rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5619

# **Abstract**

One side-effect of the Global Financial Crisis of 2008–09 was the resurgence of a debate over exchange rates. The conventional wisdom dictates that real-exchange rate adjustments are needed in order to bring about changes in trade balances across countries. However, the literature on the effect of exchange rate fluctuations and currency under-valuations on exports is surprisingly ambiguous. This note explores for the first time the potential role of foreign direct investment as an intermediate variable in the process of trade adjustment after large realexchange rate changes. Real-exchange rate devaluations might result in increases in foreign direct investment inflows, as investors can take advantage of changes in the foreign-currency value of domestic assets. If so, the response of exports will depend to some extent on the nature of such foreign direct investment inflows, with inflows motivated by “horizontal” foreign direct investment associated with negligible changes in export growth after devaluation. The author utilizes quarterly data on real effective exchange rates, foreign direct investment inflows and exports to explore the effects of large devaluations (defined as the largest observed quarterly real effective exchange rate devaluation) on foreign direct investment and exports from 1990 to 2010. The admittedly speculative evidence suggests that there were heterogeneous experiences regarding the timing and magnitude of subsequent changes in foreign direct investment and exports, but on average foreign direct investment inflows tended to precede export surges within two year horizons.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dlederman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
some caution is needed here because measurement problems could affect one term of the decomposition more than the other.

# **_3.6 Within and between misallocation: Country, states, and districts_**

We now turn to the decomposition of national misallocation into districts. As already argued, national misallocation may be about more productive establishments locally being unable to access factors and grow or about factors not being able (or willing) to move to more productive areas. These are two different phenomena, which arguably call for different policy responses.

The results for this geographic decomposition are reported in table 4. As with previous the results are stable over time so that we can focus our decompositions, extremely discussion on 2010 again. Many of the patterns already observed above still hold. For instance, there is less misallocation for output than for factors and factors are roughly equally misallocated.

The first striking new result is that national misallocation reported in the first column is often very close to the unweighted mean of district misallocation. Hence, to a large extent, the national picture about misallocation is a close reflection of what happens in an average district. Then, we can also note that the share-weighted average for misallocation across districts, _M_<sup> ̃</sup> , is typically higher than the unweighted mean. Larger districts, where output is higher and factors more abundant, are also more misallocated districts. As shown by column 5, there is a lot of variation in misallocation across districts.

At the same time we also observe that the between-district term, _M∆_ , is also negative and of about the same magnitude as the difference between the weighted and unweighted mean district misallocation. This national misallocation is about the same explains why as district misallocation. The negative sign of _M∆_ suggests that more productive districts produce more output and receive more factors of production per establishment. Because larger districts with more factors and more establishments are also often more productive 16
The background paper (World Bank 2017b) calculated a reform index over the period 1995 to 2015 for 88 developing countries and 22 OECD countries. The index was based on four subindices (regulation, restructuring, competition, and private sector participation), each given a weight of 25%, whose values were determined by the categorizations shown in table 5. Regulation stayed with an “on/off” approach; restructuring generalized the approach of Vagliasindi (2012) who distinguished full and partial privatization; competition distinguished five categories; while private sector participation was a continuous variable depending on shares of private sector participation.

Table 5: Power Sector Reform Index.

|Regulation|No r<br>|egulator<br>=0||Regul<br>=10|ator<br>0|
|---|---|---|---|---|---|
|Restructuring|Vertically<br>integrated<br>= 0|Partial<br>unbun<br>=|vertical<br>dling<br>33|Full vertical<br>unbundling<br>=67|Vertical and<br>horizontal<br>unbundling<br>=100|
||Monopoly<br>= 0|IPPs<br>= 25|Single<br>mo<br>=5|buyer<br>del<br>0<br>Bilateral<br>contracts<br>=75|<br>Competitive<br>market<br>=100|
|Private sector<br>participation|0.5 *(percentag<br>0.5 *(percentag|e of genera<br>e of distrib|tion capac<br>+<br>ution utilit|ity with private sect<br> <br>ies with private sect|or participation)<br>or participation)|

