rafmacalaba/datause-encoder-data huggingface dataset:
2

# **1 Introduction**

The aim of this paper is to analyze if, and in what way, mass violent conflict affects fertility. It is well known that shocks can have significant effects on fertility, with the nature of the shock driving the nature of the impact. In the case of mass violent conflicts, very little is known about their effects on fertility. Hence we aim to understand the effects of off-spring and sibling mortality, widowhood and sex imbalance on short- and long-term changes of fertility in the post-conflict period. This paper – a product of the Gender and Development Unit, Poverty Reduction and Economic Management Network – is part of a larger effort in the department to study gender differentials in response to conflict, with generous funding from the Government of Norway.

Our analysis contributes to two literatures. First, it teaches us how conflict affects individual human behavior and well-being, which is a small but growing area of research. Second, we learn more about the drivers of fertility in times of sudden structural change. The occurrence of mass violent conflict can be seen, from a methodological perspective, as an opportunity to analyze fertility responses to aggregate and individual shocks, and to understand fertility as a coping strategy. Given the importance of relative population shares of various ethnicities in conflict-prone societies and given the importance of fertility for individual and household well-being in very poor economies, understanding the relationship between conflict and fertility is also important from a policy perspective.

We study the specific case of the 1994 Rwandan genocide, using three waves of Rwanda Demographic and Health Surveys (RDHS) collected in 1992, 2000 and 2005. Our research strategy includes cross-sectional analyses of the post-genocide data as well as analyses of pooled data from before and after the genocide. This allows us to compare the determinants of fertility for sub-samples of women of the same age groups across the survey years. The most challenging identification of an effect of conflict is at the micro-level – especially if the survey does not ask about an individual’s exposure to conflict explicitly (Brück et al. 2010). Two alternative categories of proxies for conflict exposure are employed to distinguish women who were and were not likely to be affected directly by violence. Each category represents a transmission mechanism of conflict: (1) replacement effects and (2) marriage market effects. We interpret the coefficients for these variables as the ‘pure’ conflict effects on fertility, bearing in mind that conflict may also shape fertility indirectly through the other variables included in the regressions.
31 Third, as noted before, the new entrants chose to focus on trade finance and other specialized services although they are authorized to collect deposits. This in effect left the pre-existing large banks unchallenged in the retail market. Fourth, as discussed already the decline in concentration ratios have been primarily due to top 5 banks losing deposits to the ones below the top 5 which were in the system prior to 1980 and they already had reasonably large branch networks although the magnitude of their losses have not been sufficient to alter the rankings. Given these considerations, it is not surprising that entry did not positively influence competition in the equations estimated. Hence, results obtained does not mean entry does not matter. Rather, it highlights the importance of conditions under which entry will be more effective, i.e., when the type and size of the entrants are close to the existing firms in the industry, Hannah and Kay (1977), Kheamani and Shapiro (1988).

The results for the concentration variable are consistent with expectations -market concentration is a ddeterminant of competition in the retail banking market and is correlated with a low level of interfirm rivalry. Such a finding is in line with Heggestad and Rhoades's proposition that mobility is an element of " industry structure but rather reflect conduc **t** hat the theory would predict to arise from certain market structures". This result also corroborates the earlier result that found a positive relationship between and concentration and profits. The average fixed asset size of the top 5 banks also has a strong negative impact on competition. This result suggests that the branch network and equipment owned by the top _5_ banks are negatively affecting competition in the market and probably deter new comers to enter into the retail banking market."7 It may also be an indication of reputation effects which are thought to be significant in banking. As expected market growth is strongly related to competition which is line with most other competition studies.

