rafmacalaba/datause-encoder-data huggingface dataset:
26 economic liberalization; high tax burden; and macroeconomic instability). Consequently, countries like Georgia, Azerbaijan, and Ukraine exhibited very large unofficial economies by the 1990s, having experienced very rapid growth since the late 1 980s.

# _Nature of the Data and Implications for Measurement_

By definition, any measurement of the unofficial economy is subject to substantial margins of error. Further, errors stemming from the methodological assumptions made in our approach ought not be ruled out. Hence, it is important to not leave the reader with any false sense of precision concerning the numbers. Instead of accurate point estimates of unofficial economy, figures should be regarded as orders of magnitude, and each estimated share should be viewed as falling within a range. It should be noted that we have tried to maintain a conservative approach to the estimates. And that support for the methodology and empirical estimates is found from independent microsurvey data. Further, sensitivity analysis regarding reasonable ranges for the assumed initial unofficial economy share and for the elasticity of electricity consumption suggests that the margins of error are unlikely to be large enough to question the overall orders of magnitude and broad trends reported here.

The sheer orders of magnitude and variance in these unofficial economy estimates point to the need for furthering a country-specific understanding of the unofficial economies in the region. The lack of official recognition of these activities in most analyses implies that an implicit estimate of zero (or close to it) for the unofficial economy was chosen by default (or when analyzing trends, the "default" assumption is that the growth rates of the official and unofficial economies have moved in tandem--which is a fallacy in the FSU and CEE region). Hence, however imperfect the unofficial economy estimates may be initially, attempts at measuring and understanding the unofficial economy are bound to significantly improve measurement and understanding of the dynamics of the overall economy.

# _Implications for Policy_

Incorporating more fully the evolution of the unofficial economy into our understanding of the dynamics of the overall economy leads to some differences in approach to policy design and implementation. The details of optimal policy making would be expected to vary substantially in a setting where the unofficial economy is either ignored or rather small (or where it remains constant over time). We review these policy implications below.

# 1. Unofficial Economy: Curse or Blessing in Disguise?

The first question that needs to be addressed by the policy maker is what the official approach toward the unofficial economy should be. Should it be one of further encouraging growth, one of benign neglect, or one of active discouragement?
The results for GDP per capita suggest an almost perfect inertia for AA countries, as the estimated and statistically significant coefficient is 0.93. A hypothetical increase of $1 in the 1975 GDP per capita therefore would be almost entirely transmitted to the mean GDP per capita (see table 9). The estimated coefficient for BB economies is 0.15, but it is not statistically significant. These results suggest that initial conditions seem to play a larger role in explaining the economic performance of the better-off countries.

In the growth models, initial conditions are not statistically significant for both groups. This finding suggests no income convergence within and between groups, as already pointed out.

**<u>Table 9: Impact of initial conditions on GDP per capita</u>**

||**Dependent vari**|**able: GDP per capita**|||
|---|---|---|---|---|
|**Data**|**GDP per capita in 1975**|**Standard deviation of growth**|**R**<sup>**2**</sup>|**N**|
|AA & BB countries|1.00 (9.24)|-144.8 (-1.52)|.70|39|
|AA countries|.923 (4.83)|-181.7 (-.69)|.63|18|
|BB countries|.147 (1.35)|9.32 (.61)|.16|18|
||<br>**Dependent**|<br>**variable: Growth**|<sup>**2**</sup>||
|**Data**|**GDP per capita in 1975**|**Standard deviation of growth**|**R**|**N**|
|AA & BB countries|-.000 (-.27)|-.012 (-.10)|.00|39|
|AA countries|-.000 (-.28)|-.165 (-.63)|.04|18|
|BB countries|.000 (.47)|.100 (.96)|.09|18|

Notes: t-statistics in parentheses.

