rafmacalaba/datause-encoder-data huggingface dataset:
significance, the 2SLS results thus cast some doubt as to whether voluntary information disclosure reduces financing obstacles.

# **C. Matching**

As an alternative to 2SLS estimation, whose validity hinges on the validity of the IV, we also use two matching methods to shed light on the effect of _Audit_ on financing obstacles: propensity score matching and direct matching. The advantage of these methods is that it is transparent, easier to understand, and intuitive; its disadvantages is that it does not handle the issue of selection on unobservables. To ensure that our key results do not rest on fragile identifying assumptions, we thus also provide the matching results; we shall draw our main conclusions based on the findings from both 2SLS and the matching results.<sup>15</sup> To conduct propensity score matching analysis, we first use the full regression specification in Table 3 to estimate a propensity score, which is the probability that a given firm would have its financial statements audited. We then match the audited firm with an unaudited firm using the nearest neighbor method. We match with replacement (i.e., allow a control firm to be used multiple times as potential match for various treated firms) in the region of common support, which ensures that the matches do not fall outside of the range of propensity values given by the treated group.

The results of propensity score matching are reported in Table 4 Panel A. The second row, labeled ‘Matched’, compares the treated firms to their counterparts based on the nearest matched nonaudited firms. The results show that audited firms experience a slightly lower level of financial obstacles (i.e., the average treatment effect on the treated, or ATT) and it is only marginally significant when measured by _FinancialObstacleDummy._

> 15 In other words, we shall assign the weight of zero for the findings associated with the OLS results since the underlying selectivity of auditing is quite clear.

16
_Table 4: Aid for Trade Facilitation: Gravity Model with Dummy Variables_

||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|
|---|---|---|---|---|---|---|---|
||Baseline –<br>all positive<br>trade|Baseline<br>with per<br>capita<br>GDP|Only<br>countries<br>receiving<br>AfTF /1/|Recipients<br>only; trade<br>with<br>recipients<br>& ROW|Recipients<br>with<br>developing<br>world only|Recipients<br>with<br>developing<br>& bilateral<br>donor; no<br>intra-donor<br>|Base-line<br>(2) with<br>lags|
|||||||trade||
|ln_dist|-1.117***|-1.110***|-1.318***|-1.162***|-1.331***|-1.136***|-0.912***|
||[0.009]|[0.009]|[0.033]|[0.015]|[0.013]|[0.009]|[0.013]|
|ln_tariff|-0.141***|-0.246***|-0.320***|-0.300***|-0.245***|-0.252***|-0.356***|
||[0.021]|[0.022]|[0.066]|[0.030]|[0.027]|[0.022]|[0.040]|
|ln_rep_gdp|1.085***|1.169***|1.107***|1.202***|1.116***|1.184***|1.198***|
||[0.004]|[0.006]|[0.027]|[0.009]|[0.009]|[0.006]|[0.010]|
|ln_part_gdp|1.269***|1.244***|1.195***|1.322***|1.271***|1.254***|1.196***|
||[0.004]|[0.006]|[0.026]|[0.009]|[0.008]|[0.006]|[0.008]|
|ln_itfa|**0.009*****|**0.000**|**0.125*****|**0.002**|**0.006*****|**0.000**|**-0.016*****|
||[0.001]|[0.001]|[0.019]|[0.002]|[0.001]|[0.001]|[0.003]|
|ln_etfa|**0.012*****|**0.015*****|**0.219*****|**0.011*****|**0.011*****|**0.015*****|**0.011*****|
||[0.001]|[0.001]|[0.020]|[0.002]|[0.001]|[0.001]|[0.002]|
|comcol|1.004***|0.974***|0.794***|0.832***|0.980***|0.966***|1.292***|
||[0.034]|[0.034]|[0.075]|[0.043]|[0.037]|[0.034]|[0.077]|
|curcol|-0.897**|-0.958**|0.000|-3.119***|-3.085***|-1.009**|-0.007|
||[0.412]|[0.409]|[0.000]|[1.118]|[0.551]|[0.405]|[0.256]|
|comlang_off|0.892***|0.912***|0.936***|0.830***|0.657***|0.926***|0.936***|
||[0.019]|[0.019]|[0.060]|[0.028]|[0.030]|[0.020]|[0.028]|
|contig|1.076***|1.057***|1.255***|1.264***|1.114***|1.171***|0.833***|
||[0.048]|[0.048]|[0.111]|[0.076]|[0.053]|[0.049]|[0.071]|
|smctry|0.876***|0.882***|0.638***|0.805***|0.605***|0.774***|1.171***|
||[0.067]|[0.067]|[0.151]|[0.100]|[0.071]|[0.068]|[0.112]|
|ln_area_rep|-0.117***|-0.169***|-0.197***|-0.186***|-0.165***|-0.181***|-0.191***|
||[0.004]|[0.005]|[0.020]|[0.008]|[0.007]|[0.005]|[0.008]|
|ln_area_part|-0.096***|-0.078***|0.022|-0.098***|-0.116***|-0.081***|-0.052***|
||[0.004]|[0.005]|[0.020]|[0.008]|[0.007]|[0.005]|[0.006]|
|ln_rep_gdp_cap||-0.167***|-0.054|-0.154***|-0.142***|-0.179***|-0.330***|
|||[0.009]|[0.037]|[0.014]|[0.013]|[0.009]|[0.017]|
|ln_part_gdp_cap||0.046***|0.227***|0.005|-0.014|0.043***|0.068***|
|||[0.008]|[0.037]|[0.013]|[0.012]|[0.008]|[0.011]|
|L.ln_itfa|||||||0.002<br>[0.003]|
|L2.ln_itfa|||||||-0.006**<br>[0.003]|
|L.ln_etfa|||||||0.006***<br>[0.002]|
|L2.ln_etfa|||||||0.009***|
||||||||[0.002]|

