rafmacalaba/datause-encoder-data huggingface dataset:
Table _5_ Past and projected key economic indicators for Sub-Saharan AfErica, 1985488, 1995, 2000 (average annual percentage change unless indicated otherwise)

||1985-88|1995|2000|
|---|---|---|---|
|Domestic indicators||||
|Real gross domestic product (GDP)<br>|2.6<br>|4.0<br>|_5.0_|
|Real gross domestic investment (GDI)<br>|-6.9|8.7|7.7|
|GDI (percent of GDP)<br>|14.0|20.7|25.0|
|Gross domestic savings (percent of GDP)<br>|11.4<br>|16.6|20.0|
|Real consumption per capita<br>|-0.7<br>|0.1<br>|1.3|
|Real gross national income (GNY)<br>|-1.8<br>|4.2<br>|_5.4_|
|Real GNY per capita (1988 US$)<br>|314.0|304.0|334.0|
|Real effective exchange rate<br>|-4.0 a|-3.3|-3.4|
|ICOR (at 1988, 1994, 1999 prices respectively)|6.8|4.9|4.8|
|Trade indicators and official development assistance||||
|Import elasticity<br>|-1.6|1.0|1.0|
|Import volume<br>|-4.0|4.1|5.0|
|Imports (percent of GDP)<br>|22.9<br>|33.1<br>|39.3|
|Export volume<br>|1.9|3.6|4.3|
|Exports (percent of GDP)<br>|20.3|29.0|34.3|
|Import prices<br>|6.3|4.6|5.4|
|Export prices<br>|-4.6|4.8|5.9|
|Terms of trade index (1988=100)<br>|111.5|103.5|107.5|
|Real gross ODA<br>|13.9|3.3|1.1|
|Gross ODA (percent of GDP)|5.9|8.6|9.0|
|Debt service indicators before debt relief<br>||||
|Debt service ratio (percent of exports)<br>|-|26.5|15.7|
|Debt service (percent of GDP)||7.8|_5.4_|

Note: a. Median for about three fourths of the Sub-Saharan countries; 1986-88. Sources: World Bank data and projections.

The trade gap widens during the decade because export growth lags behind import growth in aggregate (owing primarily to the low growth in Nigerian oil exports -- see Annex), the terms of trade decline for oil-importing countries, and the base for projecting import values is about a third 39
**Table 4: Baseline Business Score and Survival Dynamics**

||1‐year<br>Survival|1‐year<br>Survival|1‐year<br>Survival|1‐year<br>Survival|5.5 Year<br>Survival|1‐year<br>Sales Growth|1‐year<br> Sales Growth|5.5 Year<br> Sales Growth|
|---|---|---|---|---|---|---|---|---|
|Baseline Business Practices Score|0.055***|0.039**|0.038**|0.022|0.110**|0.476***|0.277***|0.556***|
||(0.017)|(0.017)|(0.016)|(0.017)|(0.053)|(0.087)|(0.089)|(0.196)|
|Baseline log(paid workers)||0.025***|0.015**|0.011|‐0.019||0.110**|‐0.025|
|||(0.007)|(0.007)|(0.007)|(0.015)||(0.043)|(0.088)|
|Male|||0.028***|0.023**|||0.142***||
||||(0.010)|(0.010)|||(0.052)||
|Owner's Age at Baseline|||0.002***|0.002***|||‐0.004**||
||||(0.000)|(0.000)|||(0.002)||
|Owner's Years of Education|||‐0.001|‐0.001|||0.034***||
||||(0.001)|(0.001)|||(0.006)||
|Digitspan Recall|||0.000|‐0.000|||‐0.010||
||||(0.002)|(0.002)|||(0.011)||
|Age of Firm at Baseline|||0.003***|0.003***|||0.001||
||||(0.001)|(0.001)|||(0.002)||
|Raven test score|||0.003***|0.003***|||0.013**||
||||(0.001)|(0.001)|||(0.006)||
|Baseline Log Sales||||0.005*||‐0.697***|‐0.760***|‐0.798***|
|||||(0.003)||(0.015)|(0.017)|(0.035)|
|Baseline Log Profits||||0.006*|||0.079***||
|||||(0.003)|||(0.018)||
|Sample Size|7016|7016|7015|6847|1413|5689|5629|1152|
|Mean of Dependent Variable|0.912|0.912|0.912|0.913|0.874|0.394|0.392|0.483|

