rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

The availability of detailed firm level datasets has led to the emergence of a new line of research that relates openness to trade with firm performance. Both theoretical and empirical findings, as reviewed in Bernard et al. (2007) and Lopez (2005), show that firms that are engaged in international trade are larger and more productive than the ones that serve only domestic markets. This study contributes to the existing literature in two ways. It shows that not only exporting but also importing intermediate goods is related to better firm performance in growth and in introducing technological innovations. The firms that are exposed to foreign markets by both importing and exporting are the best performers in the economy and they are usually followed by only exporters and by only importers in respective order.

In this study, using a detailed dataset from the manufacturing sectors of 43 developing countries, I provide evidence on the positive relation between foreign exposure of firms and their evolution. The analysis has two novel features. First, I provide a complete view of the trading activities of firms by grouping them according to their exposure to foreign markets. I distinguish firms that both import and export (two-way traders), that only export, and that only import. This allows me to compare firms’ evolution with different levels of foreign exposure. Second, such detailed analysis is scarce for developing countries. As a result of globalization, firms in developing countries have been increasing their engagements with the rest of the world and determining how these engagements affect their evolution is important for identifying the right trade policies.

Recent models of trade and firm heterogeneity have mostly evolved around the exporting behavior of firms. Many studies since Bernard and Jensen (1995) have shown that exporters outperform non-exporters in many dimensions. Different explanations have been proposed for these persistent differences across firms among which the most acknowledged one is the selfselection of productive performers into foreign markets. Exporting requires extra sunk costs and only the most productive firms can compensate these costs. Clerides et al. (1998), Bernard and Jensen (1999), and Aw et al. (1998) provide empirical evidence for the self-selection hypothesis and theoretical models like Melitz (2003) and Bernard et al. (2003) build this stylized fact into general equilibrium trade models.

Another activity that is equally crucial as exporting for firms’ better performance is importing. In his survey on technology diffusion, Keller (2004) summarizes theoretical and empirical literature on how imports provide knowledge and technology transfer in a macro perspective. In studies like Romer (1990), Grossman and Helpman (1991), Kortum (1997), and 2
in this shift. Dani Rodrik recently emphasized, among the major features of the so-called new development economics, its being diagnostic rather than presumptive, experimental (in the broad sense of “a predisposition to find out what works through policy innovation”), based on monitoring and evaluation, aimed at selective and targeted reforms, suspicious of universal recipes and best practices and, instead, focused on policy reforms (Rodrik 2008, pp. 27-28). These were also characteristics of Hirschman’s analysis of development projects and policies. During the 1980s and early 1990s, and until the disavowal of the mid-1990s, the World Bank often applied a universalistic, one-size-fits-all approach, especially with its emphasis on macro policies (World Bank 2005). It is thus interesting to note that well before, in the early 1960s, the Bank was agreeing with Hirschman on positions that are in tune with today’s most articulate analyses of development economics. At that time, although it was perhaps a rather experimental stage, nobody in the business of development doubted the importance of building development policies based on thorough case diagnostics.

# **5. Diverging views on project appraisal**

Albert Hirschman and his wife, Sarah, spent one year between July 1964 and August 1965 traveling to four continents. Sarah was not simply an accompanying spouse but, as often in Albert Hirschman’s work, a partner in field research and an important intellectual interlocutor. Her contribution was fundamental in the late 1950s, enabling Albert Hirschman to expand his 1958 book into the sort of anthropological literature relevant to development studies. Likewise, she participated in field research during the summer of 1960 in Mexico, Colombia, Chile, Argentina and Brazil that would serve as the basis for Hirschman’s 1963 book, _Journeys toward Progress_ .<sup>29</sup> In 1964-65 Sarah was again “a member of the ‘team’,” and this explains why several field notes we will be using in the following pages are authored by her.<sup>30</sup> Once returned from his travels, Albert Hirschman circulated a memo with some preliminary observations.<sup>31</sup> These focused on what Hirschman called “Behavioral Characteristics of

> information about the Bank’s evaluation function, see World Bank 2006a and 2006b.

> 29 Hirschman 1958, p. vi, and Hirschman 1963, p. ix. Charles E. Lindblom was the third participant in the 1960 Latin American trip.

