rafmacalaba/datause-encoder-data huggingface dataset:
The Effects of Single-Sex Schooling on Student Achievement and Attitudes in Nigeria by Valerie E. Lee and Marlaine E. Lockheed

# Table of Contents

|Background|6|
|---|---|
|<br>Research on Single-Sex Tertiary Education|6|
|Research on Single-Sex Secondary Schooling|7|
|Education in Nigeria|11|
|Method|13|
|Sample and Data|13|
|Measures|15|
|Analysis Approach|18|
|Descriptive Differences Between the Four Sex-by-School||
|Type Groups|19|
|Results|25|
|Estimating Single-Sex Effects After Adjusting for Differences||
|in Student Background and Attitudes, Schools,||
|and Teaching Practices|25|
|Discussion|33|
|Single-Sex School Effects|33|
|Education of Females|36|
|Sex Differences in Mathematics Achievement|39|
|Endnotes|42|

Valerie E. Lee is of the University of Michigan. The contributions of the International Association for the Evaluation of Education Achievement (IEA) in making the data available are gratefully acknowledged. The useful comments of three anonymous reviewers, as well as the research assistance of Linda C. Carr, are very much appreciated.
# 114

Our data also indicate that the size of the extendec family--in terms of number of people fed-increases as income rises, although the relationship is not very strong (see Table 8.5). When we regressed the number of people fed by a Senegalese industrial worker on the worker's base wage and total compensation, we found small but statistically significant coefficients on the wage variables (Table 8.5). However, the wage variables only explain 2 percent of the variance in the number of extended family members. Our data therefore support the hypotheses that industrial wages have a positive effect on the size of the extended family, but the explanatory power of our results is very small.

TABLE 8.5 Senegal Corelation **beaween Sie** of Estended Family and **Workers** Eanis

|(Dependent Variable: Number of people fed)|
|---|

|Independent<br>Variables:|Coefficien|t|t Statistic|
|---|---|---|---|
|Intercept|11.6||10.6|
|Total Compensation|38E-5||23|
|Base Salary|.23E14||3.4|
|||.02<br>558||

Source: 1986 - Worker sample, 1986 survey of 17 industrial firms.

Sample - Senegalese workers only.

# _8.3 Secondwy Income_

The effect of the extended family on the worker's standard of living becomes clearer when one realizes that the earnings a worker receives from a job in the industriaW sector usually represent his/her total earnings. In other words, there are relatively few workers who either earn income from a secondary activity or who have a secondary earner in the household. This
**FIGURE 6: Advanced Reformers: Wage Decline and Recovery**

<!-- Start of picture text --> 20<br>15<br>10<br>.- 5<br>-0<br>-25 0 siovenia<br>-30<br>1989 1990 1991 1992 1993 1994<br>Source. PlanEcon<br><!-- End of picture text -->

# **I11. HOUSING PRIVATIZATION**

## **_3.1 Housing as One of the "Four Privatizations"_**

The Hungarian economist Janos Kornai has been the leading analyst of the structure, behavior, and performance of socialist economies. He has pointed out the affinity that exists between forms of ownership on one hand and economic control mechanisms and modes of financing on the other.<sup>6</sup> In socialist economies we have state ownership, state financing, an atrophied financial system (as we shall discuss in the next section) and widespread state monopolies over the production and distribution of goods and services. By contrast, in market economies private forms of ownership dominate and financing is provided by the banking system and the capital markets. Competition and contestable markets form the cornerstone of public policy in these economies. Even when and where public activities are significant. they are also evaluated with reference to that analytical and policy yardstick. Housing privatization is a very much a precondition to market-based housing finance: where there is no housing sold to private individuals there will be no demand for private mortgage finance, because there is no need for it.

> 6 See in particular Kornai's grand synthesis, _The Socialist System, The Political Economy of Communism,_ Princeton Universitv Press, 1992.

