rafmacalaba/datause-encoder-data huggingface dataset:
where  _G,j,t_ is the cumulative percentage increase in the CPI-measured price of good _G_ from period 0 to period _t_ and _EG,t_ is the period- _t_ cumulative measurement error in the price index since that base period. Inserting equation (6) into equation (4) gives: An estimable version of equation (7) using a time-series of cross-sectional household budget data and a temporal CPI for food, nonfood, and all consumption is: where _Dt_ are time dummy variables, _Dj_ are regional dummies, and <sup>ˆ</sup> is the intercept from equation (7), plus the coefficients of the omitted time and region dummies. In the usual time series usage, the coefficients on the time dummy variables,  _t_ , are key to the measurement of deflator bias; the calculation of real income should already have put households observed in different years on the same cost-of-living basis so there should be no temporal “drift” in the residual food share. These dummy coefficients capture relative price effects, differential bias for food and nonfood, and overall deflator bias scaled by the coefficient on income: If the degree of CPI-bias in food and nonfood is approximately equal (or if relative price movements have only small effects on food budget shares) then Hamilton (2001) shows that: 18
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27
types of propensity matching to control for the fact that export promotion services are not randomly allocated across firms. Overall, our results suggest that the entry and survival margins are the main channels through which export promotion agencies affect export growth, and tend to be unsuccessful at increasing export intensity, thus highlighting the role of fixed costs of entry into exporting activities.

The remainder of the paper is organized as follows. Section 2 describes the data and provides some prima-facie descriptive evidence. Section 3 presents the empirical methodology and section 4 discusses the econometric results. Section 5 concludes.

# **2 Data and Prima-Facie Evidence**

Our sample selection depended on panel data availability for Latin American firms. The World Bank’s Enterprise surveys for 2006 and 2010 (www.enterprisesurveys.org) provide panel data for the following seven countries: Argentina, Bolivia, Chile, Colombia, Ecuador, Peru and Uruguay. The survey covers manufacturing, but also agriculture and services. The surveys provide information regarding firms’ export status (exporter or non-exporter) with which we can build our status transition variables to explore the impact of using export promotion services on entry and survival. The survey also contains information on firms’ export intensity (direct and indirect exports as a fraction of total sales),<sup>4</sup> which we use to assess the impact of export promotion on the intensive margin. It also provides information on employment that we use as a proxy for firm size, share of foreign ownership, and the ISIC 4-digit code of the firm’s main product. In addition, the surveys provide information on firm capabilities, such as whether firms have a website or use email to communicate with clients, which we used to control for changes in firm capabilities between 2006 and 2010.

The explanatory variable of interest is whether firms have used the services offered by EPAs. In all 2010 surveys, firms were asked whether they had used export promotion services over the last 3 years. Thus, by comparing the status of firms in 2006 and 2010, we can measure the impact of having used export promotion services on firms’ export entry, exit, survival and intensity.

Table 1 provides a first look at the data by showing the share of exporters in 2006 and 2010 among firms that used services offered by EPAs and firms that did not. There is a larger share of

> 4 Indirect exports are defined as sales from a firm to an intermediary that subsequently exports the same product.

5
in the R&D and non-R&D sectors is attributed to investment-specific shocks by 56 and 38.4 percent, respectively.

The three technology shocks jointly explain 92.3 percent and 78.5 percent of the variance of outputs in the R&D sector and the rest of the economy, respectively. Ouyang (2011) argues that technology shocks are important factors in explaining the procyclicality of R&D. Our results confirm this claim since the main sources of output volatility in the two sectors, capital investment-specific and R&D productivity shocks, induce output responses of the same sign. Furthermore, technology shocks, jointly explain a moderate proportion of the variance of hours which is in the order of 46.1 percent and 56.4 percent in the R&D sector and the consumption-good sector, respectively. Table 4 displays variance decompositions when the R&D sector is not modeled as a separate sector and R&D is not treated as investment. In this case, aggregate output correponds to the GDP reported in the NIPA tables while hours correspond to aggregate per capita hours.<sup>30</sup> These results show that under this specification of the model, investment-specific shocks and neutral productivity shocks explain 40.2 and 33.3 percent of the variability of NIPA output while the combined e↵ect of technology shocks is 90.3 percent; this result is not too di↵erent from findings of previous studies that used quarterly data.<sup>31</sup> The combined e↵ect of technology shocks on productivity and hours increases significantly compared to the model where there is a separate R&D sector and R&D is treated as investment than solely as an expense.

In the second set of results (tables 5 and 6), we compute variance decompositions of the

> 30In the model of section 3, the R&D channel is closed when _↵_ 1 = 0.

