rafmacalaba/datause-encoder-data huggingface dataset:
Reports that land prices begin to rise sharply as soon as there is a credible announcement that construction has been sanctioned are commonplace. The subject came up almost always in Bell’s (2009a) discussions with villagers in Assam, Himachal Pradesh and Orissa, in which quite startling claims, often of many-fold increases, were usually followed by remarks to the effect that there had been very few, if any, actual sales. Quite some time must elapse after construction before settled valuations based on transactions displace speculative guessing. One hopes that enough panel surveys will be carried out to reveal how things actually develop over the whole course of events, from _before_ any announcement to full maturity many years afterwards. For such evidence would provide an invaluable check on the estimates derived from the approach pursued in this paper.

# **8 Conclusions**

The three big questions posed in the Introduction are addressed _seriatim_ . _Preserving and enhancing current benefits_ The benefits generated by past investments will continue undiminished only if the PMGSY roads are properly maintained. The associated outlays will be very modest in comparison, and the consequences of neglecting to make them can be readily experienced by taking a ride at random on the district and state highways to which the PMGSY roads are connected.

The technical capacity to keep this extension of the network up to scratch appears to be adequate, but the outlook for actually financing the work is poor. Although the five-year provision in the contracts is helpful, it is seen to be just a stop-gap measure when viewed against the roads’ economic lifetime. Many engineers claimed that the funds allocated to the maintenance of the P.W.D. network are but a fraction of the – amount needed to keep it in good order one-third at best, often a quarter or less.

The villagers in Assam, H.P. and Orissa with whom I spoke on my field-trip had not only a firm opinion of where the responsibility lies, but also a blithely optimistic assessment of the states’ finances. In fact, each link is an almost ideal local public good, whose beneficiaries are readily identifiable and ought to contribute to its management and upkeep, supported by a matching grant from the state government that is suitably calibrated to their community’s economic condition. All the classic problems of provision and collective action arise: the villagers will (and did) protest their poverty, 24
all variables, except the percentage change in the exchange rate and local market returns used to calculate _1B,_ which are contemporaneous, independent variables are lagged by one month. All accounting variables are based on accounting results from the most recent financial statements that are publicly available.

Table 1 presents a list of the countries and the number of stocks in each market, as well as summary statistics for the main variables used.' In order to analyze the returns on a common basis, we choose to work with U.S. dollar returns (in excess of the one-month U.S. Treasury bill rate), which also lies behind our choice of the change in the local currency/dollar exchange rate as an explanatory variable. The excess market return, r,,, is taken to be the local market index for each country, converted into dollars and less the U.S. Treasury bill rate.

There is a limited number of stocks in the database, ranging from a low of 22 for Colombia and Zimbabwe to a high of 137 for Korea, with the sample representing between 23.6 percent (Malaysia) and 88 percent (Venezuela) of total trading volume. Included in the sample are all stocks from each of the sample countries, some of which either entered or dropped out of the sample over the eight year sample period based on their liquidity. The low number of stocks is of some concern, but in most cases the stocks included in the sample are the most active and liquid in the market. Extending the sample of stocks, even if data were available, would be problematic given the low level of trading for most listed shares in these countries. For example, in Brazil, the top 10 stocks represent more than 50 percent of all trading, a fraction which is not unusual in these markets. In practice, the active market in Brazil is limited 6 Times series properties for the data have been described in Claessens, Dasgupta and Glen (1995).

9
De Mel, Suresh, David McKenzie, and Christopher Woodruff (2009a) “Are Women more Credit Constrained? Experimental Evidence on Gender and Microenterprise Returns.” _American Economic Journal: Applied Economics_ 1(3): 1-32.

De Mel, Suresh, David McKenzie and Christopher Woodruff (2009b) “Measuring microenterprise profits: Must we ask how the sausage is made?”, _Journal of Development Economics_ 88(1): 19-31 Drexler, Alejandro, Greg Fischer and Antoinette Schoar (2010) “Keeping it simple: Financial Literacy and Rule of Thumbs”, mimeo. LSE.