9
Table 21. Main Regional Activities

|**Region**|**Main Sector**<br>**of**<br>**Employment**|**Main Activities**<sup>**18**</sup>|**Population**<br>**(millions,**<br>**2013)**|
|---|---|---|---|
|TR1 - Istanbul|Serviecs|Largest city in terms of economy and population. Highly<br>diversified;industry,finance,services,high tech,etc.|14.2|
|TR2 - Bati Marmara|Services|Industrial agriculture, textile manufacturing, tourism and other<br>services|3.3|
|TR3 - Ege|Services|Industrial agriculture in the inner parts of the region and<br>manufacturing. Includes Izmir, the 3<sup>rd</sup>largest city in the<br>country,important tourism destination.|9.9|
|TR4 - Dogu Marmara|Services|Services, heavy industries, manufacturing, most industrialized<br>region|7.2|
|TR5 - Bati Anadolu|Services|Includes Ankara, capital and second largest city in the country.<br>Diversified industrial production, services (administrative and<br>governmentjobs)|7.4|
|TR6 - Akdeniz|Services|Primarytouristic destination,industrial agriculture|9.8|
|TR7 - Orta Anadolu|Services|Manufacturing, agriculture. Anatolian Tigers (lots of medium<br>and small size firms)|3.9|
|TR8 - Bati Karadeniz|Services,<br>Agriculture|Mining, forestry, industrial production, services|4.5|
|TR9 - Dogu Karadeniz|Agriculture|Agriculture(specializedproducts: tea and hazelnuts),forestry|2.6|
|TRA - Kuzey Dogu Anadolu|Agriculture|Husbandry, forestry, less-industrialized agriculture,<br>industrialization through increased construction|2.2|
|TRB - Orta Dogu Anadolu|Agriculture|Husbandry, lower skill agriculture, light manufacturing,<br>industrialization through increased construction|3.8|
|TRC - Guney Dogu Anadolu|Services|Developing industrialization (textiles and manufacturing) ,<br>industrialized agriculture,services|8.1|

_Source_ : Turkish Statistical Institute data compiled form Household Labour Force Statistics (publication), Household Labour Force Data Base (internet)

> 18 Does not include all activities. Regional specializations within the country

50
**<u>Table 6: Impact of proximity to large farms on modern input use</u>**

||**Rotation**|**Agronomi**<br>**Intercrop**|**c practices**<br>**Line sow**|**Traction**|**Mo**<br>**Imp. seed **|**dern input**<br>**Fertilizer**|**use**<br>**Pesticide**|**Access to**<br> **Extension **|
|---|---|---|---|---|---|---|---|---|
|||||**Pane**|**l A**||||
|Any new large farm <= 25 km|0.069**|0.065***|-0.002|0.029*|0.033*|0.020**|0.028**|0.013|
||(0.027)|(0.024)|(0.028)|(0.017)|(0.018)|(0.009)|(0.011)|(0.016)|
|Any new large farm 25-50 km|<br>0.009|<br>0.059**|<br>0.053*|<br>0.047***|<br>0.019|<br>0.022***|<br>0.012|<br>0.005|
||(0.030)|(0.026)|(0.029)|(0.015)|(0.016)|(0.008)|(0.014)|(0.016)|
|Any new large farm 50-100 km|<br>0.046|<br>0.048*|<br>-0.014|<br>-0.001|<br>-0.017|<br>-0.009|<br>0.001|<br>0.004|
||(0.031)|(0.025)|(0.030)|(0.012)|(0.014)|(0.009)|(0.014)|(0.018)|
|Observations|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|
|R-squared|0.166|0.190|0.295|0.574|0.166|0.305|0.250|0.128|
|||||**Pane**|**l B**||||
|Any new large farm <= 25 km|0.011**|0.013***|-0.002|0.006*|0.003|0.005**|0.004**|0.000|
||(0.005)|(0.004)|(0.005)|(0.003)|(0.003)|(0.002)|(0.002)|(0.003)|
|Any new large farm 25-50 km|<br>0.005|<br>0.011***|<br>0.007|<br>0.007***|<br>0.006**|<br>0.003**|<br>0.003*|<br>0.002|
||(0.005)|(0.004)|(0.005)|(0.002)|(0.003)|(0.001)|(0.002)|(0.003)|
|Any new large farm 50-100 km|<br>0.006|<br>0.005|<br>0.002|<br>0.003*|<br>0.000|<br>-0.000|<br>0.002|<br>0.003|
||(0.004)|(0.004)|(0.004)|(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|
|Observations|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|<br>12,202|
|R-squared|0.166|0.189|0.295|0.574|0.166|0.305|0.250|0.128|
|<br>_Note:_Other control variables th<br>adults and old people), headshi<br>included throughout and standar|<br>at are includ<br>p, head’s ag<br>d errors are|<br>ed throughout<br>e, and highes<br>clustered by E|<br>but not repor<br>t education i<br>A. *** p<0.0|<br>ted are farm<br>n the househ<br>1, ** p<0.05|<br>size, house<br>old and a ti<br>, * p<0.1.|<br>hold compos<br>me dummy.|<br>ition (no. o<br>EA fixed|<br>f children,<br>effects are|

