rafmacalaba/datause-encoder-data huggingface dataset:
We run the following regression for each measure of political institutions: _xit_ = 10 � _s_ = _−_ 10<sup>_δ_</sup> _t,_<sup>�</sup> _tb_ 1+ _s_<sup>_βs_+</sup><sup>_μi_+</sup><sup>_εit_where</sup><sup>_δ_</sup> _t,_<sup>�</sup> _tb_ 1+ _s_<sup>istheKroneckerdeltawhichisequaltooneif</sup> _t_ =<sup>�</sup> _tb_ 1 + _s_ and zero otherwise, _βs_ are coefficients, _μi_ is an unobserved country effect and _εit_ is an idiosyncratic error term. We set _s ∈{−_ 10 _, . . . ,_ 0 _, . . . ,_ 10 _}_ , so that the result is a 21year window around the break date<sup>�</sup> _tb_ 1. The first year of the slump is _s_ = 1 corresponding to _t_ =<sup>�</sup> _tb_ 1 + 1. The standard errors are robust to heteroskedasticity and autocorrelation within both country and time clusters (Cameron et al., 2011). We plot the estimates of the coefficients (including 95% confidence bands) as they represent the conditional expectation of _xit_ at time _s_ relative to ‘normal’ times.<sup>12</sup> – **Figure 2** Institutions and politics <!-- Start of picture text --> Executive Constraints Executive Recruitment Political Competition<br>−10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>Autocracy Democracy Polity Score<br>−10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10 −10 −8 −6 −4 −2 0 2 4 6 8 10<br>s s s<br>1 1<br>0<br>0 0 −1<br>−1 −1 −2<br>−2 −2 −3<br>3 0 0<br>2 −1 −2<br>1 −2 −4<br>0<br>−3 −6<br><!-- End of picture text --> _Source_ : Authors’ analysis based on data from the Polity IV project and structural breaks estimated using the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

All indicators describing a country’s political institutions from the Polity IV database are significantly lower (or higher) in the decade before a slump occurs and then gradually return to normal levels thereafter. Figure 2 shows that, ten years before a slump, countries tend to

> 12In this case, ‘normal’ refers to all observations other than the 21 years around the downbreak.

15
# **1 Introduction**

Despite global calls for improvements in education, progress towards learning for all is slow. This deficit is particularly pronounced for poor children and children in low-income countries [Akmal and Pritchett, 2019]. But why do some students learn more in some schools than others? While there are many contributing factors at system, school, and household-level, one consideration receiving growing attention is school management—the processes and practices used by principals day-today as they run their schools [World Bank, 2018]. Academics and practitioners interested in this issue face two challenges: how to _measure_ school management accurately and cost-effectively at scale across schools and countries; and how to _explain_ any observed relationship between school management and learning outcomes in a way that elucidates the underlying mechanisms to guide policy. This paper addresses both of these challenges.

Our first contribution is to develop a new approach to measurement that can, in principle, be used with any existing public dataset containing items about school management. We illustrate using two public datasets as examples: the OECD’s Programme for International Student Assessment (PISA), and the Brazilian school census survey, Prova Brasil. The essence of our approach is to benchmark against the “state of the art”, but expensive, World Management Survey (WMS) in Bloom et al. [2015a]. We show how questions from these public surveys can be classified into WMS topics (53 PISA questions into 14 WMS topics and 33 Prova Brasil questions into 8 WMS topics), how the responses can be coded using the WMS scoring rubric, and finally how these grades can be built into a school management index. Our PISA-based index covers over 15,000 schools across 65 countries, and our Prova Brasil-based index covers nearly all public schools in Brazil. These indices are well-validated and can be used by researchers interested in studying the role of management in education systems across a far wider range of countries and schools than was previously possible.<sup>1</sup> All three indices, WMS, PISA, and Prova Brasil, show a strong, positive (within-country) correlation between school management and student learning outcomes, echoing recent causal evidence from randomized controlled trials in the U.S. [Fryer, 2014, 2017].

Our second contribution is to develop a framework to explore why management matters for schools.

> 1 For example, see Wössmann [2016] for a review of education systems research using large, cross-country surveys.