# **_Robustness of Country Groups_**

This section examines the robustness of country groups by testing their statistical significance. We estimated coefficients of country groups in GDP per capita pooled and fixed-effect regression models, as follows: 2 _Yit_ = α _i_ + β _Gg_ +φ _Cit_ + λ _tt_ +η _tt_ + ε _it_ 2 Δ _Yit_ = α _i_ + β _Gg_ +φ _Cit_ + λ _tt_ +η _tt_ + ε _it_ where _Yit_ is the GDP per capita of country _i_ in year _t,_ α _i_ is the country fixed-effect, Δ _Yit_ is the growth rate of country _i_ in year _t, Gg_ is the dummy of country-group _g, Ci_ is the dummy of country _i_ , _tt_ is time,<sup>_t_</sup> _t_ 2 is time squared, and ε is the error term.

Table 10 shows the results. Country groups’ coefficients are statistically significant at the 5 percent level; they are sizable and have the expected signs. After controlling for country fixed-effects, country groups, and time and its quadratic term, 28
# **Firm Characteristics Used to Study Differences in Savings-related Measures**

In addition, rich ICA data allow us a number of firm characteristics and proxies for access to finance. We use these firm characteristics to study how our five savings-related measures differ across firms. Specifically we break down our sample based on the following characteristics:

- **Firm size** (we define small as firms with fewer than 50 employees and large otherwise). About 62% of our sample is classified as small firms and only 38% are defined as medium or large.

- **Industry** (manufacturing or services).<sup>7</sup> About 81% of our sample is in manufacturing.

- **Foreign ownership** , firms with over 10% of equity owned by foreigners. In our sample 6.5% of firms are classified as foreign firms according to this definition.

- **Government ownership** , defined as 10% of more of the firm owned by the government. In our sample 3.2% of firms are classified as government owned. Over 90% of government owned firms are in a manufacturing industry.<sup>8</sup>

- **Exporter** , defined as 10% of more of the firm sales were exported directly or through a distributer. In our sample about 23% of firms are classified as exporters.

In addition, we calculate two productivity-related measures that we can use to test whether firms that are more productive also have higher cash flows or investment. We calculate **total factor productivity (TFP)** as a residual from a regression of log sales on log fixed capital and log of wages. Unfortunately this measure is only available for manufacturing firms since we don’t have total value of fixed capital for service firms. In addition, we calculate **sales per employee** as a measure of labor productivity.

As discussed above, availability of external finance is an important factor that will determine savings and investment. Therefore we study how our savings-related proxies differ for

> 7 Further analysis could break down firms within manufacturing industry. However within industry classifications are not consistent across different waves of the surveys, so constructing industry classification will be difficult and imprecise.

> 8 We do not have a reliable indicator for female-owned firms because gender of the owner is only asked of firms with largest individual shareholder and the question also asks whether or not one of the principal owners is a female. This does not produce a sharp identification of female owned firms in our sample.

9
Countries adopted differing pension reform paths based on their initial conditions and socio political situations. The pros and cons of various types of reform are discussed in detail in Schwarz and Demirguc-Kunt 1999 and James 1999. One of the factors influencing a country's choice of reform path was the size of implicit pension debt (IPD). A huge IPD forced some countries, such as Latvia, Poland, and Sweden, to adopt a notional defined contribution system (Disney 1999). Countries have used various options to finance the transition cost or IPD. These and other issues will be discussed later in the paper.

# **Figure 1. Classification of pension reforms/systems**

<!-- Start of picture text --> Fully funded defined contribution<br>(Indindual senants)<br>(e.&,Clile, Argentina, Boliia,<br>Colombia, Mei-eo Pea',<br>Hngagry. Ka-akhnn)<br>, PAYGO-Defined Benefit<br>ke.g. U.S. social secwrity sstem \ Notional defined contribution<br>(e.g, Ltlvia, Poland nd Sweden)<br>K Employer-sponsored defined<br>contribntion system<br>(e B OECD -oanties suhls as<br>l.Aootnli Deomam nd Switzerlad)<br>INew system: PAYGO or otber<br>No formal pension system e.g. Angola, G.atsnl, Moantiq<br>Oman, Ziinb.bse<br>DC-Provident Funds PAYGO system<br>(e.g, Ma1aysia *nd Sin, a (eg., odonosia and Nigeia)<br><!-- End of picture text --> Source: Based on Schwarz and Demirguc-Kunt 1999, James 1999.

