rafmacalaba/datause-encoder-data huggingface dataset:
lead us to observe an increase in pre-trial duration just before the introduction of the decree in the event study. This is not what Figure A2 suggests: if anything, we see a decline in duration for cases entering court just before the decree application.

Similarly, the start of decree application in the first chamber may have led those plaintiffs who want a fast resolution but are expecting a long pre-trial for their case (e.g., because they are facing a defendant known for resorting to dilatory tactics) to delay their entry into court to match the decree application in their expected chamber. First, it is worth noting that postponing the filing of a case to cut court delays is not an obvious strategy in this context: total time to resolution of the dispute may not change much.<sup>31</sup> Second, should this have been the case, we would see a reduction in pre-trial duration in all chambers right after application in the first chamber, which, for that chamber, would be a true reform impact, but for all other chambers would be an artifact of the change in case composition (the cases with a longer expected delay are missing from the pool); this initial effect would be followed by an increase in pre-trial duration back to a higher level once these cases re-enter the pool of incoming cases. As a check, we estimate the event study specification (1) on pre-trial duration and likelihood of completing in four months, if all chambers were simultaneously treated at the first cutoff ( _t_ =38, first civil chamber; Panels A and B, Figure A10). As expected, this dampens the drop in pre-trial duration. However, we reassuringly observe a decline in duration after the first introduction cutoff, and a smooth downward slope thereafter, indicating no bunching of slow cases in the aftermath of the decree application.

These robustness checks unanimously corroborate that the event study design likely produces unbiased estimates of the causal impact of the reform on the speed of justice.

31From thorough interviews with civil and commercial law firms we learned that lawyer’s fees are typically composed of both a pre-agreed rate and a premium proportional to the awarded amount and they are both unrelated to realized case duration. In addition, by the time the last chamber was treated (March 2014), the first chamber had only been treated for five months; it is hard to imagine that actors had time to form clear expectations of post-decree duration within the decree application window.

69
corporates, when global risk aversion (VIX) increases, whereas neither advanced country nor emerging market sovereigns respond to such global shocks. These facts are inconsistent with many models that use countries’ own productivity shocks as the only source of shocks and/or focus on asymmetric information and sovereign risk alone as frictions to explain co-movement of inflows and outflows together with procyclicality of capital flows.

Our dataset combines data from several publicly available sources and offers a distinct advantage over existing datasets from single institutions, such as the IMF and/or World Bank. The large number of developing countries and emerging markets at the quarterly frequency, which is the preferred frequency to study the relationship between capital flows and the business cycle, is a big advantage of our capital inflows dataset relative to standard sources.<sup>1</sup> Why decompose _only_ debt inflows and outflows by sector? Debt flows are generally the largest component in total capital flows. Figure 1 illustrates this clearly.<sup>2</sup> Panel (a) shows the share of total debt in total external liabilities. Debt represents the majority of external liabilities globally and in advanced countries (AE). In emerging markets (EM), debt and non-debt liabilities are of similar magnitude. Panel (b) highlights that other investment debt (usually bank loans) is the bulk of debt stocks. Portfolio debt (bonds) in panel (c) represents nearly half of AE external debt and around a third of EM external debt. Thus, it is important 1The set of countries in our 85 country capital inflows data includes 25 advanced, 34 emerging, and 26 developing economies from 1996q1 to 2014q4. Standard sources for such a long period will only have available data for 20 or so countries. If we go to an annual frequency, we can have 89 countries for inflows, adding 4 more developing economies. For capital outflows data we have 16 advanced and 15 emerging economies for 2004q1–2014q4. This is because of the fact that foreign assets of lender types are poorly recorded. For total outflows one can have of course more countries but our aim here is to decompose outflows by banks, corporates and sovereigns as we do inflows. We combine the general government and central bank sectors into a single public sector in order to increase data coverage for outflows.

> 2This figure plots stocks. The flow version delivers a similar picture, though more noisy, and is plotted in Figure C1 in Appendix C.