# Notes:

Marginal effects from probit estimation shown in columns 1‐5, regression estimates in columns 6‐8. Robust standard errors in parentheses, *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. Columns 1 to 4, and 6 and 7 are data from Kenya, Nigeria, and Sri Lanka. Columns 5 and 8 are from Sri Lanka only. All specifications include sector and country dummies, and controls for missing values of the control variables where needed. Sector is defined as manufacturing, trade, services or other for columns 1 to 5, and defined at the detailed country*sector level for columns 6 to 8 34
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**Table 4. Sales Response to Relative Price Level Movements**

|||dep. var.: %|frm’s sales||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**A. Data at frm-destination-product-year**|**level**||||
|log(rel. price level)|0.092***|0.100***|0.022**|0.031***|
||(0.012)|(0.010)|(0.010)|(0.012)|
|log(rel. price level)*1(any exports in 1997)|||0.430***<br>(0.041)||
|log(rel. price level)*(sales share in 1997)||||0.353***<br>(0.057)|
|frm efects|Y||||
|destination efects|Y||||
|frm-product-destination efects|N|Y|Y|Y|
|year efects|Y|Y|Y|Y|
|R2|0.15|0.70|0.70|0.70|
|N|954025|954025|954025|954025|
|**B. Data at frm-destination-year level**|||||
|log(rel. price level)|0.563***|0.579***|0.236***|0.298***|
||(0.076)|(0.056)|(0.055)|(0.054)|
|log(rel. price level)*1(any exports in 1997)|||1.134***<br>(0.134)||
|log(rel. price level)*(sales share in 1997)||||0.289***<br>(0.050)|
|frm efects|Y||||
|destination efects|Y||||
|frm-destination efects|N|Y|Y|Y|
|year efects|Y|Y|Y|Y|
|R2|0.32|0.80|0.80|0.80|
|N|191793|191793|191793|191793|

Notes: Relative price level defined as in (3) in text. Variables 1(any exports in 1997) and sales share in 1997 defined at firm-destination-product level in Panel A and firm-destination level in Panel B. Robust standard errors, clustered at firm-year level, in parentheses. *10% level, **5% level, ***1% level.
The aluminum price started to rise in December 1993, and it seems likely that the prospect of an agreement to limit production was one factor behind this rise. However, the prices of the other non-ferrous metals also started to rise at the same time, and there were no special factors in these industries. Metals prices have boomed in 1994 and these relatively high prices look likely to be sustained into 1995. The major factor behind the metals price rises in general has been a surge in consumption growth, the question in regard to the MoU is whether there has been any differential price rise in aluminum relative to other metals.

The situation in aluminum is in some respects similar to that in coffee: in both industries an intervention scheme was negotiated just prior to an upturn in the price which would have taken place regardless of the agreement, although in neither case could this have been anticipated. It would therefore be quite wrong to attribute either the price rise wholly to the agreement. I have suggested that the coffee retention scheme is likely to have brought part of the price rises forward in time, but that it probably had little effect on price levels from June 1994 onwards since there is neither production levels nor subsequent stock levels have been affected. In aluminum, by contrast, the MoU was intended to cut both production and stock levels. The question, therefore, is whether there has been any such cut. Current data are insufficient to allow a firm conclusion, but preliminary figures show 1994 "western world" refined aluminum production at 14.4m tons against 15.1 m tons for 1993. This is a substantial fall which has reduced production to 1989 levels. The largest part of the fall is attributable to the USA, down from 3.7m tons to 3.3m tons.<sup>24</sup> By contrast, Russian production is ambiguous but preliminary evidence suggests that the agreed cuts have failed to materialize.25 Against this, it is widely held that production in Russia would have further increased in the absence of the MoU as the result of new capacity becoming available.

- 24 _World Metal Statistics Yearbook,_ 1995 (World Bureau of Metal Statistics, Ware, Herts.). 25 _Metal Bulletin,_ 25 July 1994.

35
Tables A.2 and A.3 report, without comment, the analogous benchmark results for Tables 4 and 5, but with the aggregate growth polarity measure using PPP growth rates. By and large, the results presented in these two tables are qualitatively similar to the measure using real growth rates. Due to the more limited coverage of the PPP series, the regressions are for a smaller sample.

