rafmacalaba/datause-encoder-data huggingface dataset:
**4.1 Literacy rates by region and gender in Ghana**

Literacy raises the productivity and earning potential of the population, and improves their quality of life. However, low literacy rates continue to be a problem in most of sub-Saharan African countries. Most of the educational systems with high enrollment rates are often plagued by high dropout rates. Even though there is no clear cut definition of literacy, we define a person to be "literate" if he can read and write. Of course this definition is very subjective. A person is considered to be "read literate" if he can read the newspaper, "write literate" if he can write a letter and "math literate" if he can do simple calculations. This section will examine the status of literacy in Ghana, based on the GLSS survey. This will help us better understand the extent of differences across such social lines as gender, region and age groups. Table 26 shows the literacy rates for the whole population. Literacy rates(read&write) have increased from 37% in 1987 to 49% in 199 15. Literacy rates are higher in the urban areas(65%) than in the rural(40%) in 1991. Also, the male literacy rates are higher than the female literacy rates. Looking at literacy rates within the age groups cohorts, we find that younger the age cohort, the more literates. Higher literacy rates in the younger age groups is an indication of advancement in the primary and secondary schooling, while lower literacy rates among age cohort 25 and above is an indication of lower access to primary education in the past. The main results are: The adult Literacy rates(read and write) is about 49%; Government estimates around 44% in the 1984 census. Male literacy is higher than female literacy. Urban literacy is higher than rural literacy and by age-cohort, the literacy rates are higher for younger people.

# **4.2 Dropouts and repetition:**

The GLSS data sets do not provide much information on dropouts and repetition, therefore one has to rely on indirect sources to ascertain information on this aspect. One such way is to look at the educational level by age groups. Obviously, one would think that age 5-10 yr children would be predominantly in primary, 11 -15 yr olds in middle, 1620 yr olds in secondary and 21-25 yr olds in higher level education. As seen in Table 27, most of the 11-15 yr old are still in primary level, when one would expect them to be in middle level, and similarly in the age group 16-20 year olds, most of the students are still in middle, when they are supposed to be in secondary. These numbers would suggest that the dropouts/repetition are higher in the 11-20 year age category.

> 5The GLSS3 is not fully compatible to the GLSSI & 2, in terms of literacy measures since the survey questions are slightly different. In the GLSS I&2 the question asked is: Can you read a newspaper or can you write a letter, while in GLSS3 the asked question is: "Can you read a newspaper in your langauage or in English"; and "can you write in your native language or in English".

38
for the 1973 – 2005 periods. Corsettin and Muller (2006) find evidence supporting the twin deficits hypothesis for Australia, Canada, the UK and the US; the relationship is stronger when the country is more open. Kaminsky, Reinhart and Vegh (2004) examine 104 countries and find some interesting patterns regarding international financial flows, fiscal and monetary policies. They also find countries with (a) capital inflows are pro-cyclical and (b) fiscal policy is pro-cyclical for most developing countries. This means that more expansionary fiscal policy is positively related to higher capital inflows (or larger current account deficits). For OECD countries, Piersanti (2000) finds that expectations of future budget deficits (such as those engendered by tax cuts) are associated with higher current account deficits.<sup>6</sup> Kearney and Monadjemi (1990) use VAR analysis on eight industrialized countries and confirm the twin deficits hypothesis for the period 1972- 1987.

# <u>Empirical Estimation of the Twin Deficits Hypothesis</u>

This section examines the relationship between fiscal and current account balances in the ECA countries. The main question of interest is whether fiscal deficits were destabilizing in the sense that they (or rather, the discretionary component of them) tended to be larger when current account deficits were larger. In order to investigate this relationship, panel regression analysis is used. The main regression of interest estimated in levels is of the following form:<sup>7</sup> CAB/GDP is the current account balance as a ratio to GDP. In order to focus on discretionary elements of fiscal outcomes rather than those influenced by cyclical fluctuations and past actions, I use the cyclically adjusted primary fiscal balance as a ratio to GDP as a measure of the fiscal stance. The adjustment used focuses on revenues, rather than expenditures as described in Annex 1.<sup>8</sup> The list of countries included in the empirical estimation is in Annex 1 **.** Other explanatory variables included in various specifications, as prescribed by theory and related empirical papers, are the lagged current account balance to GDP ratio, the output gap relative to GDP, the terms of trade (TOT), a measure of trade openness (OPEN/GDP), a measure of financial indebtedness

> 6 Expectations of future budget deficits are instrumented with past values of the deficit.