2
The student language test, which evaluated ability in English (Kenya, Nigeria, Tanzania, and Uganda), French (Senegal and Togo), or Portuguese (Mozambique), ranges from simple tasks that tested letter and word recognition to a more challenging reading comprehension test.<sup>9</sup> The mathematics test ranges in difficulty from recognizing and ordering numbers, to the addition of one- to three-digit numbers, to the subtraction of one- and twodigit numbers, and to the multiplication and division of single-digit numbers. In both language and mathematics, the tests span items from the first four years of the curriculum.<sup>10</sup> In contrast to other approaches to assess teachers’ knowledge, where teachers take exams, teachers were asked to mark (or “grade”) mock student tests in language and in mathematics. This method of assessment has two potential advantages. First, it aims to assess teachers in a way that is consistent with their regular teaching activities—namely, marking student work. Second, by using a different mode of assessment for teachers compared to students, it recognizes teachers as professionals. In the analysis, we use data on language knowledge of those teachers who teach language, and data on mathematics knowledge of those teachers who teach mathematics.

Both the language and mathematics tests for teachers covered items starting at Grade 1 level (simple spelling or grammar exercises, addition and subtraction) and included items up to the upper primary level (Cloze passages to assess vocabulary and reading comprehension, interpretation of information in a diagram and/or a graph and more advanced math story problem).<sup>11</sup> Both the student and the teacher tests have good reliability, with a reliability ratio (estimated by Cronbach’s alpha) above 0.8 in both subjects on the student test and above 0.85 in both subjects on the teacher test.<sup>12</sup>

> 9 For consistency with the other countries, in Tanzania and Uganda we do not use the part of the samples from those countries in which students were tested in Swahili.

> 10 The teacher and student subject tests were designed by experts in international pedagogy and validated against 13 Sub-Saharan African primary curricula (Botswana, Ethiopia, The Gambia, Kenya, Madagascar, Mauritius, Namibia, Nigeria, Rwanda, the Seychelles, South Africa, Tanzania, and Uganda). See Johnson, Cunningham and Dowling (2012) for details. A few items in the tests also measured grade 5 knowledge.

> 11 Cloze sentences/passages leave blank a word in a phrase/sentence and ask the test-taker to fill in that word (sometimes from a set of options) with one that completes the sentence in a way that is sensical and grammatically correct.

> 12 Cronbach’s alpha is defined as the square of the correlation between the measured test score and the underlying metric. A Cronbach alpha of 1 would indicate that the test is a perfect measure of the underlying metric (though not necessarily of student/teacher knowledge). As a rule of thumb, values between 0.8 and 0.9 are considered as good.

7
# **6. Bibliography**

Alfaro, Laura, Areendam Chanda, Sebnem Kalemli-Ozcan, and Selin Sayek. 2004. "FDI and Economic Growth: The Role of Local Financial Markets," _Journal of International Economics_ 64(1).

- Buch, Claudia, Jörn Kleinert and Farid Toubal. 2003. **“** Where Enterprises Lead, People Follow? Links Between Migration and German FDI,” Kiel Working Paper No. 1190.

- Bhattacharya, Utpal and Peter Groznik. 2003. “Melting Pot or Salad Bowl: Some Evidence from U.S. Investments Abroad,”. EFA 2003 Annual Conference Paper No.650.

- Commander, Simon, Rupa Chanda and L. Alan Winters. 2004. “Who Gains from Skilled Migration? Evidence from the Software Industry,” London Business School mimeo.

- Docquier, Frederic and Abdeslam Marfouk. 2005. “Measuring the international mobility of skilled workers (1990-2000),” World Bank Policy Research Working Paper No. 3381.

- Gleditsch, Nils Petter, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Håvard Strand. 2002. “Armed Conflict 1946–2001: A New Dataset,” _Journal of Peace Research_ 39(5): 615–637.

- Gould, David M. 1994. “Immigrant Links to the Home Country: Empirical Implications for U.S. Bilateral Trade Flows,” _Review of Economics and Statistics_ , 76: 302-16.

- Head, Keith and John Ries. 1998. “Immigration and Trade Creation: Econometric Evidence from Canada,” _Canadian Journal of Economics_ , 31(1): 47-62.

