rafmacalaba/datause-encoder-data huggingface dataset:
education has almost tripled since FY09 from LE 39.8 billion to LE 92.3 billion in FY15.<sup>13</sup> However, as a percent of GDP the allocation has shown a slight decrease to 3.78 percent in FY15 from 3.83 percent in FY09.

In terms of total government expenditure, the share of education has increased to 12.6 percent in FY15 from 11.3 percent in FY09. In FY15, education was the sector with the third largest share in the budget, preceded by general public services and social protection. Compared to other countries in the region, Egypt ranks on the lower end of spending on education, both as share of total government expenditure and as a share of GDP (Figure 1 and Figure 2) over the period FY05-FY15. It is worth noting that, in this comparison, the Islamic Republic of Iran is the only country with a similar population to Egypt, and although the two rank close in education spending as a share of GDP, the Islamic Republic of Iran ranks much higher as a share of total government spending.

As is common in this sector, the majority of spending goes to the compensation of employees. However, since FY09, the share of compensation of employees in total education spending has grown from 78.7 percent to 83.6 percent in FY15 at the expense of investments in education (which mostly include the construction of new schools) and purchase of goods and services (which cover purchase of school supplies and equipment and maintenance of equipment and school buildings). Both items have each decreased by a little over 2 percentage points in the same period.

The available government expenditure data do not allow for the identification of the amounts spent on primary, preparatory, and secondary education separately, the data available group all expenditures that are in the levels below tertiary education into one figure. As such, the cost, in nominal terms, per student in pre-university education in FY09 was LE 1,408 and has grown to LE 3,125.9 in FY15. The nominal cost per student for university education has reached LE 10,180.1 in FY15 compared to LE 5,057.6 in FY09. In total numbers, spending on pre-university education is more than double the spending on university education. However, only about a tenth of students enrolled in the primary level continue to tertiary education.

# **Figure 1. Middle East and North Africa (MENA) Public Education Spending as a Percentage of GDP (FY05–FY14 averages)**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Djibouti Malta Tunisia Saudi Morocco Syrian Algeria Kuwait Oman Egypt Iran Qatar Bahrain Lebanon<br>Arabia Arab (Islamic<br>Republic Republic<br>of)<br>Percent<br><!-- End of picture text --> _Source:_ UNESCO 2017.

_Note_ : This list excludes the countries in the MENA for which no data was available, notably Iraq, Jordan, Libya and the United Arab Emirates. It is also worth noting that Algeria only had one data point for FY08.

13 These expenditures encompass all levels of education: preprimary, primary, preparatory, secondary (general and vocational), tertiary, assistance services, education at unspecified level, and research and development activities.

10
It is plausible that some of the coefficients may be inconsistent and causality may be confounded in this regression due to potential presence of reverse causality from ratings to some of the explanatory variables. In other words, income per capita or external debt may itself depend on ratings. There are two reasons why this may not present serious difficulties for our purpose. First, this is a cross sectional study and we have deliberately used lagged data for all the independent variables, instead of contemporaneous values. Second, our purpose is to use the regression model as a best linear predictor of ratings, rather than for hypothesis-testing. In a cross section, this method gives reasonably good results.

# Specification 2: Dated model

# Dependent variable as of end-2006; but if the rating was established in year t, then use explanatory variables for year t-1

The regression model in the previous specification assumes that the latest available rating in end2006 reflects the prevailing view of the rating agency, i.e. that the macroeconomic and political situation has not improved (deteriorated) sufficiently to warrant an upgrade (downgrade) between the date the rating was established and end-2006. However, a rating established a few years back may not have changed for other reasons—a likely one being that the country may not have requested or paid for a rating. Since it is not possible to distinguish between the two with the available information, we use what we believe to be a more robust specification. We use lagged “dated” explanatory variables relative to the year in which the latest available rating was established.<sup>14</sup> For example, the latest available rating by S&P for Estonia is for 2004, we use 2003 control variables. For the latest ratings that were established in 2006, we continue to use the 2005 control variables. As before, we exclude outliers to improve the fit of the prediction model. The results are reported in table 4.

