rafmacalaba/datause-encoder-data huggingface dataset:
prices than predicted.<sup>15</sup> The reason is that the IPC regression model takes no account of small country size, rugged terrain, and remoteness which increase prices. Indeed, our own comparison of prices in Tonga to those in New Zealand suggests that the correct PPP exchange rate should be approximately the prevailing exchange rate between these two countries, not the imputed rate. The difference is quite large: the PPP rate has 1.272 Pa’anga = $1US, compared to an official exchange rate of 2.075 Pa’anga = $1US.

The second reason that we believe PPP may not be the right benchmark is that if migrants plan on returning with earnings saved from abroad, or if they are remitting, then the actual exchange rate is what matters for determining the purchasing power of income earned abroad in the home country. For both these reasons, we believe use of PPP makes incomes in PNG and Tonga appear more attractive than they are in reality.

Table 6 presents these estimates for the gain in weekly income from migration. Estimation is carried out for the sample that are currently working and who are currently not students. For the New Zealand top students, the PPP and actual exchange rate results give similar numbers. The mean gain in income from migration according to the regression is between 800 and 1000 U.S. dollars per week. The mean self-assessed change in levels is 815-899 per week, and thus of similar magnitude to the regression estimate. The mean income of those working in New Zealand is $US1250 per week, so this represents a 64 to 80 percent increase in mean income from migrating. The mean change in log income is also similar between the regression and self-assessed methods, between 0.44 and 0.56. There is thus a large gain in average income to be had in both PPP and exchange rate terms from migration.

For the Tongan sample the PPP estimates of the gains are a little lower than those using the prevailing exchange rate, but as noted, we believe the PPP estimates understate the effective gain. Using the exchange rate, the gain in income according to the regression estimates is between 700 and 1200 U.S. dollars per week, compared to a mean income in Tonga of only US$246 per week. The self-assessed change is US$728 per week, which is not significantly different from the regression estimates. In logs, the gain in income is 1.2 to 2.0 log points. Thus by any measure the average gain in income is huge.

> 15 International Comparison Program (2008).

- 24 -
Table 2 Economic Growth and Public Expenditure **Mix** (in percent) [Cross-section/Time-scries data, **69** countries, **1971-90]**

|Range|Slow-Growth|Fast-Growth|
|---|---|---|
|_Mean GrowthRate_|**-1.97**|**3.23**|
||**(163)**|(146)|
|_Te/GDP_|24.76|**22.85**|
||**(163)**|(146)|
|_Curefle_|**_75.92_**|**75.61**|
||**(163)**|(146)|
|_Ncurefle_|**68.61**|**68.87**|
||**_(155)_**|**(139)**|
|_Capefle_|24.03|24.21|
||**(160)**|(145)|
|_DeflTe_|**12.75**<br>(140)|14.92<br>**(131)**|
|_HIMte_|**6.33**|4.87|
||**_(156)_**|**(137)**|
|_EdlTe_|**_15.05_**|**_13.55_**|
||**(156)**|**(138)**|
|_Tacle_|**10.10**|**8.67**|
||**(152)**|**(130)**|
|_HospfTe_|**_5.16_**|**2.96**|
||**(83)**|**_(66)_**|
|_InhkhlTe_|**_0.56_**|**0.77**|
||**(26)**|(44)|
|_OthkhlTe_|1.41|**_1.55_**|
||**(66)**|**(72)**|
|_SchIlTe_|**8.60**|**8.99**|
||**(83)**|**(78)**|
|_UnivlTe_|**3.03**|**2.85**|
||(82)|**(76)**|
|_OthedlTe_|2.42|**2.65**|
||**(71)**|**(77)**|

_Notes: a)_ Growth rate is 5-year forward moving average of per capita real **GDP.**

**b)** Mean growth is **0.5.** Past-growth refers to the periods<sup>where growthisgreater thanthe mean</sup> growth; Slow-growth is less than the mean growth.

