rafmacalaba/datause-encoder-data huggingface dataset:
# **6. REFERENCES**

Alesina, A. and D. Rodrik (1994), “Distribution Politics and Economic Growth,” _The Quarterly Journal of Economics_ , 109, 456-490.

Beeson, P., D. DeJong and W. Troesken (2001), “Population Growth in U.S. Counties, 18401990,” _Regional Science and Urban Economics_ , 31, 669-699.

- Castro, N. (2002). "Transportation costs and Brazilian agricultural production: 1970-1996" Texto para Discussão - NEMESIS – LXVI, http://ssrn.com/author=243495", Social Science Research Network.

- Conley, T. (1999), “GMM Estimation with Cross Sectional Dependence,” _Journal of Econometrics_ , 92, 1-45.

- Da Mata, D., U. Deichmann, V. Henderson, S. Lall, and H. Wang (2005). Examining the Growth Patterns of Brazilian Cities. Mimeo.

- Duranton, G. and D. Puga (2004), “Micro-Foundations of Urban Agglomeration Economies,” in J. V. Henderson and J.F. Thisse (eds.) _Handbook of Regional and Urban Economics_ , Vol 4. North-Holland.

- Galor, O. and J. Zeira (1993), “Income Distribution and Macroeconomics,” _Review of Economic Studies_ , 60, 35-52.

- Glaeser E., J. Scheinkman and A. Shleifer (1995), “Economic Growth in a Cross-Section of Cities,” _Journal of Monetary Economics_ , 36, 117-143.

- Henderson, J. V. and H.G. Wang (2005), “Urbanization and City Growth: the Role of Institutions,” Brown University, mimeo.

- Henderson, J.V. and C.C. Au (2004), “Are Chinese Cities Too Small?,” Brown University, mimeo.

Hummels, D. (2001), “Toward a Geography of Trade Costs”, Purdue University, mimeo.

- IPEA, IBGE, and UNICAMP (2002), Configuração Atual e Tendêncies da Rede Urbana, Serie Configuração Atual e Tendêncies da Rede Urbana, Instituto de Pesquisa Econômica Aplicada, Instituto Brasileiro de Geografia e Estatistica, Universidade Estadual de Campinas, Brasilia.

- Lemos, M,. Moro, S., Biazi, E., Crocco, M. (2003). A Dinâmica urbana das Regiões Metropolitanas Brasileiras. _Economia Aplicada_ , 7, 1:213-244.

- Korenman, S. and D. Neumark (2000), “Cohort Crowding and Youth Labor Markets: A CrossNational Analysis,” in D. Blanchflower and R. Freeman, _Youth Employment and Joblessness in Advanced Countries_ , University of Chicago Press, pp. 57-105.

- La Porta, R. and F. López-de-Silanes (1999), “The Benefits of Privatization: Evidence From Mexico,” _The Quarterly Journal of Economics_ , 114, 1193-1242.

- Morandi, L. and E. Reis (2004), “Estoque De Capital Fixo No Brasil, 1950-2002,” _Anais do XXXII Encontro Nacional de Economia,_ Proceedings of the 32th Brazilian Economics Meeting.

- Persson, T. and G. Tabellini (1994), “Is Inequality Harmful for Growth?,” _American Economic Review_ , 84, 600-622.

27
# _United States Manufacturing Data_

It is useful when discussing the levels of Indian specialization and diversity to benchmark them against other countries. Surprisingly, there is very little tabulation of these metrics internationally. We will compare our Indian metrics with those that we calculate for the United States using 2011 manufacturing data from County Business Patterns (CBP). Our index calculations take the same approach, and we have two CBP data issues to highlight. First, we utilize the three-digit level of the North American Industry Classification System (NAICS). At this level of industry aggregation, NAICS contains 21 manufacturing industries, which is very comparable to the 22 industries at the NIC two-digit level. The NAICS and NIC industries are defined somewhat differently, and to a limited degree this could influence the measures. For example, a more evenly balanced distribution of employment across one set of industries would reduce the likelihood of a very high specialization value occurring. Nevertheless, given the broad comparability of manufacturing industry definitions, we believe this issue to be very minor.

