rafmacalaba/datause-encoder-data huggingface dataset:
# (iii)

# Summary

One concern raised in the discussion over regional trade arrangements (RTAs) is whether the discriminatory trade barriers applied in these arrangements encourage high cost imports from member countries at the expense of lower cost goods from non-members. However, evaluations of the actual impact of RTAs have been hampered by a lack of appropriate empirical procedures for assessing their influence on the level and direction of trade.

This study employs a new index for analyzing one aspect of an RTA, - its static trade effects. It examines changes in the regional "orientation" of exports, and shows how this information can be employed in connection with the "revealed" comparative advantage (RCA) index to identify apparent "inefficiencies" in trade patterns. The approach is applied to statistics on Mercosur countries' exports to determine if recent trade is evolving along lines compatible with these countries' comparative advantage. The paper does not comment on the many other possible effects of RTAs such as benefits from political cooperation, enhancing the credibility of reform strategies, or dynamic gains from trade. Nor does it focus directly on changes in trade with non-members which accelerated rapidly due to the 1988-91 liberalization of Mercosur countries trade barriers. Thus, the paper does not address the net welfare effect of trade creation and trade diversion relative to where member countries' trade policies started from in 1988.

The results show the most dynamic (fast growing) products in Mercosur's intra-trade generally are capital intensive goods in which members have not displayed a strong export performance in outside markets. Neither the RCA indices, nor statistics relating to factor proportions, indicate that Mercosur has a comparative advantage in these products. What factors are causing these trade changes? The evidence suggests that Mercosur's own trade barriers are responsible. Most favored nation tariffs on the fast growing products are above the average for all imports and provide Mercosur members with significant preferences. These may also be supplemented by a higher than average incidence of nontariff measures on imports from the rest of the world.

These findings constitute evidence of the potential adverse effects of regional trade arrangements on members and on third countries as judged by some variance in their trade patterns from what would be predicted by current comparative advantage. The counterfactual comparison is with an equivalent degree of liberalization on a non-discriminatory basis. Given the recent proliferation of RTAs, they highlight the need for further empirical research on the domestic and international effects of these arrangements in order to better assess the pros and cons of regionalism.
maximum at a certain level of income and decreasing from then on. The existence of this relationship as pointed by Cole (2003) has been criticized on grounds of the endogeneity problems afflicting some estimations, the unexplored impact of trade openness, the restriction of some samples to OECD countries, non stationarity of some series and heteroscedasticity on cross-section data. Cole (2003) takes into accounts these considerations and he still finds the existence of U-inverted Kuznets curve for some pollutants. However, Cole’s approach has been strongly criticized by Stern (2004) subtracting validity to his conclusions. Moreover, there is important evidence mostly based on time series that rejects the existence of the Kuznets curve.<sup>16</sup> In our approach we are sympathetic of Stern (2004) and Copeland and Taylor (2004) skepticism about the existence an environmental Kuznets curve, however if there is Kuznets curve its effect should be captured by the regional interacted dummies we use, given that this dummies are highly correlated with levels of income.<sup>17</sup> Beside these interacted terms our analyses encompasses the study of the impact of the productive structure on the environmental outcomes, in particular those of agricultural and non agricultural sectors.

The data on sectorial output, C02 emissions, and water withdrawals were obtained from the World Development Indicators 2004. The forests areas were obtained from FAO. The data on air pollution covers the 1970-2000 period, the fresh water data is from the year 2000, and the forest coverage data is from two years, namely 1990 and 2000.<sup>18</sup> Table 5 (a) reports results from country fixed-effects (FE) estimations of sectorial determinants of CO2 emissions, thus assuming that sectorial outputs are exogenous to air pollution. The probability values of the F-test of the significance of the coefficients associated with LAC for the five economic sectors are reported at the bottom of the table. Regression (1)

> 16 See Stern (2004) for a review of the literature.

> 17 Our approach is equivalent to estimate a Kuznets curve with a three piece spline.

> 18 See Appendix C for data definitions and primary sources.

23
demand for compliance verification (dependent on importing patterns) can generate delays in clearance times.