**c)** Numbers in parentheses are observations.

**26**
Table 7: Short-term impacts of _Mais Educação_ on educational outcomes

|Dependent Variable:|Dropout rates<br>Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|
||(1)<br>(2)|(3)|
|Panel A: Impac|ts on fifth-grade variables||
|Dummy_Mais Educação_* year 2009|1.057<br>−3.953**|−2.482|
||(1.283)<br>(1.664)|(1.544)|
|_R_ <sup>2</sup>|0.796<br>0.807|0.809|
|Observations|1,992<br>1,992|1,992|
|Dummy year 2009|Yes<br>Yes|Yes|
|School fixed effects|Yes<br>Yes|Yes|
|Panel B: Impact|s on ninth-grade variables||
|Dummy_Mais Educação_* year 2009|−0.137<br>−5.305***|−2.351|
||(1.100)<br>(1.889)|(1.872)|
|_R_ <sup>2</sup>|0.81<br>0.818|0.797|
|Observations|1,730<br>1,730|1,730|
|Dummy year 2009|Yes<br>Yes|Yes|
|School fixed effects|Yes<br>Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, replacing the 2011 dummy variable by the 2009 year dummy variable. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **denotes significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
# **_Have governments complied with the rules?_**

**Consider first compliance** . As noted above, it is possible for rules to influence fiscal policy even if compliance with them is low (and it is also possible for them to have little influence even if compliance is high). The crucial question is the counterfactual one: what would have happened in the absence of the rules? The available data are not sufficient to answer this question. Nonetheless, it is useful to have an idea of the extent of compliance, because a high rate of noncompliance is suggestive of problems. Given the complexity of fiscal rules—including the existence of escape clauses and subtle differences in accounting—it is difficult to provide definitive evidence even about this question.

**A quick review suggests that fiscal rules in the Western Balkans have been followed a little more often than not.** The evidence is set out in Table 3, in which green shading shows compliance with the rules, red shading shows noncompliance, and absence of shading implies that no rule was in force. The table is rough in several ways. Only the debt and overall-deficit rules are considered; no account is taken of rules on current and primary deficits, expenditure, and guarantees. The data on actual debts and deficits also come from the World Bank’s database, and may differ in some respects from the data used domestically to define the debts and deficits that are subject to the rules. Third, no account is taken of escape clauses and other subtleties in the rules. Subject to the provisos mentioned above, the table suggests the following:

- Albania has followed its debt rules in most years, though the original debt rule was repealed as soon as it was breached.

- Kosovo has followed its debt rule every year and its deficit rule most of the time.

- Montenegro has generally not complied with its debt and overall-deficit rules.

- Serbia has usually not complied with its debt rule, but, according to the approximate data used here, has often complied with its complex deficit rule.

Bosnia and Herzegovina’s rules are not considered in the table because they are subnational rules.

For the region as a whole, the evidence suggests that governments have complied with the debt and overall-deficit rules a little more than half the time, which is similar to the experience of other regions (Eyraud and others 2018).

15
We do not account explicitly for the quantity of credit, in addition to its cost, in our specification of the investment function, for two reasons. First, the evidence provided by Guncavdi, Bleaney, and McKay (1999), as well as others, suggests that the impact of credit constraints on investment is less significant nowadays than was the case in the early 1980s, following financial reforms; at the same time, the (expected) cost of credit appears to have become a more important consideration for firms.<sup>21</sup> Second, even in the context of the recent crisis, there is no strong evidence that the fall in credit to private sector firms (at least the bigger ones) resulted from a credit crunch, that is, a supply-induced contraction in lending; on the contrary, a recent study by the World Bank (2003) suggests that demand-side factors (high interest rates, low economic activity) were largely to blame. In fact, as discussed later, we assume that the supply of bank loans to private sector firms is perfectly elastic at the prevailing interest rate, and that only formal sector households are subject to credit rationing.

Formally, the desired level of investment, ZP<sup>d,isgivenby</sup> where NGDP is nominal GDP at market prices, defined as the sum of value added and indirect taxes on goods and services (including tariff revenue): or equivalently, as the sum of expenditure and net exports: Real GDP, RGDP , is defined as, using base-period prices: RGDP = X PCi,0(Ci + Gi + ZP<sup>i) + ZG+ ER0(wpei,0Ei−wpmi,0Mi),(67)</sup> i the cost of credit, for reasons that we discuss later on.

> 21It should be noted that Sancak (2002) did not find any evidence that financial liberalization led to a relaxation of the borrowing constraints faced by Turkish firms in the 1980s and 1990s. However, the methodology that he used to test for structural breaks is rather weak.

