rafmacalaba/datause-encoder-data huggingface dataset:
sector strategy and a road social contract in consultation with stakeholders that will guide expenditures and provide an agreed framework for measuring successful delivery. In Chile, the Ministry of Public Works is moving towards an in integrated planning process which will use a supply-chain approach to ensure investments are made based on multimodal analysis using improved economic and engineering assessment.

# **Box 9: EITI**

<mark>The Extractive Industries Transparency Initiative is a global program aimed at improving transparency and accountability in resource rich countries. The heart of the program is publication and verification of payments made to governments by extractive industries. Over the 2003-2006 period, 25 countries committed to implement the EITI, and the Bank was providing assistance in more than half of these countries in 2006. Multi-stakeholder committees to oversee EITI implementation had been set up in 12 countries and auditors had been appointed in 8 countries. The World Bank administers the EITI MultiDonor Trust Fund, which is used to provide technical and financial assistance to countries implementing the program (World Bank, 2006).</mark> Demand estimation will also improve the economic return to projects (World Bank, 2006b). This involves not only improved economic analysis of projects, but also better cost forecasting. Predicted outcomes should be compared with predicted and actual outcomes of a relevant reference class of past projects to determine their likely accuracy, and forecasts should be published, publicly debated, and subject to independent review (Kenny, 2007).

There is a further need to improve expenditure tracking through levels of government. In some cases, infrastructure services involve multiple levels of government simultaneously, and this can create particularly complex governance challenges. Rural roads is one such case (see Box 10). Tanzania’s Pro Poor Expenditure Tracking Survey in 2001 covered four sectors including water and rural roads. The Tanzania PETS found significant leakage. One district had recorded only 10 percent of receipts from central government for road funds over 1999-2000, for example –this encouraged the Treasury to advertise all transfers from central to local government in the media (Sundet, 2004).

The response to expenditure tracking analysis can be improved if civil society is actively engaged. For example, in South Africa the Public Service Accountability Monitor based at Rhodes University provides a database of information on budget allocations and resources available to government departments as well as plans for their use, progress reports and recommendations made by oversight bodies (Sohail and Cavill, 2007).

19
# **II. De facto regime classification: Updating**

In Levy-Yeyati and Sturzenegger (2001) we introduced a de facto classification of exchange rates that relied on clustering country-year observations on the basis of three classifying variables: the movements of the nominal exchange rate within each year, the movements in central bank reserves (intended to capture interventions in exchange rate markets) and changes in the rate of change of the exchange rate (to capture crawling-peg regimes).<sup>9</sup> The use of reserves changes distinguished our classification from later attempts at classifying exchange rate regimes that relied solely on exchange rate volatility,<sup>10</sup> and was critical to characterize exchange rate _policy_ –as opposed to exchange rate volatility. It was this measure of foreign exchange intervention that allowed us to tell whether a stable exchange rate was the result of an active policy aimed at limiting exchange rate volatility (as is often assumed), or just the reflection of a stable environment in the context of a flexible exchange rate that does not impose any constraint on macroeconomic policy. In turn, the direction of the intervention will be the key variable to identify fear of floating from fear of appreciation in the finer regime classification that we propose here.

Central Bank interventions are notoriously difficult to measure and they usually differ from a simple measure of reserve variation. To approximate as closely as possible the intervention impact of changes in reserves, we subtract government deposits at the central bank from the Central Bank’s net foreign assets.<sup>11</sup> More specifically, we define net reserves in dollars as:

> 9 The methodology classifies the country year data by the k-means algorithm, through a two step procedure with five groupings. See Levy Yeyati and Sturzenegger (2004a, 2004b, 2005) for further reference.

> 10 See, among others, Reinhart and Rogoff (2003) and Shambaugh (2004).

> 11 Oil producing countries and countries with important privatization programs are examples of cases where the latter correction matters. Calvo and Reinhart (2000) indicate other reasons (hidden foreign exchange transactions, use of credit lines, derivative transactions, or issuance of debt in foreign currency) that make it difficult to compute the real movement in reserves. To these one could add coordinated intervention by other central banks (though this should be limited to G-3 economies) and the measurement error introduced by the fact that all accounts are transformed to dollar units. If the Central Bank holds a portfolio of assets with several currencies, changes in the parities between the reserve currencies can be mistaken for foreign exchange interventions. We believe this measurement error problem should not be significant as most of the reserves are held in dollar-denominated assets.