17Bodehom (1990) also finds similar results.
**Table 3:  Percent of Urban Population with Access to Services and Housing Conditions by Reference**

**Group**

|**Reference**<br>**Group**<br>**(Percentile**<br>**Range)**|**Threshold**|**Housing**<br>**Unaffordability**<br>**rate**|**Households**<br>**with access to**<br>**safely managed**<br>**sanitation**|**Households with**<br>**access to safely**<br>**managed drinking**<br>**water**|**Households with**<br>**access to roof, floor &**<br>**walls that it is**<br>**constructed of low-**<br>**quality material**|
|---|---|---|---|---|---|
|10 to 40|3716|25.2|69.1|84.3|37.5|
|20 to 40|3911|29.1|71.1|84.2|37.8|
|0 to 60|4049|31.9|69.2|83.3|35.0|
|20 to 50|4156|33.9|72.3|83.9|36.2|

Source: Author calculations based on HIES 2018-19 data

**<mark>Comparability of expenditure and income data.</mark>** <mark>In most developing countries, consumption expenditure is used as a proxy for welfare. Theoretically, consumption is likely to remain more stable over time than income, which can be affected by seasonality and other shocks. Practically, it is extremely difficult to collect accurate income data in developing economies with high informality, a large prevalence of selfemployment or own-account workers, reliance on foreign remittances, in addition to smallholder agricultural production for home use. Income measures are especially problematic due to difficulties in estimating self-employment profits, falsified responses and underestimation (Deaton and Zaidi 2002; Deaton 2019). For these reasons, even though the HIES collects data on income, it is more reliable to continue using consumption as a proxy for income in the REM approach.</mark>

For comparison, household income aggregates were estimated using the data in HIES 2018-19. A household’s total income is given by the sum of all the following sources:

- 1) Work or job-related annual income from main, secondary and other occupations;

- 2) In-kind payments for employment as well as transfers such as pensions;

- 3) Main and secondary income from agriculture (including in-kind transfers) for those in agriculture;

- 4) Unearned income sources including local and international remittances, Zakat/Ushr, inheritances, income from insurance, the Benazir Income Support Programme;

- 5) Income earned (rents or sales) from agricultural and non-agricultural land;

- 6) Rent or sale of animals and equipment; and

- 7) Rent or sale of household enterprise property and equipment.

To enable comparisons with the consumption aggregate, self-reported imputed rents for owneroccupants are included to ensure comparability. Without accounting for these, income falls short of consumption for most households.

Estimates from Pakistan reveal that household incomes are higher than expenditures. This relationship holds across all quintiles in urban Pakistan, with the differences between the two estimates being significant - as highlighted by the non-overlapping error bars in Figure 3. While the differences between income and expenditure estimates are likely to correspond to savings, noisy income estimates would indicate that this is not a hard and fast rule.

25
# [Table 3 about here]

While the direct effect of oil production is to increase the likelihood of civil war, oil also reduces the chance of war onset for countries with a high _risk of coup d’état_ . The interaction reach conventional levels of statistical significance for the Humphreys oil production and the Ross oil and gas rents measures for small scale wars. The results are similar for the larger sample for which we have available the _history of coup d’état,_ but, in this sample, only the Fearon and Laitin oil exports significantly reduce the risk of civil war for coup threatened regimes for both small and large war onset. _Military regimes_ are also helped by oil resources to reduce the risk for civil war, with statistically significant interaction terms in the models for small war onset for Humphreys oil production and Fearon and Laitin oil export indicator.<sup>19</sup> The results for both diamond and oil endowments provide support for the ideas behind the rentier state hypotheses, in that coup vulnerable regimes appear able to use revenues from diamond and oil production to deter civil war onset. Closely related to our hypothesis and our results, Collier and Hoeffler (2007) find that high risk of coup d’état results in increased military expenditures. Their findings apply particularly to Africa where coup d’état risk has remained elevated and where the higher spending on the military reduces significantly the risk of coup d’état. Our own findings further suggest that countries with rich mineral endowments that are at risk of coup d’état can use increased spending on the military not just to reduce the risk of coup but also reduce the risk of rebellion. A cautionary point in this account is that, on their own, a high risk of coup d’état, a history of coups or a military regime generally pose a higher risk of civil war, and the coefficients for these variables are usually statistically significant.

> 19 This result also holds for the Lujala (2010) off-shore oil production indicator.