30
Policy Research Working Paper 8692

# **Abstract**

Food consumed outside the home in restaurants or other food establishments is a growing segment of consumption in many developing countries. However, the survey methods that are utilized to collect data on expenditures on food away from home are often simplistic and could potentially result in inaccurate reporting. This study addresses the potential inaccuracy of commonly used methods and tests potentially superior methods to inform best practices when collecting data on consumption of food away from home. A household survey experiment was implemented in Hanoi, Vietnam, to test these different methods. Using a food away from home consumption diary as a benchmark, the study finds that many of the alternative methods considered—including asking about consumption in one line (the existing practice in Vietnam) or asking each individual about their food away from home—lead to underreporting (33 and 22 percent underestimates, respectively). Surprisingly, using one respondent and helping them with recall with a simple worksheet as well as bounding (two-visits) results in food away from home estimates that are indistinguishable from those reported in the benchmark diary. This finding implies that there is a more cost-effective way to collect accurate data on food away from home than an intensive daily diary. Furthermore, it highlights the inaccuracy associated with collecting data on consumption of food away from home from a single question in a survey. Although limited analysis can be conducted on the implications for poverty, the study finds that the profiles of the poorest households differ across different methods of collecting information on food consumed away from home.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ research. The authors may be contacted at gfarfan@worldbank.org, kmcgee@worldbank.org, jperng@worldbank.org, and rvakis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13 Côte d’Ivoire has made substantial progress in expanding GSM coverage**

<!-- Start of picture text --> % of population<br>coverage gap efficient market gap existing access<br><!-- End of picture text --> Source:Mayer and others 2008.

A second set of simulations explored the commercial viability of limited performance broadband services based on limited institutional use and public access telecenters using WIMAX technology. Assuming a subscription rate of 0.25 percent in rural areas and that 1 percent of local income could be captured in broadband revenues, about 95 percent of the population could be provided with such service on a commercially viable basis. Even if the spending assumption were reduced to 0.25 percent of local income, 85 percent of the population could still be served on a commercially viable basis. Those areas that would require public subsidy are confined to the far north of the country and in some pockets on the eastern and western side (figure 14).

29
Policy Research Working Paper 10066

# **Abstract**

The spatial distribution of economic activity is known to depend on trade costs, both international and domestic. This paper examines the interplay between these external and internal trade costs using a model of trade and production that is tested with the organized manufacturing sector data for India from 1989 to 2009. The analysis establishes that the trade liberalization episode of the early 1990s helped spread manufacturing away from the primary region (districts closest to ports) to the secondary region between 1994 and 2000. Such dispersion of activity away from the primary to the secondary region was driven by high internal trade costs that insulated manufacturers from import competition. This trend reversed post-2000, a period of massive decline in internal trade costs, attributed to the Golden Quadrilateral highway upgrades. During this period, the districts along the highway network in the secondary region gained market access and manufacturing activity, while those off the network lost. Irrespective of the period, or the nature of trade costs, manufacturing activity in the interior region (districts farthest from ports) remained depressed, thereby emphasizing the importance of complementary conditions in driving territorial development.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Mancini, Giulia, and Vecchi, Giovanni. (2022). _On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis_ . Washington, D.C.: World Bank Group.

- <mark>Marotta, D., Yemtsov, R., El-Laithy, H., Abou-Ali, H., & Al-Shawarby, S. (2011). Was growth in Egypt between 2005 and 2008 pro-poor? From static to dynamic poverty profile.</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>5589.</mark>

- Medeiros, Marcelo, and Joana Costa. (2008). “Is there a feminization of poverty in Latin America?” _World Development_ , Vol. 36(1), pp. 115-127.

- Milazzo, Annamaria, and Dominique van de Walle. (2017). “Women left behind? Poverty and headship in Africa.” _Demography_ , Vol 54(3), pp. 1119-1145.

- <mark>Muller, J. S., Hiekel, N., & Liefbroer, A. C. (2020). The long-term costs of family trajectories: Women’s later-life employment and earnings across Europe.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>57</mark>_ <mark>(3), 1007-1034.</mark>

- <mark>Munoz Boudet, A. M., Buitrago, P., Leroy De La Briere, B., Newhouse, D. L., Rubiano Matulevich, E. C., Scott, K., & Suarez-Becerra, P. (</mark> 2018). _Gender Differences in Poverty and Household Composition through the Life-Cycle: A Global Perspective_ . Policy Research Working Papers 8360. Washington, D.C: The World Bank.

- Munoz Boudet, A. M., Bhatt, A., Azcona, G., Yoo, J., & Beegle, K. (2021). _A global view of poverty, gender, and household composition_ . Policy Research Working Papers 9553. Washington, D.C: The World Bank.