The signs of the explanatory variables in table 4 are in the expected direction, and are significant at the 10 percent level or better. The additional variable that is now significant is inflation, with higher inflation being correlated with worse ratings. All these variables together explain about 80 percent of the variation in ratings for the dated regression sample. With such high explanatory power, it is not surprising that the predicted ratings are the same or close to the actual ratings for a large number of countries in the sample (see annex table 1). Below we use this as the benchmark model for predicting ratings for unrated countries.

by the end of 2006. Another outlier is Lebanon, where the rating is affected by a high level of debt, but does not adequately account for large remittances from the Lebanese diaspora (World Bank 2005a, chapter 4).

> 14 The latest rating contains the most valid information about the macroeconomic and political fundamentals of a country in the year it was established. Therefore, the information content in the dated explanatory variable would be the highest.

16
Performance Survey could provide further valuable insights (see, e.g., Hellman and Kaufmann, 2002; Uslaner, 2010).

No matter how good political institutions may be, however, countries will always encounter difficulties in dealing with distributive issues. How one thinks about taxes and inequality reflects ideas of fairness or social justice (Bird et al., 2006). Income inequality is a central social and economic problem in many transition countries. Surprisingly, the link between income inequality and tax morale, tax compliance and tax performance is not well explored. As inequality in the distribution of wealth and income is strongly connected with public views as to how well the fiscal system addresses social objectives with respect to fairness, social justice and redistribution, a higher level of income inequality may lead to lower levels of trust in institutions and eventually to lower tax effort because of widespread tax avoidance and evasion. Highly unequal distributions of income can also lead to low levels of solidarity by the elites toward lower income groups. Furthermore, income inequality may be associated with political instability (Alesina and Perotti, 1996). It may therefore be interesting to explore empirically the link between income inequality and tax morale/tax compliance and tax performance in transition countries (exploring also CEE and FSU countries independently).

In addition, it is important to better understand tax administration‘s behavior, its behavior and its structure in CEE and FSU countries over time. Results in the literature indicate that if the tax administration tries to be honest, fair, informative, and helpful, acting as a _service_ institution and thus treating taxpayers as partners and not inferiors in a hierarchical relationship, tax morale increases and taxpayers have stronger incentives to pay taxes honestly. One could, e.g., conduct surveys with tax administrations to generate further insights.

In summary, the findings clearly indicate the relevance of models of tax compliance that go beyond the conventional economics-of-crime approach and which capture the role of institutions, more in particular, how individuals perceive their governments, in explaining why individuals pay taxes. The report indicates the importance of accountability, democratic governance, efficient, and transparent legal structures and therefore trust within the society to enforce tax compliance and tax morale.

56
_COTE D’IVOIRE: Volatility, Shocks and Growth_

**Figure 8: Cocoa Prices, Production & Yields – Deviations From Trend**

<!-- Start of picture text --> 0 .7<br>C o c o a P r ic e S h o c k s<br>( L o g o f t h e r a t io o f a c t u a l t o t r e n d )<br>0 .5<br>0 .3<br>0 .1<br>- 0 .1<br>- 0 .3<br>- 0 .5<br>0 .2<br>0 .1 8 C o c o a P r o d u c t io n S h o c k s<br>0 .1 6 (L o g o f th e r a ti o o f a c tu a l to tr e n d )<br>0 .1 4<br>0 .1 2<br>0 .1<br>0 .0 8<br>0 .0 6<br>0 .0 4<br>0 .0 2<br>0<br>- 0 .0 2<br>- 0 .0 4<br>- 0 .0 6<br>- 0 .0 8<br>- 0 .1<br>- 0 .1 2<br>- 0 .1 4<br>- 0 .1 6<br>- 0 .1 8<br>- 0 .2<br>- 0 .2 2<br>0 .0 1 5<br>0 . 0 1<br>0 .0 0 5<br>0<br>- 0 .0 0 5<br>- 0 . 0 1<br>- 0 .0 1 5<br>- 0 . 0 2<br>C o c o a Y ie ld S h o c k s<br>- 0 .0 2 5 ( L o g o f t h e r a t i o o f a c t u a l t o t r e n d )<br>- 0 . 0 3<br>- 0 .0 3 5<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->
- 28 -