> 7 In alternative specifications, a measure of the real exchange rate is also used as an explanatory variable.

> 8 This implies that all expenditures changes were discretionary. Clearly, in some of the ECA countries, automatic stabilisers on the expenditure side may be large (though revenue effects are larger). Thus, the adjustment done for this paper would overestimate the deficit in bad times and underestimate it in good times (when output is above potential).

6
countries in LAC—with the exception of Guatemala, Panama, Peru, and El Salvador— experienced a depreciation of their currencies as the 2011 PPP values are higher than the 2005 PPP values.

To understand the effect of each component mentioned above, we estimated the Shapley value of the marginal effect of each component in the change of poverty headcount. That is, by leaving two of the components constant, we change one of the components from its corresponding value in 2005 PPP values to its value in 2011 PPP values. Then, we repeat this process five more times until we have calculated all the possible combinations.<sup>29</sup> Figure 7 shows the results of the Shapley decomposition for the changes in both extreme and moderate poverty using the welfare aggregate of the most recent household survey for each country (i.e., circa 2013). In the case of moderate poverty, the decomposition shows a substantial poverty-increasing effect of 9.7 percent from changing the nominal value of the poverty line and leaving unchanged the other two components. However, the currently-used $4 per person per day poverty line at 2005 PPP values and the $5.3 per person per day poverty line at 2011 PPP values are expressed in different spatial and temporal units. With respect to the PPP effect (i.e., the spatial adjustment effect), poverty headcount would increase by 3.0 percent, everything else constant, and the base period effect (i.e., the temporal adjustment effect), would reduce poverty by 10.6 percent. The net change is approximately 2.1 percent in 2013.

Unlike the majority of countries in Africa and South Asia, where the changes in poverty measurement using the 2011 PPP values reinforced poverty reduction (Dykstra, Kenny, and Sandefur 2014), the poverty measurement with the 2011 PPP values lead to an increase in poverty rates in LAC.

# **5 Conclusions**

Most of the countries in the world monitor their own citizens’ welfare and measure their living conditions on a regular basis. However, poverty measurement methodologies vary considerably across countries, which makes cross-country comparison and aggregations into regional and global trends difficult. In an attempt to assess the world population’s welfare, international organizations

> 29 The number of possible combinations is equal to the factorial (n!) of the number of components. See the annex for more technical details.

18
country's population (Figure 3.2). But in spite of the size, institutional capacity for effective public action remains weak. The large size of the sector hampers adequate supervisory and management oversight.

<!-- Start of picture text --> Figure 3.2: Size of Civil Service in five African countries<br>0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>CD<br>25<br>& 20<br>IJ 10<br>E<br>* 5<br>t 0 I<br>Ghana Gafntia Guinea Buridna Niger<br>.Z 1992 1990 1989 Faso 1990<br>1990<br><!-- End of picture text --> Source: Adapted from Leechor (1994)

**Public Enterprises:** Public enterprises played a dominant role in the evolution of Ghana's formal sector throughout the post-independence period. They established an important position in almost all sectors of the economy. Ghana is reported to have more than 300 public enterprises- more than any other African country other than Tanzania. Commander and Ugaz (Table 1, p.<sup>1</sup> 32) collected data from official sources for 224 nonfinancial state-owned enterprises (SOEs). In 1990 the total employment in these SOEs was calculated as 224 thousand (as against total employment in the formal private sector of under 40 thousand). The authors found that between 1986 and 1990 employment in the 17 core SOEs fell by 16 per cent. The government realizes the importance of reducing the dominance of SOEs in the economy. According to Leechor 63 out of 314 firms had been "divested" between 1989 and 1991, but it seems most of these are minor ones (p. 169). Progress has been slow and bogged down in the mire of political concerns. For example, the government announced in 1992 that private companies would be free to trade in Cocoa, but this "privilege" was confined to the domestic market. Cocoa exporting remained the preserve of the monolithic Cocoa Board which was also granted the responsibility of licensing private companies supposed to be in competition with itself.

