metadata: authors: 0: author: Geiger,Michael Tobias
1: author: Trenczek,Jan
2: author: Wacker,Konstantin M.
count: Ghana
docna: 0: docna: Understanding Economic Growth in Ghana in
            Comparative Perspective
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Inflation,Food Security,Labor Markets,Food & Beverage Industry,Business Cycles and Stabilization Policies,Plastics & Rubber Industry,Construction Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Common Carriers Industry,General Manufacturing
teratopic: International Economics and Trade,Agriculture,Social Protections and Labor,Macroeconomics and Economic Growth,Industry
abstracts: cdata!: Ghana has experienced a decade of solid
            and exceptionally high growth. Between 2005 and 2015, income
            nearly doubled. This paper analyzes the factors driving this
            impressive growth performance, using tools such as
            structural change decompositions and growth regressions. For
            the comparative perspective, the paper compares Ghana with
            its structural and aspirational peers. The paper finds that
            the contribution of structural change to growth has been
            limited and attributes this to labor that was freed up in
            agriculture not being absorbed by high-productivity sectors.
            Looking at factors that drove growth since 2000, financial
            development and infrastructure had the most important
            impacts. A benchmark analysis suggests that those areas
            should remain the policy focus over the longer term, but
            that near-term priority should be given to stabilization policies.
geo_reg_key: 517191,1927320
repnme: repnme: Understanding Economic Growth in Ghana in
            Comparative Perspective
display_title: Understanding Economic Growth in
            Ghana in Comparative Perspective
listing_relative_url: /research/2019/01/30751746/understanding-economic-growth-ghana-comparative-perspective
topicv3: Agriculture and Food Security m1326205 435,Manufacturing Sectors m1326521 1281,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Labor Productivity m1504060 2756
publishtoextweb_dt: 2019-01-10T00:00:00Z
historic_topic: International Economics and Trade,Agriculture,Social Protections and Labor,Macroeconomics and Economic Growth,Industry
no_of_pages: 39
author_keys: 0: author_key: 1067392
1: author_key: 1278219
model_extractions: WPS8699 Policy Research Working Paper 8699 Understanding Economic Growth in Ghana in Comparative Perspective _Michael Geiger Jan Trenczek Konstantin M. Wacker_ Macroeconomics, Trade and Investment Global Practice January 2019
Policy Research Working Paper 8699

# **Abstract**

Ghana has experienced a decade of solid and exceptionally high growth. Between 2005 and 2015, income nearly doubled. This paper analyzes the factors driving this impressive growth performance, using tools such as structural change decompositions and growth regressions. For the comparative perspective, the paper compares Ghana with its structural and aspirational peers. The paper finds that the contribution of structural change to growth has been limited and attributes this to labor that was freed up in agriculture not being absorbed by high-productivity sectors. Looking at factors that drove growth since 2000, financial development and infrastructure had the most important impacts. A benchmark analysis suggests that those areas should remain the policy focus over the longer term, but that near-term priority should be given to stabilization policies.

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 mgeiger@worldbank.org, kwacker@uni-mainz.de, and jtrencze@uni-mainz.de.

_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
# **Understanding Economic Growth in Ghana in Comparative Perspective**

**_Michael Geiger Jan Trenczek_** _The World Bank Group University of Mainz_ <u>mgeiger@worldbank.org jtrencze@uni-mainz.de</u>

**_Konstantin M. Wacker_** _University of Mainz_ <u>kwacker@uni-mainz.de</u>

## **<u>JEL Classifications</u>** <u>: O47, O55</u>

**<u>Keywords</u>** <u>: Economic growth; Africa; Ghana; determinants of growth; structural change</u>

## **<u>Highlights:</u>**

- We investigate factors that have driven Ghana’s recent growth performance in comparative perspective.

- Structural change provided relatively little impetus to aggregate productivity growth.

- Stabilization policies, infrastructure, and job creation in higher-productivity sectors offer viable sources of income gains.
<!-- Start of picture text --> 15%<br>10%<br>5% wooPopachee-f 5%<br>1.9%<br>= tnt Cue<br>0%<br>Wa in ASTON DAMHRDAAMHROAAMH<br>& a 3 RARARARARASSSSOORS<br>a Hdd ddA HA ANANNAANANN<br>“IAgg<br>-10%<br>-15%<br><!-- End of picture text --> <!-- Start of picture text --> 4.9%9<br>4.0%<br>3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Ghana SSA LIC LMC<br><!-- End of picture text -->
the population over-proportionally benefitted. Having in mind that extreme poverty at $1.90 a day stood at 25.2 percent in 2005, growth was pro-poor in the sense of reducing poverty (Ravallion and Chen, 2003) and in terms of over-proportional income gains of the poor (Klasen, 2004).<sup>3</sup> And finally, Ghana’s per capita income level back in 1970 was similar to countries such as Malaysia, the Republic of Korea, or Ecuador that subsequently outperformed Ghana by a large margin. From a growth and development perspective it is thus informative to analyze what those countries did differently and how this fits with Ghana’s subsequent catch-up during the last decade.