1
Finally, also the variables to proxy trade costs can all safely be regarded as exogenous to internal migration within Brazil (at least for the time horizon under study).

Table A-1 summarizes by industry the coefficients obtained from the trade regressions (equation 11). Coefficients on the trade cost variables have the expected sign, and magnitudes are in line with the literature (cf. Head and Mayer, 2013). However, there are some important differences across sectors, in particular in the distance coefficient. The last column summarizes the time varying importer fixed effect, representing the sector-specific market capacity of each destination country. Appendix S1.1 provides some descriptive statistics. Appendix S1.2 calculates various alternative market access measures.

HOUSEHOLD SURVEY DATA Our main data set is the yearly household survey _Pesquisa Nacional por Amostra de Domicilios_ (PNAD) collected by the Brazilian Institute of Geography and Statistics (IBGE). The PNAD does not follow individuals but interviews a different random and representative sample of residents each year (between 310,000 and 390,000 per year). We use the PNAD for the years 1992 to 2003 (with data missing for 1994 and 2000).<sup>14</sup>

# _Migration Rates_

We identify an individual as a migrant when the answer given to the question “In which Brazilian state did you live five years ago?” differs from the actual state of residence. Our sample is limited to individuals who declare having a job in a tradeable sector, earning a positive wage, having lived in Brazil five years ago and being between twenty and sixty-five at the time of the interview.

We distinguish eleven tradeable sectors that can be matched with the trade data and construct bilateral migration rates separately for each sector.<sup>15</sup> We do not have any information about the individual’s work five years ago. Nevertheless, as argued above, we can make the reasonable assumption that individuals already

> 14 In 1994 the PNAD was not conducted because of a strike. 1991 and 2000 were years of the population census. 15 See appendix S4.1 for details on the industrial classification. 12
# **Table 2. Weighted Average Applied and Bound Rates Faced by WTO members**

|||AppliedRate|s<br>Formula||Boundrate|s<br>Formula|
|---|---|---|---|---|---|---|
||Base|Formula|plusflex|Base|Formula|plusflex|
|**Total**|%|%|%|%|%|%|
|All countries|3.7|2.5|2.9|9.7|5.7|6.9|
|High income countries|3.6|2.5|2.9|9.6|5.6|6.7|
|Developing - non LDC|3.9|2.4|2.9|10.0|5.9|7.3|
|LDCs|3.3|2.1|2.4|14.5|8.0|10.6|
|**Agriculture**|||||||
|All countries|14.5|8.9|11.8|40.4|20.7|29.9|
|High income countries|14.9|9.2|12.1|40.3|20.3|29.1|
|Developing- non LDC|14.2|8.6|11.5|39.8|20.8|30.4|
|LDCs|7.4|6.5|7.1|56.8|32.1|45.7|
|**NAMA**|||||||
|All countries|2.9|2.1|2.3|7.7|4.7|5.3|
|High income countries|3.0|2.2|2.4|7.9|4.8|5.5|
|Developing- non LDC|2.9|1.9|2.1|7.2|4.4|5.1|
|LDCs|2.8|1.5|1.8|8.9|4.8|5.9|

Source: Laborde, Martin and van der Mensbrugghe (2008). Note: Country groups defined using World Bank and UN definitions.

The “formula” columns show that the formulae being discussed are extremely ambitious. In agriculture, the tiered formula without exceptions would cut bound tariffs by roughly half, and applied tariffs by roughly a third. In NAMA, the cut in bound rates would be roughly three-eighths, and in applied rates roughly a quarter. The formula plus flexibility column shows that the flexibilities—which were essential in securing agreement on the formulas—reduce the size of the cuts in tariffs, but still leave progress in reducing both average bound and applied rates.

While only some of the reductions in bound tariffs translate into reductions in applied rates, the entire reduction has value in constraining potential future increases in tariffs (Francois and Martin 2004). While applied tariffs are now low by historical standards, and have fallen particularly sharply in developing countries, historical studies by Gardner and Kimbrough (1999) and Rama (1992) show that reductions in applied protection that are not locked-in through international agreements are frequently reversed (see Appendix). This suggests that the reductions in bound rates shown in Table 2 may have substantial long-run value even in cases where the bound rates are above current applied rates.

