rafmacalaba/datause-encoder-data huggingface dataset:
5. Following an overview of Nigeria’s recent economic performance and its impact on employment, the paper describes the basic rationale underlying the GIF framework and its methodology. The third section discusses, based on a range of criteria proposed by the GIFF, which sectors or products would be compatible with Nigeria’s latent comparative advantage and should therefore be promoted using industrial policy. The fourth section reviews the binding constraints to growth in each of these sectors and discusses specific interventions the government could undertake-- in collaboration with the private sector--in order to alleviate these constraints. In view of the fact that shortcomings in governance have in the past often undermined the effectiveness of policy interventions in Nigeria, this section also discusses how the measures could be implemented to ensure accountability and transparency.

# **I. Recent economic developments in Nigeria**

6. Since 2001, Nigeria has had the longest period of sustained expansion of the non-oil economy since independence. Growth has occurred across all sectors of the economy and has been accelerating. While non-oil growth averaged about 3-4 percent in 1995-2000, it more than doubled to over 7 percent and rose to 8 to 9 percent in recent years. Even in spite of the current global financial crisis, growth of the non-oil economy remained above 8 percent in 2009 and 2010. While the oil economy contracted in recent years owing to unrest in the Niger Delta, since 2009, the contribution of the Niger Delta has improved as a result of positive effects of the amnesty on oil production (Table 1).

**Table 1: Macroeconomic Aggregates, 2003 - 2009 (in percent)**

||**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|---|
|Real GDP|10.2|10.5|6.5|6|6.4|6.00|7|
|Oil GDP|23.8|3.3|0.5|-4.4|-4.5|-6.2|0.5|
|Non-Oil GDP|5.8|13.2|8.6|9.4|9.5|9|8.3|
|Inflation Rate(CPI annual average)|14|15|17.9|8|5.4|11.6|12.5|

7. Moreover, over the last five years, the growth of Nigeria’s non-oil economy has been superior to that of most oil-exporting and non-oil exporting countries in Sub-Saharan Africa (Table 2).

4
_Why Do People Move across State Borders? Evidence from Mexico_ Trinidad Saavedra and Gabriela Inchauste<sup>1</sup> JEL codes: J15, J61, O12 Key words: Mexico, internal migration, labor mobility, gender, gravity model, multinomial regression, double selection

> *The authors are grateful to Romina Safojan (Consultant) and Esteban Aleman (Consultant), who provided valuable inputs. The paper benefited from thoughtful comments provided by Alan Fuchs (Senior Economist, Poverty and Equity Global Practice). The authors also thank Aylin Isik-Dikmmelik (Senior Economist, Social Protection and Jobs Global Practice) and Clemente Avila ( <mark>Senior Social Protection Economist,</mark> Social Protection and Jobs Global Practice) for providing comments at an earlier stage of the research. All views expressed in this paper, and all remaining errors, are those of the authors.
One caveat with the analysis is that it cannot be established whether or not a household would be program eligible in the absence of the typhoon. One can, however, directly test for whether or not the typhoon had any effect on beneficiary status with a standard difference-in-differences (DID) framework: A binary dependent variable that is coded equal to one if the household is a beneficiary and zero otherwise is regressed on the distance treatment (either binary or continuous), a binary variable for the 2015 survey round, and the interaction effect between the two. Under standard parallel trends assumptions, the parameter estimate on the interaction term could be interpreted as a causal estimate of the typhoon on the likelihood of being a beneficiary. Results for this exercise are shown in table 6 for the 100km distance cutoff, discrete and continuous treatments, and inclusion and exclusion of fixed effects. None of the four point estimates on the interaction term comes close to being statistically significant. There is hence no indication that the typhoon itself caused households to become beneficiaries. The significant point estimate on the distance variables vanishes once fixed effects are controlled for. The positive effect of the 2015 round merely shows the secular increase in coverage across the country.

