rafmacalaba/datause-encoder-data huggingface dataset:
4 existence of breaks in the commodity price data using an endogenous-break test. The nonparametric estimator of the long-mn persistence of shocks to commodity prices is presented in section 4, as well as an evaluation of its performance under structural breaks. Section _5_ undertakes an empirical reassessment of the Prebisch-Singer hypothesis and a taxonomy of the persistence and dynamics of commodity price shocks. Section 6 presents the conclusions and discusses some policy implications of the results.

# **2. Testing** the Prebisch-Siuger Hypothesis: A **Methodological Review.**

Several studies have tried to verify empirically the PSH but a clear answer remains elusive. Prebisch (1950) and Lewis (1952) provide early estimates of a negative trend in the terms of trade of LDCs in the 1870-1945 period, in the range of 0.5% to 0.7% per year. These estimates may appear small on a year-to-year basis, but can build up an important cumulative effect; in a 70 year period the terms of trade of LDCs would have declined by 35% to 45%. An initial refutation of the PSH is provided by Spraos (1980), who found a negative trend for the 70-year period ending in the outbreak of World War II, but non-significant results when the 1945-1970 period was added.

An important limitaton of these and other early studies (eg, Sapsford, 1985) was the deficient quality of the data on terms of trade, which led the researchers to concentrate on the evolution of the commodity prices which comprise the term of trade indices. Grilli and Yang (1988) undertook a major effort to generate a reliable database for the 1900-1986 period, with which they tested for long-run trends under alternative definitions of commodity price indices and deflators. They found a negative trend for the entire period, similar in magnitude to those estimated by Prebisch and Lewis and, in addition, a large negative break in 1921, which
# **3.2 Shortage Data**

Power shortage data are reported annually by distribution companies (DISCO) to NEPRA and are published in the DISCO Performance Evaluation Report. The latest evaluation report includes shortage data at the DISCO level from year 2010-2011 to year 2014-2015. There are 11 distribution companies in Pakistan, among which 6 provide service in Punjab province. We identify service areas by district for each DISCO and match firm-level data with shortage data based on district-level identifiers in the census data for year 20102011. The providers (and corresponding number of firms) in our data are IESCO (120), GEPCO (1,517), FESCO (1,281), LESCO (1,159), and MEPCO (372).

Although power shortages are nationwide and systematic, there is large variation in shortages at the DISCO level, which is partially linked to differences in supply and demand for electricity and partially linked to differences in operating performance of DISCOs. In fiscal year 2014, for example, average transmission and distribution losses ranges from 9.4 percent to 38.2 percent, while collection rate ranges from 33 percent to over 100 percent across the 11 distribution companies.

Shortages are characterized by two reliability indices in the data: one represents the frequency of shortages, and the second represents the duration of shortages. The System Average Interruption Frequency Index (SAIFI) is the average number of interruptions that a customer experiences in a year. Specifically, SPAIFI is calculated as the total annual number of consumer supply interruptions divided by the total number of consumers that the distribution company serves in any a given year. The second measure, the System Average Interruption Duration Index (SAIDI), captures the outage duration (in minutes) that an average customer experiences in a year. Both SAIFI and SAIDI do not encompass outages caused by load shedding. However, there is still large variation in the value of SAIFI and SAIDI across regions. Among the distribution companies in our data, the SAIFI measure ranges from 0.41 (IESCO) to 185.5 (MEPCO) (Figure 3), while the SAIDI measure ranges from a minimum value of 22.6 minutes for IESCO to a maximum of 15,896 minutes (roughly 11 days) for PESCO (Figure 4). In our econometric analysis, we exploit regional variation in the reliability of power supply measured by SAIDI and SAIFI to identify the relationship between shortages and firm productivity.

For year 2010-11, these two measures of shortages have a correlation coefficient of - 0.13 across the 11 distribution companies covered in the NEPRA report, but for the six distribution companies in Punjab Province, the correlation coefficient for the SAIDI and 6
**Figure 1: Trends in intra‐regional trade in the ECA**

<!-- Start of picture text --> a. ECA’s share of global trade has been falling b. Intra‐regional trade growing faster than extra‐<br>regional trade<br>ECA Trade as a Share of Total World Intra‐ ECA trade vs Extra ECA trade<br>Trade (exports and imports) (billions of dollars)<br>37% 10000<br>35% 8000<br>33% 6000<br>31% 4000<br>29% 2000<br>0<br>27%<br>1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>25%<br>1996 2000 2004 2008 2012 INTRA‐ECA EXTRA‐ECA<br><!-- End of picture text --> Source: Author’s calculations based on UNCTAD and World Bank Development Indicators While there are potentially hundreds of ways countries can connect with varying implications for the transfer of ideas, this paper focuses on six types of economic connections: trade, FDI, migration, information and communication technologies (ICT), air transport and portfolio financial flows. A summary of the data used is provided in Table 1. While other forms of connectivity may also be important for how knowledge transfers between countries and for economic growth, these data are the only ones available on a global and country to country basis. Subsequent sections will drill down into additional layers of connectivity (for example, land transport in ECA) as well as unique aspects of the layers of connectivity. This paper takes a broad macro view of the many layers of connectivity as a means to observe general trends in the strength of connections, how ECA countries are connected to each other and the rest of the world, and how these connections influence growth.