5
**Table 3:  Cyclone strike frequency vs. mean maximum wind speed**

|**Period**|**Corr.**|**N**|**Years**|**Scaled N**|
|---|---|---|---|---|
|**1877 - 2016**|**-0.1559**|**11,328**|**140**|**11,328**|
|**1877 - 1900**|**-0.1846**|**7,916**|**24**|**6,597**|
|**1901 - 1920**|**0.3095**|**8,832**|**20**|**8,832**|
|**1921 - 1940**|**-0.4839**|**10,435**|**20**|**10,435**|
|**1941 - 1960**|**0.1024**|**7,904**|**20**|**7,904**|
|**1961 - 1980**|**0.1304**|**11,140**|**20**|**11,140**|
|**1981 - 2000**|**0.0378**|**10,207**|**20**|**10,207**|
|**2001 - 2016**|**0.5376**|**7,796**|**16**|**9,745**|

# **5.2  Impact Index Construction**

Given the quasi-independence of strike frequency and maximum wind speed, we compute three impact indices: (1) **Strike intensity:** We count the number of times a point has fallen within a cyclone impact zone. The total count is the overall strike intensity for 1877-2016. We also compute period strike intensities as period counts divided by years within periods.

(2) **Cyclone power:** For each point, we compute mean maximum wind speed by period and for 140 years. (3) **Cyclone impact:** We weight a strike occurrence by the cyclone’s maximum wind speed. For each period and all 140 years, we compute total weights for all impacted points and divide by the maximum weight in the point set to produce an impact index bounded by [0 100].

# **5.3  Strike Intensity**

Figure 5 displays color-coded maps of cyclone intensity -- cyclone strikes per year -- in each of the seven periods. These maps corroborate Figures 3 and 4, while offering additional insights into the spatiotemporal distribution of strikes. In particular, they highlight the substantial variation in coastal areas where strikes have been concentrated. The maps for 1877-1900 and 1901-1920 show why the median coastal strike location was in Odisha during this era. Both periods feature Odisha coastal stretches in the highest intensity class (0.30 - 0.80 strikes/year), with more moderate intensities in the mid-coastal region and lower intensities for most of the Bangladesh coastline. At 13
|||||small firms by 0.29 percent of<br>revenue||
|---|---|---|---|---|---|
|Pakistan|Manufacturi<br>ng firms|Power outages|Calculations<br>based<br>on<br>firm<br>survey|An increase in average daily<br>power outages by one hour<br>causes a reduction in value<br>added of 1.26percent|(Zhang, 2019)|

24
The global economic crisis of 2008 put additional stress on the MENA economies. In Egypt, the crisis was associated with a steep decline in real earnings growth; in Tunisia, it reinforced the upward trend in unemployment; and in Jordan, it slowed employment growth. Dissatisfaction with basic public services such as health care, housing, schools, and infrastructure also grew in the developing MENA countries, according to Gallup World Poll data, reflecting the erosion in the quality of public services.

By the end of the 2000s, this erosion in standards of living was felt not only by the poor, but also by other segments of the population, including the middle class. A gradual shift in government support to the elites became a particular concern (Cammett and Diwan 2013). People were frustrated because they could not get ahead by working hard and share in the prosperity generated by the relatively few large and successful Arab firms that were mostly state-owned or privately owned companies (OECD 2009).<sup>7</sup> Reflecting diminishing marginal utility, the widespread system of subsidies could not compensate for the erosion of living standards; food and energy subsidies mattered less for the well-being of the middle class than they did for the well-being of the poor and vulnerable (Ianchovichina, Mottaghi, and Devarajan 2015).

# **Unemployment and Low Quality Jobs**

Dissatisfaction with job market conditions was particularly strong in developing MENA in the wake of the Arab Spring. In the preceding decade, the MENA region’s average, aggregate and youth unemployment rates were the highest in the world. Without guarantees of secure public jobs, young people, who entered the labor market better prepared than their parents in terms of educational qualifications (Barro and Lee 2010; Campante and Chor 2012), were forced to queue for public sector jobs or take part-time or low-quality jobs in the informal sector (Chamlou 2013).<sup>8</sup> Employment in the informal sector offered little protection at old age and limited access to quality health care and benefits, such as paid maternity and annual leave (Angel-Urdinola and Kuddo 2011; World Bank 2014b).