16
The dataset is a four-round panel survey and the data collection covered the years from 1991 to 1994. The primary objective of this Kagera Health and Development Survey (KHDS) was to estimate the economic impact of the death of prime-age adults on surviving household members. Thus, the research focused on the collection of detailed socioeconomic information from individuals who resided in a household in which one or more adult family members indicated they were in poor health at the time of the baseline (Ainsworth and Semali, 2001).

The sample of households was selected from a stratified random sample of communities from the 1988 Tanzanian census (stratified on agroclimatic zone and adult mortality rate).<sup>1</sup> Within each community, the household sample was stratified according to the anticipated households’ risk of suffering a prime-age adult death. Households were divided in “high-risk” or “low-risk” categories, based on information obtained from a houseto-house enumeration of all selected communities.

Altogether, 915 households were effectively interviewed at least once over the 4 waves of the survey, and up to a maximum of four times.<sup>2</sup> Individuals belonging to the same household were defined as a group of people living and sharing meals together for at least 3 months in the last year (Ainsworth and Semali 2001). This survey collects information that makes it particularly appropriate for the purpose of our research, in particular the extensive information on household income and consumption and on individual’s health status and anthropometrics. For the present analysis, which focuses on the children’s nutritional status, we focus on the height, weight and demographic information of children aged 5 years or below and the characteristics of the households and community to which the children belong. Overall, the total sample of children surveyed amounts to 1140 different children, visited between one and four times and making up an unbalanced panel of 2871 personwave observations.

> 1Both the prevalence of HIV and adult mortality rates in Kagera were geographically concentrated, and, therefore are strongly correlated with different climates and cropping patterns. The region with the highest rural HIV infection rates was in the northeast (10% in Bukoba Rural and Muleba districts and 24% in the town of Bukoba), where tree crops (bananas, coffee) were predominant. The regions with the lowest HIV infection rates were in the south and west (0.4% in Ngara and Biharamulo districts), where perennial crops and livestock are more common (Killewo and others 1990).  Thus, a survey design stratified only on mortality rates might confound the effects of high mortality with different agricultural, soil, and rainfall patterns.

> 2 Some households dropped from the sample at each round and they were replaced. Out of the original sample, 6 percent of households dropped before their first interview. Out of the remaining sample of households, 10 percent dropped out of the remaining sample before the end of the survey (Ainsworth and Semali, 2001). The attrition rate compares favorably to other panel datasets.