Table A.2: Benchmark regressions for proximate correlates of growth polarity, 1981–2008, with PPP GDP growth rates<sup>*</sup>

||**AP1**|**AP2**|**AP3**|**AP4**|**AP5**|**AP6**|
|---|---|---|---|---|---|---|
|Population|-1.176|-0.092|-0.046|-0.313|-0.357|-2.865|
|growth|(1.02)|(0.21)|(0.28)|(1.17)|(1.12)|(2.23)|
|Investment|0.164|-0.028|-0.122|0.206|0.055|0.360|
||(0.16)|(0.11)|(0.14)|(0.18)|(0.13)|(0.21)<sup>_∗_</sup>|
|Enrollment|0.103|0.024|-0.012|-0.002|0.027|-0.050|
||(0.03)<sup>_∗∗∗_</sup>|(0.04)|(0.10)|(0.03)|(0.04)|(0.08)|
|Income per||||0.058|0.041|0.106|
|capita||||(0.02)<sup>_∗∗∗_</sup>|(0.02)<sup>_∗∗_</sup>|(0.03)<sup>_∗∗∗_</sup>|
|Life||||-0.214|-0.182|-0.286|
|expectancy||||(0.07)<sup>_∗∗∗_</sup>|(0.09)<sup>_∗∗_</sup>|(0.32)|
|Dependency||||-0.240|-0.328|0.037|
|ratio||||(0.08)<sup>_∗∗∗_</sup>|(0.09)<sup>_∗∗∗_</sup>|(0.15)|
|Government||||-0.681|-0.423|-0.673|
|size||||(0.18)<sup>_∗∗∗_</sup>|(0.18)<sup>_∗∗_</sup>|(0.25)<sup>_∗∗∗_</sup>|
|Adjusted _R_<sup>2</sup>|0.013|||0.167|||
|_F_|||1.660|||3.056<sup>_∗∗∗_</sup>|
|Hansen _J_|||62.669|||71.109|
|AR(2)|||-0.634|||-0.487|
|Estimator|OLS|RE|D-GMM|OLS|RE|S-GMM|
|Instruments|||58|||70|
|_N_|592|592|451|555|555|555|

> * Growth rates calculated from GDP data in constant 2005 international purchasing power parity-adjusted dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are five-year averages. All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported. _∗_ indicates significance at 10 percent level, _∗∗_ indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

34
# **III. ECOWAS—Historical Background**

The Economic Community of West African States (ECOWAS) was founded on May 28, 1975, when sixteen Anglophone, Lusophone and Francophone countries signed the Treaty of Lagos. ECOWAS is comprised of 15 countries which include: Benin, Burkina Faso, Cape Verde, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo (figure 1).<sup>3</sup> The primary objective of ECOWAS is to promote regional co-operation and integration, and to create a unified economic space in order to facilitate economic growth and development in West Africa. The preamble to the 1975 ECOWAS Treaty notes that the community was created because of the “...overriding need to accelerate, foster and encourage the economic and social development of member states in order to improve the living standards of their peoples.”<sup>4</sup> ECOWAS saw regional integration as a multistage process leading to a customs union and ultimately to the establishment of an economic and monetary union that would raise the living standards of its people and enhance economic stability in the region.<sup>5</sup> The key elements of ECOWAS’ policy have been to eliminate all tariffs and other trade barriers between the member states and to establish a customs union, a unified fiscal policy, a common currency and coordinated regional policies in the transport, communications, energy and other infrastructure facilities.<sup>6</sup>

> 3 In 2000, Mauritania withdrew its membership from ECOWAS.

> 4 Aryeetey, E. 2001.  “Regional Integration in West Africa”.  Working Paper No. 170.  OECD Development  Centre.

> 5 Lecture by ECOWAS Executive Secretary, Mohamed Ibn Chambas: “The ECOWAS Agenda: promoting Good Governance and Regional Economic Integration in West Africa”.

> 6 Source: Centre for Democracy and Development, 2002: “From Regional Security to Regional Integration in West Africa: Lessons from ASEAN Experience”.

7
levels of academic performance.<sup>7</sup> Although education policy can make a large difference in the long run, socio-economic circumstances continue to play a role in student learning in many middle-income EAP countries, in contrast to the average perceptions of EAP officials.

_Figure 4: Poverty and malnutrition are not considered critical for learning by policy makers in EAP._ <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LAO PHL IDN VNM MNG Non-EAP<br>Average learning poverty guess<br>Share of respondants attributing low learning to school factors<br>Share of respondants attributing low learning to poverty & malnutrition<br>Shares<br><!-- End of picture text --> Sources: CGD–World Bank survey, 2022. Factors were pooled together depending on whether they refer to external determinants such as poverty, malnutrition, lack of internet or others. School quality factors include teaching quality, school facilities and lack of learning materials. The share of officials attributing low learning to poverty and malnutrition decreases with per capita GDP, as expected.