TOl 2 - Testa of Integration, Co-Interation, and Granger Cuaillty

|Typeof Test|Bolgatanga<br>market|Tehgi_nmarket|
|---|---|---|
|TeSt ofIntc9ration|<br>|<br>|
|**maize**<br>Moizo|-Pu.~~~~~~¶5~~~~p<br>~P<br>ni<br>IIIZ.¶ *(.3.629)<br>zt.1<br>Pm t<br>matv1(<br>Mt3<br>629) X-|P<br>a-0.12 P<br>'t<br>it-I(-3.695)<br>^t<br>mt1(3fl<br>mzt-i|
|Millet|Pit<br>-Mit1<br>(-3-M)|PtIi *t mit.<br>-<br>M.=<br>32<br>t1|
|Sorghum|P9't<br>Pgt-C'0<br>219P<br>~~gc~.¶<br>~<br>353<br>Cg.tj|P<br>-p<br>-<br>.<br>2 961<br>get<br>.1<br>(-2.96) t-I|
|Testof Co-integr.tionb|||
|millet withasite|-s<br>i *nt<br>.<br>(S081.) "ituit<br>mimxt.j<br>6nit.1|-<br>(i9|
|Sorghum withumiC|**a**<br>9-<br>art<br>9mt<br>"gemtI*i(-7.1.)<br>i|9a<br>-<br>0216<br>8int<br>(-3.750)<br>'"-I*|
|Granger<br>Testsof<br>Cauealtity|0.<br>*0.0811 *0.1209<br>(0.14) (1.17)'utigf<br>*0.7106 a,,,<br>-0.487 drought<br>(8.73)<br>t 1 (0.26)<br>0t<br>a<br>065.0<br>.8<br>+O t14I&<br>it<br>(0:17)<br>(9.31)<br>Ut.1<br>- 0.040Nm<br>'0.94Z drought<br>(0.58)<br>t1<br>(0.59)|0 .193+0.529a<br>(O.181) (4.30)<br>+0;477<br>-m0.437drought<br>(6.62)<br>t<br>(0.49)<br>* a<br>.048+0.713<br>u.,<br>t<br>(0.0U) (12.17)<br>(.2<br>I,<br>imml0.521 drmuht<br>(2.2n<br>t'1<br>(0.41)|
||.a<br>0.1116 0.343a,,<br>(0.21)<br>(3.09)|**(0.26<br>+O.U<br>6mt-1<br>9<br>_.20_<br>(3 56)|
||+0.500it<br>w0.7JWdrght<br>(5.55) 9<br>**0**t-<br>.<br>( 39)d<br>t1|+0.561<br>it<br>07-O.drs_mt<br>9tt-1<br>(0.25)|
||*<br>0.097* * 0.7<br>**t**<br>(0.19)<br>(9.06)<br>"|0.028<br>+0.760<br>Mat.<br>(0.05)<br>(11.80)<br>**1**|
||**-** 0.0401IL<br>*0.93B drought<br>(0.16)<br>9't 1(O.60)|0.039<br>.<br>- 0.66J<br>drought<br>(1.06)<br>get-1g0.42g|

Mote: Critical volue are 3.37 for 5 percent **leow** of **sI,niffeanc and** 4.07 for 1 percent. * ODckey-fullor statistic in par nthesis. Reressioncms Wme desgemualn ed dgte.

b Dfekey-Fuller statistic, _aWme_ rm is the residual of millet prices regressed on mite prices, and _#I_ Is tt corresponding term with sorgira.
**<** Table **1 >**

# **Maximum Likelihood Estimates of Loan Demand** and Supply Functions

|Variable|Loan Demand Function|Loan Supply Function|
|---|---|---|
|Constant|2.82|0.55|
||(1.32)|(2.22)|
||0.69<br>(3.21)|0.31<br>(2.03)|
|_RL,_||0.25<br>(2.43)|
|_(RL-RCB),_|_-_ 0.017<br>(-1.97)||
|_(RL- RCD),_||_-_0.80<br>(-1.76)|
|_DEP,_||0.52<br>(4.05)|
|_IP1 _|0.21|0.06|
||<br>(1.83)|(0.83)|
|_DMY,_||0.14<br>(2.99)|
|2|0.00102<br>(2.75)|0.00028<br>(3.08)|

Notes all series are non-seasonally adjusted monthly data and cover the period of January 1993 to May 1998 (65 observations). The data used in this study, with the exception of the interest rates, are available in various issues of the _Monthly Bulletin,_ The Bank of Korea. The weighted average of loan rate and the interest rates on CDs are from an unpublished source available from the Bank of Korea. Asymptotic t-statistics arc provided in parentheses.