## **_Pension reform in China_**

**_Past reforms._** China used to have an urban- and enterprise-based PAYGO pension system covering only the state sector and some large collective enterprises. After some experimentation beginning in 1982, pension reform was formally begun in 1986 when State Council Document 77 encouraged pension pooling across state enterprises on a limited basis at the municipal level, which individual contribution was implemented for contractual workers only. State Council Document 33 of 1991 called for individual contributions by all workers and encouraged experimentation including a role for individual accounts. It also, for the first time, called for the establishment of three tiers in the pension system: a basic benefit, a supplementary benefit to be provided by enterprises in sound financial condition, and a benefit based on individual saving.

## **_Reform_** _of_ **_1995_**

In 1995 State Council Document 6 proposed two plans for the basic pension tier. Municipal and prefecture governments were given the right to select a reformn design and provincial governments the right to approve the choice. This led to a highly fragmented system in which provincial and local governments and line-ministries selected various combinations of the two plans. In collaboration with 7
40 by allowing land to be used as collateral for borrowing, thus reducing the cost of borrowing and increasing investment; and by allowing the transfer of land to those who value it more. In addition he considers the possibility that private rights might be endogenous in the sense that farmers who invest more in their land end up acquiring more permanent and transferable rights. Besley notes that there is no necessary connection between more private rights and investment. For example it is possible that individuals in villages are altruistic towards fellow villagers or that communal sharing rules may allow better internalization of potential externalities.<sup>57</sup> Communal rights might be just as secure, or transferability rights might not be a significant for improving efficiency. He uses data on two regions of Ghana obtained through surveys of farmers asking them to indicate the various property rights they feel they possess and the investments (such as planting trees) that they have made in a given period on the lands they control. Farmers are asked to indicate whether they have the right to sell, rent, mortgage, pledge, bequeath, or gift their lands, and whether or not they need approval of the lineage in order to exercise each of these rights. From this survey he creates an index of private property rights and proceeds to test his various hypotheses. The results from Wassa County seemed to support the idea that stronger private rights improve investment. The results from Anloga County also showed such a link but the results were not robust and were somewhat inconclusive. Measurement and econometric problems, of multicollinearity and simultaneity for example, could be responsible for this inconclusiveness.

Overall, Besley is unable to find any strong support for any particular theory about how exactly property rights affect investment. In particular he finds little support for the collateral-based argument. His general conclusion is that advocating the creation of greater private property rights should be done with caution and is not a panacea for all that ails “low-growth” agriculture. This ambiguous conclusion is consistent with other studies which show that increases in private rights increase productive investment only when producers have access to complementary markets and communal resources. For example, an empirical study of Thai farming showed that titling (representing an increase in security and transferability of rights) increased investment only when there was access to credit markets.<sup>58</sup> Of course, titling could in the longer term improve collateralization and thus the emergence of a credit market.

> 57 Besley, p. 906.

