rafmacalaba/datause-encoder-data huggingface dataset:
# Savings, Investment and efficiency of capital

This section discusses the links between some key economic policies and the model's targets and variables, indicating how policy actions would help achieve target growth for Sub-Saharan Africa. It also reviews the region's past performance to give an idea of the feasibility of reaching the desired targets in the medium term. The section starts with a discussion of the need for investment required to reach the target growth rates. The relation between investment and output growth is a key behavioral parameter determining output growth. Second, there is a discussion of factors determining the demand for investment, which argues that inadequate demand for investment in Sub-Saharan Africa is an important impairment to investment and growth. Third, this section then analyses, domestic savings. Given the importance of public savings in increasing gross domestic savings, it suggests policy measures to reduce the current public deficit. A discussion about private savings follows, with the conclusion that raising private savings will be a medium-term effort contingent on establishing predictable macroeconomic conditions and achieving growth. Fourth, this section discusses policy actions to improve the efficiency of investment, needed to reach the target growth rates. Key policies cover public sector management, financial intermediation, relative prices, and institutional and human capital development. Finally, this section summarizes the impact of principal policy actions available to modify the main behavioral parameters of the model and discusses the mechanisms through which these actions operate. l/ 3/ This discussion is based on the conceptual framework developed in the World Bank's publication Adjustment Lending Policies for Suistainable Growth.

16
# 3.3 Migration and Poverty

One of the bene...ts of the accompanying village survey is a question asked each year of village leaders about the number of registered village residents working and living outside the village. In our analysis, we consider all registered residents working outside their home county to be migrants.<sup>12</sup> Both the tremendous increase in migration from 1987 onward and heterogeneity across villages are evident in Figure 1. In 1987 an average of 3 percent of working age laborers in RCRE villages were working outside of their home villages, which rose steadily to 23 percent by 2003. Moreover, we observe considerable variability in the share of working age laborers working as migrants. Whereas some villages still had a small share of legal village residents employed as migrants, more than 50 percent of working age adults from other villages were employed outside of home villages by 2003.

In other research using this data source, de Brauw and Giles (2008) use linear dynamic panel data methods with continuous regressors to demonstrate a robust relationship between the reduction of obstacles to rural-urban migration and household consumption growth. While one might suspect that the non-poor, who have su¢ ciently high human capital and other dimensions of ability, may bene...t most from reductions in barriers to migration, general equilibrium e¤ects of out-migration may lead to greater specialization of households in villages and this may have bene...ts for the poor. In particular, de Brauw and Giles demonstrate that households at the lower end of the consumption distribution tend to expand both labor supply to productive activities and the land per capita cultivated by their households than do richer households when out-migration increases. This raises the prospect that migration may be causally related to poverty reduction within rural communities as well.

Changes in the village poverty headcount are negatively associated with the change in the number of out-migrants, suggesting that poverty declines with increased out-migration (Figure 2). Nonlinearities in the bivariate relationship are evident in the non-parametric lowess plot of the relationship. Whether obvious non-linearities are related to the simultaneity of shocks and increases in out-migration and poverty for some villages or the simple fact that we 12From follow up interviews with village leaders, it is apparent that registered residents living outside the county are unlikely to be commuters and generally live and work outside the village for more than six months of the year.

17
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Africa), a smaller percentage of roads are already paved than in southern and western Africa. Nevertheless, the regional aggregates mask variation at the country level.

Some East African countries have already made the investments necessary to pave the portions of the regional network that fall within their national jurisdictions. All of the regional roads in Rwanda, almost all of the regional roads in Burundi, and 80 percent of the regional roads in Kenya are paved. In Ethiopia, Tanzania, and Uganda the share of the regional road network that is already paved is much lower, at 60 to 70 percent. The extreme case is Sudan, where only around 12 percent of the regional road network is paved.