In the loan supply equation, both the loan rate _(RL)_ and interest differential _(RL-RCD)_ are significant and have the expected signs, suggesting that the cost of obtaining non-deposit source of external financing (CDs) as well as the loan rate appears important in determining lending decisions.<sup>20</sup> The volume of real bank deposits _(DEP)_ has a highly significant positive coefficient. This result confirms that lending decisions are heavily dependent on bank deposits. But the fact th,at the coefficient of industrial production is insignificant may imply that industrial production is not taken into account in lending in an important way. Interestingly, the dummy variable for the financial crisis

- 20 While I included the market capitalization of the equities listed in Korea stock market in the loan supply equation to capture a potential effect of firm's equity capital position on lending decisions, it turned out that its coefficient was statistically insignificant and didn't have an expected sign. So we excluded it from the supply equation.

24
<!-- Start of picture text --> BUSRO-1000 BUSEIDOHSDOO BS USsh OOD O00 BUSe Ooo<br>400 000<br>340 000<br>300 000<br>a<br>o 2460000<br>=<br>it<br>zg 200000<br>=<br>2 #60000<br>S<br>bo poo<br>50 000<br>i)<br>China China india hidia Russia Russia Brazil Brazil<br>2002 2007 2002 200 2002 2007 2002 2007<br><!-- End of picture text -->
The RBI took these steps despite the fact that consumer price inflation in March (according to figures published in mid-April) was running at 5.91 percent, at the top of the RBI’s 2-6 percent target range and down only slightly from 6.58 percent in February (Figure 9). It did so despite the possibility that CPI inflation might accelerate further, given the impact of the lockdown and other supply-side disruptions on food prices and of exchange rate depreciation on the cost of imports. Thus, the fact that inflation expectations had become better anchored allowed the RBI to temporarily disregard the fact that inflation was already at the top of the Bank’s target range and respond to this exceptional shock.

**<mark>Figure 9: Inflation and Monetary Policy during the Pandemic</mark>**

_Percent Percent_ <!-- Start of picture text --> Repo Rate Reverse Repo Rate<br>7.0 Headline Inflation (Right) Food Inflation (Right) 16.0<br>14.0<br>6.5<br>12.0<br>6.0<br>10.0<br>5.5<br>8.0<br>5.0 6.0<br>4.0<br>4.5<br>2.0<br>4.0<br>0.0<br>3.5<br>-2.0<br>3.0 -4.0<br>Source: RBI, CEIC<br>Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 Jan-19 Feb-19 Mar-19 Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20<br><!-- End of picture text --> Three further literatures speak to the consequences of such actions. One is concerned with how an inflation-targeting central bank should respond to supply shocks, Covid-19-related lockdowns and disruptions to supply chains and production being, in part, a negative supply shock. Monetary policy is an awkward instrument for dealing with the consequences of supply shocks, since it operates mainly on aggregate demand rather than aggregate supply.<sup>44</sup> The dilemma for monetary policy committee members is that raising the policy rate in response to the inflationary consequences of a negative supply shock will only worsen the output shortfall, but reducing rates will only worsen the inflation overshoot.<sup>45</sup> Thus, the standard advice for an inflation-targeting central bank is to cut rates – or at least to refrain from raising them – if the negative supply shock is temporary. If the shock is temporary, there will be higher prices and inflation now but lower prices and less inflation, or even deflation, in the future.

> 44 Other non-monetary policies of central banks designed to buttress the liquidity and stability of specific financial assets and even institutions can be thought of as supply side interventions insofar as they prevent credit-market disruptions from interfering with supply.

> 45 Especially since a classic negative supply shock will not increase the output gap – it will only reduce actual output, since potential output has fallen, while increasing unemployment.

27
23 perspective, with a factor effect of exp(-0.40) = 0.67. This finding highlights the persistent vulnerability of widows as a population subgroup.

However, the opposite is true for the demographic impact of the conflict. A shortage of potential partners continues to limit fertility (with a factor effect of exp(0.98-0.61) = 1.44), but less so than in the immediate post-conflict period. This may be due to the fact that a larger share of sample respondents in the 2005 RDHS belongs to younger birth cohorts which are less severely affected by gender imbalances than in the 2000 RDHS. With respect to other control variables, many trends identified in the short term analysis also hold in the long term.