In this paper, we thus analyze Ghana’s recent growth performance using a variety of tools. After a descriptive overview in section 2, section 3 provides a comprehensive analysis of aggregate productivity developments due to within- and between-sector productivity dynamics. Such an exercise is important because in standard neoclassical growth models, in the absence of population growth, total factor productivity is the key ‘proximate’ source of growth in the long run, whereas productivity developments in Ghana have generally decelerated since the mid-2000s. This exercise hence sheds some light on the within- and between-sector contributions to this trend. Section 4 uses a panel growth regression model to decompose growth determinants in Ghana. This approach aims to identify the ‘ultimate’ drivers of growth, i.e. changes in policy variables that might influence growth through several proximate channels, including productivity.<sup>4</sup> Section 5 looks at the growth divergence between Ghana and other developing economies since 1970 and asks what countries that we group into “comparator countries” and “aspirational peers” have done or are doing differently in terms of different macroeconomic factors. Understanding potential growth constraints through such a benchmark exercise is also important because Ghana’s most recent growth episode took place against the background of a fairly supportive external environment where several low and lower-middle income countries managed to grow at similar or even higher rates. It is thus important to understand the factors that potentially held and hold back growth in Ghana. A final section provides some discussion from a broader development and policy perspective and concludes.

Our key findings add to a vivid current debate on how to best conceptualize growth and its potential in Sub-Saharan Africa. They can be summarized as follows:

- _First_ , within-sector productivity increases explain about 80 percent of recent aggregate productivity growth, leaving a limited contribution for structural change to increase aggregate productivity (by labor flowing towards higher-productivity sectors).

- _Second_ , Ghana’s pattern of structural change is remarkable, yet not unique, for the fact that most labor freed up by productivity increases in agriculture did not find higher-productivity employment opportunities elsewhere. In fact, the sector that has absorbed most laborers is ‘wholesale and retail trade’, with the economy’s lowest productivity and disputable potential to boost sustained growth. This ‘Janus-faced’ nature of services generally supports the idea of Diao and McMillan (2015) that a clear division between agriculture, manufacturing, and services is of limited help for understanding growth in Africa.

> 3 However, overall income inequality (as measured by the Gini index) also rose, mildly but continuously, over the last two decades. Progress in poverty reduction could hence have been faster, even in the unlikely event that prudent redistribution came at the expense of slightly lower growth (see Schäfer, 2017).

> 4 This is a rather optimistic interpretation concerning the capability of growth regressions; a more conservative interpretation is that we assess to what extent changes in policy variables that have strongly correlated with growth in many countries could explain Ghana’s recent growth performance.

3
<!-- Start of picture text --> 4,000<br>3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>5000 -&§-8-8SG§-8SaAnttOT)—— NF488TASSS3SSS3S3ITTSee/aI SHNSYSooSS<br>-1,000 nt Kt Kt BR TBH TBH ANNNNNNNNNNNYFN NS<br>lm Consumption (private) mali Consumption (public) mam Investment<br>———<br> Net exports — Other<br><!-- End of picture text --> <!-- Start of picture text --> 100%9,<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0% RSESKERGBSSRRSRSARSRRSSESSSARSEDADAAAAAAAAAAAWAAAAASSEeant SFE eeeese<br>dt tat Kt et eR eR KR TR TR KR TR TR ANN NN NNN OS<br>Agriculture Manufacturing mOtherindustries Services<br><!-- End of picture text -->
<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>fo<br>6%<br>4%<br>2%<br>0% = = | =<br>-2% 2007. 2008 +=2009 2010 2011 2012 2013 2014 # 2015<br>Agriculture Manufacturing Other Industries Services<br><!-- End of picture text -->

<!-- Start of picture text --> 7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000 ee<br>1,000<br>0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Agriculture =Industry (incl.manufacturing) =—=Services<br><!-- End of picture text --> <!-- Start of picture text --> a | a a od<br><!-- End of picture text -->
**Figure 3: Growth accounting (ctd.)**

<!-- Start of picture text --> B. Capital/output ratio approach<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>1970‐2016 1970‐1980 1980‐1990 1991‐1998 1998‐2005 2005‐2012 2012‐2016<br>‐6<br>TFP K/Y Labor Schooling Overall<br><!-- End of picture text --> Source: own calculation based on PWT 9.0 and WDI. As the standard approach in the top panel may somewhat overestimate the contribution of capital accumulation that is really due to productivity-induced capital deepening, we alternatively follow an approach by Klenow and Rodriguez-Clare (1997) in the lower panel that focuses on the capital/output ratio. As one can see, there is little qualitative difference between both approaches, only that the contribution of capital accumulation is qualitatively less in the latter case, as should be. See appendix B1 for details on the methodology and the data.

A sectoral perspective highlights the role of services and structural change: Figure 2.B depicts a typical pattern of structural change where the share of value added declines over time. Somewhat atypically, this was not accompanied by an increasing role for manufacturing, which remained remarkably stable. Labor instead moved straight into the services sector.<sup>5</sup> This is mirrored in Figure 2.C that shows a strong sectoral contribution of services to growth, while the contribution of manufacturing is barely visible for most years. It is important to understand, however, that the relevant growth contribution of services does not stem from productivity increases within the aggregate service sector. If anything, productivity in services was declining after 2005 (see Figure 2.D; the more detailed analysis in section 3 finds no productivity change). Services’ relevant growth contribution rather stems from the fact that the overall size of the sector was large (a level effect) and growing (an effect of structural change).

By and large, one could expect this to be a favorable pattern of structural change, as labor productivity in services is about double as high as in agriculture (Figure 2.D). This aggregate picture, however, masks the productivity differences that exist within the services (and also industry) sector and the detailed changes in employment taking place. This motivates our more detailed analysis of structural change in section 3.

# **3. A detailed analysis of structural change**

In this section, we have a more detailed look at patterns of structural change and recent literature of decomposing aggregate productivity changes into productivity changes that take place within

> 5 Note that a revision of the national account statistic was responsible for the considerable increase of the service sector in 2005/2006 as several communication and personal services, recreation services, and professional services were not included in GDP before the revision. Figure 2.B may thus quantitatively overstate the described trends which are nevertheless qualitatively present.

