Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Developing countries, including in Africa, already have some nascent social protection programmes, but they are still at an early stage and the coverage is limited. Many of these countries are in the process of scaling up programmes and planning social protection systems, as opposed to isolated programmes. If one takes seriously the first of the new Sustainable Development Goals (‘Ending poverty in all its forms’), and the fact that not all households can have members who actively participate in income-earning activities, at least some level of social protection should be deemed necessary. In developed countries, the system-wide impacts of social protection policies are often examined ex ante using tax-benefit microsimulation models, but few developing countries have access to such models.

However, the financing of these social programmes needs to be increasingly reliant on domestic revenues, and increasing taxes can have distortionary impacts on the economy. One particular worry in a developing-country context is the potential negative impact of increasing taxes on formal sector growth and job creation. It is well known that structural change in African countries has been slow (see for instance Newman et al. (2016)), and these countries face heavy pressure to create enough good jobs for the growing population. It is a legitimate threat that increasing the tax burden of the formal sector may hinder job creation and growth.

This paper presents results from a new tax-benefit microsimulation model for Ghana (for basic information about the model, see Adu-Ababio, Osei-Darko, Pirttilä, and Rattenhuber (2017)), which was developed as a part of UNU-WIDER’s SOUTHMOD project. ¹ The model is used to simulate the impacts of expanding social protection, targeted to the poorest households with children, disabled members, or older people, on poverty, inequality, and the government budget. Results are presented for both cases where the social protection programmes are donor-funded and where they are revenue-neutral, that is financed by domestic tax increases. The basic GHAMOD microsimulation model we use, which is modelled using EUROMOD as a platform, is a static tax-benefit model.²

However, the paper also uses quasi-experimental econometric estimates on the elasticity of formal work with respect to the tax burden on the formal sector for Ghana, based on the extensive margin estimates from a background study by McKay, Pirttilä, and Schimanski (2018), where similar estimates are also derived for a set of other African countries. The estimates are based on a repeated cross-section approach, where the data are divided into groups that are formed using exogenous characteristics of household members (such as their age, education, and sex) and the impact of taxes on the share of formal work is identified from group-time interactions in a model with group and time fixed effects.³ The implied increase in the tax rate that is needed to finance the social protection policies is combined with the estimated elasticity of formal sector work, and therefore the impacts of the policy reform are calculated also taking into account behavioural responses. The formality margin is, we would argue, the main margin of interest in a country such as Ghana, where unemployment is almost non-existent and hours choice in the formal sector is not a key concern.

The paper contributes to the literature in the following ways. First, we present results from a microsimulation model for a developing country, and such literature is exceptionally scarce, with the study using Mexican data and model by Abramovsky and Phillips (2015) as a prime exception. Second, the paper uses estimates for the elasticity of formal work derived for Ghana. To the best of our knowledge, there are no earlier studies with credible identification on the elasticity of formal sector jobs using data from Africa. However, there is a series of papers demonstrating that crowding out of formal sector jobs may be a potential threat when expanding social protection in a Latin American setting: see for instance Alzúa, Cruces, and Ripani (2012); Garganta and Gasparini (2015) and Bergolo and Cruces (2014). Third, the study highlights the costs and benefits of social protection expansion in addition to presenting econometric estimates.

The paper proceeds as follows. Section 2 describes the institutional setting in Ghana. Section 3 presents the data and some descriptive analyses. The methods used in the econometric part are covered by Section 4. This section also describes the microsimulation model and the policy reform. Results are presented in Section 5. Section 6 concludes.

Ghana is a lower-middle income country (GDP per capita around USD1,500 in 2016) that is expected to return to a rapid growth path after macroeconomic turmoil in recent years. The latest household survey, which is from 2012/13, had the poverty headcount rate at 24 per cent (see for instance McKay, Pirttilä, and Tarp (2016)). Poverty has been falling consistently for 30 years, but at the same time regional disparities have risen. The poverty line stood at approximately Ghanaian Cedi (GHS) 1,300 per adult equivalent a year in 2013 (presently, one GHS equals around USD0.2). As in other African countries, poverty is based on consumption and measured on an absolute, rather than relative, basis.