> 30 Albert O. Hirschman to Dean [of Harvard’s Graduate School of Public Administration] Don K. Price, October 19, 1964, Albert O. Hirschman papers, Box 57, Folder 5.

> 31 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

15
# **7. Long-Term Effects of the Ban**

To estimate long-term effects of the ban, we follow the same cohorts examined in the short-term analysis between 2007 and 2014. During this period, the affected cohort was aged 22 to 29.<sup>29</sup> We pool the PNADs of 2007-2014 and run the same Eq. (1) controlling for survey-year dummies. We turn attention to five outcomes: completed years of schooling, probability of having at least a high-school degree, probability of having a college degree, probability of being employed, probability of being employed in the formal sector, and log of monthly earnings. As before, for binary outcomes, we estimate linear probability models.

# _Parametric Results_

We use the same three specifications used in the short-term analysis and a sixmonth bandwidth since we have higher statistical power. The results are presented in table 5.

According to the results shown in table 5, there is no evidence of long-term effects of the ban on stock of human capital and labor-market outcomes, at least when all years are pooled together.

Table 6 in the appendix shows the placebo test for long-term effects using the cohorts born in 1983 and 1984. These cohorts turned 14 before and after December 1997 and were not affected by the ban.<sup>30</sup> Even though the coefficient for college degree stands out statistically significant at 5 percent, overall, the results are statistically insignificant.

# _Long-Term Trajectories from Ages 22 to 29_

We investigate further the long-term effects by looking at trajectories from 2007 to 2014. In this analysis, we estimate Eq. (1) for each year individually and use a bandwidth of nine months to increase precision. The point estimates with 90-percent confidence interval are shown in figures 15 to 20.

> 29 The choice of 2007 as first year of the long term effects needs more clarification indeed. We used 2007 because 22-23 is the expected age of a college graduating student.

> 30 The same cohorts were used in one of the placebo tests in the short-term analysis.

23
Berlinski, S., Galiani, S., & Manacorda, M. (2008). Giving children a better start: Preschool attendance and school-age profiles. _Journal of Public Economics,_ 92(5-6), 1416-1440.

Britto, P. R., et al. (2014). Strengthening systems for integrated early childhood development services: a cross-national analysis of governance. _Ann N Y Acad Sci,_ 1308, 245-255.

Bruhn, M., & McKenzie, D. (2009). In Pursuit of Balance: Randomization in Practice in Development Field Experiments. _American Economic Journal: Applied Economics,_ 1(4), 200-232.

Carneiro, P., & Heckman, J. (2003). Human Capital Policy. In J. Heckman & A. Krueger (Eds.), Inequality in America: What role for human capital policies? Cambridge: MIT Press.

Casey, K., Glennerster, R., & Miguel, E. (2012). Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan. _The Quarterly Journal of Economics,_ 127(4), 1755-1812.

Chang, S. M., et al. (2015). Integrating a Parenting Intervention With Routine Primary Health Care: A Cluster Randomized Trial. _Pediatrics,_ 136(2), 272-280.

Cogill, B. (2003). Anthropometrics Indicators Measurement Guide (http://www.fantaproject.org/downloads/pdfs/anthro_2003.pdf), Accessed July 10, 2012. Washington, D.C.: Food and Nutrition Technical Assistance Project, Academy for Educational Development.

Copple, C., & Bredekamp, S. (2009). Developmentally appropriate practice in early childhood programs serving children from birth through age 8. Washington D.C.: Developmentally appropriate practice in early childhood programs serving children from birth through age 8.

Drouin, O., & Heymann, J. (2010). Scaling up and sustaining community-based care for preschool and school-age children -- successes and challenges in Malawi. _Vulnerable Child Youth Stud,_ 5(S1), 31-39.

Duncan, G. J., et al. (2007). School readiness and later achievement. _Developmental Psychology,_ 43(6), 1428-1446.

Elango, S., Garcia, J. L., Heckman, J. J., & Hojman, A. (2015). Early Chilhood Education. _NBER Working Paper Series,_ Working Paper 21766.