21
# **PRIVATE INVSTfiEET IN MMXICO.**

# AN **DIPIBCAL** ANALYSIS

# Table of Contents

|||P|ageNo.|
|---|---|---|---|
|1.|Introduction<br>...................|...............|<br>1|
|2.|Economic Background ..... ..<br>.........|...............<br>.|<br>2|
|3.|The Model ...............................|.|7|
|4.|Private Investment<br>...................|.............|9|
|5.|The Relative Price of Investment ..........|..............|. 14|
|6.|<br>The Real Interest Rate ...................|.............|<br>18|
|7.|<br>Concluding Remarks...<br>................|.............|<br>24|
|8.|Data ................................||27|
|9.|Statistical Tables ......................|.......... 30,|31,32|

I am grateful to Bela Balassa, Joaquin Cottani, Edgardo Barandiaran, Sreenivasa Ramachandran and Louis Fischer for their comments. All remaining errors are my sole responsibility.
<u>Table 4. Firm Innovation by size group (% of all firms)</u>

||||**Technol**|**ogical**|**Non-Tech**|**nological**|
|---|---|---|---|---|---|---|
|**Country**|**Size**<sup>**_6_**</sup>|**Product**|**Process**|**Prod. & Proc.**|**Organizational**|**Marketing**|
||All|44.0%|61.4%|77.7%|37.2%|90.6%|
|Banladesh|Small|40.9%|52.6%|75.1%|38.9%|82.7%|
|g|Medium|37.5%|68.7%|77.8%|34.4%|93.1%|
||Large|56.5%|63.2%|81.0%|40.2%|97.8%|
||All|8.1%|8.1%|14.8%|14.5%|72.0%|
|Pkit|Small|6.3%|3.4%|9.6%|5.2%|67.3%|
|asan|Medium|7.6%|13.6%|18.9%|14.6%|76.9%|
||Large|14.8%|5.4%|17.8%|19.6%|70.4%|
||All|53.2%|60.8%|83.3%|55.3%|81.8%|
|India|Small|45.5%|57.2%|77.3%|41.2%|79.7%|
||Medium|55.9%|58.8%|85.1%|49.6%|79.8%|
||Large|56.4%|67.8%|86.0%|66.1%|87.4%|
||All|12.3%|10.1%|21.2%|36.9%|87.3%|
|Nl|Small|9.0%|6.9%|15.4%|0.0%|84.5%|
|epa|Medium|20.2%|26.5%|41.8%|43.3%|99.4%|
||Large|81.7%|11.9%|90.8%|10.5%|99.5%|
||All|19.3%|12.6%|26.3%|53.3%|‐|
|ECA|Small|18.1%|10.6%|24.3%|48.2%|-|
||Medium|20.8%|15.7%|29.4%|58.5%|-|
||Large|24.1%|20.0%|35.0%|66.8%|-|
||All|25.8%|30.6%|44.6%|42.7%|-|
|Afi|Small|23.8%|28.4%|42.2%|40.2%|-|
|rca|Medium|33.0%|37.1%|51.5%|39.3%|-|
||Large|31.1%|46.5%|58.4%|51.4%|-|
||All|20.5%|16.8%|28.2%|67.9%|-|
|Russian|Small|13.2%|11.7%|18.6%|55.6%|-|
|Federation|Medium|24.9%|22.8%|35.2%|67.7%|-|
||Large|38.7%|22.4%|48.8%|83.8%|-|
||All|5.4%|6.3%|8.5%|63.1%|-|
|Tk|Small|4.3%|6.8%|8.0%|58.8%|-|
|urey|Medium|5.1%|4.5%|7.0%|55.4%|-|
||Large|14.2%|9.1%|17.6%|84.0%|-|