Table 1. Data used for measuring economic connectivity, including the data sources and sample period.

|Indicator Name|Description|Coverage|
|---|---|---|
|FDI|Total bilateral FDI stocks. Source: OECD|2002‐<br>2013|
|Trade|Bilateral total trade flows for manufacturing|2000‐|
||goods. Source: UNCTAD|2015|
|Migration|Total migration stocks. Source: Individual|2000 and|
||Countries’ Census Data, OECD and World Bank<br>estimates (See Artuc et. al 2017)|2010|

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# **Table B.1. Estimated results by Warren for the fixed and mobile penetration models**

Dependent variable for the fixed penetration model: the quantity of mainlines (mainlines per 100 inhabitants) Dependent variable for the mobile penetration model: the quantity of cellular mobile subscribers <u>(mainlines per 100 inhabitants)</u>

|Policy variable|Fixed penetration model|Mobile penetration model|
|---|---|---|
|Policy|5.26**|3.05**|
||(3.11)|(1.85)|
|Y|0.004***|0.0008***|
||(0.0003)|(0.00005)|
|Y<sup>2</sup>|-6.3E-8***|-1.9E-9***|
||(0.0)|(0.0)|
|Y<sup>3</sup>|1.3E-13***||
||(0.0)||
|HD|0.003||
||(0.003)||
|UD|-0.08*||
||(0.05)||
|Qual|-0.13***<br>(0.03)||
|PD||0.001**|
|||(0.0006)|
|Constant|12.26***|-1.3*|
||-2.66|(0.70)|
|Adj. R-squared|0.89|0.78|

Notes: the reported figures are estimated coefficients figures in parenthesis represent standard errors

*=coefficient estimates are at the 90% significance level

**=coefficient estimates are at the 95% significance level

***=coefficient estimates are at the 99% significance level Source: Warren (2000) Model 5 in Table 6.5 and Model 7 in Table 6.6

The policy variable can be separated in the following equations where b is vector of estimated coefficients on the vector of actual policy measures.

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prices for fixed annuities may exceed 20 percent of average prices (Rocha and Vittas, 2010), while data on sold annuities in Chile point to similar conclusions (Rocha and Thorburn, 2007).

Fourth, VPAs that share longevity risk among annuitants allow for a more balanced treatment of longevity risk because they do not have to rely on overly conservative assumptions about projected future mortality improvements. Longevity risk-sharing VPAs provide full protection against diversifiable longevity risk, i.e., the risk that any one annuitant may live much longer than the average, but share among surviving annuitants the non-diversifiable risk, i.e., any increase in average longevity.

Fifth, to the extent that providers do not assume the longevity, investment and inflation risks, VPAs imply smaller capital requirements for provider solvency and thus lower the cost of annuities.

# **3.2 Disadvantages of VPAs**

But VPAs also suffer from several shortcomings. Unlike holders of fixed real annuities, who are protected against longevity, investment and inflation risks, buyers of VPAs are exposed to investment and inflation risks and share the non-diversifiable component of longevity risk.

Second, depending on how the initial calculation of annual payments is made, they may start with relatively low payments. This depends on the level of the AIR relative to the level of market returns and the extent of projected mortality improvements that have been retained in the longevity assumptions.

Third, depending on the volatility of annual returns on investment funds, annuity payments may fluctuate widely from year to year. These fluctuations may cause large changes in the consumption patterns of pensioners.

Fourth, VPAs presuppose access to well functioning and liquid securities markets. If they offer extensive guarantees, they also require access to efficient hedging facilities.

Fifth, buyers of VPAs avoid the price dispersion of fixed annuities but they are exposed to the potential dispersion of investment performance, expense levels and bonus smoothing mechanisms.

Sixth, VPAs suffer from more complex charges that are both less transparent and more difficult to understand. Consumers often rely on the advice of brokers who have incentives to direct them to products and providers that offer the highest commissions. These create conflicts of interest situations, especially in markets where conduct regulation and supervision are weak.

Seventh, VPAs may also suffer from opportunistic cost transfer pricing and profit sharing policies. Because they are less transparent and are more difficult to compare than fixed annuities, such practices may proliferate.

