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2003; Buch, 2003; Buch and De Long, 2004). A more recent study emphasizes that it is not the absolute physical or cultural distance between home and host but rather a relative comparison with distances for other foreign competitors in the host market that affects location decisions (Claessens and Van Horen, 2008b).

# **4. The Consequences of Foreign Bank Entry**

What is the impact of foreign bank participation in developing economies? Studies on the consequences of foreign bank entry have predominantly focused on three main areas: the implications of foreign bank entry on the efficiency and degree of competition in the banking sector, the impact on banking sector stability, and the effects on access to finance, in particular for opaque borrowers such as small businesses. In what follows, we review the evidence on each of these topics, highlighting wherever appropriate the differences observed across regions.

# **_Foreign bank presence, efficiency, and competition_**

A series of cross-country empirical studies show that the presence of foreign-owned banks is associated with greater efficiency and competition in a host country’s banking sector. In particular, foreign bank presence has been linked to lower net interest margins, profitability, cost ratios, and non-interest income for domestic banks in developing countries (Claessens et al., 2000, 2001; Claessens and Lee, 2003; Bayraktar and Wong, 2004). Also, foreign bank presence and fewer restrictions on banks’ activities have been directly linked to greater competiveness in a host country’s banking sector as reflected in the Panzar-Rosse H statistic (Claessens and Laeven, 2003, Gelos and Roldos, 2004).

Other cross-country studies that compare the relative performance of foreign and domestic banks, find that foreign banks have relatively higher interest margins and profitability and lower overhead costs in developing host countries (Demirguc-Kunt and Huizinga, 2000; Micco, Panizza, and Yanez, 2007). Those authors, therefore, conclude that foreign banks in developing countries are relatively strong competitors in under-developed banking markets and can exert pressure on domestic banks to become more efficient and competitive.

10
Extremely high costs are associated with frequent inspections and audits. At least 15 state agencies have the authority to control economic units. An average Moldovan firm paid host to 20 inspections in 2002. Inspectors spent on average 48 days per year per firm in 2001. Although this may seem a lot of time, the corresponding figure for the previous year was almost twice as high (90 days), so there has been some improvement in this area. Inspections are extremely costly: an average firm paid USD 1280 in fines and USD 516 in bribes and “voluntary contributions”; in additions the value of confiscations was USD 300 per firm. These costs are huge not only in absolute terms, but also in comparison to those in other countries in the regions. For example, the inspection costs in Bulgaria, Belarus and Ukraine were significantly lower than in Moldavia.<sup>30</sup> This is very telling, because none of these countries represents a particularly good standard of the business environment. But Moldova fares poorly even against such a modest benchmark.

One could continue to list further examples of overregulation and associated costs, however no more examples seem necessary to arrive at the conclusion that the sate imposes an extremely high tax – in the broad sense of the word – on business activity. There is no single element that is responsible for the business environment being unfriendly in Moldova. It is the whole regulatory system, bureaucratic discretion, and the underlying philosophy of taxing businesses that are the root cause of depressed economic activity in Moldova. Therefore a fundamental overhaul of the business regulations system is necessary to improve the investment climate. The overarching objective of such an overhauls should be to deregulate business activity and to lessen the scope for bureaucratic discretion and arbitrary decisions. The monetary costs associated with regulations need to be lowered with the aim of maximizing long-term benefits instead of short-term gains. Ultimately, a radical improvement in the business environment is the only way to foster job creation and to improve employment opportunities.

The government is aware of the excessive regulation and control over business activity. For example, recently the government took measures to coordinate and

> 30 Comparison is limited to these three countries as they are the only ones where the comparable survey of regulatory costs was conducted.

39
# **TABLE 6.12**

||**S**<br>IV<br>E<br>um-<br>**(Val**<br>|**ao**<br>**(Pape** <br> <br>_<br>*<br>tr**V**<br>**u it parethe**<br>VlA|A Wood b:d<br>od4r<br>1!<br>- **L**of**Co**<br>**m awe mdrd**<br>dd|o<br>**Price V**<br> <sup>**arn)**</sup><br>Bas|**Added**<br>ed on Value-|dded|
|---|---|---|---|---|---|---|
||Base<br>in<br>1|d on aue-<br>Domestic Pri<br>2|e<br>ces<br>3|<br>i<br>4|<br>n World Price<br>_5_|<br>s<br>6|
|Intercept|4.396<br>(2X712)|6.789<br>(12 90)|2392'<br>(0.607)|3588<br>(3.571)|8.093<br>(15.066)|**2.600**<br>(0.642)|
|Trsde Variablesy|||||||
|Effective Rate of<br>Protection|40.149<br>(0.235)<br>|40.192<br>(0.165)<br>|4.188<br>(0.060)<br>|.0533<br>(0309)<br>|-0.696'<br>(0.192)<br>|-0.679<br>(0.063)<br>|
|% Epors|-2.012<br>(1.725)|0.239<br>(4.272)|_-OA51_<br>(0.677)|-3.016<br>(2270)|0.058<br>(4.986)|4.629<br>(0.716)|
|InuLts:<br>|||||||
|In (Seealese Silled<br>and Unskilled)|0.786<br>(0588)|||0.525<br>(0.774)<br>|||
|Skiled and Unsklled|-9.5E4<br>(8.7E-4)|||-0.001<br>(0.001)|||