Source: Author’s elaboration from Enterprise Survey (2014) Further, the composition of the innovation portfolio highlights another difference between both country groups. While process innovation is more important in Bangladesh and India, product innovation is more important in Nepal and Pakistan. However, there is convergence in the pattern of non-technological

> 6 Size classification consists of: small, less than 20 employees; medium from 20 to 100 employees; large more than 100 employees.

11
for people with completed primary education. The lower likelihood of HE ownership for people with completed secondary education and above is consistent with secondary education being rare, and therefore providing opportunities in relatively high paying non-farm wage and salary jobs – often a more desirable alternative. Size of NFE is positively associated education, with self-employed HE owners without family help being the least educated, HE owners with family help being more educated and ME owners the most educated, almost as well educated as public sector wage workers (figure 12). And as shown below, education does increase earnings for HE owners. So while primary education may not be necessary to start a HE or ME, judging but the sorting among occupations be education, it seems to help.

Why do owners start HEs? Obviously, to make money. But are they “pushed” or “pulled” into the sector? Consistent with divergent views of informal enterprises over time, many analysts have discussed whether HEs are the „reserve‟ sector, where people end up because they cannot find other opportunities (a view associated with the “exclusion” school of thought) or a dynamic sector, which people enter to as a positive choice, to exploit an opportunity and/or to have the independence that self-employment brings - a view associated with the “new” view of informality (Maloney, 2004). Household surveys in two of our study countries (Tanzania and R. Congo) asked HE owners to report their main reason for starting a business and found that push factors dominated the list. Not being able to find a wage and salary job was the most frequently cited reason, but it was cited by less than 40 percent of respondents (multiple responses were permitted). The need for income came in a close second, and was the most common in rural areas. The most commonly cited pull factor in R. Congo was the desire for independence, while in Tanzania the opportunity to make a profit was the most common response (this responses was not included in the R. Congo survey).

19
propagation effect, of job gains. In section 5.2, we will break down exactly what service sectors benefit from the BTA. In section 7, we will examine via what channels the job gain propagation could take place.

Our results are still strong and robust when we use another measure of employment at the household level. Specifically, when we replace the outcome variables with the change in number of working members, we obtain similar results (see Table A3 in the Appendix).