6
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<mark>health facilities and childcare where needed. Future research should attempt to identify how a refugee</mark> <mark>shock that translates into a labor market shock affects childcare and the intrahousehold division of labor.</mark>

# **<mark>References</mark>**

<mark>Akresh, Richard, and Bhalotra, Sonia, Leone, Marinella.2018. “First and Second Generation Impacts of the Biafran War”.2018.</mark> _<mark>Households in Conflict Network Working Paper 254</mark>_ <mark>Almond, Douglas. 2006. "Is the 1918 Influenza pandemic over? Long-term effects of in utero Influenza exposure in the post-1940 US population."</mark> _<mark>Journal of Political Economy</mark>_ <mark>114(4): 672-712.</mark> <mark>Almond, Douglas, and Janet Currie. 2011. “Human Capital Development before Age Five”. In</mark> _<mark>Handbook of Labor Economics</mark>_ <mark>4:1315-1486</mark> <mark>Almond, Douglas, Kenneth Y. Chay, and David S. Lee. 2005. “The costs of low birth weight.”</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>120 (3): 1031-1083.</mark> <mark>Almond, Douglas, Lena Edlund, Hongbi Li, and Junsen Zhang. 2010. “Long-term effects of early-life development: Evidence from the 1959 to 1961 china famine.” In</mark> _<mark>The economic consequences of demographic change in East Asia</mark>_ <mark>,</mark> _<mark>NBER-EASE</mark>_ <mark>19: 321–345.</mark> <mark>Almond, Douglas, and Bhashkar Mazumder. 2011. “Health capital and the prenatal environment: The effect of ramadan observance during pregnancy.”</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>3 (4</mark> _<mark>):</mark>_ <mark>56-85.</mark> <mark>Alderman, Harold, John Hoddinott, and Bill Kinsey. 2006. “Long term consequences of early childhood malnutrition.”</mark> _<mark>Oxford Economic Papers</mark>_ <mark>58 (3): 450-474.</mark> <mark>Alix-Garcia, Jennifer, and David Saah. 2010. “The Effect of Refugee Inflows on Host Communities: Evidence from Tanzania.”</mark> _<mark>World Bank Economic Review</mark>_ <mark>24 (1): 148–70.</mark> <mark>Alvi, Eskander and Seife Dendir.2011. “Sibling differences in school attendance and child labour in Ethiopia”.</mark> _<mark>Oxford Development Studies</mark>_ <mark>39: 285-313</mark> <mark>Baez, Javier E. 2011. "Civil Wars beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees."</mark> _<mark>Journal of Development Economics</mark>_ <mark>96 (2): 391-408.</mark> <mark>Baum II, Charles L. 2003. “Does Early Maternal Employment Harm Child Development? An Analysis of the Potential Benefits of Leave Taking”.</mark> _<mark>Journal of Labor Economics</mark>_ <mark>21(2) 409-448</mark> <mark>Belsky, Jay and Eggebeen D. 1991. “Early and Extensive Maternal Employment and Young Children’s Socio-emotional Development: Children of the National Longitudinal Survey of Youth.”</mark> _<mark>Journal of Marriage and the Family</mark>_ <mark>53:1083-98.</mark> 23
two-thirds can be explained by the change in diets. As China’s population growth is slowing and its total population is projected to peak around the year 2025 (at a level about 2.3 percent higher than in 2014) (FAOSTAT), the primary driver of food consumption increases is likely to be change in diet, and therefore change in _per capita_ consumption.

# **_Income-Consumption Relationship: Regression Analyses_**

While we can observe the rapid growth in China’s consumption of food over the period since 1980, this gives us little insight into the way this growth is likely to play out in the future. To gain some insights into this, we turned to econometric analysis using a large sample of countries. This allows us to view a much larger range of real incomes and to obtain a better idea of the extent to which the growth of food consumption in cereal equivalents begins to decelerate. This relationship includes most importantly the effects of income growth on the demand for basic food staples and for foods with relatively high income elasticities but also other influences on demand such as changes in the rate of assistance to agriculture with income growth (Anderson, 1995).

We estimate the CE consumption-income relationship using the functional form used in Rask and Rask (2004, 2011). Specifically, _y = ƒ_ ( _x_ ) = _A1 – A2_ e<sup>−kx</sup> , _ƒ' ˃_ 0, ƒ''˂0 (1) where y is CE consumption per capita and _x_ is PPP GDP per capita in 2005 constant prices. As _ƒ' ˃_ 0, ƒ''˂0, this functional form captures the observed pattern of the change in CE consumption, which rises more rapidly at early stages of development and tapers off at higher levels of income. It implies that, as incomes continue to increase, consumption asymptotically approaches a limit given by A1. Historical figures for CE consumption per capita are calculated using food supply, food demand and population data extracted from FAOSTAT. The GDP data at PPP are obtained 13
RCS converted these national poverty lines to a common currency using the household consumption PPPs derived from the 2005 round of the _International Comparison Program_ (ICP) (World Bank, 2008a,b). We use the same PPPs to convert the international line back to local currency for measuring poverty. The 2005 ICP is the most complete and thorough assessment to date of how the cost of living varies across countries. The ICP collected primary data on the prices for 600-1000 (depending on the region) goods and services grouped under 155 ―basic headings‖ deemed to be comparable across 146 countries. The prices were obtained from a large sample of outlets in each country. The price surveys were done by the government statistics offices in each country, under supervision from regional authorities.