22
Table 3. Role of Fuel Products in the Economy

||Share in<br>total fuel<br>consumption|Fuel intensity<br>in<br>production/<br>consumption|Fuel import<br>intensity by<br>sector|
|---|---|---|---|
|Agriculture|12.4|19.6|49.8|
|Fuel products|0.5|7.0|184.6|
|Industry|29.9|11.6|34.7|
|Transport|40.0|30.8|—|
|Other services|7.1|2.3|—|
|Households|10.1|1.2|—|
|_Urban_|_37.1_|_1.1_|_—_|
|_Rural_|_62.9_|_1.2_|_—_|

Source: Based on HBS (2005/2006) and the Social Accounting Matrix of 2009.

In general, direct effects on households from subsidy reform relate to their consumption of fuel products, whereas indirect effects relate to the change in real household incomes because of higher production costs of goods and services. Consistent with evidence from other countries, the direct expenditure for fuel is modest at 1,805 Yemeni Rials (YER) per capita per year, or about 1.2 percent of household expenditure, on average (Table 4). The per capita amount is higher for urban households (2,659 YER) than for rural households (1,363 YER) in absolute terms. However, rural households spend a higher share of their income on fuel: 1.2 percent compared with 1.1 percent for their rural counterparts. Indirect consumption of fuel matters more.<sup>8</sup> To illustrate this point, consider the case of transportation: Household expenditure on transportation (which is fuel intensive, see Table 3) is about eight times higher than expenditure on fuel and accounts for 8.7 percent of household expenditure nationwide, while urban households spend a much higher share on transportation than rural households. In summary, petroleum subsidies make up a significant share of government expenditures and play an important direct and indirect role for real household incomes. Subsidy reform is expected to reduce household welfare and alter the production costs of economic activities in the short run, while freeing up substantial resources that can be used for alternative spending. Therefore, capturing the direct _and_ indirect effects of subsidy reform will be the key for meaningful analysis. The next sections will lay out the analytical strategy to capture these effects and assess alternative options for reform Table 4. The Share of Fuel Products in Household Consumption

||Total|Urban|Rural|
|---|---|---|---|
|_Fuel products_||||
|Per capita expenditure||||
|(YER/year)|1,805|2,659|1,363|
|Share in total expenditure|1.2|1.1|1.2|
|_Transport_||||
|Per capita expenditure||||
|(YER/year)|13,281|46,130|6,677|

> 8 Key household characteristics are summarized in Table A.1 in the Appendix.

7
# **Table 5b: Detailed Variable Importance for Kiswahili – CIF and LASSO**

|**Variable**|**Conditional Inference**<br>**Forest**|**LASSO**|
|---|---|---|
|1)**Baseline Kiswahili Score**|1 (20)|200 (-)|
|2)**Are any actions taken in case of poor teacher performance?**<br>(=1 if Teacher says Yes;=0 if Teacher says No)|0.19 (20)|178 (-)|
|3) Student Age|0.15 (20)||
|4) Have you received training in 3Rs? (Teachers who received training in<br>the 3R (Reading, Writing, Counting) Program)|0.06 (18)||
|5)**Your Kiswahili teacher offers extra help to students who find the**<br>**subject difficult**<br>(Percentage of students for a given teacher who said Yes when asked this<br>question)|0.05 (20)|188 (+)|
|6) Are you satisfied by the support you get from the school<br>administration?<br>(=1 if Teacher says‘Satisfied’;=0 if Teacher says‘Not Satisfied’)|0.05 (18)||
|7) It is okay to be absent as long as I: complete the curriculum OR leave<br>students with work OR doing something useful for the community (= 1 if<br>Teacher answers‘Yes’;=0 if Teacher answers‘No’)|0.04 (16)||
|8) Lesson Facilitation (Classroom Observation)<br>(Teacher rated higher if lesson objectives are clearly articulated,<br>explanation of content is clear, teacher connects lessons to real life)|0.04 (14)||
|9) Pupil Teacher Ratio|0.02 (19)||
|10)**Relationships Index**|||
|(Teachers who strongly believe that they have a good relationship with<br>their students, colleagues & head teacher score higher)|0.02 (14)|145 (+)|
|11) Are you satisfied by the support you get from the Government?<br>(=1 if Teacher says‘Satisfied’;=0 if Teacher says‘Not Satisfied’)||144 (+)|

