rafmacalaba/datause-encoder-data huggingface dataset:
_c_ . It bears noting that the ratios are expressed in value terms. Therefore, a household that sells a given quantity the food at harvest time and buys the exact same quantity later at a higher price is considered a net buyer, because their expenditures on the food item exceed their receipts from sales of the item.

The basic model involves four simplifying assumptions that should be highlighted. First, it only captures the short-run impact, and does not take into account the possibility of consumers shifting their consumption patterns in response to higher prices. Normally, one would expect that over time consumers would substitute away from the foods whose relative prices are increasing. As a result it overestimates the negative impact of higher prices on net buyers of a commodity. While common sense and economic theory dictate that consumers will shift to less expensive staples, the magnitude of that substitution is a matter of some debate. In the short run, substitution will be constrained by available supplies from that year’s harvest. Thus substitution will push up prices for these other staples, as appears to be the case in Uganda. Recent econometric estimates by Ulimwengu and Ramadan (2009), using the same 2005–06 UNHS data, indicate a remarkably low degree of substitutability between staples for consumers. They estimate Hicksian (compensated) cross-price elasticities of 0.05 and 0.04 for the food groups “cereals” and “roots and plantains.” This implies that a 10 percent increase in the price of cereals would increase demand for roots and plantains by only 0.5 percent if the consumers’ real incomes were not affected by the cereal price increase. Similarly, a 10 percent increase in the price of roots and plantains would increase cereal demand by only 0.4 percent. Of course, real incomes are affected by the staple food price increases, which is why Ulimwengu and Ramadan’s (2009) estimated Marshallian (uncompensated) cross-price elasticities for these food groups are negative, indicating that an increase in cereals prices would lead to a small _reduction_ in the demand for roots and plantains, and vice versa.

Second, the analysis assumes that the increase in the producer price is the same as the increase in the consumer price, i.e., sellers capture the full benefits of the price increase. In practice this margin could be diminished by higher margins for middlemen and/or higher production costs, such as the increased costs of transportation or fertilizer. This assumption likely overestimates the positive impact of higher food prices on net sellers of a commodity. This is especially germane in Uganda, where much of the staple food price increase appears to have been driven by increases in transportation costs arising from disruptions in the 7
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Côte d’Ivoire could take many decades to reach the infrastructure targets. However, if inefficiencies were addressed, the goals could be reached within 20 years, even without increased spending.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure across almost all African countries, including Côte d’Ivoire. The results have been presented in reports covering different sectors of infrastructure (ICT, irrigation, power, transport, water and sanitation) and different policy areas (including investment needs, fiscal costs, and sector performance).

This report presents the key AICD findings for Côte d’Ivoire, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. A social and economic crisis in Côte d’Ivoire has crippled its growth trajectory, which had been that of a middle-income country. It will therefore be compared to low-income countries (fragile and non-fragile groups) and middle-income countries, as well as immediate regional neighbors in West Africa.

The study presented several methodological issues. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effects of short-term fluctuations. Second, the indicators and analyses were standardized to ensure that comparisons across countries were consistent. As a result, some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the five years from 2003 to 2007, Côte d’Ivoire’s economy grew at an average annual rate of 1.5 percent, considerably lower than the 2.1 percent recorded in the previous decade. Infrastructure contributed 1.3 percentage points to per capita economic growth over this period. This contribution came mainly from the ICT revolution, while deficient power infrastructure held growth back by 0.3 percentage points. Simulations suggest that if Côte d’Ivoire’s infrastructure could be improved to the level of the African leader—Mauritius—annual per capita growth rateswould increase by 1 percent, from 1.3 to 2.3 percent (figure 1).