Eighth, because of the way they handle longevity risk, some VPAs are exposed to potentially deceptive and even perverse marketing policies, affecting their risk-sharing arrangements.

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Focusing on a specific target group may reflect either the _direct_ importance of the specific policy objective or its _instrumental_ importance for another end. Appleton and Collier (PSP) examine the case for allocating benefits by gender. They argue that such a case must rest on gender being a clear dimension of disadvantage or on the existence of positive externalities accruing to others as a result of targeting benefits to women. They also share with Sen and others the view that using a group identifier such as gender has the advantages of being easy to identify and hard to manipulate. However, such broad characteristics can be highly imperfect correlates of poverty, possibly allowing only a modest impact for a given outlay.<sup>2</sup> Appleton and Collier's study brings out well the reality that multiple objectives and constraints on instruments underlie our choice of policies.

A recurrent issue concerns the weight that should be given to the preferences of the poor. Should the welfare objective be denominated in terms of utility or income? Should we be concerned with how hard the poor must work? In short, should the adopted framework be welfarist or nonwelfarist when the main objective is poverty reduction? Kanbur, Keen and Tuomala in PSP illustrate the consequences of this decision for targeting and evaluation rules. For example, they show that if the policy objective is the minimization of an income-based poverty index and given labor supply responses, accepted rules-of-thumb based on the welfarist utility framework-namely that marginal tax rates on the poor should be low-are overturned. Under the alternative nonwelfarist policy goal, simulated optimal marginal tax rates tend to exceed 60 percent (given minimal revenue requirements). Consider, further, Sahn and Alderman's empirical study (PSP) of the effects of Sri Lanka's targeted food stamp scheme on labor supply. They find strong disincentive effects: men and women lower total hours worked as a result of the transfer. Is this a good or bad outcome? If one is judging this case within a welfarist context in which leisure is accorded a high weight, the policy may then be considered a success.<sup>3</sup> However, from the point of view of the policymaker trying to achieve the greatest dent in income poverty for a given budget, such behavioral responses may be important 4
**Table 7: Pedagogical practices**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Policies applie**|**d**|||||
|COMP<br>~~U~~SE|Math policy - use of<br>computers in class|0.4345<br>(0.4957)|40800|0.6447<br>(0.4787)|4815|
|TXT<br>~~B~~OOK|Math policy -<br>same textbook|0.7905<br>(0.4069)|40557|0.7855<br>(0.4105)|4882|
|STD<br>~~C~~UR|Maths policy -<br>standardized curriculum|0.8705<br>(0.3358)|40595|0.949<br>(0.22)|4882|
|**Fromative asse**|**ssment used to . . .**|||||
|ASS<br>~~S~~CH|monitor the schools<br>yearly progress|0.9111<br>(0.2846)|40555|0.9799<br>(0.1403)|4882|
|ASS<br>~~T~~CH|make judgements on<br>teachers’ efectiveness|0.7764<br>(0.4166)|40400|0.9912<br>(0.0934)|4882|
|**Cognitive Acti**|**vation in Mathematics**|||||
|COGACT _(r)_|Cognitive activation in<br>mathematics lessons|0.2998<br>(0.975)|26217|-0.3278<br>(0.6647)|3249|
|**Classroom Ma**|**nagement**|||||
|STU<br>~~F~~EEDB|Seeking written feed-<br>back from students|0.7105<br>(0.4536)|40788|0.8419<br>(0.3649)|4882|
|CLSMAN _(r)_|Teacher classroom<br>management (in math)|0.2394<br>(0.905)|25753|0.2163<br>(0.7761)|3252|
|DISCLIMA _(r)_|Disciplinary climate|-0.0243|26242|0.3747|3254|
||in class (mathematics)|(0.9055)||(0.6926)||

Notes: The variables relate to the questionnaires administered to schools and students in the rotated booklet, marked with _(r)_ . For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different at the 95% significance level, except TXT ~~B~~ OOK. Figures in parenthesis represent standard deviations.

# **2.8 School Characteristics**

Table 8 indicates interesting basic differences between Vietnam and Dev7 school characteristics. Vietnamese schools are about half as likely to be private schools (8% compared to 17%) and less dependent on funding from student fees; in Vietnam, student fees account for 17% of the school’s financing, compared to 26% on average for Dev7. One very useful comparison comes from a question regarding the geographic location of the high school. The percentage of schools reported in a VILLAGE (defined in PISA by population below 3,000 inhabitants), was 46% of high schools in Vietnam compared to 14% of High schools in Dev7 countries. With CITY, defined by a population above 100,000 inhabitants, we find only 23% Vietnamese schools in cities, compared to 41% of high schools located in cities for Dev7 countries.