EAP officials’ low estimations of the influence of poverty and malnutrition on learning relative to other factors is reflected in the low priority given to programs that address poverty or malnutrition issues. As part of our survey, respondents were presented with several potential policy interventions randomly selected from a pre-determined set of options, including: (i) giving teachers structured early grade reading lesson plans with linked materials and training; (ii) in-service training for teachers; (iii) delivering universal, free school lunch; (iv) removing fees for secondary school; (v) laptops for schools; (vi) hiring extra specialist teachers for students with disabilities; and (vii) building hospitals. In this discrete choice experiment, respondents were asked which intervention they would select for implementation. The cost and the implementation timeline for the intervention were provided and were varied by respondent.

We compare the results between the group of EAP and non-EAP countries in Figure 6 and in Table 3. The only category of interventions for which the probability of selection is statistically different between EAP and non-EAP countries is school feeding programs (Figure 5). On average, school feeding programs are selected by 40 percent of respondents in the EAP region, lower than the 51 percent observed in the nonEAP group.

> 7 PISA 2018 Volume 2 (OECD, 2019).

8
an indirect effect in which aid affects trade by improving the investment climate in which trade takes place. The authors find that aid for trade can have a positive effect on investment climate indicators. They find that aid in the area of “trade policy and regulations” has helped reduce the costs of trading. They also find a robust, positive, and non-linear effect of aid in productive capacity on exports. These last two papers relate closer to the work of this paper in that our target is to measure aid to its direct objective instead of attempting to measure the impact of aid on growth. We, however, take a new approach by analyzing how aid affects firm perceptions.

Another important contribution of this paper is that we identify and analyze firms’ needs. There is little mention, if any, about firms in the current aid literature even though firms are the biggest source of employment, production, and growth.<sup>1</sup> From the endogenous growth literature we know that firms are a key element in the development of innovation and technology which drives growth.<sup>2</sup> Innovation, the key for long term growth, results from the process of trying to solve production problems, to learn from experience, to find new and better ways of doing things, and/or to profit from opening up new markets. Institutions, market structure, market imperfections, trade, government policy, and legal framework affect (and are affected) by long run growth through their effects on firms’ incentives to engage in innovation activities. Therefore understanding the needs of firms is essential to be able to provide aid more effectively such that it will create the right environment for innovation and growth. Also because firms interact with

> 1 The only study that we know which relates recipient country’s perceptions and aid is Birdsall and Kharas (2010). The authors create an index of aid quality for each donor using as one of their measures the support by each donor to a recipient country’s priorities. They identified priority sectors based on submissions of individuals in partner countries to surveys, asking them to identify development priorities for their country. 2 See, for instance, Aghion and Howitt (1992); Mankiw, Romer and Weil (1992); Romer (1990); Schumpeter (1942).

7
is positive and almost nil. The composition effect is small because changes in trade shares are minute over this period and are distributed tightly around zero; in more than 90 percent of industries the change in trade shares is less than .1 percentage points over the period. This finding is quite striking, given changes that we might expect to take place in comparative advantage over time the vast majority of trade shares are roughly constant. These results imply that there is a change in relative trade costs that is affecting many industries, and making distance more important.

Panels b and c show the average coefficients on distance in the first and last period for the industry data and the aggregate data, respectively. The weighted-average coefficients from the industry data are somewhat lower than the distance coefficients from the aggregate data, in both periods. The specifications are somewhat different since industry coefficients allow a different country fixed effect for each industry, while when we use the aggregate data there is only one coefficient per country-time period. Still, the change in the coefficients from the first to the last period is quite similar. The industry data suggest a change of about 0.13 while the aggregate data suggest the change is about 0.11.