6
sectors and aggregate productivity changes that are the result of labor re-allocation between sectors with different productivity levels and trends (McMillan et al., 2014; de Vries et al., 2015).<sup>6</sup> We therefore rely on data from the Groningen Growth and Development Centre (GGDC) 10 Sector database (Timmer et al., 2015), with additional information on data and methodology provided in appendix B.2.

The more disaggregated GGDC data reveal that much of the increase in the service employment share since 1990 can be attributed to expanding market services. The strongest employment increase, especially after 2000, took place in the sector comprising wholesale and retail trade, hotels, and restaurants (‘wrt’, see Figure 4 and appendix Table A.1). Not only does this sector have the lowest labor productivity among the 10 sectors we analyze, but also when comparing this sector across 29 developing countries, as done in McMillan et al. (2014: Table 2), Ghana is the country with the lowest labor productivity within this sector. In our view, this suggests that highproductivity service sectors are not able to absorb most of the labor supply that is especially freed up by productivity increases in agriculture. This may be due to supply constraints (Ghana only ranks 120<sup>th</sup> in the Doing Business report) and/or because the freed-up labor supply does not have the skills to enter high-productivity services (Ghana ranks bottom in PISA scores) and hence end up in the insulated low-productivity sectors such as retail trade.<sup>7</sup> However, also highly productive service sectors such as transport or business services expanded employment.

To quantify the contribution of structural change to aggregate productivity growth, we use different methodologies to decompose the change in average labor productivity into different components, each with its own economic interpretation (McMillan et al. 2014; de Vries et al., 2015; McMillan et al. 2017; see appendix B2 for details on the methodology). In its basic form, the decomposition splits the change in aggregate labor productivity into a component that results from productivity growth within individual sectors (e.g., innovation) and changes that result from a shift of employers between sectors (e.g. from low-productivity to high-productivity sectors).

A key result of this exercise is that structural change cannot explain Ghana’s most recent growth performance. Figure 4 plots the employment changes of 10 different sectors between 1990 and 2010 on the horizontal axis against the productivity of those sectors (relative to overall productivity) on the vertical axis. The size of each dot represents the overall importance of the respective sector (in terms of employment). Figure 4 details the above-mentioned employment dynamics: agriculture saw a drastic decline in employment, which is usually a precondition for labor to flow into above-productivity sectors. However, the sectors with the highest employment increases were trade services (‘wrt’) and “other” sectors (‘oth’), both of which had the economy’s lowest productivity. On a positive note, sectors with productivity highly above average also expanded so that in total a positive but very small effect of structural change on aggregate labor productivity remained. This is indicated by the solid regression line pointing out that, on average, sectors that saw an employment increase were those that had a relatively higher productivity.

> 6 See Osei and Jedwab (2016) for another case study. The studies of de Vries (2015) and McMillan et al. (2014) also include Ghana in their sample. The latter mentions that it was one of the relatively few countries where structural change towards higher-productivity sectors was taking place. Our analysis reveals, however, that this aspect was of very limited magnitude.

> 7 For potential policies to promote growth-enhancing structural change, see Trenczek (2016).

7
<!-- Start of picture text --> Correlation between sectoral productivity and change in employment shares in Ghana (1990-2010)<br>§<br>3<br>8<br>3>= cee\<br>83 ‘<br>g ‘i<br>s i<br>2 |<br>: cm<br>-10 s 0 5<br>‘Change in employment share (in %—points)<br><!-- End of picture text -->
The key productivity improvements took place within agriculture. Table 2 disaggregates Ghana’s pattern of structural change and productivity developments over the 1990-2010 period across the 10 sectors analyzed, using a modified decomposition formula suggested by Vries et al. (2015; see Appendix B2 for details). In the leftmost column we can observe that agriculture contributed more than half (1.4 of 2.4 percentage points) to productivity increases within sectors. The remaining within-sector productivity increases were essentially split between manufacturing (0.4 percent) and services (0.6 percent). Such productivity increases in agriculture are often seen as a precondition for industrialization and development. But growth-enhancing structural change can only take place if workers moving out from agriculture find higher-productivity job opportunities.

**-** **<u>Table 2: Disaggregated productivity development patterns (1990 2010)</u>**

|<br>Sector|||<br>Component due|<br>to:|
|---|---|---|---|---|
||Total||<br>Structu<br>|<br>ral change<br>|
|||Within|Static|Dynamic|
|**Agriculture**||**1.36**|**0.00**|**0.00**|
|**Industry**||**0.42**|**0.17**|**0.05**|
|Mining||0.05|0.02|0.01|
|Manufacturing||0.22|0.00|0.00|
|Public utilities||0.06|0.00|0.00|
|Construction||0.09|0.16|0.04|
|**Services**||**0.59**|**0.56**|**-0.20**|
|Market services||0.37|0.47|-0.17|
|Trade services||0.14|-0.01|-0.12|
|Transport services||0.27|0.16|0.07|
|Business services||-0.04|0.31|-0.12|
|Non-market services||0.22|0.06|-0.03|
|Government services||0.19|0.03|0.01|
|Other services||0.03|0.03|-0.04|
|**Total economy**|**2.93**|**2.38**|**0.70**|**-0.15**|

**Source:** Constructed from the Groningen Growth and Development Centre 10 Sector database (Timmer et al., 2015). **Note:** Productivity growth is defined as the compound annual growth rate in average labor productivity (%). See appendix B2 for details on decomposition methodology. Growth-enhancing structural change occurs when labor moves into those sectors whose - <u>productivity is above average in either productivity levels (static gains) or growth rates (dynamic gains).</u>