24
# **Table 4: Error Correction Model for Savings**

A(s l y)t = _a._ + **_yf1A(s_** / _y)_ X + a1A in _y,_ + **a 2Ar,** + _a 3AJfli_ + _a4 A7r,_ + _a5 govs,_ + _, 1ec1,l_ + **_ut_**

|Constant|CHL<br>0.004<br>(0.537)|GHA<br>0.001<br>(0.225)|IDN<br>-0.011<br>(-2.051)|KOR<br>-0.022<br>(-4.997)|MYS<br>-0.011<br>(-2.352)|MEX<br>-0.006<br>(-1.871)|TUR<br>-0.001<br>(-0.203)|ZWE<br>0.009<br>(0.467)|
|---|---|---|---|---|---|---|---|---|
|A (s/y),]|-0.082|0.072|0.149|0.110|-0.077|0.297|0.172|-0.023|
||(-0471)|(0.420)|(1.214)|(1.064)|(-0.624)|(2.281)|(0.722)|(-0.068)|
|A Iny|0.166|0.100|0.215|0.465|0.430|0.125|0.015|0.186|
||(3.216)|(1.432)|(3.269)|(8.747)|(5.463)|(1.886)|(0.176)|(0.681)|
|A r,|-0.005|-0.336|-0.318|-0.128|-0.908|0.216|0.030|-0.263|
||(-0.076)|(0.788)|(-1.486)|(1.368)|(-3.092)|(4.911)|(0.358)|(-0.533)|
|Af.|-0.010<br>(-0.862)|0.011<br>(1.212)|-0.003<br>0.470|-0.013<br>(-2.388)|0.000<br>(-0.068)|0.006<br>(0.791)|0.010<br>(1.145)|-0.014<br>(-0.436)|
|An t|-0.033<br>(-0.482)|-0.279<br>(-0.824)|-0.478<br>(-2.827)|-0.107<br>(-1.083)|-1.360<br>(-4.429)|0.271<br>(5.417)|0.036<br>(0.386)|-0.371<br>(-0.749)|
|A govs,|0.146<br>-0.586|-0.745<br>(-3.104)|-0.983<br>(-2.332)|-1.297<br>(-6.897)|-1.319<br>(-9.548)|-0.159<br>(-8.885)|-0.239<br>(-0.847)|-0.091<br>(-0.304)|
|cc,l|-0.344<br>(-1.612)|-1.071<br>(-3.473)|-0.890<br>(-4.893)|-0.429<br>(-3.078)|-0.856<br>(-3.398)|-0.779<br>(-3.644)|-0.697<br>(-2.485)|-0.274<br>(-0.806)|
|R2|0.500|0.583|0.730|0.872|0.864|0.897|0.176|0.000|
|BGtest|0.088|0.085|0.179|0.167|0.57|0.126|0.619|0.025|
|NOBS|23|23|22|22|23|23|23|18|

Notes:

1. t-statistics in parenthesis.

2. BG denotes the marginal significance level for the Breusch-Godfrey test for serial correlation up to the second order.

46
a board as well as additional **i l.in-:itiij** caused **by** localized industry-specific spillovers and natural advantages. Drawing upon their model they propose an index of geographic concentration of employment in an industry that controls for industry characteristics, namely for the size distribution of plants in the industry.' Indeed, one of the key advantages of the **EG** index is that it allows for cross-industry comparisons.

Considering the aforementioned raw geographic concentration index **Gj** for industry _j_ and its plant size distribution as measured by the Herfindahl index, **_Hj = I_** 1<sup>_z_</sup><sup>**,**where</sup><sup>**zij**</sup> is plant i's share in employment of industry **_j,_** the **EG** index for industry **_j_** in a given time period is defined **by** Equation **1:6** The subtraction of the term **(1** - **_EMIx2)Hj_** in Equation 1 accounts for the aforementioned problem that **Gj** would be larger in industries with just a few large plants even if plants chose their locations randomly. That term corresponds to the level of concentration that would be expected (in the model of Ellison and Glaeser **(1997))** if plants in the industry chose their locations independently and randomly and both industry-specific spillovers and natural advantages played no role. Therefore, the **EG** index measures the concentration of employment in an industry over and above the level of concentration that would have prevailed if plants were distributed completely at random. Greater values of the **EG** index are associated with greater geographic concentration of the industry.<sup>7</sup> Dumais et al. (2002) show that the index can be generalized to a dynamic setting. Note that we compute the Herfindahl index **_Hj_** based on our plant level data for Indian industries, rather than take it from external sources as in other studies. This is a particularly important advantage of