While these are clear improvements, the 2005 PPPs still have some limitations.<sup>8</sup> There is a problem of ―urban bias‖ in the ICP price surveys for some counties in that it appears that the surveys were largely confined to urban areas.<sup>9</sup> Based on ICP sampling information we treat the 2005 consumption PPPs as urban PPPs for Argentina, Brazil, Bolivia, Cambodia, Chile, China, Colombia, Ecuador, Pakistan, Peru, Thailand and Uruguay. We then use existing differentials in urban-rural poverty lines at country level for these countries (from Ravallion et al., 2007) to correct the national PPP.

As was argued in Ravallion et al. (1991), a further concern is that the weights attached to different commodities in the conventional PPP rate may not be appropriate for the poor. Deaton and Dupriez (2008) have estimated ―PPPs for the poor‖ for a subset of countries with the required data; the results do not suggest that the implied re-weighting has much impact on the consumption PPP, as we show in Chen and Ravallion (2010).

Figure 1 plots the poverty lines for developing countries compiled by RCS against log household consumption per capita from national accounts, also at 2005 PPP; there are 75 countries with complete data. The Figure also gives a nonparametric regression of the national poverty lines against log mean consumption. Comparing expected values conditional on mean consumption (as estimated by the nonparametric regression in Figure 1), the range is from $1.25 to $30 per day. The mean line for the poorest 15 countries in terms of consumption per capita is $1.25 while the mean for the richest 15 is $25 a day.

> 8 For a fuller discussion of these issues see Deaton and Heston (2010) and Ravallion (2010).

> 9 The greatest bias is probably in the ICP survey for China, which was confined to 11 cities. Although the survey included some surrounding rural areas of these cities, it clearly cannot be considered representative of rural China; evidence on this point is provided by Chen and Ravallion (2008b) which discusses our corrective.

6
price. As described above, we observe votes from our sample of leaders, whose job is to represent their village, and a subsample of individuals within the village, who vote their own preferences. We use responses to the referendum to estimate each community’s collective minimum willingness to accept payment for enrollment, based on leaders’ responses, and the payment that would induce the median household member to vote yes, based on the household data.

# 4.1 The question

The following question, based on standard stated preference methodology (Phaneuf & Requate 2017), was presented to all of the leaders and households in our sample: Suppose that your ejido/community were voting another time to decide if they wanted to participate in the program. The decision would be taken according to the normal voting rules of your village. If the community were to receive an amount of [X] per hectare, equivalent to [X x number of hectares in total], would you vote in favor of or against participating?

Here [X] refers to an amount between 80 and 770 pesos (US $4 and $41). Values offered in the referendum question were randomized, with a higher proportion of values offered below the actual payment received (or that would have been received in the case of the non-beneficiaries). This distribution was chosen because we assumed that true opportunity costs were likely to be less than the payments, given that all surveyed communities had already voted once to participate at these rates. The question was asked in reference to the specific parcel of land that was submitted in the most recent PSA application. After answering, a follow up question was offered at a higher (lower) value in the event that the respondent said no (yes) to the first value.

# 4.2 Minimizing strategic response

The referendum-style question was used, rather than an open ended or checklist format, because prior research has shown that the referendum format can induce truthful preference revelation in specific contexts<sup>11</sup> . Vossler et al. (2012) and Carson et al. (2014) provide theory and experimental

> 11The basic understanding of incentive compatibility in stated preference questions is drawn from the public choice literature on voting (e.g. Farquharson (1969), Gibbard (1973), Satterthwaite (1975)), which lays out conditions under

16
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Lengthy travel times in the Douala-Bangui corridor are mainly associated with inefficient operations in the Port of Douala, which is responsible for half of the time required to import to the CAR. Productivity measures record poor performance. For example, truck-processing time for the receipt and delivery of cargo in the Port of Douala is 14 days versus 7 days for an average port in Sub-Saharan Africa. Vessel berth productivity, at 12 tonnes per hour in the Port of Douala, is nearly half the SubSaharan African average. Further privatization of the customs clearance system and expansion of the port might help improve the performance of the Port of Doula. Time-consuming regulatory processes related to customs clearance and technical controls further augment the total time, accounting for about one-third of the total time (figure 5b).