(Contiued) 'A
# II. EXCHANGE RATE DETERMINATIj AND THE REDUCTION OF GOVERNMENT SPENDING: BACKGROUND AND INTUITION

The analysis of "productive", government expenditure has a long history, dating to Samuelson (1954), and Musgrave (1959). For recent surveys of the literature st 3rennan and Pincus (1983) and Johansson (1986). Our approach, which '11 be developed in do-2il in the next section, assumes that the government provides infrastructure which enhances private productivity. This approach has been developed in Grossman and Lucas (1974), Barro (1981, 1984), Barro and Grossman (1976), Johanseon, (1982), and Negishi (1974).3 A problem with the analysis in most of these articles is that they assume there to be a one-to-one correspondence between government spending and the supply of public goods: in practice increases in government spending usually lead to rapidly decreasing marginal provision of public goods.

There have been several recent studies that construct intertemporal macroeconomic models designed to analyze fiscal reduction programs. 4 There are, however, virtually no such models that consider public investment, one of the major issues that we wish to examine. Accordingly they are unable to examine the trade-offs between private production and government spending that we wish to consider.

- 3/ One might also wish to include public goods in private consumption, an approach taken in Calvo (1979), Groenwald (1980, 1982, 1984), and Johansson (1982).

- 4/ Among these are Blanchard and Sachs (1982), Cuddington and Johansson (1986), Marchand, Mintz, and Pestieau (1984, 1985), Moore and Neary (1984), and Neary and Stiglitz (1983).
“Production processes are more and more fragmented ... The nature of trade has changed, but our trade data have not ... Many goods are assembled in China, but their commercial value comes from the numerous countries ... We want to know the value added by each country in the production process of ...nal goods.” –Pascal Lamy, Director-General of WTO, “Made in the World”Initiative, 2011

# 1 Introduction

Over the past two decades, increasing global production fragmentation has allowed exporting ...rms to rely less on domestic inputs for production. Indeed, research ...nds that domestic content in exports has been declining in most countries. China is an intriguing exception.<sup>1</sup> What caused China to defy the declining trend in domestic content in exports in most countries, despite its deep engagement in global value chains? There are several possible answers to this question with con‡icting implications. The rising domestic content could re‡ect the changing composition of Chinese exports, suggesting that China has shifted its comparative advantage towards the industries with high domestic content. It could also be a result of its increasing domestic production costs, which would imply that the country has become less competitive. Yet another possible answer is that it could be due to the gradual substitution of domestic for imported materials by its exporters. This would imply that China has become more competitive, particularly in the intermediate input sectors. Understanding the determinants of China’s rising domestic content in exports can provide important development policy insights for other countries.

This paper uses customs transaction-level data merged with ...rm survey data to measure

> 1Koopman, Wang and Wei (2012) ...nd that China’s DVAR rose between 2002 and 2007. Johnson and Noguera (2014), using the GTAP IO tables, show that from 1970 and 2009, the DVAR of all countries in their sample are declining, except for the Republic of Korea and Indonesia.

1
when overall growth rates for most industries began declining.

# **2.2 Entry, Exit, and Job Growth in Fast-Growing Industries**

The figures presented in the previous section showed that the Korean manufacturing industries have undergone a striking structural transformation over the past 40 years. This shift in flagship industries was accompanied by the reallocation of resources between industries. We believe that entry and exit (i.e., the opening of a new plant and closings of plants in declining industries) played a crucial role, gradually shifting resources from one industry to another.<sup>7</sup> Given that some of these industries did not exist in the past, differences in growth rates across existing plants alone do not fully explain the transformation process. Therefore, it is important to examine the behavior of new firms and plants, which deliver new products and technologies to the economy and alter value creation in various industries.

Figure 4 plots the entry and exit rates of the four industries we focus on.<sup>8</sup> Electronics and transportation have relatively high entry and exit rates. About 30% of plants in the late 1980s and early 1990s were new. Entry rates for textiles were also high in the late 1980s, when the industry maintained a relatively high growth rate. However, as the growth rate went down in the early 1990s, 7 The Mining and Manufacturing Survey collected information for plants with more than five employees until 2006. From 2007 and on, the survey criterion changes to 10 employees and more. We construct a sample with 10 and more employees to keep consistency. Thus, plants that reduce size below 10 would be considered as an exit. In a similar way, if an existing plant with fewer than 10 employees increases the number of workers and enters the survey sample, it will be considered as an entry. Figures for entry and exit rates from the earlier survey sample (that includes plants with five and more employees) are available upon request.