- Kaufmann, Daniel; Kraay, Aart; Mastruzzi, Massimo. 2004. „Governance matters IV: governance indicators for 1996-2004,” World Bank Policy Research Working Paper No. 3630.

- Kugler, Maurice and Hillel Rapoport. 2006. “Skilled Emigration, Business Networks and Foreign Direct Investment,” _Economic Letters,_ forthcoming.

- Lipsey, Robert E. and Fredrik Sjoholm. 2004. Foreign direct investment, education and wages in Indonesian manufacturing _Journal of Development Economics_ , 73(1): 415-422.

- Lucas, Robert E.B. 2005. _International Migration and Economic Development: Lessons from Low Income Countries_ , Regeringskanliet, Sweden.

- Markusen, James. 1986. “Explaining the Volume of Trade: An Eclectic Approach,” _American Economic Review,_ 76(5): 1002-1011.

- McKenzie, David. 2005. “Paper Wall Are Easier to Tear Down: Passport Costs and Legal Barriers to Emigration,” World Bank Policy Research Working Paper No. 3783.

- Rauch, James. 2001. “Business and Social Networks in International Trade,” _Journal of Economic Literature,_ XXXIX: 1177-1203.

- Rauch, James and Vitor Trindade. 2002. “Ethnic Chinese Networks In International Trade, _Review of Economics and Statistics_ 84(1): 116-130

- Singh, Shinu. “Economic Impact of Return Migration of Highly-Skilled IT Professionals from the United States to India," Draft Thesis, Massachusetts Institute of Technology (downloaded on August 18, 2006 from

http://web.mit.edu/nruiz/www/Documents/Shinu's%20Thesis%20Draft.pdf#search=%22 Shinu%20Singh%20india%20migration%22) 12
|InternalFamiyyML|s<br>Banks<br>|Trade Credit|MFIs|
|---|---|---|---|
|Micro<br>0.01|-0.07***|-0,.03|-0.04***|
|(-0.32)|(-3.24)|(-1.27)|(-2.68)|
|Owner's Education<br>-0,.06***|0.01|0.03*|0.01|
|(-2,66)|f-1.08)|(-1.65)|(-1.33)|
|Owner's Job in Formal Sector<br>-0.07 *|0.04**|0.04|0.01|
|(-1,93)|(-2.08)|f-1.06)|(-0.70)|
|Lng Age<br>-0.02**|0.002|0.01|0.01 **|
|(-2,32)|f-0.56)|(-1.44)|(-2.06)|
|Female Owner Decision Maker<br>0.05**|-0.001|-0.02|0.01|
|(-2,34)|f-0.13)|(-1.49}|(-0.91)|
|Manufacturing<br>-0.04*|-D.002|0.02|0.01|
|(-1.81)|f-0.29)|(-1.43)|(-0. 78)|
|LAC Region<br>0.12***|0.05**|-0.02|0.0003|
|(-5,58)|f-2,06)|(-0.36)|f-0.01)|
|Private Credit to GDP<br>-0.002***|0.002***|0.001**|0.00027|
|(-2,78)|(-5,18|-2,06)|f-0.53)|
|Financial Freedom<br>-0,004***|0.002***|g9.003***|O.001***|
|(-5.52)|{-4,96)|(-5,26)|{-4,35)|
|Contract Viabilitviexpropriation<br>-0.05***|0.02***|0.04***|0.03***|
|(-3,33)|(-2,.89)|(-3.16)|(-4,30)|
|Law and Order<br>-0.05***|0.02***|0.03***|0.02***|
|(-4,60)|(-2,64)|(-3.36)|(-2.83)|
|PropertyRights<br>-0.002**|0.001 ***|0.001 ***|0.0004*|
|(-2,52)|f-4,54)|(-2,79)|(-1.75)|
|PseudoR2<br>a.08|0.08|0.09|a.o9|
Table 1: Parameter Values of Calibrated Model