The Ghana Statistical Service (GSS) started conducting the GLSS, a household survey, in 1987; for more details see Ghana Statistical Service (2014b). Since then six waves have taken place (GLSS 1 to GLSS 6), namely in 1987, 1988, 1991/92 ,1998/99, 2005/06, and 2012/13. The data are in the World Bank Living Standards Measurement Survey format, and include detailed information about individuals’ background, income, and consumption. The econometric estimates presented in this paper are based on data from the last four rounds, GLSS 3 to GLSS 6,⁴ and in particular GLSS 6 for the microsimulation exercise.

Pinpointing the exact size of the informal sector in the GLSS data requires making several assumptions as the questionnaires have changed across time. For the purpose of this study we mainly use information on the sector to define formality. Specifically, in GLSS 5 and 6 a worker is defined as formally employed if employed in the government sector or the formal private sector (including paid apprentices), by a parastatal employer, an NGO (local and international), a co-operative, or international organisations and diplomatic missions. A worker is defined as informally employed if working in the informal private sector, as a domestic employee, casual worker, or apprentice. Pensioners, unemployed, inactive, and students are considered as neither formally nor informally employed.

In GLSS 4 slightly less detailed information is available on sector and status of the job and respondents do not have an option to state that they are working in the informal private sector. We therefore consider those employed in the government sector, in the formal private sector (including paid apprentices), by a parastatal employer, an NGO (local and international), a co-operative, or international organisations and diplomatic missions as formal, as well as people who are self-employed in a business with employees. Farmers without employees and self-employed people without employees are in turn considered as informal. In GLSS 3 anyone indicating that they were self-employed or working on a farm or in an enterprise belonging to them or their household is considered as informal.

Table 1 provides basic summary statistics about the sample used in microsimulation when calculating the impacts of tax changes (that is, observations for which labour income or self-employment income data exist are used) for 2012/13. The formal sector is, as expected, composed of better-educated individuals. While around four out of five of workers in the formal sector (83 per cent) in 2012 had completed secondary education or higher, this was true for less than half of workers in the informal sector (46 per cent). Formal sector workers have better education and they reside in urban areas more often than informal sector workers do. Formal sector workers are often employees and they typically work for the service sector (which includes public sector workers), whereas many informal sector workers work in the agricultural sector. Net annual income (gross income minus income tax and social security payments paid by the employee) for formal sector workers is on average more than twice the income for informal sector workers (for whom by definition gross and net wages are the same).⁵

Our data confirm the decrease of the formal sector discussed in the literature (see Figure 1). Between 1991 and 2012 the share of formal workers declined from 33.2 per cent to 21.8 per cent. Formal sector workers also enjoyed on average higher net pay across all four waves (see Figure 2). Among employees the share of formal sector workers decreased from 71.0 to 45.1 per cent. The propensity to be paid below the minimum wage (the vertical line in the graph) was also considerably lower in the formal sector than in the informal sector.

Figure 1

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Figure 2

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For the purpose of this study we combine econometric estimation methods and microsimulation methods which are explained in more detail below. The results from both methods inform each other in the following manner: first, we estimate the elasticity of the formal sector with regard to the net pay difference between the formal and the informal sector based on four waves of the GLSS. Second, we use GHAMOD, the microsimulation model for Ghana, to simulate two different policy reforms: a stand-alone expansion of social protection and a revenue-neutral expansion of social protection financed through an increase in employee payroll tax. Next we combine the changes arising in the net pay differential between the formal and informal sector due to the policy changes with the formality elasticity estimated in the first step, to gauge the effect on the size of the formal sector. Finally, we recalibrate the weights taking into account the changes in the size of the formal sector, before calculating the overall effects of the policy and behavioural changes on poverty and distributional outcomes and the government budget.

The results are reported in Tables 3 and 4. In the first column, we report the baseline/status quo results for the year 2013. Poverty and inequality are measured using consumption. As the changes in the policy experiment refer to income changes, we turn these into changes in consumption possibilities. In other words, we examine how much consumption would change if all the additional income were spent. The next two columns are based on static microsimulation with no behavioural impacts. The first of these shows the impacts of expanding the social protection alone (Reform A: non-revenue-neutral scenario) and the second the impacts of a reform where the SSC paid by the employees is raised to recoup the revenue (Reform B: revenue-neutral scenario). The last column includes the results with the behavioural impacts, thus where the tax increase leads to a reduction in the share of formal work (Revenue-neutral reform with behavioural impacts).