- Nasri, Khaled (2020). Social Safety Nets in Tunisia: Do Benefits Reach the Poor and Vulnerable households at the Regional Level? GLO Discussion Paper, No. 440, Global Labor Organization (GLO), Essen

- NAF 2020. “Assessing Gender Equity in National Aid Fund Programs”. Jordan National Aid Fund <u>https://naf.gov.jo/EN/List/Statistics_and_Data</u>

- Nassar, Heba. (1997). “Feminization of Poverty.” The Egyptian Human Development Report; Research Papers Series, UNDP.

- <mark>Newhouse, D., Becerra, P. S., & Evans, M. (2017). New global estimates of child poverty and their sensitivity to alternative equivalence scales.</mark> _<mark>Economics Letters</mark>_ <mark>,</mark> _<mark>157</mark>_ <mark>, 125-128.</mark>

- OECD. (2018). A Broken Social Elevator? How to Promote Social Mobility. Paris: OECD Publishing. https://doi.org/10.1787/9789264301085-en

- <mark>Palestinian Central Bureau of Statistics (PCBS) and World Bank. (2018). “Measuring poverty in West Bank and Gaza: Methodology review using PECS 2016.” Technical report.</mark>

31
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census data to select localities with more than 15,000 residents. In the 2001-2002 school year, 2,200 schools enrolled in PEC. But by the 2003-04 school year, 20,600 schools or 10 percent of all Mexican primary schools received PEC support.

We construct a panel of 74,700 schools and use two common non-experimental methods to create a control group and estimate impact: regression analysis and propensity score matching. We compare and contrast the estimated impact of PEC on dropout, repetition, and failure rates. The preferred estimator, difference-in-differences with matching, reveals that participation in PEC robustly decreases drop-out rates by 0.24 percentage points, failure rates by 0.24 percentage points and repetition rates by 0.31 percentage points.

The results provide useful information for a variety of other countries, such as El Salvador, Kenya, Kyrgyz Republic, Nepal, Nicaragua, Paraguay, and Yemen, that are developing or operating similar programs. They also guide countries seeking to improve learning and inform the broader debate on education decentralization.

The paper is structured as follows. Section 2 reviews research on decentralization and school-based management. Section 3 discusses data sources and variables used in the empirical analysis. Section 4 discusses the empirical approach, while section 5 estimates program impact. Section 6 presents results, and section 7 concludes.

# 2. Decentralization and school based management

Authors generally argue that decentralization – the transfer of financial, pedagogical, personnel, or other decision-making power from central to local authorities – may make policy better reflect beneficiaries’ heterogeneous preferences, and that local management increases the accountability of political authorities. Critics emphasize that centralization may decrease costs through economies of scale, and that decentralization may decrease the quality of public services if local providers lack technical capacity or if local elites 3
The equation for the first principal component is the one with maximal variance. The weights used to aggregate the asset indicators are therefore the set (b11,b21, ... , bk1).

The next asset index we consider is also based on principal components but uses only the indicators that reflect ownership of household consumer durables: **principal components using only assets** . As discussed in the previous section, there has been some discussion in the literature about which of the indicators should be included. Some authors have expressed concern about variables that affect outcomes directly (such as water source in health outcome analysis) as well as variables that reflect availability of publicly provided utilities (such as use of electricity for lighting). This variant of the principal components index explores the implications of excluding all indicators measuring housing quality, drinking water source, type of toilet, as well as availability of electricity and source of cooking fuel.

The next index is that derived from an Item Response Theory analysis of the data, the **IRT index** . The underlying model assumes that each of the k indicators is determined through the following relationship: where, in the IRT literature, αk is the called the “discrimination” parameter for the k’th indicator, βk is the “difficulty” parameter, and Ai is the latent factor (or “ θ” ) for the i’th household. The IRT methodology estimates the αs and βs after conditioning on Ai, and then derives an estimate of Ai _ex-post_ .<sup>16</sup> The next index is the sum of the asset indicators where each asset is weighted by the share of the population that does not own the asset, the **share weighted average** . More formally:

> 16 The open source software ICL was used for estimating the IRT model.  It is available at <u>http://www.b-ah.com/software/irt/icl/. Note that only binary variables can be included, which means that rooms per person is</u> dropped.  Moreover, only assets whose ownership increases with the latent factor can be considered in this index. Some assets or housing characteristics are therefore dropped in the construction of the IRT index.

15