# **5.2 Job creation in services**

**Table 5.2: Employment change in services sectors**

||Commerce|Transport|Finance|Other services|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Panel A: Change in share of number of e**<br>**working ages at household level**|<br>**mployed members**|<br>**to number of me**|<br>**mbers in**||
|<br>Change in provincial tariffs|-0.491<sup>**</sup>|-0.128<sup>*</sup>|0.048<sup>***</sup>|-0.184<sup>*</sup>|
||(0.187)|(0.069)|(0.014)|(0.099)|
|Share of commerce workers-1|<br>-0.402<sup>***</sup><br>(0.012)||||
|Change in share of provincial|<br>0.037||||
|<br>commerce workers between 2002 and<br>1998|||||
||(0.064)||||
|Share of transport workers-1||-0.482<sup>***</sup><br>(0.025)|||
|Change in share of provincial transport<br>workers between 2002 and 1998||<br>0.124<br>(0.076)|||
|Share of finance workers-1|||-0.444<sup>***</sup><br>(0.073)||
|Change in share of provincial finance<br>workers between 2002 and 1998|||<br>0.065<br>(0.096)||
|Share of other services workers-1||||-0.384<sup>***</sup><br>(0.017)|
|Change in share of provincial other<br>service workers between 2002 and<br>1998||||<br>-0.051|
|||||(0.040)|
|Constant|-0.035<sup>*</sup>|-0.009|0.005<sup>***</sup>|<br>-0.005|
||(0.018)|(0.007)|(0.002)|(0.009)|
|_N_|<br>18957|<br>18957|<br>18957|<br>18957|
|adj_R_<sup>2</sup>|<br>0196|<br>0251|<br>0215|<br>0179|
|.<br>**Panel B: Changes in working hours as**|.<br>**shares of total pot**|.<br>**ential working ho**|.<br>**urs by member**|.<br>**s in working ages**|
|Change in provincial tariffs|-0.776<sup>**</sup>|-0.136|0.038<sup>**</sup>|-0.211|
||(0.328)|(0.097)|(0.016)|(0.130)|
|Share of commerce-working hours-1|<br>-0.385<sup>***</sup>||||

20
characteristics, that have been highlighted in the literature, matter in our data and whether our main results are robust to including them. First, in column (1) we include as control an index of bureaucrats’ perception on management quality<sup>36</sup> . Rasul and Rogger (2016), drawing upon Bloom and Van Reenen (2007), have documented a positive correlation between management practices and project completion in Nigeria. Including a variable on management practices does not change the positive effect of politicians’ integrity, and the coefficient on the management variable is not statistically different from zero, although the point estimate is positive and large.

We also examine whether ethnic heterogeneity among the districts’ bureaucrats and politicians play a role in service delivery in columns (2)-(3), as in the literature on the role of ethnic diversity in teams (Hjort, 2014) and in economic development more generally (Alesina and La Ferrara, 2005; Alesina et al, 2017). Our proxy for ethnic diversity is a Herfindahl Index of self-reported ethnicities. We first include as control the index for all surveyed individuals, and in column (3) we include separate herfindahl indices for bureaucrats and politicians. Once again the correlation between integrity and service delivery remains broadly unchanged. The point estimates on the herfindahl indices are small and not significant. In column (4) we include both ethnic diversity and management practices measure as controls; the coefficient on politician integrity remains significant, and with little change in size across all these different specifications.

Finally, we consider whether the integrity of different types of bureaucrats–those with greater leadership powers, such as the Chief Administrative Officer (CAO) and the Resident District Commissioner (RDC), versus those within the technical health bureacracy, such as the District Health officer– matters for service delivery, and whether this disaggregation by bureaucrat type changes the results for political integrity. We calculate the integrity index separately for the leaders of the bureaucratic organization - CAOs and RDCs - and for bureaucrats working in the health sector, and include these in the specification reported in column (5). The coefficient on bureaucratic leaders’ integrity is very small in magni-

> 36It includes six questions on whether bureaucrats believe that senior management recruit and promote the best employees.