Emran, M. Shahe, AKM Mahbub Morshed and Joseph Stiglitz (2007) “Microfinance and Missing Markets”, Mimeo.

Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodruff (2011) “Female Microenterprises and the Fly-paper Effect: Evidence from a Randomized Experiment in Ghana”, Mimeo. World Bank.

Field, Erica, Seema Jayachandran, and Rohini Pande (2010) “Do traditional institutions constrain female entrepreneurship? A field experiment on business training in India”, _American Economic Review Papers and Proceedings_ 100(2): 125-29.

Gine, Xavier and Ghazala Mansuri (2011) “Money or ideas? A field experiment on constraints to entrepreneurship in rural Pakistan”, Mimeo.

Imai, Kosuke, Luke Keele, Dustin Tingley and Teppei Yamamoto, (2011). “Unpacking the black box: Learning about causal mechanisms from experimental and observational studies”, Mimeo. Princeton University.

Imbens, Guido (2000) “The Role of the Propensity Score in Estimating Dose-Response Functions”, _Biometrika_ 87(3): 706-710.

Karlan, Dean, and Martin Valdivia (2011). “Teaching Entrepreneurship: Impact of Business Training on Microfinance Institutions and Clients”. _Review of Economics and Statistics_ , 93(2): 510-27.

Kevane, Michael and Wydick, Bruce (2001), "Microenterprise Lending to Female Entrepreneurs: Sacrificing Economic growth for Poverty Alleviation?", _World Development_ , Volume 29(7): 1225-1236.

Lloyd-Ellis, Huw and Dan Bernhardt (2000) “Enterprise, inequality, and economic development”, _Review of Economic Studies_ 67: 147-68.

McKenzie, David (2011) “Beyond baseline and follow-up: The case for more T in experiments”, _Journal of Development Economics_ , forthcoming.

Valdivia, Martin (2011) “Training or technical assistance for female entrepreneurship: Evidence from a field experiment in Peru”, Mimeo.

# **Appendix 1: Business Practices Score**

The _total score_ – the composite business practice score -- ranges from a minimum of -1 to a maximum of 29. The total is the sum of the following component scores: the _marketing score_ , the _stock score_ , the _record s score_ , and the _financial planning score_ .

The _marketing score_ ranges from 0 to 7, and it is calculated by adding one point for each of the following that the business has done in the last 3 months:

- Visited at least one of its competitor’s businesses to see what prices its competitors are charging

26
**Figure 4:** Pollution estimates by region

<!-- Start of picture text --> Pollution density<br>ón Aisén del Gral.Carlos Ibáñez del Ca Región de Antofagasta Región de Arica y Parinacota Región de Atacama<br>250<br>200<br>150<br>100<br>50<br>0<br>Región de Coquimbo Región de La Araucanía Región de Los Lagos Región de Los Ríos<br>250<br>200<br>150<br>100<br>50<br>0<br>ión de Magallanes y de la Antártica Chi Región de Ñuble Región de Tarapacá Región de Valparaíso<br>250<br>200<br>150<br>100<br>50<br>0<br>Región del Biobío ión del Libertador Gral. Bernardo O’Hig Región del Maule Región Metropolitana de Santiago<br>250<br>200<br>150<br>100<br>50<br>0<br>0 10 20 30 0 10 20 30 0 10 20 30 0 10 20 30<br>Mean PM2.5<br>Count<br><!-- End of picture text --> Source: Authors’ calculations using van Donkelaar et al. (2016)

# **5.2 City-level productivity data**

Our economic dataset consists of region/city-level data, which contains several productivity indicators that are relevant for our the analysis. For each of the 346 cities ( _i_ ) there are 21 sectors and 183 sub-sectors ( _j_ ). For each of the indicators within each city, the annual data ( _t_ ) span from 2005 to 2016. From the city level data we construct real output per worker, our proxy for labor productivity.<sup>3</sup> The data tracks nominal sales of firms within each city. We have estimates of city-level sales values, but not for individual firms. As we do not have access to city-level CPI data, or to sub-sector CPI data, we deflate nominal sales using sector-level national account deflators for Chile as a whole. The pollution data is provided in a coordinate-system. We merge this data using city-level coordinates for both the productivity and the pollution data-set. Table 2 shows that annual pollution concentration for Chile during the period of study exceeds the recommended annual 5 _ug/m_<sup>3</sup> .