How do import tariffs and NTMs affect firms’ responses to export demand shocks? To fix ideas, let us assume a depreciation of the yuan versus the USD, which is the case for most of our sample period (Figure 1). For Indonesian exporters to Japan, where they invoice in USD, this exchange rate movement represents a negative demand shock, particularly for those goods where Chinese competition is stronger. We are interested in how import measures on firms’ intermediate inputs affect the heterogeneity of responses to this shock across Indonesian exporters. Once hit by the exchange rate shock, the exporter could counter the associated increase in competition by lowering its marginal costs and/or its mark-up. The extent to which the Indonesian exporter can alter its mark-up depends on a number of factors such as the degree of market power of the firm, but not directly on the intermediate input choice.

The firm can lower its marginal costs by adjusting its variable costs, particularly those associated to intermediate inputs.<sup>5</sup> That is the case as the other variable cost–labor–is typically sticky in the short-term especially if firing costs are high (Adhvaryu et al., 2013), as in the case of Indonesia.<sup>6</sup> Reducing input costs typically involves switching to cheaper intermediate inputs, whether from the same supplier or a different one. As exporters are especially dependent on imported inputs, including in Indonesia (Rahardja and Varela, 2015), then import measures can affect the exporter’s ability to switch inputs and, hence, to respond to the shock.

> Consider first the case of NTMs. As NTMs increase uncertainty and/or the fixed cost

> 5Lowering the marginal costs may entail switching to a lower quality segment or sourcing from a new supplier.

> 6According to World Bank Doing Business data, during the period of analysis, Indonesia had the most onerous employment termination rules among middle-income and Southeast Asian countries. Indonesian employers had the obligation to both notify the workers’ representatives in case of dismissal and to seek approval for the worker’s dismissal by the judicial bodies in case no agreement with the worker can be found. In addition the severance payment accruing to the worker upon dismissal was particularly generous in Indonesia. For example, a worker with 10 years of tenure was entitled to 95 salary weeks for redundancy dismissal versus 50 in Thailand, 43 in Vietnam and the Philippines, 33 in Malaysia and 21 in India.

7
Figure 1: fDi Markets FDI flows vs.UNCTAD-BOP FDI flows <!-- Start of picture text --> Outward FDI flows Inward FDI flows<br>US A CHN<br>JPNDEU<br>GUY ZWE BLR JORKENKGZTUNPERECULKAVNM UKR SAUSYRSRBPAKBGRJAMEGYLVAROUIRNLTULBYHRVSVKTHAESTSVNCZECYPPHLTUR VEN ARGFIN COL GRCCHLHUN ZAF IDNKWTMYSPOLDNKISLMEXAUSINDPRTLUXISRKORAUTBRACHNSGPIRLNOR RUS SWEITAHKGCAN BEL CHEESPNLDGBRFRA SWZBTNPNG ZWE GMBKENSLE MUS GUYLAOBOLKWT SYC SENKGZMDGMNGMDATJKMOZYEMGHA MKD NIBLRMMR KHMALB ARMLKASLVCODUGASYRGTMZMBCETHTZAHNDSVNBGD GEO ECUURYOMNLVALBY AGO TTOCRIDOMLTUMLT BHS DZACYPTUNPHLVENESTJOR MARVNM DNKZAF SDN QATAZEISLPAKHRVSRB PER GRCJPNIRNNZLNGAIDNSVKKAZUKRARGMYSBGREGYFINNORHUNCOL PRT CZECHLISRROU AUT THASAUTURINDKORLUXPOLAUSSWECHEBRARUSMEXCANSGPNLDITAESPDEUHKGBELFRAGBRUSA<br>MKD JAM<br>ARM NPL<br>PRY<br>MUS<br>TTO<br>−4 −2 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14<br>ln(cumulated outward UNCTAD−BOP FDI values) ln(cumulated inward UNCTAD−BOP FDI values)<br>12 12<br>10<br>10<br>8<br>8<br>6<br>6<br>4<br>4<br>2<br>ln(cumulated outward greenfield FDI values ) ln(cumulated inward greenfield FDI values ) 2<br>0<br>0<br><!-- End of picture text --> Note: Cumulated values over the period 2003-2006.

which reflects the actual use of external debt financing in the economy, has been extensively used in the growth, finance, and international trade literature (Levine, 2005). Data come from Beck et al. (2009). We lag this variable by one year to reduce any potential simultaneity bias and we adopt a logarithmic transformation to attenuate the influence of outlying values. The private credit to GDP ratio varies a lot across countries with a mean value of 56% and a standard deviation of 50% over the 2003-2006 period.