The labour supply estimates reported in Table 2 suggest an elasticity of 0.10 (Column 3). When calculating the impacts of the reduction in the share of formal workers we assume that the larger number of workers in the informal sector does not change informal sector wages. While there is of course concern for more general equilibrium effects, we consider the effects of the proposed interventions on labour supply in the informal sector to be not important enough to exert downward pressure on wages in the informal sector.

In a revenue-neutral reform scenario government needs to increase the general component of the SSNIT contribution by eight percentage points (from 3 per cent to 11 per cent).¹² The decrease in the difference in net wages between the formal and informal sector wage together with the elasticity estimated in Table 2 then implies that formal sector work is decreased by approximately 1 per cent.

Reforming LEAP and the pension system reduces poverty overall (Column 2), and in particular among households with older persons (defined as households with at least one member above 65). While the headcount rate (FGT(0)) overall would decrease by less than one percentage point in the reform scenario, it would decrease by more than four percentage points among households with older persons. Female-headed households also benefit slightly more than male-headed households. The wide coverage of the simulated pension benefit explains why households with older persons benefit particularly. The poverty gap index (FGT(1)) is reduced relatively more, especially among households with older persons. The decrease in poverty is accompanied by a decrease in inequality as the proposed reform of the benefit system mainly benefits those at the bottom of the income distribution.

In a revenue-neutral reform scenario (Column 3 of Table 3) these effects remain largely the same: the decrease in poverty is smaller overall but still sizeable for households with older persons. Interestingly, the effects of the proposed reforms remain stable even when considering negative effects on the share of formal sector workers due to higher social security payments by employees in the formal sector (Column 4 of Table 2); the decrease in the formal labour share barely affects poverty rates as households benefiting from the increased social protection are mostly employed in the informal sector.¹³

The proposed reforms entail significant costs to the government’s budget (see Table 4). Extending coverage of LEAP increases expenditure on the LEAP programme by more than 120 times (Column 2 in Table 4). Extending coverage of the pension system to those aged 65 years and above with no pension entitlement so far implies additional expenditures of GHS 437 million. Both reforms together thus amount to a total of GHS 710 million of additional expenditure for the state budget. In a revenue-neutral setting, on the other hand, government more than doubles its income from employee SSC (from 486 to 1192 GHS million, Column 3 in Table 3) to offset the additional expenditure due to the extension of social protection.¹⁴

Factoring in behavioural responses of labour supply (Column 4 in Table 3) shows that the consequences of a lower share of formal sector employees for the state budget are negative but not drastically so. The government now has lower receipts of employee SSC, due to the smaller formal sector, but the decrease is overall around 1 per cent. The receipt of employer SSC and income tax is reduced by a similar amount in the scenario with behavioural responses.

This paper has studied, for the first time to our knowledge in a developing-country case, the impacts of expanding social protection and financing it using a tax-benefit model with behavioural elements. For the latter part, we presented estimates that identify the causal impact of tax changes on the share of formal work in Ghana. These estimates were then taken into account in the simulation and the overall impacts were calculated for the case both with and without behavioural changes.

The simulated policy included expanding the amounts and the eligibility threshold of the existing LEAP cash transfer programme and introducing a new universal old-age pension. The amounts offered in these reforms are still very modest and they alone would not raise the households above the poverty line. This is why their estimated impact on the poverty headcount rate was also fairly small: less than a one percentage point reduction for households with children and a four percentage point reduction for households with older people. The cost of the programme would be approximately GHS 700 million and financing it with an increase in the social security contribution of formal sector employees would imply raising the rate by approximately eight percentage points. The increase is sizeable, given the narrow base of the tax.

