows. Pipeline flows are usually calculated by the World Bank's Debtor Reporting System from information about existing loans. Expected flows are computed exogenously in a separate debt module based on the modelers assumptions about new loan commitments and terms. All other factor service and net capital items as well as net current transfers are projected by means of exogenous growth rates. These growth rates are expressed in nominal terms and so must include both value and volume effects.

# The Resource Balance

34. Exports (EXP) are equal to real exports in local currency (X) multiplied by an exogenous export price index (XPI) and exchange rate (E). The same relationship holds true for imports.

RESBAL G EXP - IMP 27) EXP - X * XPI * E 31) IMP M * M1P * E 32) Although the exchange rate and price indices affect the valuation of the resource balance, they do not induce any changes in real trade volumes. The overall trends are all determined on the real side of the model. It is there that the modeler must imDute reactions to prices and exchange rates by manipulating the real export growth rate and the import elasticities.

14. Many modelers choose to use the World Bank's Half Yearly Commodity Price Forecast for the export and import price indices. These are based on global supply and demand projections which include assumptions about large producers and consumers for certain commodities and countries. The modeler may treat each country as a "price taker" when these indices are used.
take the information that firms report about access to credit. First, when constructing the mean access to finance at the country-location-sector-size average we use as measure of firm size the firm‘s _average_ size (between period t-3 and t) rather than the current firm size. The latter is arguably more exogenous to the firm’s current investment in job training than the former (especially if we worry with reverse causality). Second, we match the averaged indicators to each firm based on the firm _initial_ size, not the current size. We estimate the same model as the one reported in table 7 but based on these variables and report the findings in table A5. It is reassuring to see that the relation between the investment in job training and each measure of market imperfection are qualitatively very similar than the ones in table 7. The main differences relate with the lack of statistical significance of one of the variables proxing access to capital (share of reinvested profits) and with the variable proxying the perceived uncertainty of the economic environment.<sup>37</sup>

# **6. Conclusion and Policy Implications**

The importance of the SME sector for growth in the developing world is consensual and policymakers all over the world worry with how to foster productivity and growth among this group of firms. Moreover, in a modern economy, the investment in human capital is crucial to foster technological adoption and, thus, achieve higher productivity growth. This paper explores a unique firm level data set across 99 countries in the developing world to (1) document how different are the propensities to invest in job training across firm sizes; and to (2) investigate the reasons behind the large differences in the investment in job training. We conjecture that SMEs could be less likely to invest in job training because of three broad sets of reasons. First, the expected return on the investment in job training might be smaller for SMEs. This could happen if either SMEs have lower expected benefits or higher marginal costs of the investment in

> 37 We have also tested the robustness of our findings by averaging the responses of firms: (1) Considering the current firm size (instead of the initial size); (2) Excluding the size dimension (i.e., computing means by country, sector and location). Reassuringly the results (not reported but available on request) are supportinve of the low explanatory power of the proposed variables in explaining the large training gaps across firm size within countries and sectors. Moreover, the qualitative results for each of the proposed explanatory variables remains very similar to the ones reported in table 7.

29