Table 2 reports tabulations of the results when the trade growth regression (equation 11) is estimated for each of the 766 industries individually, using country-time and partner-time fixed effects, over the change from the first five-year period to the last period. For nearly 30 percent of industries and about 37 percent of trade, the coefficient is negative and significant (columns 2 and 3), while for almost 70 percent of industries the coefficient is not significant. There are only a handful of industries for which the sign is positive and significant. Columns 4 and 8 record the average coefficient and the 16
Table 6.13 (Con'L) <!-- Start of picture text --> Based on Value-Added Based on VaheAdded<br>in Domestic Prices _ in Wodd PEiop<br>1 2 3 4 S 6<br>InK 0.331* 0380* 0.284' 0313* 037 0.274'<br>(0.066) (0.067) (0.043) (0.070) (0.070) (0.046)<br>rime 4082' 4O.W65 -0.062 40.090* 4.07 -OC7*<br>(0.032) (0.031) (0.031) (0.033) (0.032) (0.033)<br>R2 0.903 0.894 0.882 0.947 0.943 0.935<br>N S8 S8 58 58 58 58<br><!-- End of picture text --> g

* Ski;ficant at the S % statistical test level or better.

+ - Significant at the 10 % statistical test level or better. Our sample in this industry consists of 10 frm and it contans no state-owned _fums_ or _firms_ emploing other African middle or upper manages Many fums also display mssg us for ownerp and in the remaining _frms_ te ownrship variables are higbly correlated wnth the other explanatory variables (espcialy the effecive rate of proteaion). The ownership variables _am_ hbene excluded in the above regressions.
# Industrial Policies vs Public Goods under Asymmetric Information<sup>�</sup>

Constantino Hevia Norman Loayza Universidad Torcuato di Tella The World Bank Claudia Meza-Cuadra The World Bank Keywords: Industrial Policy; Public Goods; Uncertainty; Private Information; Firm Subsidies and Taxes JEL Classi...cation: H2, H4, O1, O2

> �We thank Aart Kraay, Claudio Raddatz, and Luis Serven for insightful comments and discussion. We gratefully acknowledge funding from the Knowledge for Change Program (KCP) of the World Bank. The ...ndings and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the institutions to which they are a¢ liated.
Figure 5: Correlation between household consumption and the share of land allocated to coffee <!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Share of land allocated to coffee in 2011 Share of land allocated to coffee in 2011<br>95% CI 2011 2013 95% CI 2011 2015<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>Log food consumption (adult equivalent) Log food consumption (adult equivalent)<br>-.4 -.4<br><!-- End of picture text --> Note: This figure shows the correlation between log household adult equivalent consumption and the share of land allocated to coffee in 2011. The effect of rainfall and price inflation (year fixed effects) are removed before plotting. The figure shows that in 2011, when coffee price boomed, there is a significant positive correlation between household consumption and the fraction of household land allocated to coffee. This fact is totally reversed in the years 2013 and 2015, when international coffee price is about 50% less than its 2011 level (see figure 1).

expenditure is stronger than the effect on non-food expenditure.

The coefficient estimates in panel A can be interpreted as percentage change in consumption following a 1% change in coffee price for a household that allocates all its farm land to coffee (for the most exposed household). In equation 3, the effect of a percentage change in coffee price is given by _β_ 1 _×_ LandShareCoffee _i_ , which varies across households depending on the fraction of farmland allocated to coffee. For a household that allocates all its farmland to coffee, LandShareCoffee _i_ = 1, the effect of a percentage change in coffee price is given by _β_ 1. For a household that does not produce any coffee, the effect of the price change is zero. Accordingly, for a household that allocates all its farmland to coffee, a 50% decrease in coffee price results in about a 40% decrease in household adult equivalent food consumption and about a 30% decrease in non-food consumption.

Panel B reports the implied changes in household total, food, and non-food expenditures following a 50% price drop between 2011 and 2013 for households at different points in the distribution of the share of land allocated to coffee. The first row in Panel B reports the implied decrease in consumption of an average farmer in ESS data (rural sample). An average farmer in the ESS data allocates only 5% of its farmland to coffee production (see table 2), and as a result, consumption decreases by only 2.5% following a 50% decrease in coffee price. However, because the share of land allocated to coffee is highly positively skewed (slightly more than three quarters of households do not produce coffee at all), it is important to quantify the loss in 17
<!-- Start of picture text --> Number of agreements<br>=}N<br>wo<br>=<br>=<br>wo<br>° &x» Sse Xss- esGiS: se<br>S<br>ME with EU EEE with Japan<br>EEE North-South MEE South-South<br><!-- End of picture text --> <!-- Start of picture text --> Number of agreements<br>&<br>©<br>2<br>wo<br>oSsoSSCe £— § FS€se© Ff SFS ceCF&rESB OCySX rsHS C7oH<br>HE with EU EEE with Japan<br>GEE with USA MEE North-South<br>MEE South-South<br><!-- End of picture text -->