_Source:_ World Bank 2017b The conclusions to that study found that the rapid diffusion of policies in the decade 19952005, with the aggregate reform index for developing countries growing from 12 points to 35 points, was followed by a much slower diffusion in the next decade (35 points to 43 points). The slowdown was especially notable with respect to regulation, and privatization of generation and distribution. By the end of the period, IPPs had been introduced in 96% of developing countries, 72% had a regulatory agency, 44% had undertaken vertical unbundling, but only 7% had a wholesale power market and 12% still were operating with a vertically integrated state-owned monopoly. It is also important to note that even among the OECD countries there was not full uptake of these policies– one-third of these countries had a reform score below 70 in 2015.

These figures do not support an unqualified acceptance of the reform model. Rather they point to important differences between countries in terms of their willingness to adopt certain reform steps. <mark>World Bank (2017b)</mark> shows how the degree of uptake varies with a number of country characteristics, not just income level. Progress on adopting reform was slowest in Sub-Saharan Africa where the performance as measured by QFDs is known to be weak. These findings point to the need for deeper analysis of why these countries were slow to adopt reform– including the possibility that the reform model was completely unsuitable and as a result they either failed to adopt it at all, or else they just undertook a small segment of the whole range of policies and then made little further adjustment.

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Figure 5: Percentage of ECTs mentioning technologies from WIPO green patents, 2012-2021 <!-- Start of picture text --> Construction Energy Farming and Forestry<br>2.5 Con s truc t ion Materia l 2.5 Wind Turbine s 2.5 T r ansgeni c Crops<br>Air Conditioning Li Ion Battery Soilless Agriculture<br>Led Technology Biofuels Water Saving Irrigation<br>2 Sensors 2 Solar Energy 2<br>Inverter Technology<br>1.5 1.5 1.5<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>PMP Transportation<br>2.5 Ca r bon Capture Techno l ogy 2.5 Hybridelectr i c Vehic l e<br>Insulating Glass Drones<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of ECs mentioning green patents<br><!-- End of picture text --> Notes: The figure depicts the share of ECTs that belong to each of the selected technologies within each of WIPO Green sector in the period 2021-2021.

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and transparent assessment of the size of the parameters described in the model, including non-oil revenues, oil revenues and the number and location of citizens, statistics which would contribute to a better understanding of the costs of conflict. Likewise, more extensive transparency in government and military expenditures by both actors (verified by third parties) following the example already set by the CPA Monitor could contribute to more complete information and reduce the likelihood of unwanted conflict.

Additionally, note that asymmetric information was assumed here for convenience, not to reflect the actual conditions on the ground in Sudan. If, more realistically, we assume that each side faces uncertainty about the other’s expected gains from going to war and that each can increase military expenditures, then, given some very reasonable assumptions on the parameters, Khartoum could have an incentive to increase its military spending to discourage Juba from opting for partition, while Juba might want to build up its military power to deter Khartoum from responding to partition with war. If there is any dynamic feedback from observed military spending to actual military spending, this could result in an “arms race” that could be quite dangerous in a situation in which both actors do not know the other’s payoff associated from war (and have incentives to signal that the payoffs are high). Military spending may in fact lead to a situation in which the actors’ preference for peace is overcome and they prefer to go to war regardless of the other’s actions. In addition, the existence of private information and incentives to misrepresent that information can lead to war through another causal mechanism, which does not require overcoming the preference for peace. Both actors may start feeling threatened by the other’s military spending, perceived as aggressive, 27
# **Table 15 The Unemployed by Employment History and Reasons for**

# **Unemployment, 2001**

|Reasonsforunemployment|Unemployed|
|---|---|
|Allunemployed|100.0|
|Newentrants|14.1|
|Joblosers|85.9|
|Alljoblosers|100.0|
|Layoff|45.7|
|Terminationoftemporaryjob|22.4|
|Voluntaryquits andotherreasons|31.9|

_Source: Labor Force Survey, May 2001, Bank staff calculatons._ Finding new employment is a lengthy process. On average, it takes over three years of job search before a new job is found.'<sup>7</sup> This means that unemployment in Lithuania is of a long-term nature, and it is thus very costly for both the affected individuals and the society as a whole. International experience shows that long-term unemployment often leads to disenfranchisement and social exclusion, as chances to find a job decrease with unemployment duration. This so called "duration dependence" results from the erosion of skills and motivation on the part of the long-term unemployed as well as from the employers' reluctance to hire workers who were jobless for a long time, whom they consider as less productive.