36
|**Table 3: Portf**<br>**Enterprise**|**olio Performance**<br>**Business**|**Rep**|**ayment Due**||**Actuals Pa**|**id(end of con**|<br>**tract)**|**Amount Due**|**Arbitration**|**Litigation**|
|---|---|---|---|---|---|---|---|---|---|---|
|||||**Return if**|||**Actual**||**Payments**|**Payments**|
|**Code**|**Sector**|**Capital**|**Revenue**<br>**Share**|**repaid**<br>**(%)***|**Capital**|**Revenue**<br>**Share**|**Return**<br>**(%)**|**(at end of**<br>**contract)**|<br>**received (upto**<br>**end 2017)**|<br>**received (upto**<br>**Jan 2019)**|
|A|Vehicle repair<br>(auto electrical)|500,000|113,124|22.6|500,016|113,119|22.6|n.a.|n.a.|n.a.|
|B|Tyre sales|700,000|108,113|15.4|106,060|37,161|‐79.5|664,887|50,000|in process|
|C|Motor coil rewinding|250,000|62,738|25.1|250,008|62,754|25.1|n.a.|n.a.|n.a.|
|D|Printer|500,000|51,494|10.3|137,753|22,107|‐68.0|391,650|57,500|334,150|
|E|Vehicle painting|500,000|46,184|9.2|155,621|11,779|‐66.5|378,800|95,000|in process|
|F|Vehicle/boat servicing|500,000|689|0.1|306,687|689|‐38.5|193,329|30,000|in process|
|G|Yoghurt and<br>flavored ice|450,000|44,139|9.8|449,785|44,343|9.8|n.a.|n.a.|n.a.|
|H|Coir and Rope|360,000|45,206|12.6|22,214|4,986|‐92.4|378,003|0|in process|
|I|Processed fruit drinks|412,500|117,060|21.3|25,000|9,300|‐93.8|495,260|0|inprocess|
|Notes:<br>Arbitration ag<br>Of the six firm<br>Litigation proc<br>n.a. denotes n<br>Returns are ca|**Total/ Average**<br>reement was for firms to ma<br>s with amounts outstanding<br>ess began with the issuing o<br>ot applicable, since no arbit<br>lculated as repayments rece|4,172,500<br>ke an initial p<br>at the end of<br>f Letters of De<br>ration or litiga<br>ived as a perc|588,747<br>ayment of 25<br>the contract,<br>mand. One fi<br>tion was nee<br>entage of am|13.7<br>% of balance<br>four respond<br>rm settled ou<br>ded with this<br>ount invested|1,953,143<br>due, and remai<br>ed with some p<br>t of court at th<br>firm.<br>, and are in no|306,239<br>nder in 11 pa<br>ayment of fu<br>at time. The o<br>minal terms o|‐47.6<br>yments ov<br>nds due to<br>ther 5 cas<br>ver three y|2,501,929<br>er the year.<br>arbitration.<br>es are currently<br>ears.|232,500<br>in court.|334,150|

27
|Travel time to work for breadwinner|27.02|45.4|18.37|***|581|
|---|---|---|---|---|---|
|Volume of drainage system per capita by sub-<br>ward|0.06|0.13|0.07|**|581|
|Elevation of dwelling location|26.84|34.12|7.285|*|581|
|Electricity|77%|90%|13%|**|581|
|Piped water in dwelling|20%|31%|12%|**|581|
|Access to informal lending|53%|76%|23%|***|581|
|Proof of ownership dwelling|79%|88%|10%|**|581|
|Holds a bank account|33%|45%|13%|**|581|
|Access to community saving group|18%|26%|8%|*|581|
|Option of lending towards income generating<br>activities within saving group|10%|16%|7%|*|581|
|Owns insurance against floods|2%|6%|4%|*|581|
|Source of income breadwinner: Private<br>company|13%|22%|10%|*|574|
|Source of income breadwinner: Self-employed|65%|55%|-11%|*|574|

_Figure 13 - Distribution of the expenditure proxy and comparison between households that recovered and did not recover from flood_ <!-- Start of picture text --> Affected - recovered Affected - not recovered<br>0 200000 400000 600000 800000<br>Monthly expenditure proxy in Tsh<br><!-- End of picture text --> _Note: Expenditure proxy are calculated with SWIFT. The vertical line represents the poverty line in Tanzania._ 27
# 2.7.1 Student progression

Rates of student progression are low in Malawi, as a result of high rates of dropout and repetition, driven in part by large class sizes and limited teacher effort. We anticipate improvements in class sizes, as a result of improved allocation of teachers to lower grades. We also anticipate improvements in teacher effort. As a result of these, we anticipate improvements in student progression, particularly in lower grades.