> 5The main prediction of the model is that the relationship between average levels of concentration and industry characteristics is the same whether concentration is caused **by** from spillovers, from natural advantage, or from a combination of both. This prediction implies that one can construct an index that controls for industry differences, regardless of the cause of concentration. 'The Herfindahl index is defined as usual covering all plants in the industry without a geographic dimen- **S10n. 'A** value of zero for the **EG** index indicates that industry employment is as concentrated as would be expected from a random location process. Ellison and ( I **-** i **(1997)** suggest that industries with **EG** index values higher than **0.05** are **highly** concentrated while those with **EG** index values below 0.02 are not very concentrated.

**6**
relevant for public enterprises **as well** as private firms, since it constrains the public enterprise's goal of welfare-maximization and distorts their input mix.49/ It can therefore be concluded, that rateof-return regulation imposed on public sector productiun is a potential source uf inefficiency.

28. A pervasive consequen(.e of regulation in transport is the creation of a protected market -- essentially granting monopoLy power to the public or private enterprise. This regulation is often called "institutional" barriers to entry, rather than a barrier to entry arising from the nature of the market or of production itself. Typical examples in transport include: licensing requirements for vehicles, restricted entry into urban transport, area and route licensing for buses, trucks, and civil aviation, zoring or planning commission regulations, and high costs of conducting environmental impact studies and legal fees for entry. Private transport enterprises may also cooperate to reduce competition by self-regulating operations and setting fares.50/ If these regulations are effective, then the public or private firmn en,oys a monopoly position and, as discussed below, may have little incentive to minimize costs.

# Market Structure

29. It has long been observed that a firm which enjoys monopoly power might produce output at a higher cost than would a firm operating in a competitive market. It is believed that lack of competition widens the area for discretionary behavior in the organization and eliminates the discipline on the enterprise to lower costs.51/ Standard microeconomic theory, however, implicity rules out any connection between 49/ Op Cit, Dieter Bos, p. 156. 50/ In Yemen driver cooperatives effectively operate route licensing systems and set tariffs for passenger and freight transport. Transport Sector Review, Study Report, Yemen Arab Republic Central Planning Organization, February 1982, p. 5.

51/ Op Cit, A. Israel, p. 93.

21
34 _The dependent variable (p* - p*R )_ The variable (p* - p*R ) was constructed as an annual average for all mills individually (i.e. the measure (p* - p*R ) is _mill-specific_ ) over 26 years from 1974/75 to 1999/2000. Recall that p* is the perfectly competitive international benchmark price for different Costa Rican origins. It follows that individual mills will have individual annual average p* based on the composition of different origins (bean types) in their harvest. Mill-specific p* was therefore calculated using Icafé data on the proportions of different bean types in a mill’s harvest, and then applying the specific Costa Rican price series to the harvest for those bean grades. The price series were constructed from data collected from the ICO, the New York Coffee, Sugar and Cocoa Exchange (CSCE) where Arabica futures are traded, and using quality differentials obtained from private traders in Switzerland and London.<sup>35</sup> The variable p*R , the annual average mill-specific price reported by mills to Icafé, was collected during six months of field work in Costa Rica with coffee producers, mills and the relevant staff at Icafé. This careful data work permitted the construction of a series of continuous markdown measures (p* - p*R ), which differ across mills and across years.

# _Independent variables_

* It has been seen that (p* - p R ) indicates to what extent (if any) the market, through its structure, its actors and other factors, reduces the returns to the Costa Rican coffee producer from the perfectly competitive MES level.  In addition to locating cooperatives and Fairtrade mills in the data to test hypotheses (i) to (iii), any variations in the mark-down induced by market power can be understood in terms of simple rotations and shifts of the demand and supply curves (see Figure 7).  Shifts in demand represent movements along the supply curve. These affect the size of the * deviation between pM and pPC (the size of the margin (p* - p R ) when this is due to market power) since the wedge between supply and MPC differs along the supply curve.<sup>36</sup> Input factor demand, D, is:

# VMP = p* x MPcherries ,

since Costa Rican coffee mills are not unreasonably assumed to be price takers in the output market. The demand shift factors that affect the VMP therefore include changes in the output price (world price for coffee, p*) and/or changes in their marginal product, including determinants of mill specific costs, such as bean types or changes in downstream technology.