Note: (i) Variables in bold are selected as important by both CIF & LASSO (ii) Numbers for CIF show relative variable importance (variables ranked in terms of loss in predictive power if that variable is permuted) and figures in parenthesis show the number of times the variable showed up in 20 runs of the model (iii) Figures for LASSO show the number of times the variable appeared in 200 runs of the model along with the coefficient sign in parenthesis 36
The main characteristics of each publication and the R&D programs evaluated were also collected. Table 2 shows that the average year of publication is 2010 and around 70 percent of the publications explicitly developed methodology that used a counterfactual to assess impacts. The average sample size of the data used to conduct the assessment in each paper is 2,143 observations with a relatively high standard deviation.<sup>11</sup> Around 70 percent of R&D programs included in the papers were preceded by laws and enactments and only 43 percent were in the later stages of implementation. On average the impact coefficient was 0.32 with a standard deviation of 0.13. The average cumulative investment of R&D programs surveyed was 175 USD million (PPP).

On average, each paper had around 110 web views, taken from each website/publication traffic statistics. But only some of these papers were cited in other publications 15 times on average. Around 80 percent of papers and publications followed a peer review process. Only 1 out of 4 papers had sections devoted to robustness checks of impact estimates.

Table 2. Database Summary Statistics (Program and Papers)

||Mean|Std Dev.|Min|Max|N|
|---|---|---|---|---|---|
|Program Characteristics||||||
|Year of Publication|2010|2008|2004|2011|37|
|Use of Counterfactual|0.70|0.46|0.00|1|37|
|Sample size|2,143|2,968|66|12,566|31|
|Firms Benefited by Program|103,305|170,236|1,000|761,000|34|
|Late Stage of implementation|0.43|0.50|0.00|1|37|
|Program preceded by Law|0.70|0.46|0.00|1|37|
|Years of Implementation|12.49|7.89|4.00|38|37|
|Impact Estimator|0.32|0.30|0.03|1|33|
|Standard error estimator|0.13|0.14|0.01|1|33|
|Total<br>cumulative<br>program<br>investment (PPP, usd millions)|175|324|3|1,500|35|
|Paper Characteristics||||||
|Number of Views (web-based)|111.41|115.83|11.00|552|37|
|Citations (google_scholar)|14.59|23.31|0.00|104|37|
|With Fast View Server|0.54|0.51|0.00|1|37|
|Number of pages for analysis|10.23|6.84|3.00|39|37|
|Paper peer review|0.81|0.40|0.00|1|37|
|Rate of paper quality (=10 best)|7.27|1.16|5.00|10|37|

> 11 From the 37 papers surveyed the smallest sample sized used was 66 and the largest was 12,500.

18
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Table **2** - **Granger Causality Test (Jan 1983** - **Dec 1992)**