> 58 For excellent surveys of the effects of different property rights and contractual arrangements on farm productivity, see the excellent surveys by Deininger and Feder [2000] and Binswanger et. al. [1995].
||Female<br>mortality ma<br>rate per<br>100,000(1)|Ratio of <br>le-to-femal<br>mortality<br>(2)||Age-slope<br>e offemale<br>mortality<br>(3)|Age-slope<br>ofmale<br>—‘mortality<br>(4)|Female<br>mortality <br>rate per<br>100,000(1)|Ratio of <br>male-to-femal<br>mortality<br><br>(2)|| Age-slope<br>e offemale<br>mortality <br>(3)|Age-slope<br>ofmale<br>—_mortality<br>(4)|Female<br>mortality <br>rate per<br>100,000(1)|Ratio of<br>male-to-femal<br>mortality<br>(2)||Age-slope<br>e offemale<br>mortality<br>(3)|Age-slope<br>ofmale<br>—_smortality<br>(4)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||All-caus<br>(expect|e deaths<br>ed):2020|||Excess de|:<br>aths:2020|||Excess de|:<br>aths:2021||
|Average (all countries)||1.69||||||||1.84|||
|Tercile<br>1 average||1.61||||||||1.37|||
|Tercile 2 average||1.77||||||||1.71|||
|Tercile 3 average||1.71|||||||||||
|Note: Columns 1-4 are <br>Deaths dataset. Column<br>65. Columns 3&4 is th<br>population weighted av|regression c<br>1<br>is the pre<br>e increase in <br>erages ofthe|oefficients <br>dicted fem<br> mortality <br> country s|based on th<br>ale mortality<br>riskforwom<br>pecific coeff|e country-spe<br> per 100,000<br>enandmen <br>icients show|cific Poisso<br> atage 65. C<br> respectively <br>n in Table 2 o|n model des<br>olumn2<br>is <br>associated w<br>verall and b|cribed in the<br>the predicted<br>ith an addit<br>y income ter|text applied <br> ratio ofmale<br>ional 10 year<br>ciles.|to theWHO <br>-to-female m<br>s in age. This|Global Exce<br>ortality ata<br> table show|ss<br>ge<br>sthe||
<!-- Start of picture text --> ��<br>R� �Log ����� �DistPop��� �� �<br><!-- End of picture text --> <mark>where</mark> _<mark>c</mark>_ <mark>are refugee camps, Pop</mark> �<sup><mark>is the average population in 1993-1995 in each refugee camp and</mark></sup> <mark>Dist</mark> ��<sup><mark>is the distance in</mark></sup><sup>_<mark>km</mark>_</sup><sup><mark>of each cluster</mark></sup> <mark>k to each refugee camp. This distance is alternatively modeled with ∂ �0.5, 1, 2 and 3 (squared, linear, quadratic and cubic functions). The coefficient of interest is therefore β</mark> �<sup><mark>which measures the effect of the interaction between the treatment group and the</mark></sup> <mark>intensity of the refugee presence adjusted for the inverse of the distance from the 13 refugee camps. Figure A3 shows the geolocation of our 608 clusters and 13 refugee camps based on which we compute R</mark> �<sup><mark>.</mark></sup> <mark>T</mark> �� <mark>is our treatment variable. We will consider five flavors of this variable (q=1, 2,3 4, and 5) depicted in Figure A2 and described as follows:</mark>

- <mark>T</mark> ���� is a binary variable equal to 1 if the mother j of child i was exclusively born or solely spent <mark>her early childhood during either the first (1993-1996) or the second period (1997-2001) of refugees’ influx and 0 otherwise. This is the main cohort of treated mothers we consider (“main cohort” in Figure A2).</mark>

- <mark>T</mark> ���� is a continuous variable measuring the number of years spent under the presence of refugees <mark>for mothers who were born or exclusively spent their early childhood in the presence of refugees. This is also the main cohort we consider but measured in terms of duration of exposure to refugees rather than simple exposure as in</mark> _<mark>q</mark>_ <mark>=1.</mark>

- <mark>T</mark> ���� is a categorical variable equal to 2 if the mother j of child i was exclusively born or solely <mark>spent her early childhood during the first (1993-1996) refugees’ inflow period; equal to 1 if the mother j of the child i was exclusively born or solely spent her early childhood during the second refugee influx period (1997-2001) and 0 otherwise. This variable essentially compares the treated groups across the two refugee influx periods against mothers who were not exposed to refugee influxes (“control cohort” in Figure A2).</mark>

- <mark>T</mark> ���� is a binary variable equal to 1 if the mother j of child i spent some years of her early childhood <mark>out and others inside the first period of refugees’ presence. This is the 1988-1993 cohort (“mixed cohort” in Figure A2), and 0 otherwise (“control cohort” in Figure A2).</mark>