Some worker groups are more successful than others in finding new employment. Two groups stand out in this respect. These are young workers and workers with university education, (Table 16). Both these groups on average find work much faster than their older and less educated counterparts. For example, it takes younger workers (aged 15-24) half the time needed to find ajob by older workers (aged 55-64). Similarly, university educated workers need about half the time necessary to find a job by workers with less than upper secondary education. Put differently, 50 percent of younger workers and university educated workers find a new job within less than 18 months, while other

> 1 The average estimated duration of an _uncompleted_ unemployment spell is 21.1 months. In a steady state, the average duration of a _completed_ unemployment spell is twice as long as that of an uncompleted spell.

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among many EMs and a number of developed markets.<sup>6</sup> Thus, the evidence based on the assumption of free flow of capital and corresponding market indices may not reflect the true benefits of diversification.

The purpose of this section is tn provide new evidence on the benefits of interrational diversification. The emphasis will be on EMs, many of whom are closed to foreign investors. We **use** the freely accessed National Index Funds that are also freely traded on the U.S. stock ex%zhanges together with various freely accessible developed market portfolios to document gains fro-m international diversification. We also use the EM and DM market indices (many of which are not freely accessible) to provide comparisons with past work and distinguish among theoretically desirable versus practically attainable diversification opportunities.<sup>7</sup> Following the tradition, we begin by reporting the pairwise correlations, and then the systematic risks of the various NIFs with respect to the benchmark local and U.S. market indices. We proceed to develop various mean-variance efficient frontiers to document theoretical gains from diversification based on various market indices and the diversification potential based solely on the U.S. market that includes the National Index Funds. Finally, we provide the evidence of attainable diversification gains from the U.S. perspective based on all available assets, including all NIFs from developed and emerging markets as well as all freely accessible 6Stulz (1981) and Errunza and Losq (1989), among others, develop asset pricing models that suggest higher expected returns on securities from markets that cannot be accessed freely.

'Note that the debate as to whether the National Index Funds serve as a substitute for direct portfolio investments in the corresponding markets is moot in the case of markets otherwise closed to foreign investors. In the statistical sense, though, the behavior of NIFs will he compared to the underlying assets and market inc<sup>1</sup> 'ces in this and the next section.

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_Formal institutions are needed as markets become complex‐ and partly replace informal ones._ Peoples’ actions are governed simultaneously by formal and informal institutions. The more complex that transactions become, the more separated in time and space, and the greater the share of transactions between counterparts that are not known to each other through social or business networks, the higher will be the relative importance of formal institutions with third party enforcement mechanisms (see Box 2). For example, competitive, small scale, village markets where you buy daily provisions with cash have little need for formal rules to regulate behavior. Problems encountered with this simple transaction (e.g. poor quality or use of counterfeit money, for example) are generally dealt with through informal codes of conduct. However, engaging a financial institution or advisor to support your retirement accounts, or exporting goods to a country where the firm cannot use its connections to gain customers, will require more formal rules to facilitate transactions. An example of how formal institutions may replace informal ones is when court enforcement of contracts is preferred towards the use of arbitration through community leaders. It is easier to hold a firm to account through a court than through a community enforcement mechanism when the transaction relationship is “arms‐length” that is, outside the community. The WDR 2002 provides many examples of how increasing complexity in transactions (as described above) raises the demand for more formal institutions to complement or substitute for more informal ones.