> 3The industry classification follows ISIC Rev.4. Furthermore, we did not obtain data on hours worked, which precludes analysis on this measure of productivity.

9
An important consideration in applying this decomposition concept to the WBG salary data is that employees are hired at a specific grade. This suggests that our “entry salaries” component should be subdivided into the grades at which men and women are hired, and the entry salaries they receive _conditional on their hiring grade._ This leaves us with five components: attrition, salary growth, entry salaries, grade composition of hires and legacy.

Appendix 2 describes in detail how the decomposition is implemented using simulations. In brief, the procedure involves the following steps. First we estimate from our data simple parametric models for each decomposition component, in each year and for each group of employee that we are concerned with. For example, we estimate the probability of leaving the bank in 1995 for a Part 1 male employee hired as a GF, as a function of his current salary. Next, taking as given the initial 1987 distribution of salaries, we use the estimated models to simulate the salary raise that each employee receives in each year, whether they leave the WBG in each year, as well as the salaries of new hires, all the way to 2015. After verifying that the simulations for 2015 reproduce accurately the 2015 data, we can use the simulation model to produce counterfactual salary distributions in which, for example, the parameters governing the salary growth for women are replaced by the parameters governing salary growth for men.

The procedure described above decomposes the difference in salaries between two groups of employees in a given year, which we have called the “aggregate gap”. However, the same method can also be adapted to conduct an analysis by tenure. The object of interest in that case is salary gaps as they develop over the employee's careers, which we call the “career gap”. To do so, the data are arranged by years of tenure instead of by calendar year. Hiring salary, attrition, and salary growth processes are also estimated for each year of tenure. The simulation algorithm starts by drawing a distribution of entry salaries and proceeds to simulate salary growth and exits for each year of tenure.

This decomposition method has several strengths. First, the factors identified in the decomposition correspond to well-defined Human Resources levers. Namely, the policies concerning raises and promotions, the policies regarding new hires and the policies regarding retention of employees. An important point regarding the interpretation of the results, is that this decomposition is not concerned with determining why each of these policies have favored men over women or some nationalities over others. For example, if male hires receive higher salaries than female hires, we do not attempt to separate whether this is due to discrimination or to objective differences in qualifications among male and female candidates. Instead, the type of statement that we can make is for example: “80 percent of salary 30
WPS7844 Policy Research Working Paper 7844 Poverty and Shared Prosperity Report 2016: Taking on Inequality _Background Paper_ Who Are the Poor in the Developing World?

_Andrés Castañeda Dung Doan David Newhouse Minh Cong Nguyen Hiroki Uematsu João Pedro Azevedo with Data for Goals Group_ Poverty and Equity Global Practice Group October 2016
5 among the ten funds in this survey. The reason for choosing the top ten funds was to provide a set that captures a high market share. Although some smaller funds might behave differently than the funds analyzed here, the chosen fumd set in this study is sufficiently representative of the system as a whole during and after the privatization process to enable conclusions about the role of funds.

In order to describe and analyze various funds, a more detailed conceptual framework shall be provided by classifying between different types of funds.

Table 2: The top ten investment funds in the Czech Republic

|Investment<br>fu|nd group 1/|% of total|% of points|
|---|---|---|---|
|Abbreviatio|n Full name|points|acquired by funds|
|CS|Ceska Sporitelna|11.1|15.6|
|IB|Investicni Banka|8.4|11.9|
|HCC|Harvard Capital & Consulting<br>4/|7.5|10.5|
|KB|Komercni Banka|5.4|7.6|
|CP|Ceska Pojistovna|3.9|5.5|
|CA|Creditanstalt<br>5/|1.9|2.7|
|PPF|PPF|1.4|1.9|
|ZB|Zivnostenska<br>Banka|1.4|1.9|
|AB|Agrobanka<br>6/|1.3|1.8|
|YSE|YSE 2/|0.8|1.2|
||Subtotal fund set|43.1|60.6|
||Other funds 3/|28.3|39.4|
||Total funds|71.4|100.0|

1/ For bank sponsored funds IPFs are classified by sponsors (see also next chapter).