8 The trend is the fitted line from the regression of the variable against the fourth-order polynomial function. We prefer using this methodology to the HP (Hodrick-Prescott) filtering for samples with a shorter time period.

8
under the VG component), we included a question in our census of 100 study villages that asks whether an individual had “ever heard of TASAF.” We suggest that this variable provides a similar gateway that is necessary, but not sufficient, for an individual to be a program beneficiary: while many people who have heard of the program do not benefit, an individual who has not been informed as to the existence of the program cannot seek to benefit from it. Our data show that, among households eligible for TASAF II according to the exogenous vulnerability criteria, less than half of survey respondents had ever heard of the program. Non-vulnerable households, i.e. those who were ineligible to receive support under this program, were actually eight percentage points _more_ likely to have heard of the program than the eligible nonbeneficiaries. This finding is dismaying, indicating that half of the target population under TASAF II had never heard of TASAF at baseline, by which time, as described earlier, VG groups had been formed and were ready to be funded in each of these 100 villages.

We therefore conclude our empirical analysis by exploiting the village-level census of eligible households provided by the listing exercise in the 100 study villages with VG subprojects and examining the determinants of program awareness among eligible households, and the determinants of being a beneficiary among those who had heard of the program at baseline. This analysis uses the listing data, meaning that the sample size for the analysis is greatly enhanced relative to Table 5. However, detailed consumption data are not available for this larger sample, so we proxy per capita consumption (and poverty) with several simpler questions: the number of days in the past week that a household consumed proteins (meat, eggs, or milk).

In Table 6 we analyze awareness of TASAF within the eligible non-beneficiary sample. In columns (1) through (4), we cumulatively include covariates intended to proxy for wealth (consumption), information (proximity to village center, education, and ownership of radio/phone), political activity (attendance in village meetings) and political connectedness (household members holding office, blood relation to village elites, and benefitting from other development projects for vulnerable groups). The last row of the table presents an F-test on the joint significance of the coefficients newly added in that column. This analysis of the degree of program awareness is interesting because it is distinct from the earlier targeting analyses in being entirely and more broadly regressive: not only is program awareness regressive in education and access to media, as well as political activity and connectedness, but it is also regressive in consumption, with those households eating more protein-based diets more likely to be aware of 18
the survey. Also related to context and framing, respondents and managers have different attitudes to reporting “true” information; we proxy this attitudes with a dummy measuring whether the firm reports having been exposed to the payment of bribes. The direction of the effect is ambiguous. On the one hand a manager that is more likely to report having been exposed to bribes can be thought of as more likely not to provide true information, because more inclined to circumvent the system. On the other hand, one can also claim that reporting having been exposed to the payment of bribes, as opposed to paying bribes but not reporting it, is an indication of being inclined to provide accurate answers during the interview. Third, firms' characteristics may impact response behavior. Larger, older, foreign owned, and exporting firms may be more likely to provide accurate information due to reputational issues. Also, it is interesting to analyze the performance context of the firm since firms that are expanding (contracting) could have less incentives to provide inaccurate (accurate) information since they can show potential investors and clients a good performance. This is represented by the annualized growth in employment over a three years period. Finally, to explain the differences in answers between the two surveys it is important to consider the recall period, measured by the number of days elapsed between the implementation of the two surveys. The hypothesis is that the longer the time between the surveys, the lower the anchoring effect, as it is more likely that respondents forget the response provided in the first survey.

To empirically verify whether these elements play a role in explaining respondents’ behavior we regress the differences in the answers between the two surveys on the variables identified above. We first use a probit model in which the dependent variable takes the value of 1 if the answer in the two surveys is different and 0 otherwise. However, the incentives that play a role in providing different answers in the two surveys maybe be different in yes to no answers –that is, yes to the short questionnaire and no in the long questionnaire, as compared to no to yes answers. In order to capture these different incentives we also estimate a multinomial logit model, where we model three different outcomes. Specifically, we consider the outcomes for _Yi_ equal to 0, 1 and 2; where 0 represents no difference between the two surveys, 1 represents that firm i reports innovation in the short questionnaire and no innovation in the long questionnaire; and 2 when firm i reports no innovation in short questionnaire and innovation in the long questionnaire. Therefore _Yi >0_ represents discrepancy between questionnaires.

Table 5 shows the results of estimating the probit model with country and 2 ISIC digit industry fixed effects. Columns (1) and (2) show the results for the full sample, while column (3) and (4) are the estimates for the group of countries in which show cards where used. The time interval that passes between the two surveys and the number of interviews per supervisor increases the probability of having different 19
_Handbook of Development Economics_ . Vol. 4. Amsterdam and New York: North Holland.

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