The service sector absorbed most of the labor moving out of agriculture. The fact that productivity _levels_ in the former are higher than in the latter contributed 0.56 percentage points to aggregate productivity growth, mostly driven by business and transportation services (table 2). Despite the relatively small increase in their employment shares, their productivity levels were quite above average (compare Figure 4). However, it seems that Ghana’s service sector struggled with absorbing additional labor at levels of marginal productivity similar to existing workers. This can be seen from the dynamic term in Table 2 that captures different productivity _trends_ rather than levels. Services’ negative contribution of -0.20 percentage points to aggregate productivity growth indicates that while the sector had higher productivity levels than sectors that freed up labor, its increase of productivity over the 1990-2010 period was smaller. Especially trade services, that absorbed most laborers, do not enjoy scale economies and are likely to experience a strongly declining marginal productivity of labor (partially because they are not tradeable from an international perspective). This is reflected in their strongly negative ‘dynamic’ term. In a sense, these sectors are ‘insulated’ as they cannot easily integrate into value chains or provide forward 9
spillovers. Overall, this important sector did not contribute to aggregate productivity increases as small improvements within the sector were washed off by dynamic structural change effects.

Manufacturing, where prospects for scale economies are more promising, saw an employment decline and in Ghana historically suffers low productivity levels (see Figure 4) inter alia due to lagging technical efficiency of firms, firm dynamics, and a strong link between wage levels and firm size that weighs on international competitiveness (see Teal, 1999 and Davies and Kerr, 2018). The latter does not only constitute a problem in itself but also limits the potential for business services or what Diao and McMillan (2015) call ‘in-between firms’ to form linkages. This is illustrated by the fact that business services experienced negative within-sector and ‘dynamic’ productivity developments (table 2). After identifying key drivers of growth in the next section, we hence apply a benchmarking analysis in section 5 that might provide some indication about potential macroeconomic constraints.

# **4. Drivers of the recent growth performance**

To better understand the nature of growth in the past two decades, we use insights from crosscountry growth regressions to decompose growth determinants in Ghana. This methodology is based on recent studies by Araujo et al. (2016) and Moller and Wacker (2017). It essentially uses panel growth regressions for a sample of 126 countries (listed in Table B.5) over 5-year averages in the time period 1970-2010 and estimates the respective parameters using System GMM (Arellano and Bover, 1995; Blundell and Bond, 1998). Those parameters are then multiplied with developments of the explanatory variables in Ghana over several 5-year periods until 2015.<sup>9</sup> This allows us to predict growth based on the econometric growth model and identify the determinants that are most likely to have driven structural growth in Ghana over the respective period.

This approach is well-founded by growth theory and captures long-run drivers of growth. The setup of the econometric model can be shown to be a functional representation of the standard neoclassical growth model. Specifically, the lagged dependent variable (‘persistence’) captures the (smooth) transition of a country towards its own steady state while the remaining explanatory variables capture structural factors that are most-conventionally thought to drive growth in the long run, such as human capital accumulation or financial development. As Tables 3 and B.6 indicate, all parameter estimates of the model are in line with economic theory. It should be noted that this method aims to identify structural, not cyclical drivers of growth. This is especially achieved through 5-year averaging and the System GMM methodology which is careful not to confuse changes in a variable with a temporary shock. For example, temporary government consumption might help to cyclically bring up growth, especially in times of a severe output gap. However, structurally high government consumption (conditional on existing variables such as educational attainment) will most likely lead to distortions in the economy and lower potential output.

> 9 We use the exact model and parameters of Araujo et al. (2016) and Moller and Wacker (2017) but had to extend our sample until 2015. Technical and data details about the model and its extension to 2015 for Ghana can be found in appendix B3.

10
These drivers of growth are grouped into the categories of structural, stabilization, and external effects.<sup>10</sup> “Stabilization” variables contain inflation, banking crises and the real exchange rate,<sup>11</sup> capturing the idea that macroeconomic fluctuations can influence growth over an extended period. “Structural” variables capture a broad set of fundamental country characteristics. This includes secondary school enrollment as a proxy for human capital, a measure for trade openness (trade-toGDP ratio adjusted for population), an institutional variable (polity2), private credit-to-GDP as a measure of financial development, fixed telephone lines per capita as a proxy for infrastructure, and government size measured by government consumption/GDP.<sup>12</sup> “External factors” are reflected in terms of trade and commodity prices, more specifically net barter terms of trade and the country-specific commodity export price index of Arezki and Brueckner (2012). Furthermore, global conditions will also be reflected in the time dummies (although they are not countryspecific). The variables can also be inferred from Table 3 and are described in more detail in appendix Table B.4.

The model explains Ghana’s past growth experience reasonably well: when looking at the two 5- year periods of the 2000s, the annualized average prediction error of the model is about 0.35 percentage points, as some overprediction in the earlier half is balanced by some underprediction in the latter half (see Table 3). This may be the case, for example, because growth policies in the earlier period did not immediately materialize in de facto growth but only with a delay of some years. Compared to the average annual growth rate of 3.1 percent, the prediction error is quite low and clearly outperforms other growth regression models for African economies, such as IMF (2013). A key deviation, however, arises for the early 2010s (the period between the average 20052010 and the average 2010-2015). As we will see, this is not per se a shortcoming of the model, which aims to capture drivers of potential output, but to a large degree driven by the cyclical nature of growth after 2010.

Growth was driven by structural improvements, particularly in infrastructure and financial development: as Table 3 highlights, our variables gauging infrastructure (telephone lines) and financial development (credit/GDP) improved markedly especially during the early 2000s, which explains most of Ghana’s growth performance in the 2000s. For the latter half of the decade, this is also reflected in the persistence term, as initial improvements had a fading-out effect on the growth rate.