3
attempt to enter a new product or a new market. Through contractual arrangement, Original Equipment Manufacturer (OEM), or Original Design Manufacturer (ODM), and joint ventures, foreign investors allowed China to utilize the advantage of backwardness through learning by emulating, and eliminate the risks for innovation. Between 1993 and 2005, 70-80 percent of FDI inflows were concentrated in manufacturing which helped capital deepening as China‘s capital-labor ratio increased from 0.4 in 1985 to nearly 4.0 in the manufacturing sector. _―Foreign Invested enterprises (FIEs) created about 20 percent of tax revenue, 55% of import and export, and over 80% of high-tech product export‖_ (MOFCOM 2010).

- In the last five years (2005-09), **FDI inflows are** shifting toward higher value added sectors including services, as showed by Figure 3.4. Taiwanese investors have also provided much needed technology and managerial skills that Chinese firms need in electronics and information technology.  Wholesale and retailing have shown the fastest growth rate in recent years as China moves more toward promoting domestic consumption.

- The process of 3-stage upgrading shows the importance of <u>learning by importing, to learning by exporting; from lower end manufacturing goods  to higher value added goods,</u> and then to services.  Initial learning activities were within sectors, and gradually spilled over horizontally to other new sectors, and eventually diversified through  outward FDI to other countries (Lin and Yan Wang 2008).

**Figure 3.4. Changing Structure of FDI flows to China: from Manufacturing to services: Growth of approved Taiwanese outward foreign direct investment into China by Industry,** **<u>2005-2009 (%)</u>**

<!-- Start of picture text --> Fabricated Metal Products Manufacturing<br>Chemical Material Manufacturing<br>Computers, Electronic and Optical Products ...<br>Electrical Equipment Manufacturing<br>Basic Metal Manufacturing<br>Non-metallic Mineral Products Manufacturing<br>Machinery and Equipment Manufacturing<br>Plastic Products Manufacturing<br>Electronic Parts and Components Manufacturing High Value Added<br>Wholesale and Retail Trade Service sector<br>-20 -15 -10 -5 0 5 10 15 20 25<br>Source: Rosen and Zhi Wang, 2011. Page 35 based on data from the MOEA, Taiwan (China), approved direct<br>investment to mainland China- rapid growth of service sectors.<br><!-- End of picture text --> In sum, in this section we have argued that industrialization failed in many parts of the world for various reasons. Import substitution and protectionism in the 1970s and 1980s played an 41
well as changes in the ratio of the consumer price index and the NDP deflator.

Similarly to the results for urban-rural growth, there is a marked contrast between the two periods. For all three poverty measures we find that the sectoral pattern of NDP growth mattered in the pre-1991 period. Growth in both the primary and tertiary sectors was poverty reducing, but this was not so for the secondary sector. Note that the coefficients on secondary and tertiary are of similar magnitude but opposite in sign. This suggests that it is really the difference in the growth rates between the two sectors that matters. Given that those working in the secondary sector pre-1991 are likely to be relatively well off compared to the tertiary sector, the pattern seen in these results can be interpreted as an effect of inequality on poverty reduction, whereby higher secondary sector growth relative to tertiary implies greater inequality and hence higher poverty.

By contrast, we find that the pattern of growth does not matter in the post-1991 period; instead, it is the overall rate of growth that drives poverty reduction. By implication, the most notable change is the significant poverty-reducing effect of secondary sector growth. A plausible explanation is the rapid growth in the labor-intensive construction sector during this period.

**_NAS-NSS drift:_** This result on the post-1991 sector neutrality of poverty effects of growth is open to a potential empirical challenge.  As noted in Section 2, there has been a growing drift between the series on consumption derived from NAS and that from the NSS, with the drift being particularly pronounced since the 1990s.  If the drift is reasonably neutral to sector then our main conclusion will not alter.  However, insofar as the drift may reflect “missing” growth in the surveys, then it is likely that this missing component is higher for the fastest growing sectors, in particular, the tertiary sector in the post-1991 period.  There is thus a concern that absent any control for the drift, the sector-neutrality result could be biased by the drift being correlated with the tertiary sector growth rate.