2/ Due to limited data coming from investment fund Bohemia (No. 10 in size with 1.0% of all the points), which is the IPF from Banka Bohemia, the private fund YSE was included in our set of top ten funds. This change insignificantly influences the overall importance of the top ten Czech funds among all funds operating in the Czech Republic (60.6% instead of 60.8%) 3/ of which some bigger Slovak Investment Funds that invested in Czech shares, e. g. VUB (5.8% of total points), Slovenske Investicie (2.2% of total points), SSK - Investment Company of Slovak Savings Bank and VSZ Kosice (2% of total points), Slovenska lnvesticni Banka (1.7% of total points) and Slovenska Pojistovna (1.4% of total points). These five bigger Slovak funds together collected 13.1% of all points and amoun **t** o 18.2% of total points invested in funds.

4/ includes 0.8% of total points from the Slovak fund of Harvard.

5/ includes 0.3% of total points from the Slovak fund of Creditanstalt.

6/ includes 0.5% of total points from the Slovak fund of Agrobanka. Source: Fund interviews and Mejstrik (1994).
Duranton G., 1997, « La Nouvelle Economie Géographique : agglomération et dispersion », _Economie et Prévision_ , 131, 5, 1-24.

Duranton G., 1999, « Distance, sol et proximité : analyse économique et évolution urbaine », dans Bailly A., Huriot J. M. (dir.), _Villes et croissance_ , Anthropos, 91131.

Eaton J., Kortum S., 2002, « Technology, geography and trade », _Econometrica_ , 70, 5, 1741-1779.

Eaton J., Kortum S., 1997, « Technology and Bilateral Trade », _NBER working paper 6253._ Frankel J., Stein E., Wei S., 1993, « Continental Trading Blocs: Are They Natural, or Super-Natural ? », _NBER working paper 4588._ Fu X., 2004, « Limited linkages from growth engines and regional disparities in China », _Journal of Comparative Economics,_ 32, 148-164.

Fujita M., Hu D., 2001, « Regional disparity in China 1985-1994: the effects of globalization and economic liberalization », _The Annals of Regional Science,_ 35, 3-37.

Fujita M., Thisse J. F., 2001, « Agglomération et marché », _Cahiers d’Economie et Sociologie Rurales,_ 58/59, 11-57.

Fujita M., Thisse J. F., 2002, _Economics of agglomeration,_ Cambridge University Press.

Gao T., 2004, « Regional industrial growth: evidence from Chinese industries », _Regional Science and Urban Economics,_ 34, 101-124.

Ghio S., Van Huffel C., 1999, « Politiques de libéralisation commerciale dans les pays en développement et niveaux d’infrastructures : les effets sur l’hyperconcentration urbaine », dans Catin M., Lesueur J. Y., Zenou Y., _Stratégies, concurrence et mutations industrielles_ , L’Harmattan, Paris.

Golub P.S., 1999, « Recentrer la croissance », _Le Monde Diplomatique,_ Janvier. Hansen N., 1990, « Impacts of small and intermediate-sized cities on population distribution : issues and responses », _Regional Development Dialogue_ , 11, 60-76. Henderson J. V., 2000, « The effects of urban concentration on economic growth », _NBER working paper_ , _7503_ .

Henderson J. V., Shalizi Z., Venables J., 2001, « Geography and development», _Journal of Economic Geography,_ 1, 81-105.

He C., 2003, « Location of foreign manufacturers in China : agglomeration economies and country of origin effects », _Papers in Regional Science,_ 82, 3, 351372.

Hu D., 2002, « Trade, rural-urban migration, and regional income disparity in developing countries : a spatial general equilibrium model inspired by the case of China », _Regional Science and Urban Economics,_ 32, 311-338.