> 10 We again follow Loayza et al. (2005), Araujo et al. (2016), and Moller and Wacker (2017) in this regard. 11 A decrease in the real exchange rate is equivalent to a currency depreciation. As the interpretation of the exchange rate variable is somewhat difficult in the cross-country context it should rather be seen as a control variable, i.e. controlling for the fact that an undervalued exchange rate might boost growth temporarily.

> 12 Increases in government consumption / GDP (including increases of the public sector wage bill) are empirically associated with lower long-term economic growth. This is because this type of non-productive public spending would need to be financed either by distortionary taxation (Afonso and Furceri, 2010) or increased public borrowing, both of which are associated with lower growth in the long run. Growth-enhancing public spending, such as infrastructure development or education are already captured by other variables in the model, including lagged GDP.

11
<!-- Start of picture text --> 2000-2015 Early 2000s Late 2000s Early 2010s<br>ooo<br>.<br>3<br>Qa wv wv vw<br>;<br>=<br>-<br>y¥ ¥<br>y<br>ME persistence [EM structural MEE stabilization<br>MO external residual (model error)<br><!-- End of picture text -->
**_Table 3: Parameter values, changes and predicted growth effects (real GDP per capita)_**

|||**2000‐2**|**015**|**early 2**|**000s**|**late 20**|**00s**|**early 2**|**010s**|
|---|---|---|---|---|---|---|---|---|---|
||parameter|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|_change _|_predicted_<br>_effect_|
|_Persistence_|0.781|0.189|0.118|0.084|0.066|0.115|0.089|0.189|0.148|
|_Structural:_||||||||||
|Δln(schooling)|0.018|0.508|0.016|0.056|0.001|0.245|0.004|0.207|0.004|
|Δln(credit/GDP)|0.074|0.565|0.072|0.264|0.020|0.104|0.008|0.196|0.015|
|Δln(trade/GDP)|0.082|0.086|0.012|‐0.135|‐0.011|0.222|0.018|0.000|0.000|
|Δln(govt C)|‐0.262|0.142|‐0.064|‐0.082|0.021|‐0.085|0.022|0.308|‐0.081|
|Δln(tele lines)|0.141|0.346|0.084|0.627|0.088|‐0.129|‐0.018|‐0.152|‐0.021|
|Δln(institutions)|‐0.003|0.379|‐0.002|0.314|‐0.001|0.065|0.000|0.000|0.000|
|_Stabilization:_||||||||||
|Δln(inflation)|‐0.011|0.145|‐0.003|‐0.016|0.000|0.253|‐0.003|‐0.092|0.001|
|Δln(exch rate)|‐0.064|‐0.076|0.008|‐0.202|0.013|0.336|‐0.021|‐0.211|0.013|
|Δ bank crisis|‐0.040|‐0.200|0.014|‐0.200|0.008|0.000|0.000|0.000|0.000|
|_External:_||||||||||
|Δln(TOT change)|0.118|0.004|0.001|‐0.017|‐0.002|0.085|0.010|‐0.064|‐0.007|
|Δln(commodity<br>prices)|10.482|0.002|0.028|0.001|0.013|0.002|0.017|‐0.001|‐0.014|
|predicted growth|||0.312||0.216||0.126||0.057|
|**annualized**|||**1.9%**||**4.3%**||**2.5%**||**1.1%**|
|**actualgrowth**|||<br>**5.3%**||<br>**2.3%**||<br>**3.8%**||**9.9%**|

Source: Authors’ calculation, obtained by inserting Ghana’s values for the explanatory variables and using the regression coefficients of the baseline model presented. Note that results for the period 2000-2015 need not be consistent with the aggregation of the three included 5-year periods because the former uses the growth between the early 1980s and the late 1990s a lagged dependent variable in the persistence term which is not reflected in the 5-year intervals.
Observed growth rates since 2010 are largely cyclical and not justified by the policies captured by the model. This can be seen from the fact that the model generally predicts growth quite well but not for the period 2010-2015 (see rightmost panel in Figure 5). During this period, especially excessive government consumption held back structural growth drivers (see Table 3). It is important in this context to distinguish between actual output (a combination of potential output and cyclical factors) and long-run potential output. This is illustrated in Figure 6 which uses the standard Hodrick-Prescott filter to decompose actual GDP into an estimate of the trend of potential output (blue line) and cyclical fluctuations. Our econometric model, particularly the use of 5-year averages and of the System-GMM estimator, focuses on the determinants of long-run potential output. That is also why government consumption enters the model negatively: having already controlled for the growth-enhancing factors that public spending achieves (such as education and infrastructure), large public consumption puts a burden on output as the taxes needed to finance it act as a distortion to the economy. Figure 6 adds another important insight: actual output in the period after 2010 was way above potential. This is in line with fiscal spending only temporarily improving output and explains why our model ‘underpredicts’ growth so severely: because potential output (which the model is supposed to capture) by far did not grow as much as observed GDP. Especially the right panel of Figure 6 highlights that the cyclical deviation from the trend of potential output was enormously large during the most recent growth episode.