> 31Altig et al. (2011), find that capital investment-specific shocks explain 41 percent of the variation of output while neutral technology shocks explain 11 percent for the period 1982:1-2008:3. Fisher (2006), finds that investment-specific shocks explain 42-67 percent of the variation of output while neutral technology shocks explain 8-33 percent for the period 1955:1-2000:4.

37
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# **1. Introduction**

In recent decades, China has become a major player in global trade. Between 1995 and 2015, its share of world exports grew from about 4% to over 15%. At the same time, China’s share in world imports of agricultural and mining products rose from less than 2% to more than 10%. China is also a major importer of manufactured inputs used in the production of its own exports. As emphasized by Autor et al. (2013), the rising importance of China’s trade over this period reflected several intertwined developments: (i) the transition to a market-oriented economy, which involved the rural-urban migration of more than 150 million workers (Chen et al., 2010); (ii) increased access to previously banned foreign technologies, capital goods and intermediate inputs (Hsieh and Klenow, 2009); (iii) the fact that multinationals were increasingly allowed to operate in the country (Naughton, 2007); and (iv) the accession to the WTO giving China most-favored nation status among WTO members (Branstetter and Lardy, 2006).

The Belt and Road (B&R) Initiative seeks to deepen China’s international integration, notably by improving infrastructure and strengthening trade and investment links among the economies involved (Table A1 in the Appendix reports the unofficial list of B&R economies considered in this study). The _Belt_ links China to Central and South Asia and onward to Europe, while the _Road_ links China to the nations of Southeast Asia, the Gulf Countries, North Africa, and on to Europe. The B&R initiative is composed of five main priorities: (1) infrastructure connectivity; (2) coordination of development strategies and policies; (3) trade facilitation to ensure “unimpeded trade”; (4) financial integration; and (5) people-to-people exchange.

This paper uses detailed bilateral trade data for the period 1995-2015 to assess the degree of exposure of B&R economies to China trade shocks. The paper proceeds in several steps. First, it examines the main destinations and sources of China’s trade over the period 1995-2015, devoting especial attention to trade relationships between China and B&R economies. It then examines econometrically the extent to which competition and demand shocks associated with China’s trade growth impacted industry-level exports of B&R economies in recent decades. This analysis 2
Union, were not aware of directly paying taxes (Kornai, 1990). Indeed, during the Soviet era, ―taxpayers‖ were ―large in size and small in number‖, and the state had many other levers of control besides taxes (Martinez-Vazquez and Wallace, 1999). However, in a shift from a centrally controlled to a market economy, the fiscal system needed to be reformed. These changes represented significant shocks to ordinary citizens. In particular, individuals were faced for the first time with the direct payment of taxes, including being asked to file different tax returns. The transition process brought up many policy questions, among others: the tax system, the structure of tax administration, and the degree of political participation. There are numerous research papers regarding the transition process. However, less evidence is available in regards to tax compliance. Voluntary compliance and self-filing, two important pillars in a modern tax system, were completely absent just after the planned socialism (Martinez-Vazquez and McNab, 2000). Following the early transition, tax evasion and avoidance reached very high levels, as the new tax administration was not prepared to enforce taxes in a market-based economy with large numbers of taxpayers. Further, the connection between the payment of taxes and the provision of public goods had been largely concealed under socialism, which might have reduced the identification with the state and thus the willingness to pay taxes. Easter (2007) reports the findings of a poll conducted by the tax administration in the Tambov region in the early years of the transition, which indicated that only one-third of the respondents argued that paying taxes to the state would provide any benefit to themselves. Fiscal concerns might be a key element and it is also often argued that tax evasion is widespread in transition countries (see, e.g., Alm and Martinez-Vazquez, 2003; Martinez-Vazquez and McNab, 2000). Alm and Martinez-Vazquez (2003) point out that ―In developing and transition countries (DTCs) in particular, tax evasion is often widespread and, indeed, systematic. Thus, the problem of tax evasion tends to have far more serious consequences in DTCs than in developed economies‖ (p. 147). In the transition process, revenue needs are an important issue. Gordon (1994) points out that in the reform process, governments in Eastern Europe are confronted with expenditure needs such as investment demand, infrastructure improvements, and social insurances. In such situations, the degree of the individuals‘ tax morale might be a key determinant. Martinez-Vazquez and McNab (1997) argued that it is not surprising to see taxpayers‘ 44
receivable, including claims on other insurance companies. In fact, 5 companies (3 small, 1 medium and 1 large) have other assets well in excess of 40 percent of total assets (in 3 cases by a large margin). Small and medium size companies report higher equity ratios than large companies although, as already noted above, some of these equity positions may be overstated.