East African countries vary substantially in their track record on maintenance of the regional road network. Overall, 77 percent of the regional network in EAC is in good or fair condition, compared with 56 percent for East Africa as a whole (table 2.3). As a general rule, all EAC members except Uganda are succeeding in maintaining their portions of the regional network in good or fair condition. In Uganda, only 61 percent of the regional road network is in good or fair condition.

Further north, the quality of roads in Ethiopia and Sudan is also very poor. Only around half the regional roads in Ethiopia and around 15 percent of the regional roads in Sudan are in good or fair condition. The large share of poorly maintained roads may be symptomatic of wider deficiencies in the funding and implementation of road maintenance works in these countries, or it may point to the low priority given to regional routes in national road plans. Significant resources are being allocated to Ethiopia and Uganda by the respective governments to improve the regional road network. More recent data will reflect this change.

**Table 2.3 . Condition of East Africa’s regional road network by member country, based on 2008 data**

|Percent||**Co**|**ndition**|||**Type**||
|---|---|---|---|---|---|---|---|
||Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|
|Burundi|42.4|27.9|24.2|5.6|94.4|5.6|0.0|
|Kenya|40.5|38.4|21.1|0.0|79.5|20.5|0.0|
|Rwanda|66.7|33.3|0.0|0.0|100.0|0.0|0.0|
|Tanzania|44.9|35.7|3.7|15.7|60.0|40.0|0.0|
|Uganda|13.5|47.4|4.3|34.8|69.9|21.9|8.2|
|**EAC**|**38.0**|**38.7**|**13.2**|**10.2**|**73.3**|**25.3**|**1.3**|
|Ethiopia|43.5|10.9|11.3|34.4|69.9|30.1|0.0|
|Sudan|0.0|15.3|9.1|75.6|11.6|21.4|67.0|
|**East Africa**|**29.8**|**26.5**|**11.7**|**32.0**|**57.2**|**25.4**|**17.4**|

Source: AICD various sources.

# Railways

The national railways in East Africa do not constitute a true regional network (figure 2.5), and the demand for rail services is relatively low. The exception is Tanzania, which is linked into the railway network of southern Africa via the Tazara railway. The economic insignificance of railways in East Africa stands in contrast to southern Africa, where interconnected national railway systems form a railway network that spans half a dozen countries and extends from southern Democratic Republic of 18
unprocessed foods, supply of solar power, machinery or medical goods, among others, are exempt from VAT. Rates on excise taxes and custom duties vary depending on the good or service<sup>10</sup> . VAT collections in Uganda alone represent 4.3 percent of GDP, almost a third of total tax revenue and almost as much as direct taxes, at 4.7 percent (Figure 2).

_Figure 2. Tax revenue in Uganda_ <!-- Start of picture text --> 30%<br>25%<br>2.6%<br>20%<br>6.9%<br>15% 1.5% 1.9%<br>2.10%<br>3.3% 3.5% 1.9%<br>10% 4.8% 3.6% 5.6% 4.8% 2.9% 4.30%<br>4.3% 4.20%<br>5% 3.0% 2.9% 8.5% 1.8%<br>2.0% 2.7% 2.7% 2.3% 4.30%<br>0%<br>Ethiopia (2010) Ghana (2012) South Africa Tanzania Uganda (2016) Kenya (2015)<br>(2010) (2011)<br>Personal and payroll taxes Corporate taxes Other direct taxes VAT Excises Other indirect taxes<br>% of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers. For Kenya, World Bank (2018a)_ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, and for Kenya to 2015._

# 2.3. Direct taxes

Revenue from direct taxes (when measured relative to the economy’s size) is higher in Uganda than in Ethiopia, but lower than in Tanzania, Kenya, Ghana and South Africa (Figure 3b). Direct taxes are mostly levied on individual income and corporate income. PAYE is the main direct tax revenue source, accounting for twice as much as corporate tax revenue (Table 1). Uganda’s revenue from corporate income taxes (as a share of GDP) is notably lower than in all other countries appearing in Figure 2, attributable to costly exemptions and multiple deductions that lower the effective tax rate.