The initial boom during this period, peaking in 11.3 percent growth in 2011, mainly reflected increased prices of Ghana’s main commodity exports, notably gold and cocoa, and the initiation of commercial oil production in 2011, together with a loose (and possibly election-driven) fiscal policy stance. The bust thereafter saw growth declining four consecutive years, down to 1.6 percent in 2015, and reflected a combination of declining commodity prices, energy rationing (partly due to reduced hydropower output in face of drought conditions), and a large fiscal crisis in 2013. Avoiding such boom-and-bust cycles is an important goal of stabilization policies, since such volatilities can themselves have negative impacts on long-term output.<sup>13</sup>

**Figure 6: Cyclical and trend GDP p.c.**

<!-- Start of picture text --> A. Real GDP p.c. against its trend potential B. Cyclical component of real p.a. GDP<br>1960 1970 1980 1990 2000 2010<br>year<br> HP 'trend' ln GDP p.c. ln GDP p.c. 1960 1970 1980 1990 2000 2010<br>year<br>.2<br>8.2<br>8 .1<br>7.8 0<br>7.6<br>-.1<br>7.4 lngdppc cyclical component from hp filter<br>-.2<br><!-- End of picture text --> Source: own calculation based on annual data from PWT9.0. The smooth blue line in the left panel is an estimate of potential output (measured as log GDP p.c.) with cyclical fluctuations (red line) around it. The latter component is separately displayed in the right panel. A positive value here indicates that output is ‘above potential’.

13 There is a vast literature addressing this relationship, staring with the seminal paper by Ramey and Ramey (1995).

14
Given the potential role of pro-cyclical policy and growth volatility in Ghana and the recent literature on macroeconomic frameworks in Sub-Saharan Africa (Calderon and Boreux, 2016), we also re-estimated the baseline model with the 5-year standard deviation of growth in the respective 5-year period as an additional explanatory variable.<sup>14</sup> According to our estimates, cyclical volatilities cost Ghana about 0.3 percent of growth p.a. over the whole 2000-2015 period, with the strongest drag on growth in the early 2010s (0.7 percent p.a.). This is in line with the magnitudes of cyclical volatilities documented in Figure 6 and supports our interpretation that the deviation of our growth model from the observed growth rate is mostly due to the cyclical nature of growth after 2010.

Overall, these results suggest that Ghana should revert to more conventional stabilization policies and prioritize structural reforms going forward. On the one hand, pro-cyclical fiscal spending and associated macroeconomic volatility have been a restraint to economic growth. Correspondingly, recent inflation developments also show a trajectory towards highly critical levels. While economies like the Ghanaian clearly do not need a level of price stability similar to most-industrialized countries, some modest improvement in macroeconomic stabilization and inflation containment would potentially lead to large benefits (see Kremer et al., 2013). On the other hand, potential output and its long-run drivers have developed much slower than actual GDP for other reasons as well. Hence, structural improvements are needed to increase potential output and sustain the recent growth performance. Given fiscal and political constraints, it is important to assess which reforms will potentially bring the highest output improvements or, put differently: which current areas are most constraining to growth of potential output. The next section will offer such an assessment.

# **5. The long-run challenges of economic growth: A comparative analysis**

In this section, we compare Ghana to two groups of countries: a ‘comparator’ group and a group of ‘aspirational peers’.<sup>15</sup> While the former are economies that are structurally similar to Ghana and stand at similar income levels, the latter have overtaken Ghana’s income level at some point. We compare Ghana to these peer groups by a benchmarking exercise covering levels and historical trends in those key drivers of income and growth presented in section 4 and furthermore look in more detail at patterns of structural change in four countries from the group of aspirational peers. This exercise informs prioritizing policy and structural reforms in the context of fiscal constraints, as identified in the previous section, and also helps understanding Ghana’s development from a comparative perspective.

**<u>Table 4: Benchmarking groups and levels of GDP p.c. (PPP)</u>**

|<br>**Comparator group**||<br>**Aspirational peers**||
|---|---|---|---|
|Myanmar|5,344|Republic of Korea|35,104|
|Nicaragua|4,453|Malaysia|23,158|
|Mauritania|3,409|Belarus|20,290|
|Kyrgyzstan|3,359|Algeria|12,812|

14 This somewhat alters other results of the model, see also the results in Araujo et al. (2016: Table B.20) who use the output gap and fluctuations around potential output as volatility measures. Results for the overall regression are available upon request. Note that our result of a growth drag of 0.3 percent hence cannot be simply subtracted from the prediction results of Table 3 and Figure 5.

15 The countries selected as comparator and aspirational peers are identified using the same methodology of the parallel Systematic Country Diagnostics, which was carried out in 2017 and 2018 through the World Bank Group Country Team working on Ghana. See Annex 1 for details: World Bank 2018. However, we added, on a normative basis the Republic of Korea, Malaysia, and Vietnam to the group of aspirational peers, as they are prominent in the contemporaneous policy discourse in Ghana.

15
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variables in Ghana and the group of aspirational peers and their subsequent developments until the late 2010s.<sup>17</sup> The resulting graphs, combined in Figure 8, are essentially convergence plots for the respective variables. For example, the top left panel shows that sampled countries starting out with lower schooling levels in the early 1970s have, on average, seen faster improvements thereafter. Applying this exercise to the potential drivers of growth identified above gives us some candidates of explanatory variables that have contributed to the growth and income divergence over that time period.