14
and used the same sampling frame. LECS3 is a nationally representative survey that contains a household questionnaire and a village questionnaire. The school survey collected detailed information from each primary school serving the catchment area for the households surveyed in LECS3. If a village did not have a school at the time of the survey, the closest school attended by most children in that village, usually located in a neighboring village, was covered by the survey. Information about the school principal or head and data on the school were collected through interviews of the school principal. Individual teachers were interviewed also and were asked whether they offered tutoring lessons to students outside of school hours, and if they did, whether or not those tutoring lessons were for pay and the number of students they tutored. Teachers were also asked about the number of hours they spent preparing teaching materials and grading homework.<sup>18</sup> After deleting the observations with many missing values, our analysis file covers more than 1,500 teachers in 322 schools. To test whether this final sample resembles the population of schools, we compared summary statistics of similar variables from our estimation sample with those from a national school census data for the same year. For example, the female teacher ratio and the student-teacher ratio are, respectively, 0.52 and 30.4 in our final sample, which are almost identical to 0.51 and 31 from the 2002-2003 school census database (ESITC, 2011).

The school survey collected information related to pecuniary and non-pecuniary incentives. Our measure of pecuniary incentives (P) is the number of months a teacher’s salary was in arrears in the past year; again, we focus on salary delay rather than salary levels because the salary scale is given. Non-pecuniary incentives (A) consists of two types of measures: The first set includes binary variables reflecting the level of teacher autonomy in a school: whether

> 18 Although the survey did try to measure teacher absence rates, there were more missing data and the rest hardly had any variation.

13
consideration the market conditions such as prices and other energy policies that govern the delivery of energy services.

A quite different way to estimate energy poverty is based on energy expenditures as a proportion of total expenditures (Pachauri and Spreng 2004). Many international and government agencies routinely collect expenditure data for countries. The rationale for this expenditure-based approach is that many expenditure surveys indicate that poor households spend a large part of their total expenditure on energy and this obviously would be a hardship if the expense levels are too high. Generally, as household expenditures rise, less and less money is spent on energy as a percentage of the total income. Poor households on the other hand spend higher and higher shares of their income on energy and this obviously means that there is some basic essential energy service. Rising expenditure of energy becomes more and more difficult for households and they begin to cut back on their energy use to minimum levels. Pachauri and Spreng (2004) adopt a cutoff point of 10 percent of total expenditure because it is frequently mentioned in literature as common level of expenditure for poor households.

Energy poverty can also be based on the types of energy used by households at or below the overall expenditure poverty line already estimated for a country (Foster, Tre, and Woodon 2000). The basic assumption behind this measure is that expenditure-poor households (in terms of per capita expenditure) are also likely to be energy-poor. That is, the energy poverty line is related more to consumption expenditures than to physical energy requirements. The steps involved in developing this measure are fairly simple. The expenditure poverty line is determined first, following one of the standard techniques. This information is often already available through national statistical offices. Next, households are selected, whose per capita total expenditure falls below the expenditure poverty line. Finally, the average per capita energy consumption for these households is calculated, which is deemed to be the energy poverty line.

7
# **Benefits and Costs of Public Schooling in Ghana**

Dhushyanth Raju Stephen D. Younger Originally published in the Policy Research Working Paper Series on _April 2022._ This version is updated on _February 2023._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL codes_ : I20, I26, I25, I28 _Key words_ : Ghana, public education, returns to education, education quality, cost-benefit analysis

> Raju, World Bank, <u>draju2@worldbank.org; Younger, Commitment to Equity Institute, sdy1@cornell.edu. The study</u> was prepared under the World Bank’s advisory services and analytics task Ghana Human Development Public Expenditure Review (P175117). We thank Kehinde Ajayi, Jishnu Das, David Evans, Paul Andres Corral Rodas, and Michael Weber for valuable comments. We also thank Eunice Ackwerh, Chris Burningham, Yoko Nagashima, and Karishma Talitha Silva for feedback and for support in obtaining relevant data and documentation.