Our primary measures of student progression are dropout and repetition rates.

**Dropout Rate:** This is measured by the number of students in each grade dropping out in the most recently completed school year, divided by the total number of students enrolled in that grade for that year.

**Repetition Rate:** This is measured by the number of students in each grade repeating a grade in the most recently completed school year, divided by the total number of students enrolled in that grade for that year.

As these measures are drawn from school records, we measure them at the school level and report them overall across grades, and for lower and upper primary (Grades 1-4 and 5-8 respectively).<sup>11</sup>

# 2.7.2 Student test scores

As a result of improvements in class sizes, teacher effort, and school learning environments as described above, we anticipate improvements in the test performance of sampled students in learning assessments in mathematics and English. We utilize data from norm referenced assessments that cover competencies from Grade 1 through Grade 6 (see section 2.8. We measure these outcomes at the student level through a longitudinal sample (see section 2.9.4). Raw scores are converted to _knowledge scores_ on a mean-centred scale using Item Response Theory (see section A).

**Mathematics Knowledge Score:** This is measured by the knowledge score of each student in the mathematics test for each school.

> 11 Owing to the fact that our data is not collected annually (see section 2.8), we are unable to use standard definitions which divide the number of repeaters by the previous years’ enrollment.

15
Similar to Collier and Hoeffler (2004b) we take a weighted average level of militarization of countries that are “contiguous” ( _Contiguous militarization_ ). The weight is GDP and contiguity is defined as either land contiguity or water contiguity up to 400 miles of water. Data are from the Correlates of War (COW) project and were taken from Bennett and Stam (Bennet and Stam 2003). In a context of rivalry, the militarization level of contiguous countries can capture local arms race phenomena. In a context of non-rivalry, it can capture emulation, imitation and coordination effects. The contiguous militarization variable is not without problems, however. In effect, it introduces a spatial lag into the model (Anselin 1988) and often captures variables omitted from the model (Simmons and Elkins 2004). We believe our model is relatively comprehensive, but it would be difficult to say with confidence that there are no omitted variables. For this reason, we run tests with and without the contiguous militarization variables included.

Contrary to Collier and Hoeffler (2004b), we do not include a measure of predicted civil war. Such a variable creates all kinds of statistical problems. Instead, we control for the risk of civil war directly by our range of explanatory variables, which will capture the risk of civil war under the assumption that the factors triggering such war are time-persistent. Finally, we include year-specific dummies to capture any trends over time and year-specific international tension that influences defense spending globally, such as the end of the Cold War, the Persian Gulf War, or NATO action in the Balkans. Table 2 provides summary descriptive variable information.

# _The estimation method_

The estimation of TSCS data presents some special problems, particularly because of complex correlation patterns between and across panels (Beck and Katz 1995a; Beck and Katz 1995b). Since our data is unbalanced to an extent that no time periods are common to all countries in the sample, the standard version of the Panel Corrected Standard Errors (PCSE) method of Beck and Katz cannot be used. As an alternative, we therefore use a random-effects estimator with robust standard errors, assuming that observations are independent across countries, but not necessarily within countries over time, i.e. observations are assumed to be clustered. The robust-cluster option produces consistent standard errors even in the presence of serial correlation and heteroskedasticity, but it is potentially inefficient in estimation (Wiggins 1999). To ensure that results are not specific to our estimation technique, we additionally use the Generalized 10
# _Small Is Not Beautiful_

The impact of trade imbalance and low volume is less documented than transit overheads, but anecdotal evidence show that this is a major concern. Weak positioning in the global market entails low trade and prevents most LLDCs from reaping scale economies. The average traffic at many “major” border posts is often in the range of 5-10 containers per day and the busiest border post in East Africa (Malaba) only sees 200 to 300 vehicles per day. Rwanda or Burundi’s annual containerized imports would fit into a single large container vessel. This implies that almost no shipper has the required scale to have a strong bargaining position with global logistics groups. Transit to Rwanda and Uganda is dominated by large freight forwarding groups with large truck fleet. In the case of Tanzania, Tanzania Railways Corporations charges 30% more for a transit container to Rwanda from Dar Es Salaam to Isaka (990 km) than for the same container for domestic traffic to Mwanza, while this town is 1,230 km from Dar on the same railway line<sup>45</sup> .