2
# **Table 1. Major Socioeconomic and Demographic Variables**

|Variable||Mean|Std|||
|---|---|---|---|---|---|
|Name|Definition|Value|Dev.|Min|Max|
|WaterSupp|ly:|||||
|Cityws|1=the household is connected to city water<br>supply system; 0=otherwise|0.908|0.277|0|1|
|Satisfy|1= the respondent is satisfied with current city<br>watersupply service; 0=otherwise|0.231|0.422|0|1|
|Cwp|Current waterprice (yuan/ton)|2.18|0.25|1.80|2.50|
|Avemwu|Averagemonthly wateruse (ton)|6.18|8.26|1|250|
|Personal In|formation:|||||
|Male|1=male; 0=female|0.70|0.35|0|1|
|Age|age of the respondent|47.97|9.61|18|84|
|Edu|education<br>level<br>of<br>the<br>respondent<br>(1=elementary school or less; 2=middle school;<br>3=high school; 4=vocational school; 5=college;<br>6=bachelorand above)|2.36|1.03|1|6|
|Urban|Householdlocation(1=urban; 0=suburban)|0.503|0.5|0|1|
|Income|Householdmonthlyincome (yuan)|1211.1|697.2|132|2676|
|Questionna|ireTreatment<br> <br> <br> <br>|||||
|Wwqtype|1=WTP<br>questions<br>for<br>water<br>service<br>improvement are asked with a reminder that an<br>additional wastewater treatment fee also needs<br>to be paid separately; 0=without areminder|0.446|0.497|0|1|
|order1|1=WTP questions in an ascending order of<br>price; 0=otherwise|0.238|0.426|0|1|
|order2|1=WTP questions in a descending order of<br>price; 0=otherwise.|0.292|0.455|0|1|
|order3|1=WTP questions start with the most likely<br>prices and the rest in an ascending order of<br>price; 0=otherwise|0.214|0.41|0|1|
|order4|1=WTP questions start with the most likely<br>prices and the rest in a descending order of<br>price; 0=otherwise|0.256|0.44|0|1|
|ordernorm|Order3 + order4; 1=WTP questions starts with<br>themostlikely prices; 0=otherwise|0.47|0.499|0|1|
<!-- Start of picture text --> Southern Europe and Ireland<br>aso |<br>160 eer __>= a ———_—_———-<br>140 Jl eee<br>120 eS a<br>100<br>80 —= oe Ee<br>60)<br>my<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>—Greece ——treland ——Italy ——Portugal ——Spain<br><!-- End of picture text -->
# **3.1 Education Structure in Brazil**

Brazil’s basic education system is divided into: pre-school; primary school, an eight-year cycle (ensino fundamental _)_ ; and secondary, a three-year intermediate cycle (ensino médio _)_ . Pre-school education includes the social development of children age 6 and younger. Primary encompasses children between the ages of 7 and 14 and is divided into two stages (grades 1-4 and 5-8), of primary school and at the end of secondary school. Secondary school is comprised of grades 9-11 and is intended for students aged 15-17 (Table 3.1). National testing is conducted at the end of each stage.

|**Table 3.1: Structure of the Brazilian Basic Education System**|
|---|

|<br>**Age **|<br>**Grade**|<br>**Brazilian Structure**<br>**Paper Terminology**|
|---|---|---|
|7|1st.|_Ensino Fundamental_<br>Primary|
|8|2nd.|(Series 1<sup>st</sup>-4<sup>th</sup>)|
|9|3rd.||
|10|4th.||
|11|5th.|_Ensino Fundamental_<br>Lower Secondary|
|12|6th.|(Series 5<sup>th</sup>-8<sup>th</sup>)|
|13|7th.||
|14|8th.||
|15|1<sup>st</sup>|_Ensino Médio_<br>Upper Secondary|
|16|2nd.||
|17|3rd.||
|_Source:_Alber|to Rodriguez (2002).|Shading indicates grade at which SAEB is applied.|

State systems in Brazil enroll 47 percent of grade 1-4 students, while municipal systems enroll 43 percent (Alberto Rodríguez 2002). The picture for grade 5-8 is quite different, with state systems accounting for nearly 70 percent of enrollments and municipal systems accounting for 20 percent. Secondary education is also mainly taken care of by the state, 72 percent of enrollments are in state schools, 20 percent in private schools, 6 percent in municipality schools, and 2 percent of enrollment is taken care of by the federal government.

Public school’s primary education is heavily dependent on the delivery of the service by municipalities, which create a challenge for the state since the capacity is not the same in relatively richer and poorer municipalities. To address this problem the state government has created 12 regional management offices in Paraíba. These offices plan, coordinate, and monitor the state’s policies and investments and, furthermore, play a core role in coordinating efforts between the state and municipalities in the area of education, as the municipalities are essentially autonomous.