To test for this possibility we re-estimated (11) adding a control for the drift between NAS and NSS mean consumption. Here our reasoning is as follows: Suppose the true values poverty and growth variables are denoted with a *. Then, the regression specification (suppressing sectoral terms) in terms of the true data is:

* _P_ The true values are related to the observed values by  ln _Pt_   ln _Pt_  _t_ and

25
- [28] Mankiw, N.G., 2000. “The savers-spenders theory of fiscal policy.”American Economic Review 90 (May (2)), 120—125.

- [29] Obstfeld, M. 1994, "Risk-taking, Global Diversification, and Growth," American Economic Review 84, 1310-29.

- [30] Ostergaard, C., B. E. Sorensen, and O. Yosha. 2002. “Consumption and Aggregate Constraints: Evidence from US States and Canadian Provinces.”, Journal of Political Economy, 110, 634-45.

- [31] Quah, D. 1990. “Permanent and Transitory Movements in Labor Income: An Explanation for the “Excess Smoothness”in Consumption.”Journal of Political Economy, June, 98:3, 449-475.

- [32] Quinn, D., 1997, “The Correlates of Change in International Financial Regulation,”American Political Science Review, Vol. 91, pp. 531—51.

- [33] Quinn, D. P., M. Schindler, and A. M. Toyoda. 2011. "Assessing Measures of Financial Openness and Integration." IMF Economic Review. 59(3):488-522

- [34] Sørensen, B. E., Y. Oved, Y. Wu, Z. Yu. 2007. "Home bias and international risk sharing: twin puzzles separated at birth." Journal of International Money and Finance 26 (4), 587—605.

- [35] Weber, C.E., 2002. “Intertemporal non-separability and “rule-of-thumb” consumption.” Journal of Monetary Economics, 49, 293-308.

15
43 inflationary, on the contraryl This constitutes without doubt the basic premise on which the economic team acted and the major departure from the earlier strategy that had emphasized adjustment from the demand side" Peru learned in 1987-88 that continued rapid growth and massive real wage increases are incompatible with m;oderate inflation. In 1986, inflation was only 63 percent, far below the level at which the program started. But the experience of high growth with moderate inflation, after a while, became an artifact of the controls and subsidies on public sector prices and on foreign exchange. Subsidies and controls were used to avoid price increases in politically sensitive areas. In 1987-88, most petroleum products sold at one-third their July 1985 price. Electricity prices, the price of rice, and bus fares had declined by more than a third, as had the real price of foreign exchange.

Table 6 Real Levels of Controlled Prices (July 1985-100)

||Dec.1986|Dec.|1987|Dec. 1988|
|---|---|---|---|---|
|Averagea|75|59||58|
|Rice|73|61||47|
|Light Elect.|<br>49|40||16|
|Telephone|84|87||32|
|Gas|58|33||25|

aWeighted by expenditure shares Source: ADovo When adjustments in the controlled prices had to be made, inflation accelerated rapidly. Initially, the real wage gains and demand expansion pushed up inflation, but the catching up of lagging prices became
component is to provide technical assistance to both private and social organizations to better reach the poorest, by means of social innovation pilots with strong expansion potential, as well as promoting a culture of social innovation.

# **Implementation challenges**

Evaluations of _Red Unidos_ showed that it had an impact on the life of the poor, but in its first years, several implementation challenges may have prevented exploiting its full potential (Econometría Consultores et al 2012). Among the challenges, sporadic visits of _Cogestores_ ; the quality of services provided (in particular in remote areas); and lack of coordination stood out. There was, for instance, no inventory of existing social services at the local level, which made it difficult to establish whether households were able or not to access services. Decisions on preferential access made at the national level were also not systematically transmitted to the regional and program levels. Further, while budget was devoted to _Cogestores_ , social spending aimed at _Red Unidos_ beneficiaries was managed by different institutions (the Ministry of Social Protection, Ministry of Education, among others; see Quiroga, et al. 2010). This contrasts with the model of _Chile Solidario_ , where most of the budget was devoted to grant and finance preferential access to its beneficiaries.

There are also indications that in its early years, the intervention was not meeting its intended informational and guidance roles. Some households in the sample reported that they did not know about the existence of some programs or about their right to access them. Households also complained about the ease of access of social programs, such as hours of operation, and cost and availability of transportation, particularly for households living in rural areas, though not a _Red Unidos_ issue. Similarly, there was a perception of receiving “unequal” treatment by government employees, just for the fact of being poor or displaced (Econometría Consultores et al 2012).