The public enterprises enjoy considerable advantages over private concerns in the form of financial assistance, use of state owned commercial assets, special tax concessions, as well as monopolistic market situation. It is clear that, given the narrow savings base in the economy, public investment has tended to "crowd out" private investment particularly in the formal sector. It is remarkable that the private investment rate has been well below the public throughout the post-ERP era, except for 1990-91. After registering a recovery between 1987 and 1991, the private rate of investment fell back to low levels, and in 1994 was hovering around its usual level of around 4.4 percent compared to the public investment rate of 11.4 percent of GDP.

12
Table 3: Summary statistics and balance

||Mean/SD|General|Conditional|Price|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Year parcel was acquired|1992.487|-.009|-.024|0.018|
||(13.505)|(0.017)|(0.019)|(0.015)|
|Parcel is rented out|0.388|-.001|-.0006|0.0008|
||(0.512)|(0.0007)|(0.0007)|(0.0006)|
|Parcel was inherited|0.113|-.0004|-.0008|0.0006|
||(0.332)|(0.0005)|(0.0004)<sup>_∗_</sup>|(0.0004)|
|Electricity access|0.398|0.001|0.001|-.001|
||(0.514)|(0.0007)<sup>_∗∗_</sup>|(0.0007)<sup>_∗∗_</sup>|(0.0006)<sup>_∗∗_</sup>|
|Recent investment in parcel|0.214|0.0004|0.0007|-.0005|
||(0.43)|(0.0006)|(0.0005)|(0.0005)|
|Muslim household|0.569|-.0004|-.0004|0.0004|
||(0.522)|(0.0007)|(0.0007)|(0.0006)|
|Monthly income (TSh ’000)|387.497|-.915|-.817|0.86|
||(686.831)|(0.957)|(0.744)|(0.71)|
|Total assets, Log(TSh ’000)|7.518|-.003|0.0005|0.0008|
||(1.238)|(0.002)|(0.002)|(0.001)|
|Average schooling of hh|12.219|-.002|-.002|0.002|
||(2.895)|(0.004)|(0.004)|(0.003)|
|Household size|5.044|0.007|0.003|-.005|
||(2.711)|(0.004)<sup>_∗_</sup>|(0.003)|(0.003)|
|Parcel Area, Log(_m_<sup>2</sup>)|5.115|-.002|-.0008|0.001|
||(0.579)|(0.0008)<sup>_∗∗_</sup>|(0.0008)|(0.0007)|
|HH lives on parcel|0.794|0.0004|0.0005|-.0004|
||(0.425)|(0.0006)|(0.0006)|(0.0005)|
|Single female-headed household|0.189|-.0003|-.0007|0.0005|
||(0.413)|(0.0006)|(0.0005)|(0.0005)|
|Woman has rights over sale|0.582|-.0004|-.0002|0.0003|
||(0.593)|(0.0008)|(0.0008)|(0.0007)|
|De facto female owner|0.266|-.0005|-.0006|0.0005|
||(0.464)|(0.0007)|(0.0006)|(0.0005)|
|Would hypothetically cotitle|0.355|-.0004|0.00005|0.0001|
||(0.507)|(0.0007)|(0.0007)|(0.0006)|
|Women’s share of hh income|0.307|-.001|-.001|0.001|
||(0.546)|(0.0008)|(0.0007)<sup>_∗_</sup>|(0.0006)<sup>_∗_</sup>|
|Obs|1148|1148|1148|1148|

Column (1) displays the mean and standard deviation for each variable. Columns (2)-(3) display the mean and standard error of _αG_ and _αC_ from the linear regression of each variable _var_ = _α_ 0 + _αGvGi_ + _αC ∗ vCi_ , where _vGi_ and _vCi_ are the general and conditional voucher values for each parcel _i_ . Column (4) shows the results of a single bivariate regression of each variable on the overall price households faced, net of all vouchers. Voucher values are measured in (’000 TSh). Robust standard errors<sup>_∗_</sup> ( _p <_ 0 _._ 10) _,_<sup>_∗∗_</sup> ( _p <_ 0 _._ 05) _,_<sup>_∗∗∗_</sup> ( _p <_ 0 _._ 01) 13
Table 2. Characteristics of the Population and Labor Force <u>(Individuals between 15 - 64 years old)</u>