16
**Table 1**

<!-- Start of picture text --> Lag-0 and Lag-1 Momentum Trading<br>All Sample Non-Crisis Crisis<br>Whole-Fund Momentum<br>LOM 2.36*** 0.98*** 5.13***<br>T-statistic 5.63 3.19 4.55<br>Observations 4924 3288 1636<br>LIM 0.20 0.25** 0.11<br>T-statistic 1.53 2.35 0.40<br>Observations 4852 3214 1638<br>manager-only MoNtu<br>LOM 0.8 6 *** 0.29 2.01***<br>T-statistic 2.90 1.27 2.68<br>Observations 4929 3287 1642<br>LIM 0.16 0.18** 0.11<br>T-statistic 1.58 2.11 0.61<br>Observations 4849 3210 1639<br>Investor-Only Momentum<br>LOM 1.70*** 0.81*** 3.46***<br>T-statistic 6.12 3.45 4.09<br>Observations 4954 3292 1662<br>LIM 0.08 0.05 0.16<br>T-statistic 1.06 0.75 0.75<br>Observations 4854 3221 1633<br>LOM is the point estimate for the mean lag-0 momentum trading measure. LIM is the point estimate for the mean lag-1 momentum<br>trading measure (measured from return over the previous month). Whole-Fund momentum tests whether the mean of (AQj/j i, ,k is<br><!-- End of picture text --> zero, per equation **(1).** Manager-Only momentum controls for investor redemption effects as in equation (2). Investor-Only momentum reflects only investor redemption effects as in equation **(3). All** t-statistics are corrected for heteroskedasticity across fuinds. Full sample: quarterly data from April **1993** to January **1999.** The crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the sample. The total of roughly 4400 observations is **13** funds times an average of about **35** stocks per fund, times an average of about **10** quarters of available data per fund. For robustness, results in each cell are based only on observations within three standard deviations of the mean.

- Statistically Significant **at** the 10-percent level

- ****** Statistically Significant at the 5-percent level

****** Statistically Significant at the 1-percent level

**26**
50 Table 3: Tests of the parallel trend assumption and reverse causality

||T<br>Random<br>mo|ests of the p<br>trend<br>del|arallel trend<br>Differen<br>mo|assumption<br>tial trend<br>del|<br>|Tests o<br> <sup>Lead paym</sup>|f reverse ca<br><sup>ent dummie</sup>|usality<br><sup>s test on the</sup>|
|---|---|---|---|---|---|---|---|---|
||Generalized<br>test on|Hausman<br>eqn(8)<br>|<sup>Non-linear</sup><br>test on|<sup>restriction</sup><br>eqn(12)||pr|eferred mod|el|
||||||Preferred|||p-value|
|Dependent variable|chi-square|p-value|_F_|p-value|model|_FFSi,t+1_|_PBPi,t+1_|(joint)|
|Inpatient admissions|0.14|0.934|0.30|0.746|DID|0.10|0.32|0.625|
|Hosp discharges - circulatory|3.68|0.159|2.20|0.132|DID|0.74|61.01|0.366|
|Hosp discharges - cerebrovas|5.98**|0.050|0.19|0.827|RT|-1.47|11.40|0.426|
|Hosp discharges - respiratory|4.29|0.117|0.05|0.948|DID|73.98|79.77|0.165|
|Hosp discharges - digestive|4.94*|0.084|0.07|0.933|RT|41.67|78.58|0.155|
|Hosp discharges - musculo|1.00|0.608|0.37|0.696|DID|21.31|43.68|0.104|
|Length of stay|0.13|0.935|0.26|0.776|DID|0.09|-0.09|0.478|
|Hospital beds|0.99|0.611|3.37**|0.050|DT|0.03|-0.07|0.858|
|Bed occupancyrate|4.82*|0.090|1.31|0.290|RT|1.51|0.90|0.167|
|Health expenditures - Total|0.13|0.938|0.16|0.852|DID|38.86|-8.56|0.155|
|Health expenditures - Public|3.81|0.149|0.09|0.913|DID|28.18|-1.50|0.224|
|Health expenditures - Private|1.76|0.415|0.02|0.978|DID|10.68|-7.05|0.393|
|SDR ischemic heart disease|6.41**|0.040|0.42|0.659|RT|-2.80|-4.05|0.473|
|SDR diabetes|4.98*|0.083|0.32|0.730|RT|0.96*|0.59*|0.063|
|SDR cerebrovascular diseases|0.82|0.662|1.60|0.223|DID|-8.99|-1.94|0.211|
|SDR female breast cancer|2.46|0.292|0.00|1.000|DID|0.04|-0.10|0.972|
|SDR appendicitis|2.78|0.249|0.24|0.790|DID|-0.09|0.04|0.254|
|SDR hernia & intestinal|2.19|0.334|1.19|0.323|DID|-0.36|-0.19|0.141|
|SDR adverse effects|1.60|0.450|1.70|0.215|DID|0.06|0.08|0.516|