|Parameter|Description|Value|
|---|---|---|
|_I_|Number of economic agents|5|
|_ai_|Agents’ ability|_{_0.2,0.4,0.6,0.8,1_}_|
|_κ_|Informal residency productivity penalty|0.2|
|_σ_|Agglomeration scaling factor|3|
|_γ_|Income efect of agglomeration|5, 11, 100|
|_η_|Rural wage elasticity of adverse shocks|0.2|
|_b_|Congestion scaling factor|1|
|_δ_|Congestion elasticity of agglomeration|0.3|
|_λ_|Land price to rent ratio|0.3|
|_μ_<br>|Cost of land tenure formalization|0.3, 0.9|
|_π_<sup>_P_</sup><br>|Probability of adverse shock in rural area|0.3|
|_π_<sup>_G_</sup>|Probability of land grab attempt / eviction enforcement|0.05, 0.1, 0.5|
|_α_|Risk aversion parameter<br>|2|
|_β_|Discount rate (10 year period)|0_._86|

We set the value of the risk aversion parameter, _α_ , to 2, which is consistent with reported estimates in the meta-analysis conducted by Havranek et al. (2015). We use an annual discount rate of .985, which is consistent with the number adopted by long run dynamic computational forward-looking economic models, e.g., Nordhaus’ DICE model (Nordhaus, 2008). Given that a period spans over 10 years in our model, this translates to ( _._ 985)<sup>10</sup> = _._ 86.

Given a significant range of heterogeneity for some model parameters, we conduct a sensitivity analysis to demonstrate the model’s robustness to chosen parameter values. Specifically, we analyze our results’ sensitivity to variations in following model parameters: (a) income effect of agglomeration, _γ_ , of 5 (high agglomeration potential) and 100 (low agglomeration potential); (b) cost of land tenure formalization, _μ_ , of .9 (high formalization cost); and (c) probability of land “grab”, _π_<sup>_G_</sup> of .05 (low chance of eviction) and .5 (high chance of eviction).

# **5 Simulation Results**

We solve equations (17) and (19) as explained in section 3 for a set of parameter values described in section 4 to obtain the value functions that can be used to infer stationary states and corresponding optimal decision rules. We then simulate dynamic urbanization paths corresponding to the social optimum and the Markov perfect equilibrium based on the realization of randomly drawn shocks _ε_<sup>_P_</sup> _t_<sup>and</sup><sup>_εG,i_</sup> _t_ .

Figure 4 illustrates one of such simulated paths that corresponds to solving for a Markov-perfect equilibrium under a low formalization cost, low agglomeration potential and low risk of eviction. For all simulations we start with an empty set of the city with no residents.<sup>31</sup> We see that

> 31We have also run simulations for an alternative assumption of one, the most productive, informal urban

23
of 9,597 surveys were administered across the 11 countries in the study.

Data on climate were gathered from two sources (Mendelsohn et al. 2007). We relied on temperature data from satellites operated by the Department of Defense of the United States (Basist et al. 2001). The precipitation data came from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

Soil data were obtained from FAO (2003). The FAO data provide information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the results of an analysis of climate change impacts on African hydrology (Strzepek & McCluskey, 2006). Using a hydrological model for Africa, the authors calculated flow and runoff for each district in the surveyed countries. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data is derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# **4. Empirical Results**

Tables 1, 2, and 3 summarize the data on irrigation and crop choice in Africa. About 25% of the farms in our sample irrigated their land. The irrigation clearly depends on where the farm is located. Farms in dry places such as dry savannah, semi-arid, and deserts are highly likely to irrigate their land whereas farms in sub-humid and humid forest, especially in the lowland, do not.

Table 2 summarizes eight crops or combinations of crops that are chosen most often by African farmers. For Africa as a whole, maize (32%), millet (5%), wheat (7%), and fruits and vegetables (10%) are chosen widely as a single crop to manage. Most farms choose a mix of some crops: fruit/vegetables and maize (17%), maize and ground nuts (14%), millet and ground nuts (11%), millet and sorghum (6%). The crop shares reflect the percent of farms that select this particular crop or crop combination.