Second and more important, CBP data do not disclose values if tabulations risk violating the confidentiality of individual plants. That is, CBP will not provide the employment for the chemicals industry in a location if there are only one or two chemical plants, as this would be very informative about the underlying plants themselves. This issue is very prominent at the county level, and it becomes increasingly less important with higher levels of spatial aggregation. We thus consider states and metropolitan areas (which we refer to as cities for simplicity). CBP reports the total manufacturing employments for cities, and thus we can calculate the share of the total employment that has been allocated over industries. We report below tabulations that use four levels of aggregation (observation count): states and District of Columbia (51), the top 100 cities in terms of manufacturing employments (100), cities with at least 90% of manufacturing employment allocated (193), and cities with at least 60% of manufacturing employment allocated (746).

# _Specialization and Diversity Indices_

We follow Duranton and Puga (2000) in the indices that we use to measure specialization and diversity for Indian districts. Before providing the formulas, we outline the key building 6
respectively, from the first approach is fairly stable across parametric (OLS) and nonparametric (matching) models. Similarly, and given that the consequences of model misspecification are more serious in the RDD approach, the empirical models in this design were run for a number of low-order polynomial functions as well as other more flexible functional forms including third- and fourth-order polynomials and nonparametric models. The direction – and even in some cases the magnitude – of the RDD estimates to test for school completion are robust to the inclusion of different polynomial terms in the control function.

Finally, another concern is that the higher rates of matching survey data and test records observed for the treatment group may be driven by differences in the merging procedures and quality of information rather than by the effect of the program. In particular, considering that part of the information used to construct the treatment and control groups come from different datasets, it could be that the individual-level variables used to merge them (name, date of birth and national identification number) may be more accurate for people in the treatment group. For instance, surveyors that collected the data for the short-term impact evaluation could have been more careful to correctly keep the information to identify individuals in the treatment group for the subsequent rounds of data collection. Similarly, as part of the regular updates of the data entered in the information systems of FA (SIFA), program administrators may be more likely to correct mistakes in names, birth dates, and national identification numbers of program beneficiaries. As a result, individuals in the treatment group may be more likely to be matched with records in the _Icfes_ database, not because they are more likely to take the test, but because of better information quality. In practice, however, measures computed from the matching algorithms such as the _Levenshtein_ distance do not indicate systematic differences in the accuracy of data between the treatment and control groups. Therefore, it seems unlikely that this could be a reason to find children covered by the FA program to be more likely to be matched to _Icfes_ registration records.

30
|||**Unadju**<br>**sted**<br>**Net**<br>|**Use**<br>**rights**<br>**held**|**Use**<br>|**right gra**<br>|**nted**<br>|**Adju**<br>|**sted net wea**<br>|**lth**<br>|**Adju**<br>**net we**<br>|**sted /Unadju**<br>**alth****_(percen_**<br>|**sted**<br>**_tages)_**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Year**||**wealth**||**(H1)**|**(H2)**|**(H3)**|**H1**|**H2**|**H3**|**H1**|**H2**|**H3**|
||**Area**||||||||||||
||**North**|250978|2086|346|648|734|252718|252416|252330|0.7|0.6|0.5|
||**Center**|243844|1098|979|1578|1731|243963|243364|243211|0.0|-0.2|-0.3|
||**South and**||||||||||||
||**Islands**|152412|1828|18|30|32|154222|154210|154208|1.2|1.2|1.2|
||**Total**|217574|1803|367|635|707|219010|218742|218670|0.7|0.5|0.5|
|**2016**|**Age (years)**||||||||||||
||**Up to 40**|108612|2384|39|56|56|110957|110940|110940|2.2|2.1|2.1|
||**41-50**|186292|1198|0|0|0|187490|187490|187490|0.6|0.6|0.6|
||**51-65**|256688|1220|374|816|965|257534|257092|256943|0.3|0.2|0.1|
||**> 65**|231667|1972|23|65|87|233616|233574|233552|0.8|0.8|0.8|
||**Area**||||||||||||
||**North**|239417|1443|234|509|604|240626|240351|240257|0.5|0.4|0.4|
||**Center**|231262|1486|19|40|47|232729|232708|232701|0.6|0.6|0.6|
||**South and**||||||||||||
||**Islands**|141700|2154|6|17|21|143848|143837|143833|1.5|1.5|1.5|
||**Total **|206422|1680|117|255|303|207985|207847|207799|0.8|0.7|0.7|

Source: Our elaboration on SHIW data.