_Notes:_ The symbols * and ** indicate rejection of the null hypothesis of the corresponding parallel trend test or reverse causality test at the 10% and 5% levels, respectively (p-values from two-sided _t_ -tests with cluster-robust standard errors). The last column of the parallel trend tests shows the preferred model—difference-in-differences (DID), random trend (RT) or differential trend (DT)—implied by the test results for the corresponding dependent variable. Results of the reverse causality tests refer to the individual coefficients of the lead fee-for-service (FFS) and patient-based (PBP) payment methods dummies (respectively) in the preferred model, and p-values from twosided _t_ -tests (with cluster-robust standard errors) for the null hypothesis of joint insignificance of these two variables.
- 10 -

specific dummy variables. The model is also estimated separately for each sector in order to allow the individual sectors to have different cost structures.<sup>5</sup> Three infrastructure services are examined: electricity, water supply and transport. For power, the level of service quality is measured by the number of days with power outages per year ( _ZE_ ). Similarly, the quality of water services is measured by the number of days without sufficient water ( _ZW_ ). The quality of transport infrastructure service is difficult to measure, because it is affected by multiple factors with various transport modes involved. One of the good proxies that are available in the BEEPS data is firm inventory, i.e., the number of days measured by days of production or sales ( _ZT_ ). In the conventional economic order quantity model, the optimal firm inventory increases with the cost of purchasing goods, including transport and handling costs. Our paper is supportive of this (see Iimi et al. (2014)).

The cost function is estimated by two estimation techniques: seemingly unrelated regression (SUR) and stochastic-frontier analysis (SFA). To have a well-behaved cost function, the following symmetry and homogeneity restrictions are imposed: These are primarily theoretical necessities. Particularly, homogeneity of degree one in the factor prices seems essential to avoid unusual estimation results, as discussed in the traditional industrial organization literature (e.g., Greene, 1997).

In addition, by Shephard’s lemma, the following factor share equations can be derived from Equation (2):

> 5 In reality, it is reasonable to expect that different industries could be affected by infrastructure in different ways. For instance, one might think that manufacturers would necessitate more stable electricity supply than construction businesses. Quality water supply may be of particular importance for agrobusiness or restaurants.
The Net Interest Margin measures the gap between what the banks pay the providers of funds and what they get from firms and other users of bank credit and it equals interest income minus interest expense divided by interest bearing assets, averaged over the banks in each country. It is frequently used to measure efficiency despite the fact that differences in net margins may reflect differences in bank activities rather than differences in efficiency or competition. Net Interest Margin tends to decline with a country’s income, suggesting bank efficiency improves with development (Figure 3a). Unlike measures of depth, dispersion in efficiency figures tends to be higher at the lower end of the income distribution (Figure 3b and Table 1).

While measures of financial depth and margins are available for a large set of countries, measures of the reach of the sector have been much more difficult to obtain across countries. Until recently, we did not have answers to simple questions like what proportion of the population has a bank account or loan. Beck, Demirguc-Kunt and Martinez Peria (2005) is the first study to develop cross-country measures of access to and use of banking services. One of their indicators, Number of loans per capita, captures the use of credit services, with higher numbers indicating mode widespread use. Figure 4a shows that use of bank credit increases drastically with income. But differences within income groups are also large: while there are 770 bank loans per 1000 people in Poland, there are only 94 in Venezuela (Figure 4b and Table 1). Further research in this area is moving in the direction of developing better indicators of access to different financial services, using surveys both at the financial institution and household level.