Our preferred estimate for the elasticity of the share of formal work with respect to the difference in the net pay when in formal versus when in informal work is approximately 0.10. It is statistically significant, albeit at the 10 per cent level, but also very small. This also means that the difference in the poverty-reducing impact of social protection when estimated with or without behavioural linkages is small. This finding is reinforced by the fact that poor households do not typically work in the formal sector and that is why they are not affected by the tax increase. The costs of financing the programme increase by approximately 6 per cent due to the reduction in the share of formal work.

A number of caveats need to be kept in mind when interpreting the results. First, the estimated elasticity could be larger, and we have carried out some sensitivity analysis with respect to the size of the elasticity. Second, since the data set was originally prepared to study consumption, the income data are not necessarily as reliable. Third, the poverty eradication effects would be greater if, instead of modelling the tax increase via the flat social security contributions, progressive income tax were used. One further issue is that all those working in the formal sector are modelled to pay taxes. However, while their income from their main job is subject to third-party withholding by employers, they can still evade taxes from other income they earn and, arguably, the extent of evasion may also depend on the tax rate. This means that the exact size of the tax rate increase is only estimated with some margin of error. As always in microsimulation models, we have not allowed for any “leakage” due to administrative costs or misspent revenues by the social protection administration: a point with perhaps more relevance in a poor-country context with more limited government capacity. Tackling many of these issues is on our future research agenda.

As already mentioned in the main text, estimating Equation (2) poses a number of challenges.¹⁵ First, the right-hand side regressor is correlated with e and so endogenous. The most obvious reason is that both taxes and benefits are direct functions of income. An additional reason is that unobserved variables (for instance, tastes for work and savings) might affect the choice of working in the formal sector. Since the individual is only observed in at most one state at a given time, income in the other state needs to be imputed.

Our approach to tackling these issues is to utilize the repeated cross-section element of the data. This allows us to compare groups of individuals over time and, thereby, address these endogeneity issues by constructing instruments. Following Blundell et al. (1998), we partition the sample into group cells based on country, sex, age, and education level. The key idea behind the grouping procedure is to compare otherwise similar groups of individuals who have been affected differently by tax reforms (the difference-in-difference setting), while retaining the ambition of estimating structurally meaningful parameters, in this case the formality elasticity.

Let g denote group cell. Suppose that ε_it = α_g + μ_t + η_it, where E[η_it\h_it > 0,g,t] = 0. According to this assumption unobserved heterogeneity, conditional on g and t, can be captured by a permanent group effect α_g and a time fixed effect µ_t. This assumption can also be modified in such a way that it allows for instance for education-group-specific linear time trends. Let ω_gt be a vector that contains the full set of interactions between group and time. By assumption, these are uncorrelated with η_it. This is the central exclusion restriction for identification. We can then estimate

by two-stage least squares (2SLS) while using ω_gt as excluded instruments for (x_f − x_s). The instrument needs to have sufficient predictive power and it must affect the outcome variable only via changing the net pay variable. As the variation in the second-stage equation is entirely at the group level, Equation A1 can also be estimated by collapsing the data into time-specific group averages of the relevant variables. We then estimate the parameters from

by GLS, using group size as weights. Using either Equation A1 or A2 yields identical results. Note that the estimation of the probability of working in the formal sector not only hinges on tax and benefit reforms, but is also identified from shocks affecting the gross pay in the two different states.

To deal with missing income in either of the states, we proceed using a simple and transparent approach utilizing cell means. We use the cell means $\overline{Y_f-T(Y_f)+B(Y_f)}and\overline{Y_s+B(Y_s)}$ to get estimates for the net income in the states of formal work and informal work, respectively. We average over individuals with and without formal earnings, respectively. Cells are, as in the regression analysis, constructed using sex, education, household type, and age. Likewise, the cell mean labour income for those whose earnings are strictly positive is used to get an estimate of the expected income for the individual when working.

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Author details

1. Robert D Osei

   Institute of Statistical, Social and Economic Research (ISSER) University of Ghana, Ghana

   For correspondence

   rdosei@yahoo.co.uk

2. Jukka Pirttilä

   1. University of Helsinki and VATT Institute for Economic Research, Finland
   2. UNU-WIDER, Finland

   For correspondence

   jukka@wider.unu.edu

3. Pia Rattenhuber

   UNU-WIDER, Finland

   For correspondence

   rattenhuber@wider.unu.edu