(*) The reference person is defined as the member with the highest income.

**Table A5 – Differences between adjusted and unadjusted* net wealth shares held by tenth of households, Italy, 1991-2016** _(percentages)_

|**Tenth of**<br>**households**|**1991**|**2004**|**2006**|**2008**|**2010**|**2012**|**2014**|**2016**|**Average**|
|---|---|---|---|---|---|---|---|---|---|
|**1**|0.02|0.01|0.06|-0.04|0.02|-0.03|-0.04|0.05|0.01|
|**2**|-0.27|0.05|-0.03|0.08|-0.01|0.06|0.01|-0.03|-0.02|
|**3**|0.27|0.21|0.24|0.31|0.22|0.23|0.21|0.26|0.24|
|**4**|0.29|0.16|0.26|0.28|0.32|0.29|0.25|0.21|0.26|
|**5**|0.15|0.09|0.11|0.12|0.19|0.11|0.08|0.06|0.11|
|**6**|0.08|0.06|0.04|0.04|0.04|0.05|-0.01|0.01|0.04|
|**7**|0.01|0.02|0.00|-0.04|-0.01|-0.01|-0.06|-0.03|-0.02|
|**8**|-0.04|-0.03|-0.06|-0.09|-0.04|-0.06|-0.09|-0.06|-0.06|
|**9**|-0.13|-0.15|-0.14|-0.14|-0.13|-0.16|-0.10|-0.11|-0.13|
|**10**|-0.38|-0.42|-0.47|-0.52|-0.59|-0.47|-0.24|-0.37|-0.43|

Source: Our elaboration on SHIW data. * Adjusted estimates = average of H1, H2, H3.
WPS3650

# **INSTITUTIONAL AND POLICY ANALYSIS OF RIVER BASIN MANAGEMENT**

# **_The Alto-Tietê River Basin, São Paulo, Brazil_**<sup>**_1_**</sup>

**Rosa Maria Formiga Johnsson Karin Erika Kemper**

## World Bank Policy Research Working Paper 3650, June 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This paper is a product of the study, “Integrated River Basin Management and the Principle of Managing Water Resources at the Lowest Appropriate Level – When and Why Does It (Not) Work in Practice?”  The Research Support Budget of the World Bank provided major funding. The project was carried out by the Agriculture and Rural Development Department at the World Bank.  The Water Resources Management Group and the South Asia Social and Environment Unit at the World Bank have provided additional support.  The study core team includes Karin Kemper and Ariel Dinar (Co-Task Team Leaders, World Bank), William Blomquist and Anjali Bhat (consultants, Indiana University), and Michele Diez (World Bank), William Fru (consultant), and Gisèle Sine (International Network of Basin Organizations).  Basin case study consultants include Maureen Ballestero (Tárcoles - Costa Rica), Ken Calbick and David Marshall (Fraser - Canada), Rosa Maria Formiga Johnsson (Alto Tietê and Jaguaribe - Brazil), Consuelo Giansante (Guadalquivir - Spain), Brian Haisman (Murray Darling - Australia), Kikkeri Ramu and Trie Mulat Sunaryo (Brantas - Indonesia), and Andrzej Tonderski (Warta - Poland). Reviews and valuable comments by Larry Simpson, Stela Goldenstein, Gerôncio A. Rocha and Rebecca Abers are gratefully acknowledged.
variations reported in the study are not as important as those observed between the HBS and NPS data sets.

Food consumed outside the household is captured in the HBS through an additional diary filled in only by adult household members, while it is collected in the NPS by way of recall of the last seven days asked to all household members. The HBS 2007 does include the data on food consumed outside.

Non‐food items were collected using both the diary and recall methods in the HBS, while in the NPS they were collected using the recall method only. To avoid the duplication of expenditures under the diary and recall module in the HBS, an effort was made in both years to exclude non‐food items already recorded in the diary from the recall module. In 2011/12, interviewers were asked to carefully check potential duplication between non‐food items reported in the recall and those recorded in the diary. Potential duplication was also carefully checked at the analytical stage during the evaluation of the welfare aggregates and poverty estimates. In the HBS 2007, interviewers were asked to “request details of irregular purchases of consumer durables and costs of other services during previous twelve months excluding the survey month”. Excluding the survey months from the recall most likely also intended to reduce double‐counting. Potential duplication was also checked at the analytical stage following the same procedure as in 2011/12.