The mismatch between educational attainment and economic opportunities created a gap between reality and expectations, lowering youth’s life satisfaction, amplifying perceptions of inequality and unfairness, and potentially contributing to social unrest (Campante and Chor 2012). In the literature, the negative association between happiness and unemployment is well-established and can be explained by a combination of income loss and psychic costs related to psychological

> 7 According to OECD (2009), very few large Arab firms are publicly traded companies.

> 8 The informal sector consists of firms, workers, and activities that operate outside the legal and regulatory frameworks.

8
# **The cost of road infrastructure in low and middle income countries**<sup>_∗_</sup>

Paul COLLIER Martina KIRCHBERGER _University of Oxford Columbia University_ Måns SÖDERBOM _University of Gothenburg_

**Keywords:** construction, infrastructure, transport.

**JEL classification:** O1, L7, H5, R4.

**Sector board:** TRAN, PSM.

> _∗_ Paul Collier is a Professor of Economics and Public Policy at the Blavatnik School of Government, University of Oxford, 10 Merton Street, Oxford OX1 4JJ, United Kingdom; his email is: paul.collier@bsg.ox.ac.uk. Martina Kirchberger (Corresponding Author) is an Earth Institute Post-Doctoral Research Fellow at Columbia University, Hogan Hall, 2910 Broadway, New York, NY 10025; her email is: martina.kirchberger@columbia.edu. Måns Söderbom is a Professor of Economics at the Department of Economics, University of Gothenburg, Vasagatan 1, P.O. Box 640, SE 405 30 Gothenburg, Sweden; his email is: mans.soderbom@economics.gu.se. We are grateful to the three anonymous referees and the editor for very helpful comments and suggestions. We would also like to thank Orazio Attanasio, Steve Bond, Stefan Dercon, Kirsten Hommann, Francisco Ferreiro, Sergio Oliete Josa, Clare Leaver, Marta Troya Martinez, Guy Michaels, Elias Papaiannou, Gerhard Toews, Tony Venables, and Greg Yetman, as well as participants at the IGC workshop on Fragile States, CSAE Conference, ESOP (Oslo) Seminar, Tanzania Roads Fund Workshop, DFID Lunchtime Seminar, EEA Annual Conference, WB Africa Economics Seminar and the OxCARRE brownbag seminar for helpful comments and discussions. We are particularly grateful to Rodrigo Archondo-Callao for providing detailed information on the ROCKS database and to Michael Agyapong for providing excellent research assistance. This work was supported by the BP-funded Oxford Centre for the Analysis of Resource Rich Economies, the Norwegian Agency for Development Cooperation, the International Growth Centre, and the AXA Research Fund. All errors are our own.
and precautionary motives. It is possible that as more and better stock data become available allowing detailed modeling of transactions and precautionary stocks it will be possible to use a model of this form to isolate the speculative components of stock demand.

E4. Interest Rate Effects in the Gilbert and Palaskas Model 62. A second respect in which the Gilbert and Palaskas model differs from the model in Trivedi (1990) is in the treatment of interest rates. The Pesaran-Trivedi model allows a constant interest rate, but since this enters as a parameter it does not generalize to variablerates. The semi-logarithmic functional specification adopted by Ghosh _et al._ (1987) and Gilbert and Palaskas, by contrast, allows the interest rate to enter as a separate state variable. It has been suggested, see e.g. Currie _et al._ (1988), that interest rate effects on primary commodity prices provide an important route by which monetary policy in the developed world affects LDCs. The inclusion of interest rates in the commodity price model in a non-parametric manner is therefore of ;me interest in order to quantify the importance of this effect.