|Variable|E|E|VAT|VAT|
|---|---|---|---|---|
|constant|4.377<br>(0.32)|-2.736<br>(-.37)|-52.546<br>(-1.39)|-11.182<br>(-0.67)|
|E(-1)|0.424<br>(2.22)|0.411<br>(4.19)|0.416<br>(0.80)||
|E(-2)|0.311<br>(1.59)|0.373<br>(3.52)|0.623<br>(1.16)||
|E(-3)|40.060<br>(-0.31)|0.071<br>(0.63)|-0.166<br>(-31)||
|E(-4)|0.176<br>(0.89)|0.135<br>(1.20)|0.907<br>(1.69)||
|E(-5)|-0.119<br>(-0.62)|-0.168<br>(-1.50)|-1.118<br>(-2.11)||
|E(-6)|-0.0314<br>(-0.16)|0.0785<br>(0.70)|-0.480<br>(-0.88)||
|E(-7)|-0.0539<br>(-0.27)|-0.045<br>(-0406)|-0.637<br>(-1.19)||
|E(-8)|0.0472<br>(0.24)|0.109<br>(0.10)|0.788<br>(1.47)||
|E(-9)|0.2175<br>_(1.15)_|0.190<br>(1.83)|0.719<br>(1.39)||
|VAT(-1)|-0.007||0.466|0.623|
||(-0.I0)||(2.49)|(6.31)|
|VAT(-2)|0.030<br>(0.41)||0.258<br>(1.28)|0.403<br>(3.49)|
|VAT(-3)|0.049<br>(0.66)||0.143<br>(0.71)|-0.527<br>(-0.43)|
|'VAT(-4)|-0.036<br>_(-0.50)_||-0.270<br>(-1.37)|-0.305<br>(-0.25)|
|VAT(-5)|-0.028<br>(-0.40)||0.146<br>(0.75)|-0.142<br>(-1.20)|
|VAT(-6)|0.0461||0.333|0.298|
||(0.66)||(1.75)|(2.50)|
|VAT(-7)|0.002||0.081|-0.065|
||(0.03)||(0.42)|(-0.53)|
|VAT(-8)|-0.016||-0.351|-0.161|
||(-0.24)||(-1.87)|(-1.38)|
|VAT(-9)|-0.017||0.060|0.197|
||(-0.26)||(1.39)|(1.81)|
||R2= 0.79|R2= 0.79|R<sup>2</sup>= 0.93|R<sup>2 </sup>= 0.°2|
||DW= 2.00<br>RSS= 53571.7|DW= 2.00<br>RSS= 54396.5|DW= 1.93<br>RSS= 401350.5|DW= 1.92<br>RSS- 488537.4|
||N= 111<br>F(9,93)= 0.16|N= 111|N= 111<br>F(9,93)= 2.25|N= 111|

Note: E is the monthly **tax** effort index **and** VAT **real** value added **tax** revenues. T **statistics are** shcnvn in parenthesis. R<sup>2</sup> is the explan uDry pow/er of the regression; DW is the Durbin-Aitson Statistic; RSS is the residual sum of **squares** of the regressions; N is the number of observtions. F is a Fiaher test of joint-significance of the **laed terms** of the independent variable.
While the cost-effectiveness of conventional guidance is almost self-evident, it is almost equally self-evident that there are diminishing returns to it as it becomes more elaborate, and particularly when it involves costly workshop experience. But there are no empirical evaluations and it is doubtful whether any attempt at one could carry conviction. The second issue concerns the effects of guidance on attitudes and the differing views of educators and employers concerning the role of the school. In some countries employers feel that schools impart values which actually make the transition from school to work more difficult. For example in Britain, which has a long-standing and unenviable reputation for producing ill-educated and unmotivated school-leavers, "employers especially have complained that schools do not inculcate in school leavers positive attitudes toward business/industry, but instead even promote negative attitudes to authority, entrepreneurial activity, and the fundamental concept of a marketdriven, profit-oriented economic system" (Noah and Eckstein, 1986, citing a 1984 memorandum of the Confederation of British Industries to the House of Lords Subcommittee on Education, Employment and Social Affairs.) Unsurprisingly, when canvassed, employers invariably would like to see guidance go much further than the rectification of damage done. For example, in Britain, France and Germany, Noah and Eckstein found representative employers groups in favor of the positive reinforcement of business values as in the more developed examples of arbeitslehre, and moreover would be happy to see it extended to the teachers as well. But what may be acceptable in the German context, where "there is an extensive consensus about fundamental aims and means... in vocational education and training" and where 'it would be difficult to explain the behavior and actions of employers, unions, Government(s) and individuals without this context" (Hayes, Anderson and Fonda, 1984), may encounter resistance elsewhere. Since there is no dividing line between guidance and indoctrination, it is clear that costeffectiveness rapidly becomes subordinate to ideological considerations.

# 7.7 Conclusions

Although individual case studies frequently make a distinction between the different aspects of vocational education, evaluative surveys often have not. The quantitative evaluations reviewed are particularly deficient on this point and their limitations in other respects are so serious that one must view their findings with circumspection. The following conclusions must therefore be regarded as tentative.

# Non-job-specific technical education versus job-readiness training

As a first approximation, the literature suggests that formal, school-based education can be a cost-effective way of providing general manpower development embracing not only academic education and the development of basic skills but also instruction in technical subjects.

As with vocational guidance, the real questions relating to this general type of vocational education are not whether but how much should be available (probably much less than is sometimes supposed, in view of the fact

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