# **c) The impact of conflict exposure on fertility by age group**

In order to explore the cohort-specific effects of conflict exposure on fertility, the original model is estimated by cohorts. Results for the youngest group of women are displayed in Table 7. Women in this age group surveyed in 2000 were 9-18 years old during the genocide; those surveyed in 2005 were 4-13 years old during the genocide. Very few sample women had given birth to a child before 1994 or were married at that time. Hence, CHILDDEATH, SONDEATH, DAUGHTERDEATH, and WIDOW are not used as proxies for conflict exposure in this age group. Interestingly, the only proxy for conflict exposure with a significant impact on fertility rates in the short term is the death of a sister during the genocide (SISTERDEATH). As argued above, this proxy possibly captures the effect of trauma and stigmatization as a victimized survivor of the genocide. However, this effect does not significantly impact fertility in the long term. Neither before nor after the genocide do sex ratios have a significant impact on the fertility of young women. Possibly, for many women of this age group, the matching of partners in the marriage market is not yet complete and gender imbalances among young women are not as severe compared to older birth cohorts.

Results show the opposite effect for women in the middle age grouping (of age 25-34 years during the survey collection, Table 8): On the one hand, a larger share of these women were likely exposed to conflict, while on the other hand, conflict exposure had a strong effect on their fertility in the post-conflict period. There is evidence for the replacing-the-lost hypothesis: Women of middle age who lost a child in 1994 (CHILDDEATH) give birth to a significantly larger number of children compared to other women in both 2000 and 2005, holding other factors constant. Widowhood significantly reduces the fertility of middle aged women compared to other women in this age group. Being a widow reduces fertility in the short term in both the pre- and post-genocide period. The fertility rate of widows in 2005
Table 1: The response of private investment to government investment.

||**All IDA Countrie**|**s**|**IDA Cou**|**ntries in Sub-Saha**|**ran Africa**|
|---|---|---|---|---|---|
|||Control for<br>||||
||Excluding|Lagged||Excluding|Control for Lagged|
||All Observations<br> <br>Influential<br>Observations|<br>Dependent<br>Variable|All Observations|<br>Influential<br>Observations|<br>Dependent<br>Variable|
|_Panel A: Ordinary Least Squares(De_<br>|_pendent Variable is Change in Private_<br><br>|_Investment)_<br>||||
|Change in Government Investment|-0.0858<br>-0.113|-0.0780|-0.0900|-0.108|-0.0778|
||(0.125)<br>(0.116)|(0.126)|(0.167)|(0.148)|(0.166)|
|_Panel B: Two-Stage Least Squares(D_|_ependent Variable is Change in Priva_|_te Investment)_||||
|Change in Government Investment|1.881*<br>2.298*|1.892*|1.891|1.668|1.939|
||(1.066)<br>(1.297)|(1.059)|(1.526)|(1.204)|(1.557)|
|_Panel C: First-Stage Regression(Dep_|_endent Variable is Change in Govern_|_ment Investment)_||||
|Change in Predicted Disbursements|0.290***<br>0.270***|0.290***|0.248**|0.326***|0.246**|
||(0.0778)<br>(0.0750)|(0.0775)|(0.0918)|(0.0732)|(0.0915)|
|First-Stage F-Statistic|13.94<br>12.98|14.00|7.30|19.88|7.24|
|Weak Instrument Consistent 95%||||||
|Confidence Interval for|[0.583,4.863]<br>[0.772,6.275]|[0.594,4.878]|[0.137,9.526]|[0.292,5.219]|[0.180,9.854]|
|Number of Observations<br>Notes: This table reports the results<br>constant local currency units and are<br>Africa (second three columns). Pane<br>regressions. Weak instrument-consi<br>loans from official creditors are used<br>clustered at the country level are ind|916<br>908<br>from a series of regressions of chan<br>scaled by lagged GDP. The sample c<br>l A reports OLS estimates, Panel B re<br>stent confidence intervals are based<br>as an instrument for changes in gov<br>icated in parentheses. * (**) (***) i|916<br>ges in private investm<br>onsists of IDA-eligible<br>ports 2SLS estimates,<br>on the Moreira Likeli<br>ernment investment i<br>ndicates significance a|611<br>ent on changes in publi<br>countries (first three c<br>and Panel C reports the<br>hood Ratio statistic. Ch<br>n Panel B. Heterosked<br>t the 10 (5) (1) percent|607<br>c investment. All<br>olumns) and IDA-e<br>corresponding fir<br>anges in predicted<br>asticity-consistent<br>level.|611<br>changes are in<br>ligible countries in<br>st-stage<br>disbursements on<br>standard errors|

19