The personal income tax, pay as you earn (PAYE), is collected on a monthly basis and exempts individuals making less than 235,000 Ugandan shillings (UGX) per month,<sup>11</sup> employees of the armed forces and judicial services, pension incomes and allowances of members of parliament. Rates range from 10 to 30 10 For instance, cigarettes are charged an excise duty of UGX 55,000 per 1,000 sticks of locally manufactured soft caps (or UGX 80,000 per 1,000 sticks of hinge rids) and a VAT of 18 percent; whereas beer is charged an excise duty of 60 percent of the ex-factory price per liter (or UGX 1,680, if the latter is higher), and also a VAT of 18 percent (see URA, 2017 for a full list of tax rates).

> 11 About 10 percent of Uganda’s per capita GDP.

9
Substituting _Q_<sup>_PM_</sup> in equations (1) and (2), we get the ex-ante profit and welfare of a private monopoly, where The value of operating the firm after the investment is made is measured by _V_ .<sup>19</sup> A monopoly is _privately feasible_ if it is ex-ante profitable, i.e. if _E_ Π<sup>_PM_</sup> _≥_ 0. This requires <u>1</u><sup>_≥K_and</sup><sup>_F_(</sup><sup>_λ_)</sup><sup>_∈_[0</sup><sup>_,_</sup><sup><u>1</u></sup><sup>_−K_].Similarly a monopoly is</sup><sup>_sociallyvaluable_if it brings ex-</sup> 2<sup>_V_</sup> 2<sup>_V_</sup> ante positive welfare, i.e. if _EW_<sup>_PM_</sup> _≥_ 0. It is easy to check that monopolies are socially valuable but privately infeasible if 43<sup>_V>K>_</sup> <u>12</u><sup>_V_.</sup> The ex-ante welfare _EW_<sup>_PM_</sup> ( _λ_ ) increases linearly with _F_ ( _λ_ ). The maximal entry fee that the government can collect is the maximum price a risk neutral entrepreneur would agree to pay for the monopoly concession: In practice international capital flows depend on country risk ratings so that developing countries’government do not collect the maximal fee _F_<sup>_∗_</sup> (see Brewer and Rivoli 1990). Because of the service of their debt, the perception of corruption in the administration and the social instability, the lack of transparency and predictability of their political and judicial institutions, developing countries get bad risk ratings.<sup>20</sup> A consequence of the bad ratings is that private investors, especially foreign ones, are very reluctant to invest in these countries. For instance in 1999 foreign direct investment (FDI) inflows to the

> 19One can check that _V_ =<sup><u>(</u></sup><sup>_a−_</sup> 2<sup>_E_</sup> _b_<sup>_<u>β</u>_</sup><sup><u>)2</u></sup> +<sup>_<u>σ</u>_</sup> 2 _b_<sup>2.Itcanbeseparatedintotwocomponents:thevalueatthe</sup> average cost, ( _a − Eβ_ )<sup>2</sup> _/_ 2 _b_ , and the value of the cost spread, _σ_<sup>2</sup> _/_ 2 _b_ .

> 20The ratings combine a range of qualitative and quantitative information. They reflect the ability and willingness of a country to service its financial obligation. There are five different measures of country risk: political risk, financial risk, economic risk, composite risk indices and institutional investor’s country credit ratings. The leading rating agencies are Standard and Poor’s, Moody’s, Euromoney, Institutional Investor, Economist Intelligence Unit, International Country Risk Guide. See for instance Global Risk Assessments web site, www.grai.com/links.htm.