The relatively minor share of traffic to/from landlocked countries also limits their bargaining power for preferential treatments in coastal ports: in West Africa, transit traffic has not reached 10% of the total traffic through Abidjan, even when more than two thirds of Mali and Burkina Faso trade transited through its piers. The same can apply to Chad and Central African Republic with Douala and even Uganda is not more than 15% of the Mombasa port traffic, while the port handles 90% of its external trade.

In small developing economies, including landlocked countries, arranging small-scale shipments and consolidating them in a single container remains an issue both in terms of cost and in terms of service availability. In a case study of Laos, Arnold (2005) showed that availability and cost of these services are critical to export growth and diversification. In Central Asia Ojala et al (Ojala 2005) documented the steep increase occurred by loads below one container size.

> 45 Data collected from the Northern Corridor TTCA and TRC, 2005.

41
_Anne A. Methodology_ _25_ Table A3. Countries or territories with less reliable reports on causes of death, year of data used, and indices of coverage and data quality, by region

||||_Reporte_|_d deaths_<br>|
|---|---|---|---|---|
|||||_Percent due_|
||||_Pt_|<br>_illd_|
||||_ercen o_|_to -e-_|
|_Country or territory_|_Year of_<br>_data used_|_Number_|<br>_estimated total_<br> <br>_deaths_|<br>_fined_<br>_conditions_|
|_Latin Amenca and the Caribbean_<br>|||||
|Antigua and Barbuda<br>|1983<br>|300<br>|na|na<br>|
|Bahamas<br>|1985|1200|na|<5|
|Barbados<br>|1984<br>|2000<br>|95|<5<br>|
|Belize<br>|1984<br>|700<br>|na|15|
|Dominica<br>|1984<br>|400<br>|na<br>|na|
|Dominican Rep.<br>|1985<br>|28000<br>|62<br>|15<br>|
|Ecuador<br>|1985<br>|51000|70|15|
|El Salvador<br>|1984<br>|29000<br>|62<br>|20-25<br>|
|Guatemala<br>|1984|66500|86|10|
|Guyana<br>|1984<br>|4800<br>|91<br>|10<br>|
|Martdnique<br>|1985|2200|88|10|
|Mexico<br>|1983<br>|410000<br>|85<br>|5<br>|
|Panama<br>|1985|9000|80|5-10|
|Paraguay<br>|1985<br>|12600<br>|50<br>|20-25<br>|
|Peru<br>|1983|94000|50|5-10|
|Puerto Rico<br>|1985<br>|23200<br>|100|<5|
|St. Kitts and Nevls<br>|1984<br>|500|na|15|
|St. Vincent and the Grenadines<br>|1985<br>|700<br>|na<br>|10<br>|
|Suriname<br>|1985<br>|2300<br>|95<br>|15<br>|
|Trinidad and Tobago<br>|1983<br>|7600<br>|95|<5|
|Venezuela<br>|1983|77000|81|5|
|_Sub-Sahamn Africa_<br>|||||
|Mauritius<br>|1985<br>|6600<br>|100|<5|
|Sao Tom6 and Principe<br>|1985<br>|1100|na|20|
|Seychelles<br>|1985|500|na|10|
|_Middk East and North Afnica_<br>|||||
|Bahrain<br>|1985<br>|1600|90|20|
|Kuwait|1985|4300|85|5|
|_Asia_|||||
|China<br>|1985|580000|7|<5|
|India<br>|1986|18000|0.2|na|
|Korea, Rep. of<br>|1985<br>|192000<br>|75|15|
|Sri Lanka<br>|1983|96000|96|30|
|Thailand|1985|218000|57|50|

na - not ascertained.

Note: Data for around 1970 were also used for these countries except for Guyana, Seychelles, Sao Tome, Bahrain, Israel, China, India, the Republic of Korea, and Sri Lanka. In addition, 1970 data were used for five additional countries: Colombia, the Arab Republic of Egypt, Honduras, Nicaragua, and St. Lucia. The quality indices, in ten cases, are for a later data year, either 1986 or 1987. The data used for China and India were sample survey data.

For each country for the first three quinquennia, life tables are chosen from the Coale-Demeny set. The first step is to select that family of life tables (North, South, East, or West) for which the ratio of life expectancy to infant mortality is as close as