||Bangl<br>|adesh<br>|Pe|ru|Tha|iland|
|---|---|---|---|---|---|---|
||2000|2010|2004|2010|2000|2009|
|Population and demographics|||||||
|Total (millions)|71.0|89.8|27.6|31.4|58.1|64.7|
|Men (percent of total)|50.4|48.4|49.7|49.2|47.9|47.9|
|Women (percent of total)|49.6|51.6|50.3|50.8|52.1|52.1|
|Urban (percent of total)|21.8|28.0|64.5|63.5|29|31|
|Rural (percent of total)|78.2|72.0|35.5|36.5|71|69|
|Average household size|5.18|4.5|4.4|4.2|4.4|4.1|
|Number of adults per household|3.13|2.94|2.7|2.6|3|2.9|
|Share of adults per household|63.4|68.4|68.0|70.7|69.7|73.3|
|Occupied adults (as a share of number of adults)|48.4|50.1|73.2|75.7|78.3|77.8|
|Labor force participation (percent of working age population)|||||||
|All|49.4|49.2|73.5|76.2|81.1|80.5|
|Men|83.3|84.9|83.1|84.4|87.6|86.7|
|Women|14.9|15.6|64.1|68.4|75.3|74.9|
|Employment (percent of working age population)|||||||
|All|46.6|47.8|71.2|74.9|78.5|79.8|
|Men|81.6|82.9|80.9|83.4|84.4|85.8|
|Women|11.1|14.8|61.6|66.8|73.2|74.4|
|Unemployment (percent of labor force)|||||||
|All|5.6|1.4|5.1|3.8|2|0.7|
|Men|2.0|2.0|4.5|3.3|2.6|0.9|
|Women|25.9|0.8|5.8|4.4|1.5|0.5|
|Education levels (percent of working age population)|||||||
|Illiterate & Incomplete primary|57.2|47.1|19.5|16.4|44.7|34.4|
|Complete primary & lower secondary|33.8|43.4|31.6|29.4|32.2|39|
|Higher secondary & Tertiary|8.9|9.4|47.4|52.2|23.1|26.7|
|Labor relation (percent of employed population)|||||||
|Farmer|28.6|25.0|17.2|16.4|40.1|36.0|
|Daily workers|33.5|32.4|...|...|...|...|
|Self-employed|17.8|17.3|25.2|26.8|12.1|14.3|
|Salaried|20.2|25.4|38.7|43.0|41.2|42.6|
|Family worker|...|...|18.9|13.8|6.6|7.1|
|Economic Sector (percent of employed population)|||||||
|Agriculture|49.0|41.6|35.9|30.0|49.3|40.6|
|Industry|22.2|23.9|9.8|10.4|17.3|20.1|
|Private Services|24.8|30.8|46.0|50.8|24.6|30.2|
|Public Sector|4.0|3.6|8.4|8.8|8.9|9.0|
|Area (percent of employed population)|||||||
|Rural|78.6|71.6|36.5|34.6|68.4|66.8|
|Urban<br>Source: Own estimations based on Peru's ENAHO 2004 -2010, Thailand's SES 20|21.4<br>00 -2009, and Ban|28.4<br>gladesh's HIES|63.5<br>2000 -2010.|65.4|31.6|33.2|

29
C8_howfind3 How did you find this previous MAIN job?

# **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM LIST.]**

1. By starting my own business

2. Contacted employer directly 3. Through a public employment agency 4. Through a private employment agency 5. Through friends or relatives 6. Through school/university employment center 7. By responding to a vacancy 8. Through a union/professional register 9. Through job training programs/courses 10. Other 888. Don’t know 999. Refused C9_numemp3 About how many persons (including yourself) worked in the enterprise or household business where you were employed in this previous MAIN job?

1. 1 2. 2-5 3. 6-9 4. 10-25 5. 26-50 6. 51-100 7. 101-249 8. More than 250 888. Don’t know

C10_hours3 How many hours (on average) did you work during a normal WEEK in this previous MAIN job?