12
All districts in mining regions have limited ability to raise their own taxes. Thus, their investments in infrastructure and poverty alleviation programs depend on revenues transferred from the central government. The main ones are the _Fondo de Compensación Municipal (FONCOMUN)_ , and, since 2002, the _Canon Minero_ . While the allocation rule of _FONCOMUN_ favors remote and poor districts, the _Canon_ is mainly allocated on the basis of mining production value. The _Canon’_ s rule is as follows: 50 percent of mining tax revenues are distributed back to subnational governments; of this amount, 10 percent goes directly to the corresponding producing district; 25 percent is distributed among all districts in a producing province; 40 percent is distributed among all districts in a producing department; and the remaining 25 percent is transferred to departmental governments and universities.<sup>4</sup> Apart from the 10 percent transferred directly to producing districts, the allocation of the Canon across all (producing and non-producing districts) also depends on district characteristics that include socioeconomic conditions.

Before the implementation of the _Canon_ , _FONCOMUN_ represented the main sources of revenues for most districts. However, as a result of Peru’s mining boom, the _Canon_ has grown substantially, particularly for districts in producing regions. In 2007, the year of our analysis, _FONCOMUN_ represented 32.9 percent of districts’ budget and the _Canon_ 21.4 percent (excluding the Department of Lima). The correlation between the amount of FONCOMUN received from the start of decentralization until the years of our analysis (2002-2006), and being a mining district, is statistically insignificant, hence there may be little crowding out of other government transfers from the _Canon_ . Accordingly, we shall find that controlling for additional transfers from the central government does not affect the results.

# **III. Data and Methodology**

Our outcome variables are a set of district-level living standards measurements for 2007: average per capita expenditure, the poverty and extreme poverty headcount indexes, a measure of uncovered basic necessities, the illiteracy rate, and the Gini coefficient of consumption inequality.

We collect living standard measurements that are representative at the district level by drawing directly from the 1993 and 2007 Censuses and from a poverty map developed by the Peruvian Statistical Institute that combines data from the 2007 Census with 2007 household survey data from the _Encuesta Nacional de Hogares sobre Condiciones de Vida_ (INEI, 2009). The advantage of using districts as our unit

> 4 Districts are the smallest administrative entity in Peru. A group of districts forms a province and a group of provinces forms a Department. Peru is divided in 25 Departments, 195 Provinces, and 1841 districts.

8
In this paper, we aim at filling this gap in the literature and document the link between political connections and access to credit using a large sample of firms from Bulgaria, Hungary, Romania, the Russian Federation, Serbia and the Slovak Republic observed over the period 2008 _−_ 2013. Using these data, we show that connected firms, in comparison to their unconnected peers, have higher levels of leverage, are less profitable, have lower marginal productivity of capital, and do not invest more. A major finding is that political connections appear to ease credit constraints. In addition, we find evidence that this privileged access to credit generates distortion in the allocation of capital as connected firms which benefit from easier credit tend to be less productive. In fact, it seems that credit constraints are reduced disproportionally more for the least efficient and least profitable firms, leading to clear welfare losses for these economies.

The paper is organized as follows: the next section describes the data and how we identify and measure political connections. Section 3 provides an initial assessment of the differences between connected and unconnected firms in terms of their leverage, profitability, and investment activity. Section 4 documents that availability of cash flow is less crucial for investing in the case of connected firms, demonstrating that they have easier access to credit than comparable unconnected firms. Section 5 shows that these connections lead to capital misallocation and welfare losses. Some final remarks follow in section 6.

# **2 Measuring political connections**

We implement our empirical strategy using two sources of data. First, we rely on a proprietary database that documents in detail an exhaustive list of politically exposed persons (PEPs). The data are primarily used by private institutions to undertake due diligence. In the PEP data set, the definition of politically exposed person depends on the classifications by intergovernmental bodies such as Financial Action Task Force (FATF) or legislation such as the European Union’s Anti-Money Laundering Directive. More importantly, for our purposes, a wide range of information is collected for each politically exposed person. For example, the data set reports association with specific companies, and whether these linkages are established directly, or through family members, close business associates or advisers of each individual PEP. Each politically exposed person is further classified into four sub-categories based on their primary affiliation with Local Government, State 3
Appendix A

# **A3: A note on price deflation prior to 1990s**

In a hyper inflationary environment such as in Brazil prior to 1994, anyone interested in evaluating poverty dynamics in Brazil faces the difficulty of selecting an appropriate deflator. It is therefore not surprising, that different studies come up with very different results. By and large studies on the development in Brazil seems to agree that the Real Plan of 1994 had a substantial effect on reducing poverty in Brazil, while since 1995 no significant progress in poverty reduction has been made. (Figure 1).