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# **3. Data**

The main source of data is the 1997 Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Survey of Demographic Dynamics) conducted by Mexico’s national statistical agency (INEGI) in the last quarter of 1997.<sup>6</sup> The ENADID is a large nationally representative demographic survey, with approximately 2000 households surveyed in each state, resulting in a total sample of 73,412 households.

# **3.1 Which migrants to look at?**

The survey asks whether household members have ever been to the U.S. in search of work. This information is collected for all individuals who normally live in the household, even if they are temporarily studying or working elsewhere, and includes information on the number of times an individual has been abroad, and the date of the last visit. Secondly, additional questions are asked about migration during the last five years, with detailed questions collected on the last trip. Finally, the survey also asks whether there are any individuals who were living in the household five years ago who have moved abroad, regardless of whether or not they are currently considered part of the household.

With this information we can examine whether a 15 to 49 year old individual migrated for the first time during the period 1996-97. This restriction is made for several reasons. Firstly, the role of networks is believed to be less important for individuals making a repeat migration trip (Massey, Goldring and Durand, 1994), and therefore we wish to model the initial migration decision. However, since we only know the number of trips an individual has made, and the date of last migration, we can only model the initial migration decision for individuals making their first trip in the recent past.<sup>7</sup> Secondly, by comparing migration from different communities in a single two-year period, we are much less concerned about the interaction between community networks and macroeconomic shocks. Thirdly, this short period makes recall bias which varies with education much less of a concern than looking at migration over a lifetime.

> 6 Survey methodology, summary tables, and questionnaires are contained in INEGI (1999).

> 7 In particular, an individual is defined to be a first-time migrant if they migrated during 1996 or 1997, and have only made one trip to the United States.

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# **1. Introduction**

The enormous increase in foreign direct investment (FDI) flows across countries is one of the clearest signs of the globalization of the world economy over the past 20 years. Total FDI flows increased from some US $55 billion in 1985 to US $1,511 billion before falling back to US $573 billion in 2003 (World Bank 2005a). Even as a share of gross domestic product (GDP), we do observe an enormous increase in the significance of FDI. In high-income countries, this share increased from some 0.5 to 1.0 percent in the 1980s to more than 5 percent in 2000 and then declined to 1.4 percent in 2003 (Figure 1). While the increase in FDI inflows was less drastic in low- and middle-income countries, the percentage of FDI in GDP remained at more than 2 percent after the year 2000, indicating a slightly higher significance of FDI flows in developing countries in the most recent period.

Figure 1: Foreign Direct Investment Inflows as a Share of GDP, 1970-2003 <!-- Start of picture text --> % 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>1970 1980 1990 2000<br>High-income countries Low- and middle-income countries<br><!-- End of picture text --> Source: World Bank (2005a).

Over the last couple of decades, the theoretical literature on the impact of foreign investment flows on host economies has identified several channels for FDI to influence per capita income growth rates. As a start, FDI may provide new capital, allowing additional investment in both human and physical capital, which can be very beneficial for (developing) countries 2
The late response of Ethiopia to AGOA preferences, and the importance of SEZs and FDI suggest that the binding constraint in this case was traditional country-level inadequacies, like poor transport infrastructure, high transaction costs, and lack of access to capital, that were partially addressed by SEZs. The future success of Ethiopian exports of apparel to the US will depend on how well learning-by-doing and other dynamic comparative advantage factors materialize for firms inside and outside the SEZs.

# **9. What explains the heterogeneous impact of AGOA on apparel exports across countries?**

Our analysis shows that AGOA had a differential impact on apparel exports across African countries, sub-periods, and sub-regions. While some African countries took advantage of the opportunities created by the US trade preferences, others failed to do so, and the durability of the AGOA impact varies across sub-regions. We consider some factors that could have contributed to such heterogenous effects.<sup>45</sup> We re-estimate Eq. (1), allowing the AGOA apparel term to enter by itself but also interacted with policies that could be linked to developing export capacity. We include average tariffs imposed by African countries on their own imports to capture ease of participation in international supply chains, the cost of starting a business as an indicator of the business environment for both domestic and foreign firms, internet access as a channel for knowledge about manufacturing and consumer tastes, and oil rents as an aspect of endowments that could affect competitiveness.<sup>46</sup>

> 45 Frazer and Van Biesebroeck (2010) found differential short-term impacts of AGOA on apparel and non-apparel across countries, but no meaningful correlations of those with measures of corruption, rule of law, or import tariffs imposed by African countries.

> 46 The variables differ across countries and over time. Tariff data are taken from Tet <mark>i (2020) described in the Online Appendix, the cost of starting a business is taken from the Doing Business database, while the number of internet users per 100 people and oil rents as a percentage of GDP are taken from the World Development Indicators.</mark>

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