# **D. General Insurance**

As in most other developing countries, motor insurance is the largest component of general insurance, accounting for 50 percent of gross premiums (Table 5). Medical insurance represents 20 percent of general premiums, while fire, marine and general accident insurance, that tends to cover large industrial and commercial risks, accounts for about 30 percent of the market. The fall in the relative market share of these three branches between 1997 and 1998 is due to the reclassification of medical insurance from life to general business. The use of product and professional liability insurance is nonexistent.

**Table 5: Composition of General Insurance, 1997-2002**

||1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Motor|55.1|52.5|53.1|50.6|47.8|50.3|
|Fire|16.0|12.4|12.1|12.2|10.2|10.6|
|Marine|14.4|9.6|8.4|9.0|8.8|8.2|
|General Accident|14.5|8.5|8.9|9.2|10.0|10.7|
|Credit||||0.3|0.2|0.1|
|Medical||17.0|17.6|18.7|22.9|20.1|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

Data for 2002 are preliminary Source: Insurance Commission Most large industrial and commercial risks rely heavily on reinsurance. Fire, marine and general accident have reinsurance ratios in excess of 80 percent (Table 6). In contrast, motor and, to a lesser extent, medical insurance operate with high retention ratios. Several lines are reinsured with proportional treaties, although non-proportional reinsurance is used in fire, marine and motor business.

**Table 6: Reinsurance Ratios, 1997-2002**

||1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Motor|8.8|11.0|11.5|13.0|10.8|8.0|
|Fire|94.5|94.3|94.1|94.1|94.1|95.7|
|Marine|81.5|79.6|79.8|78.2|80.5|81.4|
|General Accident|67.5|80.0|79.2|80.8|81.0|87.2|
|Credit||||74.7|71.3|46.7|
|Medical||50.8|48.2|46.9|37.0|37.6|
|Total|41.5|40.5|39.7|41.5|38.6|37.8|

Data for 2002 are preliminary Source: Insurance Commission 10
Table 7: Heterogeneity in the Impact of the Program by Eligibility Criteria

||Childre|n’s Outcomes|Parental Outcome|
|---|---|---|---|
||Overall Score|Enrolled Secondary|Engagement Index|
|API Original|0.088|0.134|-0.018|
||[0.419]|[0.205]|[0.887]|
|API Original_×_ Poverty 1|0.090|-0.104|-0.046|
||[0.637]|[0.420]|[0.816]|
|API Original_×_ Poverty 2|0.091|-0.033|-0.009|
||[0.490]|[0.767]|[0.949]|
|API Plus|0.320|0.273|0.464|
||[0.047]|[0.024]|[0.000]|
|API Plus_×_ Poverty 1|0.094|0.015|0.100|
||[0.638]|[0.902]|[0.643]|
|API Plus_×_ Poverty 2|0.010|-0.216|-0.173|
||[0.963]|[0.128]|[0.386]|
|Original(Pov. 1)=Original(Pov. 2)|[0.995]|[0.681]|[0.873]|
|Plus(Pov. 1)=Plus(Pov. 2)|[0.816]|[0.297]|[0.444]|
|Number of clusters|224|182|224|
|Observations|1045|468|1045|

_Notes_ : This table shows OLS estimates and the associated _p_ -values (in brackets) on student and parental outcomes measured after two academic years of exposure to the API program under the second experiment designed and implemented by the authors in collaboration with the government. For a detailed descriptions of the test scores used in this table, see Appendix A.2. The dependent variables in the first and third columns are standardized with respect to their means and the standard deviations in the control group. The dependent variable in the second column is computed from administrative school records (see Appendix A.1). All _p_ -values account for clustering at the school level.

results reveal limited variation in program impacts along both poverty measures, with effect sizes for the interaction terms with the indicator variables for the program modalities that are not statistically different from zero, and not statistically different from each other.

Taken together, the evidence shown in this section documents that differences across populations (if any) are unlikely to represent a meaningful threat to scalability in this context. There is a very high degree of similarity in observable characteristics between the experimental sample and the overall population of schools in Chiapas. Program impacts are also not heterogeneous along the determinants of the rollout of the program under the government implementation, which is indicative of the fact that the community/school targeting process is unlikely to play a role for the scalability of the program.

23
_-5-_

# Extent of Coverage and Use

Some writers classify excise systems into three classes: limnited, intermtdiate, and extended, on the basis of coverage. The limited systems, such as those of the United Statt and Trinidad, apply only to the traditional commnodities plus motor fuel and a very few other items. The other two apply to broader ranges, the third resembling a sales tax if the coverage is broad enough.