As an alternative outcome-based measure of financial development, we also use the stock market capitalization to GDP ratio, also expressed in logarithmic form and lagged by one year. While the private credit to GDP ratio and the stock market capitalization to GDP ratio conceptually capture different sources of external finance, debt financing and equity financing, they may in practice act as proxies for a common factor.

We also verify that our results are robust to a time-invariant institution-based measure of financial development. This measure corresponds to the log of the average values of two _World Bank Doing Business_ indexes measuring the quality of financial institutions: the strength of legal rights index, which indicates “the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders,” and the depth of credit information index, which assesses “the rules and practices affecting the coverage, scope and accessibility of credit 16
at time _t_ . The main result from column (1) is that none of the eects of the policy variables in our model, including the impact of trade agreements and BITs, are statistically signicant. A possible explanation for this result is that some of the most signicant determinants of FDI are country-specic variables on the origin and/or on the destination side (e.g., corporate tax rate, corruption, bureaucratic red tape, quality of institutions, etc.). However, such determinants are fully controlled for and absorbed by the origin-time and the destinationtime xed eects in our specication. Moreover, it is also possible (cf. Eicher et al., 2012; Blonigen and Piger, 2014) that a number of time invariant characteristics (e.g., bilateral distance, common ocial language, etc.) are important for FDI. However, similar to the country-specic variables, these eects are also absorbed in our econometric model (by the pair xed eects). Our nding on the insignicant impact of BITs may seem particularly strange, however, this result is common in the related literature, cf. Lesher and Miroudot (2006) and Laget et al. (2021).

Next, in column (2) of Table 2 we allow for heterogeneous eects between shallow vs. deep trade agreements by using the same _DTA_ variable which we constructed for our trade regressions. Even though the estimate on _DTAij,t_ is positive, it is economically small and not statistically signicant. Thus, unlike their signicant impact on trade, our estimates suggest that DTAs per se do not promote FDI.

Motivated by Crawford and Kotschwar (2020) and Laget et al. (2021), in our next specication (in column (3) of Table 2), we isolate the eects of DTAs that include investment provisions. To this end, we again rely on the World Bank's DCRTA, cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), which includes 66 possible investment provisions. Based on this information, we construct a dummy variable, _INVij,t_ , which takes a value of one if an agreement includes at least one investment provision, and it is equal to zero otherwise. Thus, by construction, the observations that take a value of one in the _INVij,t_ indicator are a subset of the observations that are equal to one in the _DTAij,t_ dummy from column (2).

The main nding from column (3) is that we obtain a positive, sizable, and statistically 26
second set of regressions on the full sample that control for supply-side effects via dummy variables corresponding to Ghana’s ten regions.

We recognize, however, that the availability of financial services at the local level might not be well captured by regional dummy variables and so we run a final set of regressions that include dummy variables for each enumeration area from which we drew observations. Within these local sampling areas, fifteen households were randomly selected to answer the Financial Services Survey, and then each was randomly assigned a question format so that one-third received questionnaire 1, one-third questionnaire 2, and the final third questionnaire 3. This is therefore a highly localized control variable.

Our main conclusions remain intact when we control for supply-side effects in these ways. As in the base regressions, household usage is very similar for full enumeration and when the head of household is the informant, and reported usage of banking services is significantly lower for the random informant (Appendix B). For individual usage, results are also very similar to the base regressions (Appendix C). Reported usage is significantly higher for product-based questions for informal savings, formal credit, and insurance. This pattern holds for the limited sample when we include the question on travel time to the nearest bank, and for the broader sample when we include region or enumeration area fixed effects. It seems unlikely therefore that the omission of supply-side variables from our base regressions could be driving our results.