Figure 1 produces a poverty profile based on the headcount index and the methodology described in the paper. The three poverty profiles only differ by the underlying time deflators. _P0_Def2_ is based on the historic series of INPC, _P0_Def1_ also uses the historic series of INPC, however calculates the PNAD deflators as the geometric average of the month and the previous month, _P0_Def3_ uses deflators derived from the real minimum wage series from IBGE – which are also INPC based, but subject to IBGE modifications.

<!-- Start of picture text --> Sensitivity of Poverty Profiles to Deflators<br>33.0%<br>31.0%<br>29.0%<br>27.0%<br>25.0%<br>23.0%<br>21.0%<br>19.0%<br>P0_Def1 P0_Def3 P0_Def2<br>17.0%<br>15.0%<br>1981 1985 1988 1990 1993 1995 1997 1998<br><!-- End of picture text --> Even though the time deflators are very similar, the different dynamics in poverty are quite astonishing:

||Def1|Def2|Def3|
|---|---|---|---|
|1981|48.989|47.750|56.514|
|1985|2308.064|2192.602|2517.196|
|1988|145.592|129.913|151.435|
|1990|67.902|63.626|65.548|
|1993|77.813|77.813|77.350|
|1995|0.849|0.833|0.848|
|1997|1.000|1.000|1.000|
|1998|1.023|1.024|1.032|
|1998|1.023|1.024|1.032|

51
- 3 -

## **II. NATIONAL TRADE DEVELOPMENT**

**1.** Since independence, India has been a rather marginal participant in world trade, as shown below.

# Growth of Indian Import and Export Trades

||19|50|19|86|Compound Growth Rate|
|---|---|---|---|---|---|
||US$ (bn)|share<br>(Z)|US$ (bn)|share<br>(Z)|1950-1986<br>(Z)|
|World Exports|60.7|100|2,113.6|100|11.5|
|India Exports|1.2|1.9|9.2|0.4|7.0|
|World Imports|63.6|100|2,213.1|100|11.5|
|India Imports|1.2|1.8|14.8|0.7|7.4|

(Note: exports on f.o.b. and imports on c.i.f. basis)

**Source: UNCTAD Handbook on International Trade and Development Statistics, 1987.**

2. The above figures demonstrate that India's foreign trade has grown at a much slower pace than world trade. Actually, both export and import trades as a proportion of global trade have been shrinking. The 1986 share of Indian exports in world trade was only one fifth of the corresponding figure in 1950. As an explanation: markets for commodities which have traditionally formed the backbone of Indian exports -essentially the bulk trades- have displayed very modest growth trends. But over the last few years, the structure of Indian exports has been changing. The share of manufactured goods in the total export structure is increasing, whereas the bulk trades show declining trends; see table below.

# Structure of India's Export Trade

|Commodity Group|P|ercent Shar|e|
|---|---|---|---|
||1970|1980|1986|
|**Manufactured goods**|**45.1**|**57.5**|**64.6**|
|All food items|29.7|28.2|22.5|
|Agricultural raw materials|5.6|4.0|3.8|
|Fuels and Combustibles<br>|0.8<br>|0.4<br>|0.4<br>|
|Ores and metals|18.5|8.6|7.9|
|Unallocated|0.3|1.3|0.8|

(Note: Comparisons are in terms of value.)

**Source: Department of Statistics, Ministry of Planning, GOI.**