Table I indicates the revenue importance of excises (including special service taxes) by country and by region, based on the International Monetary Fund tabulation. Only market economy countries were included. Some arbitrary classification decisions must be made by the IMF staff in preparing tables, but the figures nevertheless give a good overall impression of the relative reliance on this form of taxation. Figures by county are shown in Appendix Table Al.

For all countries for which data are available (108), the lowest income countries show only slightly more reliance on excises than do the countries in the $501-$2,000 range, basically because they have less domestic industry and rely more heavily on customs duties. The countries above show a definitely lower percentage. But statistical analysis shows no meaningful correlation between per capita GNP and reliance on excises. For the African and Asian countries, however, there is a definite inverse relationship between per capita GNP and reliance on excises, a pattern also found in Central and North America. All but one of the South American countries show approximately the same relative dependence on excises. Previous studies of excises as a percentage of indirect tax revenue show a definite positive
Table 2a (continued)

||Share of e|mployees in<br>are women|state who|Share of<br>female-o|female emp<br>wned establ|loyees in<br>ishments|Share of<br>male-ow|male empl<br>ned establi|oyees in<br>shments|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|A & N Islands|0.100|0.161|0.263|0.576|0.597|0.925|0.940|0.895|0.901|
|Andhra Pradesh|0.403|0.509|0.426|0.902|0.964|0.940|0.713|0.711|0.780|
|Assam|0.252|0.296|0.163|0.890|0.950|0.934|0.848|0.857|0.923|
|Bihar|0.263|0.344|0.198|0.869|0.956|0.789|0.778|0.783|0.855|
|Chandigarh|0.104|0.226|0.311|0.663|0.897|0.977|0.964|0.934|0.951|
|Dadra & Nagar Haveli|0.074|0.260|0.165|0.417|0.818|0.960|0.952|0.972|0.913|
|Daman & Diu|0.123|0.239|0.227|0.754|0.960|0.962|0.974|0.986|0.965|
|Delhi|0.038|0.041|0.051|0.409|0.476|0.595|0.983|0.986|0.983|
|Goa|0.331|0.208|0.159|0.917|0.831|0.969|0.779|0.927|0.960|
|Gujarat|0.144|0.171|0.357|0.681|0.759|0.945|0.895|0.903|0.871|
|Haryana|0.104|0.124|0.141|0.723|0.832|0.846|0.949|0.965|0.922|
|Himachal Pradesh|0.217|0.276|0.182|0.789|0.898|0.868|0.890|0.889|0.926|
|Jammu & Kashmir|0.285|0.251|0.244|0.935|0.944|0.984|0.764|0.885|0.969|
|Karnataka|0.436|0.513|0.430|0.937|0.966|0.967|0.803|0.751|0.846|
|Kerala|0.470|0.462|0.442|0.925|0.930|0.943|0.771|0.786|0.827|
|Madhya Pradesh|0.324|0.335|0.322|0.900|0.853|0.940|0.775|0.752|0.854|
|Maharashtra|0.207|0.199|0.250|0.726|0.826|0.883|0.869|0.923|0.899|
|Manipur|0.746|0.757|0.679|0.986|0.983|0.989|0.923|0.906|0.878|
|Meghalaya|0.272|0.293|0.278|0.826|0.781|0.787|0.910|0.889|0.877|
|Nagaland|0.331|0.419|0.419|0.953|0.943|0.979|0.925|0.892|0.934|
|Orissa|0.391|0.459|0.304|0.711|0.839|0.875|0.671|0.634|0.798|
|Pondicherry|0.339|0.263|0.408|0.790|0.723|0.937|0.741|0.899|0.882|
|Punjab|0.215|0.270|0.181|0.833|0.964|0.941|0.945|0.955|0.974|
|Rajasthan|0.272|0.217|0.208|0.892|0.853|0.897|0.844|0.923|0.938|
|Tamil Nadu|0.439|0.495|0.432|0.928|0.949|0.941|0.730|0.723|0.758|
|Tripura|0.380|0.184|0.255|0.604|0.857|0.959|0.659|0.913|0.861|
|Uttar Pradesh|0.259|0.269|0.296|0.889|0.881|0.918|0.843|0.870|0.883|
|West Bengal|0.385|0.454|0.462|0.887|0.930|0.967|0.740|0.821|0.847|
|Nationwide|0.321|0.363|0.337|0.876|0.916|0.934|0.795|0.817|0.859|

Notes: See Table 1.