1. Fewer than 15 hours per week 2. 15-30 hours per week 3. 30-45 hours per week 4. 45-60 hours per week 5. 60-75 hours per week 6. More than 75 hours per week 888. Don’t know 999. Refused

C12a_earnings_cash3 How much did you usually earn PER MONTH in cash from this previous MAIN job (in Taka)?

**[FOR SELF-EMPLOYED, EARNINGS REFER TO PROFITS, NOT REVENUES. FOR FAMILY MEMBERS WORKING IN A HOUSEHOLD BUSINESS, RESPONDENT SHOULD SAY HOW MUCH HE OR SHE EARNED, NOT HOW MUCH THE BUSINESS EARNED.]**

**[If answered then go to C12c]**

**[If don’t know then put “Don’t Know” and go to C12b] [If refused then put “Refused” and go to C12b]**

2
Figure 6: Pre-Exit Behavior of Establishments (Data) <!-- Start of picture text --> 7000<br>6000<br>5000 >11<br>4000<br>Sales 9-11<br>3000<br>6-9<br>2000 3-6<br>0-3<br>1000<br>1 2 3 4 5 6 7 8 9 10 11<br>Age<br><!-- End of picture text --> heterogeneity in size at the startup occurs by the variation in efficiency levels. The model captures the shadow of death effect especially for the 3-6 and 6-9 year cohorts, but in the data this effect is more pronounced. In Klette and Kortum (2004), all producers start with one product; hence, there is no dispersion in size at startup. Lentz and Mortensen (2008) introduce randomness in size of each product and heterogeneity. However, there is no relation between the startup size and the post-entry performance. In the proposition below, I show how the model explains this relation.

Proposition 2 Consider a cohort of establishments all entering at the same time. At any age  ≥ 1 within this cohort, establishments that survive longer are larger in size than the exiting establishments (i.e. for  representing the establishment size,  representing the time of the exit, ∆  0 0 ≤  [ () | = ] ≤  [ () | =  + ∆]). Proof. See Appendix.

The positive relation between the startup size and the likelihood of survival is not unique to Chilean Data. Audretsch (1995) performs a logit estimation using US establishments and concludes that startup size has an impact on the likelihood of survival. Similar results are found in Dunne, Roberts, and Samuelson (1989). This evidence shows that it’s an important feature that needs to be understood in order to explain causes of size dispersion, heterogeneous responses of producers to exogenous shocks, and responses to policy changes. The model has the potential to explain these issues.

for Food industry not the all industries combined because I intend to show how model explains this relation qualitatively.

20
Other studies of the RHVP have been undertaken since the program was launched in 2009. To provide as complete a picture as possible in this evaluation report, the authors draw data and findings from these other sources.

In 2011 Mazzilli conducted a cost-modeling exercise for the HealthyBaby voucher service, adopting a Ugandan SP’s perspective of public or external resources. She drew on voucher sales and claims data, as well as cost data, which included reimbursements to voucher SPs for actual services offered to clients, along with program operation costs (Mazzilli 2011). These costs were obtained from the program headquarters in Mbarara and drawn from the program database, a review of financial reports, and interviews with program staff. The government facility costs were drawn from national sources (Mazzilli 2011).

Brody and colleagues conducted semistructured interviews on workload and job satisfaction in a total of 35 facilities, including eight that accepted the HealthyBaby vouchers, seven that accepted the HealthyLife vouchers, another seven that accepted both vouchers, and 13 nonvoucher facilities (Brody, Irige, and Bellows 2015). In each of the facilities, SPs were selected for interviews based on the following criteria: (1) proprietors and administrators, (2) clinical officers, midwives, and laboratory technicians, and (3) nurses, nursing assistants, and laboratory assistants. A total of 76 SPs were interviewed, with 56 from the intervention facilities and 20 from comparison facilities (Brody, Irige, and Bellows 2015). Job satisfaction was measured using an adapted version of the ten-item Minnesota Job Satisfaction Scale.

# **<<A>>** Analysis

The evaluation of the population-level impact of the voucher program was initially designed to approximate the counterfactual—that is, what the outcome of the program would have been in the absence of OBA voucher subsidies for comparison with the outcome of the actual interventions. However, since the design was not implemented as planned, the analytical approach adopts a post hoc definition of the counterfactual. Two definitions are used: (1) respondents in the 2010–11 survey who had never used the maternal health voucher (nonvoucher clients), and (2) respondents from villages that were included in both baseline and follow-up surveys, but where no voucher clients were present in 2010–11.