63. Note that interest rates enter the Gilbert-Palaskas price adjustment equation (46) in two distinct ways. First, there is the positive effect of the interest rate level. This Hotelling (1931) effect requires that commodity prices rise with the rate of interest, but the effect is moderated in relation to the degree of market risk aversion. Second, the commodity price is seen as responding negatively to a change in the interest rate. This is a standard asset market effect. A higher interest rate requires, through the level effect, that the price rise faster; if there were no fall in price, this would result in a price higher at every subsequent point in time, which would create excess supply of the commodity. Hence the price must jump down in order that it can subsequently rise faster. A difficulty with standard models which simply enter current and lagged interest 38
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.4 . Cost of importing goods by road through alternative gateways**

a. In U.S. dollars <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Djibouti-Addis Dar es Salaam- Dar es Salaam- Dar es Salaam- Kigali<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>b. By step (% of total time)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Djibouti- Mombassa- Dar es Mombasa- Dar es Mombasa- Dar es Mombasa-<br>Addis Bujumbura Salaam- Kampala Salaam- Kigali Salaam- Juba<br>Bujumbura Kampala Kigali<br>Ports Transport Border Customs<br>Share of costs for importing goods<br>importing goods<br>Costs (US$ per tonne) of<br><!-- End of picture text --> Source: Data collected from ―Trading Across Borders‖; Nathan Associates 2010; and AICD ports database.

In order to understand overall corridor performance, it is helpful to examine the national performance of the various modal components. The performance of the corridor can be only as good as the performance of the national transport systems that comprise it. To this end, the performance of the national road, rail, and ports sectors is briefly reviewed in the remainder of the section, with a view to identifying national weaknesses that may have serious repercussions at the regional level.

# Roads

Data from 2008 reveal that around 73 percent of roads in EAC are paved. A much lower 57 percent of the regional road network in East Africa is paved (table 2.3). This section takes a national perspective on the regional road network. For this purpose, the regional road network is defined as the network needed to connect all national capitals with one another and with the major deep sea ports. In EAC (and East 17
# **Table D3**

# **Summary Statistics of 21 Cottonseed Processing Companies in Burkina Faso**

||**_I_**|**_II_**|**_III_**|**_IV_**|**_V_**|**_VI_**|**_VII_**|**_VIII_**|**_IX_**|**_X_**|**_XI_**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**I. Nature of operation**||||||||||||
|**Number of employees**|33|7|13|103|20|55|42|107|8|8|46|
|**Capacity (tons/day)**|30|30|25|70|20|31|50|40|3|6|60|
|**Year operations began**|2007|2006|2006|2007|2007|2006|2008|2006|2005|2007|2004|
|**Wage rate (CFAf/month)**|40,000|30,000|48,000|—|—|32,000|38,000|32,000|25,000|25,000|31,115|
|**II. Seed statistics**||||||||||||
|**Crushings in 2008 (tons)**|200|100|700|5,773|—|2,000|2,000|7,500|120|80|490|
|**Crushings in 2009 (tons)**|700|100|1,140|5,240|730|1,050|2,000|6,000|200|180|1,600|
|**Seed price in 2008 (CFAf/ton)**|92,000|90,270|75,000|75,000|—|90,250|75,000|75,000|92,270|90,250|90,270|
|**Seed price in 2009 (CFAf/ton)**|92,000|90,270|75,000|75,000|75,000|90,250|75,000|75,000|92,270|90,250|90,270|
|**III. Oil and meal statistics**||||||||||||
|**Oil price in 2008 (CFAf/jc)**|—|—|13,000|12,000|—|10,500|13,000|9,000|11,250|—|12,500|
|**Oil price in 2009 (CFAf/jc)**|—|—|10,000|11,750|—|13,000|12,000|9,000|11,250|—|9,500|
|**Meal sold in 2008**|—|—|612|—|—|—|1,700|6,200|—|70|320|
|**Meal sold in 2009**|—|—|997|—|—|918|1,700|5,020|—|150|1,040|
|**Meal price in 2008 (CFAf/ton)**|90,000|90,000|100,000|100,000|—|—|100,000|70,000|100,000|95,000|92,500|
|**Mealprice in 2009 (CFAf/ton)**|90,000|110,000|100,000|105,000|—|60,000|110,000|70,000|100,000|95,000|92,500|

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