From Figure 8 one can clearly see that all countries of the group of aspirational peers for which data are available outperformed Ghana in terms of increases of secondary school enrollment and reductions of inflation rates. The weak performance concerning inflation is particularly worrisome as Ghana already started out with high inflation rates in the early 1970s. Additionally, Ghana was one of the few countries in that group where government consumption has increased, not declined. Against the background that key public goods that governments should finance to spur growth (such as education or infrastructure) did not improve considerably over the investigated period, this is additional evidence for potential shortcomings in the macro framework that could be improved, for example, by more prudent monetary policy and a better prioritization of public investment spending. Finally, it is worth noticing that almost all of the sampled countries with strong growth performance have opened up more to trade than Ghana since the early 1970s. Finally, one can see convergence in real exchange rate valuations: countries that started out with overvalued exchange rates in the 1970s saw them decline over the following decades and vice versa, as economic theory suggests. One can also see that Ghana started with the most overvalued real exchange rate and hence also saw a strong subsequent decline. Falling behind the sampled countries with strong growth performance hence cannot be attributed to particularly unfavorable real exchange rate developments.<sup>18</sup> In terms of financial development (top right panel of Figure 8), Ghana started out at a very low level to begin with but did not manage to catch up to other aspirational peers. Especially the fastest-growing economies of Korea and Malaysia saw credit/GDP increasing about twice as fast as Ghana. In terms of infrastructure, the analysis provides a mixed picture. Ghana started out with a comparably low level of telephone lines, which also developed slower than in other countries thereafter. Similarly, coverage of mobile phones was low in the late 1990s, but Ghana managed to catch up at reasonable speed in the decade thereafter. In terms of roads, Ghana started at an average level in the late 1980s and saw the fastest increase among the sampled countries in the two decades thereafter.

> 17 Reliance on the 2010 data allows us to use the data set from the growth regression exercise above. Besides consistency, this also avoids missing data points in more recent years whereas these years are unlikely to substantially change the depicted pattern. Note that for data such as roads and cell phone subscriptions we had to use later starting points since no (or not enough) data were available in the 1970s.

> 18 We did not compare terms of trade changes, banking crises, and commodity price changes because those are cyclical ‘shock’ variables whereas all depicted variables are mainly ‘level’ variables. For example, a low _level_ of schooling in 1970 could suggest a catchup in education over the following decades, reflected in higher _changes_ of schooling. However, a banking crisis in the early 1970s or a positive terms of trade _change_ in the early 1970s is not indicative of any changes in banking crises or accelerated/decelerated (=2nd differences) terms of trade developments in the subsequent decades. Inflation is a special case because even though it is no level variable in the strict sense (since it captures price changes), we view it as capturing the level, or stance, of macroeconomic inflation management.

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Next, we compare key variables in terms of quantiles that allow assessing distance to the frontier. For the variables in Figure 8 above, we compute Ghana’s quantile within the two respective benchmark groups, ‘aspirational peers’ and ‘comparator countries’ which have been identified according to four criteria that would make a country relatively similar to Ghana.<sup>19</sup> That means, if Ghana performs best on the respective variable (within the comparison group) it gets a value of (close to) 1 and if it performs worst, a value of (close to) 0. A value of 0.5 would accordingly indicate that 50 percent of countries do better, whereas the other 50 percent do worse.<sup>20</sup> This exercise can inform us where policy gaps are particularly large compared to comparator countries and thus helps to prioritize reforms as large gaps usually entail that advancement in this dimensions is not particularly costly and that the existence of the gap might be a particularly binding constraint for further economic development.

The results are depicted in Figure 9. As one can see, roads and institutional quality (proxied by the polity2 index) do not seem to be areas of urgent need for policy action: Ghana comes close to the best performers in those categories, even if compared to the aspirational peers with a much higher income level. Key gaps, however, exist concerning inflation management and aspects of infrastructure, but also regarding schooling, trade openness, and financial development. From Figure 9 it is obvious that Ghana is considerably lagging behind in terms of inflation management and telephone lines: for both variables, it is the worst performer within both peer groups. The same is true for schooling and mobile phones within the group of aspirational peers, which have a much higher income level though. But Ghana also does worse than over half of the countries within the group of comparably similar countries in those two variables (right panel). Finally, Ghana falls short of more than 50 percent of countries in both peer groups concerning trade openness and levels of credit to GDP, a measure for financial development.<sup>21</sup> Additional insights come from the results of the World Bank Enterprise Surveys. For the Ghana survey, business owners and top managers in 720 firms were interviewed from December 2012 through July 2014. Key data are presented in Figure 10 and compared to results from other SubSaharan African countries. Consistent with our benchmarking exercise, access to finance and key aspects of infrastructure are seen as major business constraint by firms—to an extent beyond the Sub-Sahara African average. This is especially true for electricity and water insufficiencies, while transportation seems to be less of an issue (consistent with our benchmarking exercise). The only deviation arises in the area of schooling, where our benchmarking exercise identifies a major gap whereas only 15.3 percent of surveyed firm managers see an inadequately educated labor force as a major business constraint. At the same time, Ghana ranks last in PISA scores for students’ mathematics and science performance among 76 countries analyzed. Note, however, that surveys were conducted with existing enterprises only. With a broader educational background of its labor force, new firms may possibly have developed in more productive sectors.

> 19 These criteria were whether a country is/has: a) commodity exporter (World Bank DEC definition), b) agrarian (above world average), c) population between 3 million and 55 million, and d) income level (USD 900 to 2,200). This methodology is based on a World Bank tool called “Find My Friends” that allows to compare development data to identify common characteristics of countries.

> 20 More precisely, we calculate quantiles by Hazen’s rule, i.e. the quantile is given as Ghana’s (inverse) rank (within the respective comparison group) minus 0.5, and then divided by the number of countries in the respective comparison group. Note, accordingly, that the best (worst) performer will not get a value exactly equal to 1 (0). We multiplied variables with a negative effect on growth (government consumption and inflation) with (-1) for the ease of interpretation.

> 21 The shortcomings in Ghana’s financial system are also investigated in Biekbe (2011).