The outcomes of interest include targeting of beneficiaries, knowledge, health service access and utilization (new use and general use of ANC, health facility delivery, and PNC), outof-pocket expenses, and socioeconomic inequities in service utilization. An analysis of targeting 16
# **Four critiques of the redistribution hypothesis: An assessment**

# **Branko Milanovic**<sup>a,b *</sup>

a World Bank, Research Department, 1818 H Street NW, Washington DC 20433, USA b School of Public Policy, University of Maryland at College Park JEL classification: D31, E62 Key words: Median voter; Income distribution; Redistribution ________________________________

> * Tel: +1-202-473-6968; Email address: <bmilanovic@worldbank.org>.
Table A-1(b). China: Basic Indicators by Province

|----------------------------------------------------------<br>----------------------------------------------------------|-------<br>Urban<br>Popu<br>(%)<br>1994<br>---------|Length<br>of roads<br>(kms)<br>1994|Average<br>years of<br>educ.<br>1987|Average<br>life<br>expctncy<br>1987|__----------_<br>Wage&<br>cost<br>index<br>1992<br>__--------_|
|---|---|---|---|---|---|
|All China|23.6|1117821|5.68|70.59|1|
|Beijing|66.6|11532|8.12|74.93|1.19|
|Tianjin|57.7|4156|7.42|73.64|1.02|
|Hebei|16.5|50496|5.90|73.26|0.98|
|Shanxi|25.8|32693|6.29|69.77|0.81|
|Inner Mongolia|37.0|44202|6.19|69.24|0.82|
|Liaoning|45.2|42763|7.00|73.32|0.93|
|Jilin|43.9|29581|6.69|70.11|0.87|
|Heilongjiang|49.7|48356|6.55|70.33|0.82|
|Shanghai|70.5|3721|7.92|75.97|1.29|
|Jiangsu|23.9|25891|5.91|73.63|1.04|
|Zhejiang|16.5|33170|5.82|71.82|1.21|
|Anhui|17.2|30876|4.71|71.21|0.87|
|Fujian|18.3|44608|5.12|70.90|1.19|
|Jiangxi|20.9|34556|5.15|68.10|0.86|
|Shandong|17.4|50225|5.52|72.88|0.90|
|Henan|14.8|47704|5.43|71.68|0.83|
|Hubei|29.0|48349|5.92|68.91|0.94|
|Hunan|16.5|58803|6.00|68.82|1.01|
|Guangdong|16.1|75716|5.96|73.83|1.57|
|Guangxi|14.1|39550|5.54|70.81|1.07|
|Hainan|34.1|13015|5.84|69.76|1.22|
|Sichuan|16.3|100002|5.40|68.70|0.90|
|Guizhou|15.4|32398|4.37|70.12|0.95|
|Yunnan|15.9|65578|4.13|64.25|0.94|
|Tibet|14.1|21842|1.91|63.50|1.17|
|Shaanxi|21.3|39058|6.29|69.74|0.91|
|Gansu|18.8|34984|4.40|70.24|0.97|
|Qinghai|32.6|17061|3.79|66.40|0.90|
|Ningxia|28.8|8324|5.15|69.74|0.93|
|Xinjiang|47.7|28611|6.14|69.25|1.00|
|----------------------------------------------------------|-------||||__----------_|
|Source: State Sta|tistical<br>|Bureau<br>(1|995). Cost|data are|from Wu Renhong,<br>1995,|
|"China's<br>Inflatio|n<br>and Re|gional<br>D|isparity,"|and Worl|d<br>Bank,<br>1994,<br>China-|
|Tnternal<br>Market<br>|and<br>Rgilat-ion|<br>.<br>Soc|ial indica|tors are|from Yasuko Hayase and|
|Seiko Kawamata,|1991, Po|piilat-io|n Ponliy an|iVital|qt-atiJt-iJr.<br>in<br>China,|
|Institute of Dev|eloping Eco|nomies, T|okyo, Japa|n. Autho|r's own calculation.|

60