The distribution of crops chosen differs widely across different AEZs, which is shown in Table 3. Maize is chosen very frequently in mid elevation dry or moist savannah, but by fewer farms in 7
# **2. From Engel to Kindleberger and Prebisch-Singer**

Based on expenditures of 153 Belgian families in 1853, Engel (1857) noted that ʺ[t]he poorer a family, the greater the proportion of its total expenditure that must be devoted to the provision of food” and later concluded that “... the wealthier a nation, the smaller the proportion of food to total expenditure.” (Quoted in Stigler 1954, p. 98). Following Engel’s observation, numerous competing theories attempted to explain the long term behavior of the ToT faced by developing countries (Rostow 1950, Kindleberger 1958, Toye and Toye 2003). The first view focused on the supply‐side and predicted that primary commodity prices will increase faster than manufacturing prices due to the resource constraints of the former and technological improvements of the latter—a view somewhat consistent with a Malthusian outcome. A second view assumed that ToT will follow investment cycles: investment expansion will induce a supply response in manufacturing goods, leading to lower prices, thus increasing the ToT. Conversely, an investment contraction would lead to a declining ToT. Proponents of a third view argued that ToT will follow a downward path because income growth leads to smaller demand increases in primary commodities than manufacturing products, a path consistent with Engel’s Law.

While no dominant view emerged during the early part of the 20th century, the negative ToT‐income relationship became the prevailing position after World War II. A turning point was Kindleberger’s (1943) judgment that, in order for commodity‐ dependent countries to mimic the growth path of rich nations, they must transfer resources from agriculture and mining sectors to manufacturing industries. Other authors, however, warned against such interventions (see Akiyama and others (2003) for a literature review in the African context). Johnson (1947) argued that the agricultural sector should not be subjected to interventions. Friedman (1954) disputed the benefits of managing income variability for agricultural producers. Johnston and Mellor (1961) criticized the pro‐urban policies pursued by many developing countries. Even Prebisch (1950) was cautious on how far industrialization policies should be pushed. Despite such warnings and cautious assessments, Kindleberger’s view dominated the post‐war development agenda and set the stage for the industrialization policies pursued by low‐ income countries.

Little attention was paid to the long‐term ToT trend during the first few decades after World War II, most likely because commodity prices did not exhibit consistent trends or move in a synchronous manner. For example, while crude oil prices remained virtually unchanged and metal prices experienced sustained increases during the three decades after the war, agricultural prices on the whole declined, although subject to two spikes (during the Korean war in the early 1950s and just before the 1973 oil crisis). Interest in analyzing ToT reemerged in the 1980s for at least three reasons. First, following the 1970s boom, virtually all commodity prices declined, subsequently stabilizing at _3_
Across World Bank income categories, the responses are fairly consistent: respondents found the analytical and technical skills of the local MBAs to be their strongest asset. Here, an interesting result reflects a discussion in a previous section: for higher income categories, a smaller percentage of firms find the communication/language skills to be the local MBAs’ strongest asset.

Neelankavil also looked at the strengths of MBA graduates from U.S. business schools (as perceived by Fortune 1000 executives) and found that their main strengths were technical skills, computer skills, oral communication and strategic planning. So, both Neelankavil’s results and the IFC MBA survey results show that the greatest strength of new MBAs is their technical and computer skills.

What this seemingly indicates is an emphasis in both Western country and developing country MBA curricula on imparting technical skills and teaching finance. This is particularly useful since any program to assist developing country business schools could build upon the technical training already in place in the developing and transition countries.

**Figure 6**

<!-- Start of picture text --> MBA Survey Question 8: In your experience, what do locally<br>educated MBAs lack most?<br>16%<br>28%<br>10%<br>3%<br>9%<br>12%<br>4% 18%<br>Work Experience/Practical Knowledge Knowledge of Languages<br>Communication/ Interpersonal Skills Analytical Abilities<br>Managerial Skills Finance Skills<br>Technical Skills/Computer Skills Other<br><!-- End of picture text --> The perceived weaknesses of locally trained MBAs are particularly interesting. Figure 6 shows that 28% of the responding firms found that MBAs lacked sufficient work experience and practical knowledge. This result is common across regions and across income categories, which implies a need for greater work experience and practical training, through pre-MBA work experience, internships, and real world case studies, in developing country business programs. Many of the firms specifically pointed this out to be a weakness caused by lack of work experience before and during MBA programs.

19