# **6.  Conclusions**

Measuring the breadth of outreach of financial sectors in developing countries remains a challenge, but one that must be met if we are to better understand how financial services (or their absence) affects the livelihoods of the poor. Surveys of individuals and 34
records."<sup>4</sup> Monitoring firms with a record of previous violation of environmental regulations more frequently than firms with no such record is found to lower the enforcement costs of obtaining a given aggregate level of compliance from the regulated industry. That is, costs are lower when past performance is used as a factor in targeting enforcement expenditures than when all firms are monitored with the same frequency regardless of their past performance. This feature of optimal enforcement practice emphasizes the importance of accurate record keeping regarding (non) compliance.

# D. Constraints on Environmental Policy in Developing Countries

There are a number of differences between developing and developed countries that should be considered when setting enviLonmental policy in the former. Among these differences are the facts that in developing countries per capita income is much lower, access to quality medical care is lower, the baseline level of pollution control is lower, the capital base is often older and not as well maintained, institutions in charge of administering fiscal reforms and promulgating and enforcing environmental policies are generally weaker (among other things this means the expertise, funds, and technology for data collection and monitoring are often lacking), and the demand on the public sector for the provision of public goods is greater while the revenue base is much narrower.

53
**Evidence shows that new SOEs have been created across several markets after 2011** . About 52 percent of the 1,399 SOEs  matched to the MoF firm-level data were incorporated between 2011 and 2019 ( <mark>Figure</mark> 3). The proportion of 2-digit sectors with at least one SOE present increased over the sample period of 2011-2020 by about 3 percentage points from 75 percent to 78 percent.

Figure 3: SOE Creation and Presence Across Sectors <!-- Start of picture text --> (a) New SOEs Created (b) Percent of Two-digit Sectors with at Least<br>One SOE Present<br>160 79.0%<br>140<br>78.0%<br>120<br>77.0%<br>100<br>80 76.0%<br>60<br>75.0%<br>40<br>74.0%<br>20<br>0 73.0%<br>1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> Source: World Bank Global Businesses of the State (BOS) database, 2019. In the database, SOEs are corporations where national or subnational governments have ownership stake of at least 10%, either directly or indirectly. Corporations are business entities that are (a) capable of generating a profit or other financial gain for their owners, (b) recognized by law as legal entities separate from their owners who enjoy limited liability, and (c) set up for purposes of engaging in market production (See World Bank 2022 for details).

**The number of firm observations increased over the sample period, possibly confirming new business creation.** Table 3 presents the distribution of firms–private firms and SOE–with revenue and employment data for the period 2011-2019. On average, SOEs represent about 0.24 percent of the sample, with most of the SOEs being directly owned by governments (88 percent) and owned by local or municipal governments (73 percent). Out of a total 613 4-digit NACE industries, SOEs in Romania operate in 192 different 4-digit NACE industries, representing about 31 percent of industries. There is no industry where an SOE is a monopoly. Overall, total sales of SOEs are greater than that of POEs in 24 industries, and in 27 industries, the total employment of SOEs is greater than that of POEs. Table 4 presents the distribution of firms with revenue and employment data for the period 2011-2019 across the sectoral taxonomies developed by Dall'Olio et al. (2022). Approximately 93.5 percent of firm-year observations are in competitive sectors, about 2.1 percent in partially contestable sectors, just 0.5 percent in natural monopoly sectors, and the remainder in sectors that are excluded from the taxonomy. There is a larger proportion of SOEs observations, about 10.3 percent, in natural monopoly sectors relative to competitive sectors, which feature just about 0.15 percent.

Table 3: Distribution of Firm-Year Observations (with Revenue and Employment Data), 2011-2019

||Observ|ations||State|-owned enterpri|ses (SOEs)||
|---|---|---|---|---|---|---|---|
|Year|All firms|SOEs|% Of<br>firms|% Directly<br>owned|% Indirectly<br>owned|% Centrally<br>owned|% Locally<br>owned|
|2011|393,578|841|0.21|84.3|15.7|30.0|66.0|
|2012|409,253|903|0.22|85.5|14.5|27.6|68.4|
|2013|419,707|986|0.23|86.3|13.7|26.0|70.3|