_Formal and informal institutions also work together_ . Examples of formal and informal institutions complementing each other are many. For example, (i) when formal laws and regulations regulate property transfer, but, in cases of conflict, adjudicated decision rules favor one party over another due to social expectations as determined by religious or traditional practices; (ii) when public services, such as electricity connections are only delivered once informal systems of payment (a system of _baksheesh_ or bribes) are added to the regulated formal system or when connections are prioritized 4
impact on years of schooling in 95% of the surveys. Only in 2.3% of the surveys was there a negative estimated effect of life expectancy on schooling, and in only 3 of the 919 regressions was the coefficient negative and statistically significant.

A simple average of life expectancy coefficients across these 919 surveys shows that for each additional year of gain in life expectancy at birth, individuals spent approximately 0.155 year more in school. Figure 4 presents region specific kernel distributions of the coefficients of life expectancy at birth obtained from survey-specific regressions. The region-specific median value of the coefficient, as indicated in the graph, reveals that the highest median life expectancy effect is observed in Latin America, while the lowest in Central Asia and Eastern Europe.<sup>15</sup> Over 97% of the estimates are positive and the median estimates for the regions vary from a low of 7.3% in Eastern Europe and Central Asia to 17.4% in South America.

# **6.2 Estimates from the regression on pooled surveys**

As reported in table 2, life expectancy at birth is positively associated with schooling. The coefficients of life expectancy at birth, ��, imply that a one-year increase in life expectancy at birth increases years in school in the range of 0.13 to 0.15 years. Reflecting our discussion in section 4, we acknowledge that cohort-specific fixed factors could bias the estimates. We control for such cohort-specific fixed effects in two different ways. In specification IV, dummy variables defining five-year birth cohorts are incorporated into the estimation.<sup>16</sup> In specification V, birth year cohort fixed effects are introduced. All the estimates suggest that an additional year of schooling results from about 7.5 years greater life expectancy at birth. We also see some modest evidence of a small positive effect of parent’s life expectancy on their children’s schooling.

> 15 For comparison, the distribution of all survey-specific estimates shows a median life expectancy effect of 0.148. 16 While constructing the five-year birth cohorts, we collapse all individuals aged 25-65 into 13 different five-year birth cohorts. Since the number of observations before 1930 is small, they are grouped into a cohort spanning more than five years. Similarly, all individuals, who were born during 1985-87, were collapsed to form the last cohort spanning only 3 years.

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The quadratic term reflects the nonlinear shape of the net revenue of the climate response function (Equation 2). When the quadratic term is positive, the net revenue function is U- shaped and when the quadratic term is negative, as in Figure 1, the function is hill-shaped. We expect, based on agronomic research and previous cross-sectional analyses, that farm value will have a hill-shaped relationship with temperature. For each crop there is a known temperature at which that crop grows best across the seasons. Crops consistently exhibit a hill-shaped relationship with annual temperature, although the peak of that hill varies with each crop. The relationship of seasonal climate variables, however, is more complex and may include a mixture of positive and negative coefficients across seasons.

The change in welfare, ΔU, resulting from a climate change from C0 to C1 can be measured as follows.

If the change increases net income it will be beneficial and if it decreases net income it will be harmful.

Cross-sectional observations across different climates can reveal the climate sensitivity of farms. The advantage of this empirical approach is that the method not only includes the direct effect of climate on productivity but also the adaptation response by farmers to local climate. This farmer behavior is important because it mitigates the problems associated with less than optimal environmental conditions. Analyses that do not include efficient adaptation (such as the early agronomic studies) overestimate the damages associated with any deviation from the optimum. Adaptation thus explains both the more optimistic results found with the Ricardian method and the generally pessimistic results found with purely agronomic studies.

Adaptation is clearly costly. The Ricardian model takes into account the costs of different alternatives. For example, if a farmer decides to introduce a new crop on his land as climate warms, the Ricardian model assumes the farmer will pay the costs normally associated with growing that new crop. That is, the farmer will have to pay for new seeds and new equipment specific to the crop. The Ricardian model does not, however, measure transition costs. For 6
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