The HBS 2007 used a 12‐month recall period for the collection of non‐food items with the exception of rent, while the HBS 2011/12 used recall periods of 1, 3 and 12 months depending on the item (see Table 3). The NPS used a 12‐month recall for some non‐food items and recall periods of 1 and 4 weeks for others such as transportation, health and education. While changes in the recall period can affect the welfare and poverty estimates, the induced variations would not be expected to be as high as those observed above in Section II (see Beegle _et al.,_ 2012).

# **ii. Evaluation of home‐produced food**

For many Tanzanians, particularly those living in the rural areas, most of their caloric intake comes from food that they produced themselves. The value of own‐produced food is difficult to evaluate as its market price cannot be directly observed. Different methods have been suggested in the literature to estimate own‐produced food values, each with its many pros and cons. In the HBS, the value of own–produced food is reported directly by the household. Respondents are asked to report a shilling value for all food consumed, whether it is purchased or produced at home. In the NPS, the valuation of own‐produced food is based on the prices paid by the household for similar items in the same geographic stratum.

16
Table 6. Features of government debt booms

||World|Advanced<br>economies|EMDEs|World|Advanced<br>economies|EMDEs|
|---|---|---|---|---|---|---|
|**All debt booms**|||||||
|Amplitude|30|25|32***||||
|(Percentage points of GDP)|[17, 52]|[13, 42]|[18, 56]||||
|Duration|7|7|7||||
|(Years)|[4, 11]|[4, 13]|[4, 11]||||
|Speed|4.3|3.7|4.6***||||
|(Percentage points of GDP per year)<br>**All debt booms: Completed versus On**|[2.5, 7.3]<br>**going**<br>|[2.4, 5.0]<br>|[2.6, 8.4]||||
||**Completed**|||**Ongoing**|||
|Amplitude|29|25|32|31|29|31|
|(Percentage points of GDP)|[17, 57]|[13, 41]|[18, 60]|[22, 49]|[12, 58]|[23, 48]|
|Duration|6|7|6|9**|11|9***|
|(Years)|[4, 10]|[4, 11]|[4, 10]|[5, 12]|[3, 13]|[6, 12]|
|Speed|4.5|3.2|5.0|4.1|4.5**|3.9**|
|(Percentage points of GDP per year)|[2.5, 8.3]|[2.3, 4.6]|[2.7, 9.4]|[2.6, 6.4]|[3.3, 6.4]|[2.6, 6.4]|
|**Completed debt booms: With crises v**|**ersus Witho**<br>|**ut crises**<br>|||||
||**With crises**|||**Without cri**|**ses**||
|Amplitude|44|30|45|20***|19***|21***|
|(Percentage points of GDP)|[25, 75]|[21, 65]|[27, 77]|[12, 36]|[9, 32]|[13, 37]|
|Duration|7|7|6|6|7|6|
|(Years)|[4, 12]|[4, 11]|[4, 12]|[4, 9]|[4, 10]|[4, 9]|
|Speed|6.7|4.9|7.0|3.5***|2.7***|3.6***|
|(Percentage points of GDP per year)|[4.0, 11.2]|[2.8, 8.2]|[4.1, 12.0]|[2.0, 5.4]|[2.1, 3.9]|[2.0, 6.2]|
|**Completed debt booms: Before 2000**|**versus After**|**2001**|||||
||**Before 200**|**0**||**After 2001**|||
|Amplitude|34|29|37|24***|19|27***|
|(Percentage points of GDP)|[18, 64]|[15, 42]|[18, 66]|[17, 38]|[13, 31]|[17, 40]|
|<br>Duration|<br>7|<br>8|<br>7|<br>6**|<br>6*|<br>6|
|(Years)|[4, 12]|[4, 15]|[4, 11]|[4, 8]|[4, 8]|[4, 8]|
|Speed|4.6|3.0|5.2|4.3|3.7|4.6|
|(Percentage points of GDP per year)|[2.5, 9.1]|[2.3, 4.4]|[2.7, 10.6]|[2.6, 6.8]|[2.2, 5.9]|[2.8, 7.6]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), based on 493 government debt booms, of which 103 booms are in advanced economies and 390 are in EMDEs. Amplitude is the trough-to-peak change in the government debt-to-GDP ratio during a government debt boom. Speed is the ratio of the amplitude to the duration. ** and *** denote that medians between advanced economies and EMDEs in all debt booms, and between completed and ongoing booms or between completed booms with and without crises in respective country groups, are statistically significantly different at the 5 percent and 1 percent levels, respectively.