26
sumption basket of the poor and reduce their real income to a level below the poverty line. Financing through higher income taxes may reduce disposable income and the ability to spend; it may also drive over time activity underground, leading to a fall in the overall tax ratio, which in turn could force cuts in expenditure (including transfers to the most needy). Financing through a tax on capital (usually a limited option for governments, due to concerns about capital flight) may lower private investment capital; as noted earlier this may reduce skilled employment and mitigate the overall, positive effect on total employment. Finally, financing through a cut in government spending on infrastructure may affect aversely the overall productivity of production factors in the private sector (including labor) and lead to a reduction in wage income; it may also have a negative impact on private investment. This, in turn, will have an adverse overall effect on the demand for labor (due to the negative effect of investment on growth) as well as an adverse effect on the demand for skilled labor through the complementarity relationship between human capital and physical capital. Thus, although the direct effect of the reduction in the payroll tax rate on the demand for unskilled labor may well be positive (both in the short and medium run), overall employment (and thus unemployment) may not change much, as an increase in the number of unskilled jobs is offset by lower employment of skilled labor. In all of those cases, again, unemployment may fall, but poverty may increase-because of adverse effects on income, due either to lower after-tax wages or lower public transfers. There may therefore be a trade-off between unemployment and poverty goals.

In addition, of course, labor market reforms may themselves be complementary, if labor market institutions are complementary as well. As emphasized by Coe and Snower (1997), this implies that partial or piecemeal labor market reforms are unlikely to achieve significant and persistent reductions in unemployment rates or poverty. For instance, active labor market policies (such as retraining schemes) may not be very effective in the presence of substantial passive policies (such as stringent job security provisions). Reform programs must be sufficiently broad (in the sense of covering a wide range of complementary policies) and deep (of substantial magnitude) to have much of an effect. In addition, these reforms may also need to be combined with measures that address more efficiently the distributional objectives of the pre-reform policies, such as the imposition of high minimum wages.

The Mini-IMMPA prototype developed here can be used to analyze a variety of additional labor market policies. First, it can be used to analyze a 45
44 Aggregate travel demand models may be used to estimate generation, distribution, and modal choice simultaneously. These are of the form:

- Tijk f( population, income, travel time and cost),

where Tijk is trips from origin i to destination j on mode k. These models have been mainly used in an inter-urban context, where zones are large. Timberlake (1988) suggests that this aggregate demand approach is superior to applying the classic transportation model in developing countries, citing a study of the Karthoum-Wad-Medani Corridor in Sudan.

Finally, in the classic model, the trips by mode are assigned to the network, matching travel demand (estimated for the baseline level of service) to network characteristics, such as distance and capacity, as well as public transport availability. Congestion may be included in defining an equilibrium according to the Waldrop principle: "Under equilibrium conditions, traffic arranges itself in congested networks such that no individual trip maker can reduce his path costs by switching routes" (Ortuzar and Willumsen, 1991, p. 254). Congestion results in rerouting, mode switching, or demand reductions according to a cost minimization algorithm, where costs may include travel time, convenience, waiting time, and out-of-pocket costs. Alternative versions of these models seek network equilibrium (within one mode), multi-mode equilibrium, or system-wide equilibrium (including switching time-of-day and destinations for trips). These models are capable of estimating welfare effects of alternative policies, although they have not been used in this way. They permit a wide variety of policies to be modeled, particularly those affecting short-run decisions on mode choice and route. Vehicle ownership and residential and
# **<mark>Table 2C - Impact of FD Law on States’ Poverty and Inequality Given Their Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on poverty and inequality of states of varying ex ante public</mark> indebtedness. The variable _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Posty_ is an indicator variable that equals one from 2016 onwards. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||Povert<br>|ys,y<br>|Moderate<br>|Povertys,y<br>|Extreme P<br>|overtys,y<br>|Gini<br>|s,y<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Posty|-0.04***||-0.00||-0.04***||-0.04***||
||(0.01)||(0.01)||(0.00)||(0.01)||
|Posty*DebtStates,16Q1|-0.02*|-0.02*|-0.04***|-0.04***|0.02**|0.02**|0.00|0.00|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|
|Observations|150|150|150|150|150|150|150|150|
|R-squared|0.96|0.96|0.91|0.91|0.96|0.97|0.54|0.58|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

47