9
|**Table 3.10: State Spendi**<br>**Education, 1995 and 1**<br>**(percentage of GDP**<br>|**ng on**<br>**999**<br>**)**<br>|
|---|---|
|1995|1999|
|**Alagoas**<br>3.4|3.6|
|**Bahia**<br>2.6|3.3|
|**Ceará**<br>2.7|3.2|
|**Maranhão**<br>5.0|4.5|
|**Paraíba**<br>3.4|5.3|
|**Pernambuco**<br>1.8|2.0|
|**Piauí**<br>4.8|5.9|
|**Rio Grande do Norte**<br>3.4|5.4|
|**Sergipe**<br>4.1|4.1|
|_Source:_Ministério da Fazenda/ IBGE.||

The state government has shown its commitment to improving education in Paraíba. Education spending there has been steadily increasing and so has its share of GDP (Table 3.10). Education’s share of total GDP increased 1.9 percentage points to 5.3 percent during 1995–99. In 2000 Paraíba spent R$459 million on education, which was 24 percent of total spending that year (Table 3.11). During 1995-2000, the annual educational spending grew in real terms 11.3 percent, nearly the double of the national average (of this amount, 70 percent was allocated to basic education, 6.2 percent to secondary education, 3.4 percent to higher education, 1.7 percent to special education, and 0.2 percent to preschool).

**<u>Table 3.11:  Education Expenditures, Shares of Total, and Growth</u>**

||**Expen**<br>**(thousan**|**ditures**<br>**ds of R$)**|**Education**<br>**Total Exp**|**al Share of**<br>**enditures**|**Annual Growth Rate**<br>**(percent)**|
|---|---|---|---|---|---|
||**1995**|**2000**|**1995**|**2000**||
|**Alagoas**|203,156|260,971|0.20|0.17|5.14|
|**Bahía**|1,020,096|1,318,855|0.17|0.21|5.27|
|**Ceará**|509,919|763,584|0.17|0.19|8.41|
|**Maranhão**|380,867|301,164|0.25|0.14|-4.59|
|**Paraíba**|268,948|459,271|0.20|0.24|11.30|
|**Pernambuco**|471,100|1,357,138|0.15|0.14|23.57|
|**Piauí**|226,797|413,795|0.18|0.09|12.78|
|**Rio Grande do Norte**|242,301|471,626|0.19|0.24|14.25|
|**Brazil**|21,423,146|27,962,191|0.17|0.18|5.47|

30
Table 10: Over-reporting less: Ratchet effect

||**Under-repor**<br>|**ting**: 1(Reporte<br>|d growth<Pred<br>|icted growth)<br>|**Fulfilling**<br>|**target**: 1(Re<br>|ported growth><br>|Target)<br>|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Whole<br>Sample|(2)<br>2×BW|(3)<br>1.5×BW|(4)<br>1×BW|(5)<br>Whole<br>Sample|(6)<br>2×BW|(7)<br>1.5×BW|(8)<br>1×BW|
|**Panel A: All city secretarie**|**s**<br>||||||||
|1(Predicted GDP >Target)|0.185<sup>***</sup>|0.187<sup>***</sup>|0.186<sup>***</sup>|0.177<sup>***</sup>|0.426<sup>***</sup>|0.193<sup>***</sup>|0.122<sup>**</sup>|0.095<sup>*</sup>|
|**(i.e. Solid performer)**|(0.029)|(0.037)|(0.040)|(0.046)|(0.029)|(0.042)|(0.048)|(0.057)|
|# of Observations|2895|2605|2335|1797|2895|2043|1633|1115|
|# of Cities|281|280|279|278|281|279|278|277|
|Adj R<sup>2</sup>|0.15|0.14|0.15|0.17|0.57|0.47|0.42|0.30|
|**Panel B: City secretaries w**<br>|**ith tenure leng**<br>|**th less than 2**<br>|**years (i.e., less**<br>|**likely to influenc**<br>|**e target setting)**<br>||||
|1(Predicted GDP >Target)|0.159<sup>***</sup>|0.157<sup>***</sup>|0.134<sup>**</sup>|0.150<sup>**</sup>|0.458<sup>***</sup>|0.257<sup>***</sup>|0.202<sup>***</sup>|0.103|
||(0.039)|(0.049)|(0.055)|(0.062)|(0.034)|(0.048)|(0.055)|(0.065)|
|#of Observations|1437|1226|1057|800|1437|1133|963|685|
|#of Cities|281|279|275|269|281|278|273|261|
|Adj R<sup>2</sup>|0.18|0.18|0.20|0.21|0.59|0.51|0.48|0.39|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes_ : This table presents the ratchet effect of GDP manipulation using a regression discontinuity design. The running variable, i.e., the difference between the predicted real GDP growth and the target growth, is interacted with the dummy variable of interest to allow for differential slopes across the cutoff. The optimal bandwidth is chosen according to Calonico, Cattaneo, and Titiunik (2014). City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

48