In addition to demand shift factors, the variation in the degree of exercise of market power across mills and across years may be affected by shifts in supply, for example, due to natural disasters or changes in farmer technology. Variation in the exercise of

> 35 For a detailed description of the data set construction used for the analysis, see Ronchi (2005).

> 36 MPC = ∂TC = ∂AC Q +AC (since TC = AC*Q). Supply is AC, which differs from MPC by ∂AC Q , which ∂Q ∂Q ∂Q clearly grows with Q.
Policy Research Working Paper 8527

# **Abstract**

This paper aims to document a set of stylized facts characterizing business cycle dynamics in smaller economies. The paper uses a large sample of countries spanning 1960–2014 to show that country size is a significant factor affecting countries’ volatility, comovement with gross domestic product and real interest rate, and persistence. Specifically, analysis finds that smaller countries (i) tend to have more volatile gross domestic product; (ii) have more volatile, less procyclical, and less persistent investment; (iii) exhibit more volatile trade balance and current account, have more procyclical exports, and thus less countercyclical trade balance; (iv) have more volatile government consumption and more procyclical public revenues and fiscal balance; and (v) possess more procyclical inflation. The effects of country size remain robust even after we control for the level of economic and institutional development, the presence of fiscal rule(s) and fixed exchange rates, and the commodity exporting status.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/research. The authors may be contacted at Viktoriya.Hnatkovska@ubc.ca or fkoehler@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
about 3 percentage points from a base of about 13%, which represents a 23% reduction in teacher absenteeism. We observe no impact of student enrollment.

# **6.2. Discussion, Interpretation, and potential mechanisms**

The Whole School Development program, over time, had a positive impact on student and teacher school attendance. In theory, increased participation should translate into increased learning outcomes. However, in this case we observe increased participation but no change in test scores. We explore four potential explanations for this finding: (1) Selection, (2) Poor teacher quality, (3) Human capital in the community, and (4) Improvements in the control schools.

## **6.2.1. Selection as treatment effect**

One plausible explanation could be that the increased student participation brought back students that perform poorer than the average. If the intervention has brought in worseperforming students in the intervention group, then the average treatment effect (ATE) may be biased downward. The distribution of test scores shown in Figure 7 shows a shift to the left, albeit only at the left tail. This is suggestive evidence for the hypothesis that the WSD program attracted more low-performing students into schools. Miguel and Kremer’s (2004) analysis of a de-worming intervention in Kenya found large effects on participation but no effect on test scores: this same kind of selection was a potential explanation in that context as well.

If students who attended more because of the WSD were also students who otherwise perform more poorly, then one might expect the treatment effect to be larger at higher percentiles of the performance distribution. To verify this, we first look at the treatment effect in the each quantile. Figure 8 shows an upward trend, which partially supports this story. However, for this effect to be interpreted as the effect of the intervention on the students on the respective quintiles, the rank preserving assumption between the baseline and the end-line needs to be true. (In other words, one must assume that students would occupy the same rank in the test score distribution independent of the intervention: If Student A had better scores than Student B before the intervention, she would continue to outperform Student B after the intervention, even though one or both of them may have improved.) This is a strong assumption and there is no way to test it.

Nevertheless, we address this selection issue by bounding the treatment effect using Lee's trimming procedure (Lee 2009). The procedure consists of dropping a proportion of the lower tail of the distribution in the WSD group – i.e., those low-performing students drawn to schools by the intervention – in order to construct an upper bound of the effect of the intervention. Then we drop a proportion of the upper tail in order to construct a lower bound. Lee shows that the proportion to trim is given by 16
bond issues) at the transaction level in both domestic and foreign capital markets. The inclusion of foreign markets is important because these economies have been undergoing a process of financial liberalization and the experience of other emerging economies suggests that a large fraction of transactions could take place abroad.

The patterns of financing using capital raisings differ from the ones using the more standard measures. For example, the amount raised in domestic equity markets declined steadily during the 1990-2005 period, though it increased after 2005. As a percentage of GDP, new capital raising issues through equity securities from Chinese (Indian) firms in domestic markets declined from an average of 1.1 (0.4) percent of GDP per year between 1991 and 1994 to 0.5 (0.3) percent between 2000 and 2004, only to bounce back to 1.0 (1.3) percent during the 2005-2010 period (Figure 2, Panel A). These patterns suggest that increasing equity prices might explain to some extent the boom in equity markets in China and India over the 20-year period.