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growth is overall positive (except for Malaysia), but quite small. Static gains are generally depressed by dynamic losses.<sup>24</sup> In no investigated country is there a single period when sectoral change on its own induced relevant aggregate productivity improvements. Fundamental improvements that boost productivity within sectors are hence once again most promising for sustained growth – especially in the case of Ghana because the exercise in section 3 revealed no high-productivity sector that is capable to absorb a huge amount of labor.

Finally, within-sector productivity developments are also a key explanation for the historical divergence between Ghana on the one hand and Korea and Malaysia on the other hand: while sectoral labor productivity grew rapidly in Korea and Malaysia, Ghana experienced a decrease in labor productivity within sectors of more than -1 percent in the period 1975-1990. Since then, within-sector productivity growth has been consistently above 2.3% and only modestly below the improvement of Korea and Malaysia.

# **6. Concluding remarks**

In this paper, we analyzed the considerable growth performance in Ghana since 1990 that especially accelerated in the 2000s. We find that this period coincides with a positive turnaround in the contribution of productivity and capital accumulation to growth. Interestingly, we found that the contribution of structural change to the productivity performance is relatively limited, which stands in contrast to seminal studies in the literature (especially McMillan et al., 2014) but is consistent with our analysis of several peer countries. We attribute this to the fact that labor freed up in the agriculture sector due to productivity improvements did not find employment opportunities in higher productivity sectors on a large scale. While some highproductivity services such as business services and communication have grown and hence had positive effects on aggregate productivity, they could not absorb labor on a large scale; maybe due to their skill requirements, maybe because business services need linkages to demand from other sectors. On the other hand, the sector that absorbed most employment increases was wholesale and retail trade, the sector with the economy’s lowest productivity, consistent with Rodrik’s (2013b) reasoning that “economic activities that are good at absorbing advanced technologies are not necessarily good at absorbing labor.” A key question for macroeconomic development strategies going forward is hence to understand how countries absorb labor freed up from productivity improvements within sectors, so that they do not end up in lowproductivity sectors.

The ‘Janus-faced’ nature of high- vs. low-productivity services documented in the case of Ghana generally supports the idea of Diao and McMillan (2015) that a clear division between agriculture, manufacturing, and services is of limited help for understanding growth in Africa. However, we are less optimistic about the identified pattern of structural change in the particular case of Ghana. While productivity developments within sectors provide a somewhat positive picture, the large labor absorption by wholesale and retail trade is potentially problematic, as this sector is to a significant degree insulated and its ability for linkages with modern and exporting sectors is limited to narrow, usually less labor-intensive, activities that promote the formation of value chains (“supermarket revolution”).<sup>25</sup> In terms of policy, there may be

> 24 This is not surprising from an economic perspective, as the declining marginal product of labor makes it unlikely that an above-average productivity sector will continue to experience above-average productivity growth when absorbing a lot of labor—especially when less developed financial markets prevent capital to quickly accumulate in such a sector.

> 25 In this context, also see an earlier contribution by Breisinger et al. (2009) on Ghana, who argue that services can support rather than drive economy-wide growth and that considerable focus on the agriculture sector and its

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potential for narrow productivity gains, e.g. by promoting foreign direct investment in retail. But, as Rodrik (2018: 16) put it, “achieving productivity gains along the entire retail sector is extremely difficult in view of the heterogeneity of organizational forms and the range of prerequisites across different segments.” Looking into the drivers of overall growth, we especially found financial development and infrastructure to be key drivers of Ghana’s growth performance since 2000. This is consistent with the picture that extended periods of growth above the 2 percent historical average call for improvements in structural fundamentals and with the two best-performing economies in our comparative analysis, Korea and Malaysia, that also both saw considerable improvements in those variables.

Going forward, our analysis underscores the relation between short-run and long-run policies. In the short run, fixing the macroeconomic stabilization framework should be a priority given the recent boom-bust episode, our result that the most recent episode of growth (since 2010) was not justified by economic fundamentals, and large policy gaps compared to peer countries in this dimension. The need for further improvements of structural fundamentals to enjoy continued substantial growth rates in the longer run is supported by our benchmark exercise (and consistent with firm-level surveys) showing substantial gaps in some variables capturing structural factors, foremost financial development and aspects of infrastructure. The latter will also be important to foster regional and global trade integration. Improving access to finance, together with other reforms such as removing firms’ business constraints and broadening skill levels, may support the development of new firms in less isolated and more productive areas than the traditional wholesale and retail trade sector, boosting growth-enhancing structural change.

Without prudent stabilization policies, necessary investments in many of those growth fundamentals are hard to finance. Additionally, there could be an important macro relation between the exchange rate regime and structural transformation, as moving away from the current overvaluation would also make the tradeable sector internationally more competitive and thus potentially offer job prospects in above-average productivity sectors where the marginal product of labor is not declining as much as in insulated service sectors. In our view, moving towards a consistent macro policy framework that aligns longer-term development aspirations with short-run challenges is hence of utmost importance for Ghana going forward and a challenge for Sub-Saharan African countries more broadly (see Calderon and Boreux, 2016). Moller and Wacker (2017) have documented for the case of Ethiopia that growth reduction through moderate heterodox macro policies may be overcompensated if they finance investment in areas with higher growth return. For the case of Ghana, we have documented the limits to such policies, as the costs of the post-2010 fiscal boom were relatively large while macroeconomic policies created few structural improvements that lifted the economy’s longterm output potential.

linkages should remain the mainstay of Ghanaian development. Also note that a modern wholesale and retail sector that is well-integrated with the rest of the economy is not ad odds with fast growth and structural transformation, as the example of Thailand shows (where the sector ranges at average economy-wide productivity).

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