According to a representative household panel survey, the ELCA 2013, _Red Unidos_ is favorably viewed by 52% of beneficiaries, as compared to a favorable rating of 92% of _Familias en Acción_ (Camacho 2014). This seemingly low rating may be due to a culture where people are accustomed to receiving money or goods rather than guidance and psychological support. This is demonstrated by the fact that households recognized that they had learned from _cogestores_ , but also claimed that the improvement in their life 23
Figure 2 identifies the products, which, between 1990 and 2002, underwent a variation above the general average for import rates, measured in terms of the ratio of import to domestic production values. This group encompasses the major enterprises which had demanded trade defense measures — among others, manufacturers of artificial yarn and fabrics whose share of imports in domestic production rose from 2.9% in 1990 to 29.6% in 2002; pharmaceutical and cosmetic products, from 7.1% to 27.9%; chemical products, from 6.7% to 16.7%; rubber, from 4.9% to 13.1%; plastic products, from 2.2% to 10.4% and metals from 2.5% to 8.2%. Other sectors, such as electronics, motorcars and vehicles and auto parts were given additional protection through an increase in tariffs.

Figure 2 The Ratio of Import to Domestic Production values for Certain Products: 1990 to 2002 (%) <!-- Start of picture text --> 1990 2002<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Yarn & artificial Tractores Plastic artilces Chemistry Electric material Auto parts & motor Metals Electronic equip Fuel oils Rubber Chemicals Other minerals Wheat Machines Coal Total<br><!-- End of picture text --> Source: Brazil’s national accounts, IBGE, several years. Author's calculations.

# **3. The Brazilian Antidumping and Safeguard System**

# **3.1 Legal, Institutional and Operational Aspects**

Until 1988, Brazil had two instruments to fight dumping which enabled the fiscal value of imported goods to be changed in order to estimate import duties. One of them, known as the <u>minimum</u> 10
boundary where control was contested. As such, conflict or violence may be the source of the negative effects in the development outcomes.

We test this mechanism in several ways. First, we estimate equation (1), controlling for the segments of disputed areas where the Salvadoran government and the guerrillas usually fought. Second, we use a doughnut-hole approach to exclude all observations within 80 m from the boundary of guerrilla-held territories.<sup>34</sup> Results for the main outcomes of interest from these exercises are in Table 9. In general, the coefficients are negative, statistically significant, and similar in size. This suggests conflict is not the main factor behind the negative effects. We obtain similar results using outcomes of war crimes, including number of deaths, disappearances, and other conflict-related crimes as reported by the Truth Commission. Results in Table D.24 once again support the idea that areas under guerrilla control did not experience disproportionately higher crimes relative to other areas. The negative coefficient associated with the war crime estimates suggests that former guerrilla areas experienced less crimes, leading to lower-bound estimates of our main outcomes.

Finally, we appraise the role of guerrilla control in contemporaneous measures of crime to judge whether the historical presence of guerrillas prevented the development of criminal actors such as gangs. The social capital in former guerrilla areas may have done so (Sviatschi, 2020); tightly knit communities with strong social ties can better prevent crime because they raise detection probabilities and attach shame to criminal behaviors (Buonanno, Montolio and Vanin, 2009). If social capital lasts, we expect fewer crimes linked to non-state armed actors, which are pervasive in El Salvador. But if our results stemmed from violence during or after guerrilla control of these areas, we should expect more violence today. To test these hypotheses, we considered homicide rates during 2017 using police data, and victimization rates from 2004 to 2016 in LAPOP surveys.

Table D.25 in the Appendix presents the results. Consistent with the finding that violence during the conflict was not greater in guerrilla-controlled areas, the results largely suggest no differences in homicide rates between areas once under and outside FMLN control.<sup>35</sup> If anything, the estimates are negative, which suggests that the documented differences in long-term development did not arise from increases in conflict or violence. Additionally, there is evidence that residents of

> 34 We excluded all segments within an 80 m distance to shut off almost every segment close to the boundary and inside the guerrilla zone with an immediate neighbor outside it.

> 35Figures D.17 and D.18 confirm this result for different bandwidths.

40