The decline in equity financing in domestic markets during the 1990s and early 2000s does not appear to be related to the use of foreign equity markets. Similarly to the trends in domestic markets, new capital raisings in foreign markets also declined during the 1990s and then significantly increased in the second half of the 2000s. Nonetheless, foreign markets have come to represent a sizeable share of the equity financing, especially for Chinese firms (Figure 2, Panel A). Noticeably, capital raising activity by Chinese firms in Hong Kong SAR, China alone was almost as large as that observed in domestic markets between 2005 and 2010. Total foreign equity financing represented over 50 percent of the total equity financing for firms from China and 22 percent for firms from India. However, trading activity remained concentrated in domestic markets, as suggested by the data for firms with depositary receipt (DR) programs. A direct comparison of the secondary market activity for these firms indicates that most of the 13
**Table 7**

# **Cross-national Growth Regressions 1994-2004**

||(1)<br>**OLS**|(2)<br>**OLS**|(3)<br>**OLS**|(4)<br>**OLS**|(5)<br>**OLS**|(6)<br>**IV**|(7)<br>**IV**|(8)<br>**IV**|(9)<br>**IV**|(10)<br>**IV**|
|---|---|---|---|---|---|---|---|---|---|---|
|Log of GDP pc 1994|-0.012**|-0.017***|-0.022***|-0.022***|-0.022***|-0.008|-0.014|-0.019**|-0.020**|-0.022**|
||(0.006)|(0.005)|(0.006)|(0.006)|(0.006)|(0.011)|(0.008)|(0.009)|(0.009)|(0.010)|
|Log of EXPY 1994|0.043***|0.035***|0.035***|0.038***|0.040***|0.032|0.024|0.024|0.030|0.039|
||(0.015)|(0.012)|(0.012)|(0.012)|(0.014)|(0.030)|(0.024)|(0.025)|(0.024)|(0.033)|
|||0.008**|0.006*|0.006*|0.006*||0.009**|0.007*|0.007*|0.006*|
|Log of % secund. school att. 1985||(0.003)|(0.003)|(0.003)|(0.004)||(0.004)|(0.004)|(0.004)|(0.004)|
||||0.007**|0.008**|0.008**|||0.007**|0.007**|0.007**|
|Rl f L Id 1996|||||||||||
|ue o aw nex|||(0.003)|(0.003)|(0.003)|||(0.003)|(0.003)|(0.003)|
|Domestic credit provided by banking||||-0.000|-0.000||||-0.000|-0.000|
|sector, % of GDP||||(0.000)|(0.000)||||(0.000)|(0.000)|
|Diversification (1 - Herfindahl Index)|||||-0.011<br>(0.023)|||||-0.011<br>(0.032)|
||-0.275***|-0.183**|-0.134*|-0.152*|-0.166*|-0.204|-0.111|-0.062|-0.106|-0.159|
|Constant|(0.092)|(0.078)|(0.079)|(0.082)|(0.087)|(0.188)|(0.152)|(0.158)|(0.153)|(0.202)|
|Observations|73|59|59|59|59|71|58|58|58|58|
|R-squared|0.21|0.25|0.32|0.34|0.34|0.21|0.24|0.30|0.33|0.34|
|First stage F-statistic of excluded<br>instrument p-value (H0:<br>underidentified/weakly identified)||||||0.019|0.018|0.019|0.021|0.089|
|<br>Hansen J statistic p-value (H0:<br>instruments are valid and correctly<br>excluded -overidentification test)||||||0.130|0.042|0.025|0.009|0.012|
|<br>Stock-Wright S-statistic p-value (H0: all|||||||||||
|endogenous regressors==0 and overid.<br>rest valid, weak-instrument-robust<br>inference)||||||0.149|0.098|0.054|0.014|0.023|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** sig<br>Instruments for IV regressions: log of populatio|nificant at 1%<br>n 1994 and log of lan|d area|||||||||

32