**Figure 5. Shares of different components involved in the time and cost of importing through alternative gateways**

<!-- Start of picture text --> a. Cost b. Time<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Douala-Ndjamena Douala-Bangui Pointe Noire to Douala-Ndjamena Douala-Bangui Pointe Noire to<br>Brazzaville to Brazzaville to<br>Bangui Bangui<br>Border Administrative Port Transport Administrative Border Transport Port<br>Source: World Bank 2010a; Teravaninthorn and Raballand 2009.<br>Share (%) Share (%)<br><!-- End of picture text -->

# Roads

## **Challenges**

Road network coverage poses a critical challenge, considering the large size of the CAR and its relatively low population. Total road density over arable land is around one-third of the density of low-income, fragile countries (41 km compared to 145 km/1,000 km<sup>2</sup> ). There are only 6 km of roads per 1,000 people. The rural network density is even lower, at 25 km/1,000 km<sup>2</sup> (table 3).

The CAR’s network appears to be in relatively poor condition except for its strategic corridors. The condition of the classified paved road network is below the level of comparable peer countries: 62 percent is in good or fair condition in the CAR versus 80 percent in fragile states. Meanwhile, it is estimated that only 2 percent of the unpaved network is in good or fair condition (table 3). This divergence in the quality of paved and unpaved roads can be partially explained by the fact that 75 percent of the resources of the Road Fund—most for routine maintenance—are directed to the primary network, which is almost totally paved (table 3).

11
|**Table 8. Labor supply by gender: IV r**|**esults**<br>(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Dependent variable:_**|**Wage**<br>**Empl.**|**Paid**<br>**Self-empl.**|**Unpaid**<br>**work**|**Wage**<br>**Empl.**|**Paid**<br>**Self-empl.**|**Unpaid**<br>**work**|**Wage**<br>**Empl.**|**Paid**<br>**Self-empl.**|**Unpaid**<br>**work**|**Hours**<br>**WageEmpl.**|**Hours Paid**<br>**Self-empl.**|<br>**Hours**<br>**Unpaidwork**|
||||||||**FEMALE**||||||
|Any hh migrant|-0.130*|0.273***|-0.098|-0.114|0.248***|-0.093|||||||
||(1.65)|(3.51)|(1.12)|(1.50)|(3.31)|(1.09)|||||||
|Current hh migrants|||||||-0.19|-0.270*|0.319**|-0.752|-0.908|1.101**|
||||||||(1.45)|(1.73)|(2.12)|(1.54)|(1.58)|(2.03)|
|Past hh migrants|||||||-0.084|0.380***|-0.188*|-0.264|1.415***|-0.881**|
||||||||(0.88)|(3.36)|(1.73)|(0.75)|(3.40)|(2.24)|
|Past indiv. migration|||||||0.663|-1.156|-0.016|2.179|-4.092|0.307|
||||||||(1.11)|(1.62)|(0.02)|(0.97)|(1.56)|(0.12)|
|_Instruments:_|||||||||||||
|Migration density at municip. in 1995|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Family/friends living abroad in 1990|no|no|no|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|More than 1 man in the hh|no|no|no|no|no|no|yes|yes|yes|yes|yes|yes|
|F-test 1st stage||87.06|||44.01||22.17|5.29 ,|39.29|21.64|5.34|38.53|
|P-value joint||0.00|||0.00||0.00|0.00|0.00|0.00|0.00|0.00|
|Overid Sargan test||||1.385|7.198|0.088|||||||
|<br>Chi-sq(1)P-val||||0.2393|0.0273|0.7673|||||||
||||||||**MALE**||||||
|Any hh migrant|-0.165|-0.259|0.177|-0.171|-0.268|0.217|||||||
||(0.77)|(1.37)|(1.02)|(0.84)|(1.48)|(1.30)|||||||
|Current hh migrants|||||||-0.228|0.214|-0.031|-0.922|0.721|-0.193|
||||||||(1.03)|(0.90)|(0.17)|(1.08)|(0.80)|(0.25)|
|Past hh migrants|||||||-0.071|-0.964|0.499|-0.391|-3.445|3.038|
||||||||(0.09)|(1.13)|(0.77)|(0.13)|(1.07)|(1.12)|
|Past indiv. migration|||||||<br>0.005|<br>0.048|<br>-0.027|<br>0.072|<br>0.175|<br>-0.322|
||||||||(0.03)|(0.27)|(0.20)|(0.11)|(0.26)|(0.58)|
|_Instruments:_|||||||||||||
|Migration density at municip. in 1995|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Family/friends living abroad in 1990|no|no|no|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|More than 1 man in the hh|no|no|no|no|no|no|yes|yes|yes|yes|yes|yes|
|F-test 1st stage||28.15|||14.09||<br>15.95|<br>45.16|<br>15.7|<br>15.26|<br>48.85|<br>15.39|
|<br>P-value joint||0.00|||0.00||0.00|0.00|0.00|0.00|0.00|0.00|
|Overid Sargan test||||0.02|1.026|0.305|||||||
|Chi-sq(1)P-val||||0.8876|0.311|0.5809|||||||

_Notes_ Absolute value of z statistics in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%; All individual, household and regional level controls are included as in Table 6 28