11
the country by the US as food aid, and a rich set of characteristics of countries and years that they use as controls.<sup>2</sup> Using these data, they estimate two types of regressions to describe the relationship between food aid and conflict. The first is an ordinary least squares (OLS) specification where the indicator variable for the presence of conflict in a given country during a given year is regressed on the quantity of wheat food aid delivered to that country, including country and time fixed effects and a broad set of controls. In this specification, additional food aid is associated with a lower incidence of conflict, but this result is not statistically significant. This result implies with within countries, years when a greater quantity of wheat aid is received from the United States are associated with no greater risk of conflict, and within years, countries that receive a greater quantity of wheat aid have no greater risk of conflict. NQ justifiably worry that this relationship could be biased, however, because food aid deliveries may be endogenous to conflict incidence if the US government either prefers to send aid to conflict affected countries, which would bias the OLS estimate upward, or avoids sending aid to such countries, which would bias the estimate downward.

To obtain a causal estimate of the effect of food aid on conflict, they estimate a two stage least squares (2SLS) specification using an IV constructed from the interaction of lagged annual total wheat production in the US with a country’s propensity to receive food aid over time, defined as the proportion of the 36 years that the country received at least some food aid in the form of wheat from the US. Their justification for such an instrument is that US domestic commodity price stabilization policies obligate the US Department of Agriculture (USDA) to purchase wheat in high production years in order to keep supply shocks from

> 2 The main variables of interest are taken from the UCDP/PRIO Armed Conflict Dataset Version 4-2010 (conflict), the Food and Agriculture Organization’s (FAO) FAOSTAT database (food aid deliveries), and the USDA (wheat production).

5
student performance was positively related to mathematics achievement in Swaziland.

SMall grou2 instruction. Small group instruction takes the form of teacher-led or student-led instructional groups, cooperative learning groups, and peer tutoring (cross-age or same-age). Studies of peer tutoring effects on achievement are consistently positive (Allen, 1976), and peer tutoring has recently been identified as a highly cost-effective teaching practice (Levin, Glass and Meister, 1984). Although observational studies rarely have exemined cooperative group effects on achievement (Brophy & Good, 1986), results from experimental studies show strong positive effects (Slavin, 1980; Sharan, 1980).

Practical activities. Research from industrialized countries provides evidence that children's scientific learning is enhar:ced by activity-based, experimentative, science instruction. A review of 57 studies of the effects of three types of activity-based elementary science programs compared with regular science instruction, found that the overall mean effect size was .52 for science process tests and .16 for tests of science content, with disadvantaged students gaining more than other students.from the programs (Bredderman, 1984). The low effect size (.16) for science content indicates that the activity based programs were no different from regular programs in teaching scientific content; they were significantly more effective in teaching scientific literacy, however. Haddad (1986) also notes that practical activities in science teaching seem to be important for elementary school students at the concrete stage of 10
<!-- Start of picture text --> ygars of education m Interquartile range<br>° * Mean<br>8 2]<br>4 a<br>I<br>‘<br>0—_ss_ ls<br>Club 1 (High) Club 2 Club 3 Club 4 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> “99 productivity = Interquartile range<br>| @ |<br>: Mean<br>8 gq<br>6 —_Sc_<br>Club 1 (High) Club 2 Club3 Club 4 (Low)<br><!-- End of picture text -->

<!-- Start of picture text --> Political stability m Interquartile range<br>1.6<br>0.812 = [| Mean<br>0.0<br>-0.804 [|<br>-1.2<br>Club 1 Club 2 Club3 = Club 4 (Low)<br>(High)<br><!-- End of picture text -->

<!-- Start of picture text --> Eeonomic complexity index u Interquartile range<br>Lc .<br>[]<br>ra<br>-2<br>Club 1 (High) Club 2 Club3 = Club 4 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> patio of trade to GDP m Interquartile range<br>Mean<br>08 > e<br>. Eg<br>Sse<br>0.0) ———<br>Club 1 (High) Club 2 Club 3 Club 4 (Low)<br><!-- End of picture text --> <!-- Start of picture text --> Investment (per cent GDP) @ Interquartile range<br>0.3<br>* Mean<br>02 =a a ]<br>0.1<br>0<br>Club 1 (High) Club 2 Club 3 Club 4 (Low)<br><!-- End of picture text -->