26
devise systematic statistical tests for, as the biases might affect individual country scores in one direction or another without introducing systematic biases into the source as a whole. Nevertheless, careful comparisons of many different data sources can often turn up anomalies in a single source that require more careful scrutiny.

# _Surveys of Firms and Individuals_

We now turn to governance indicators derived from surveys of firms and individuals. Such indicators have the fundamental advantage that they elicit the views of the ultimate beneficiaries of good governance, citizens and firms in a country. Wellcrafted survey-based governance indicators can capture the _de facto_ reality on the ground facing firms and individuals, which as we have discussed above can be very different from the _de jure_ rules on the books. The views of these stakeholders matter because they are likely to act on those views. If firms or individuals believe that the courts and the police are corrupt, they are unlikely to try to use their services (Hellman and Kaufmann (2004)) Individuals are less likely to vote, and to hold their elected leaders accountable, if they think that elections are not free and fair.

A further advantage of governance indicators based on surveys of domestic firms and individuals is their greater domestic political credibility. Governments can and do often dismiss external expert assessments of governance as uninformed pontification by outsiders. But it is much harder for governments to dismiss the views of their own citizens, or of firms operating in their country, when these point to failures of governance. Survey-based data on governance can therefore be particularly useful in galvanizing the politics of governance reforms. The experience of many countries implementing their own in-depth Governance and Anti-Corruption Diagnostics (assisted by the World Bank Institute and other agencies, and implemented with institutions in the requesting country), based on in-country surveys of enterprises, of users of services, and of public officials, supports this point: the reports on their views and experiences about many governance dimensions provided by thousands of stakeholders in the country provide a powerful input for action to reformist policy-makers and civil society groups.

Set against these important advantages of surveys there are again a number of disadvantages. First, we have the usual array of potential problems with any type of 20
Forecasts of expenditures with existing statutory authority are developed for inclusion with the ARLU process and presentation in the Main Estimates and the Reports on Plans and Priorities (RPPs). The forecasts are based on the most recent demographic, economic and/or demand information. Significant changes in forecasts during a fiscal year may be presented in Supplementary Estimates One final issue is the extent to which recurrent and capital expenditure is distinguished in the MTEF budget formulation process, and whether this is reflected in requirements to identify the recurrent cost implications of capital budget proposals. To some extent this is often a function of the ‘dual budgeting’ structure of legal appropriations; in Australia, for example, there is a constitutional requirement to present appropriations separately as ‘recurrent’ and ‘capital’, whereas in Austria there is no such requirement in basic budget laws. In principle, however, it is preferable to ensure that consideration of capital proposals (including the recurrent costs of operating and maintaining assets) is integrated with a single annual budget formulation process.

The most instructive example of integrated capital budgeting comes from Australia. As in many other advanced economies, the Australian MTEF budget process contains a ‘gateway’ review process for major projects, with a focus on major information and communication technology (ICT) capital procurement (Australian Government Information Management Office 2012). The process focuses on the strengthening of business case preparation for large or complex ICT proposals considered as new policy within the annual budget process. In particular, the business case review process is designed to ensure that the costs of new capital expenditure are fully developed prior to requesting funding. For instance, proposed ICT capital projects should reflect a ‘preferred option’ based on a cost-benefit analysis that accounts for ‘whole-of-life’ costs associated with using and maintaining the asset (such as depreciation) and the operational expenses (such as staffing) projected to be required over the forward estimates period. This process is set out in Exhibit 2 (above).

# **5.2 Application of costing approaches as part of forecasting methodologies**

This theme covers requirements and practices for forecasting methodologies including the treatment of entitlement programs, the application of non-economic parameters (e.g. escalation 32