Hu A.G.Z., Jefferson G.H., 2002, « FDI impact and spillover : evidence from China’s electronic and textile industries », _World Economy,_ 25, 8, 1063-1076. Jones D.C., Li C., Owen A.L., 2003, « Growth and regional inequality in China during the reform era », _China Economic Review,_ 14, 186-200.

Kanbur R., Zhang X., 1999, « Which regional inequality ? The evolution of ruralurban and inland-coastal inequality in China from 1983 to 1995 », _Journal of Comparative Economics,_ 27, 686-701.

Kim T. J., Knaap G., 2001, « The spatial dispersion of economic activities and development trends in China : 1952-1985 », _The Annals of Regional Science,_ 35, 39-57.

21
13 large as 10 per cent of GDP, of which 2.8 per cent was accounted for by a fall in government consumption, 0.4 per cent by cuts in government investment and the rest by discretionary increases in taxes net of transfers."' Finally, in the monetary area, the package was accompanied by the announcemen **t** hat _the exchange rate of the Danish kroner versus the German mark would henceforth be fixed._

# _2.2.2. Ireland._

In Ireland, the height of instability prevailed at the beginning of 1982 when the government fell on the issue of a tight budget. Certainly the need for austerity had been clear at least since 1981. CPI inflation peaked during that year, reaching 20.43 per cent. Similarly, the long term interest differential with Germany increased sharply toward the end of 1981 and the beginning of 1982. Public finances were in much worse situation than those in Denmark. As a share of GDP, the primary-full employment rudget deficit was 8.4 per cent, debt service absorbed 8.. per cent, and total national debt was 87 per cent. This situation persisted until early 1982 when a political consensus emerged to push forward specific measures for disinflation, austerity and fiscal consolidation. According to Dornbusch (1989), the consensus supported four specific policy measures. First, a shift towards consistently positive and high real interest rates"<sup>2</sup> . Second, a fiscal policy of consistently reducing the primary deficit (by 1984 the full-employment primary deficit had been reduced by more than 7 percentage points of GDP) mostly through higher discretionary taxes. Third, partly as a result of rising unemployment, partly as a result of wage agreements, the increase in labor compensation slowed down from 18.1 per cent in 1981 to only 11 According to our figures from OECD National Income Accounts and Economic Outlook, the fiscal consolidation implied an improvement of 6.1 per cent in the structural budget balance, of which 5.1 per cent was achieved through cuts in government consumption.

12 According to Giavazzi and Pagano (1990), ex-ante real interest rates not only fell in Ireland but, moreover, they turned negative.
35 it were, placed in different "niches". Social workers do not think of the poor or the indigent in terms of a generic category of people whose transitory, or sometimes long-term, income is below a certain minimum.

Since the beginning of the transition, spending on family benefits as a share of GDP has increased in the FSU countries where universal family allowance was introduced in 1989 only. It is now fairly high in Central Asia, where, due to demographic structure, it reaches 2% percent of GDP. In Eastern Europe it has been flat in terms of GDP, and, of course, declining in real terms (see Table 8). Comparable data on social assistance spending are not available. However, for the countries where we have the data, there was a clear increase in spending as a share of GDP. Higher spending was driven by —in some cases— huge increases in claimants and recipients of social assistance. Thus, in Poland, the number of recipients rose from little over 1 million in mid-1980s (22 percent of the population) to more than 3 million in 1993, and spending rose from 0.1 percent of GDP to 0.5 percent;* in Hungary, the number of claimants doubled between 1990 and 1993. Social assistance offices are sometimes overwhelmed by the sheer number of claimants; even more often, they run out of resources. In Riga, for example, the city-defined poverty line at the end of 1993 was Lats 12.9 (about $22) per capita per month. The city authorities, however, estimated that they had only 5 percent of the total amount needed to help everyone who could have claimed benefits based on that poverty line. In Poland, in November 1993, the legislature passed the law authorizing special benefits for expecting mothers with low incomes. The financing, supposed to come from the Center, never materialized, and local authorities, short of funds, simply failed to provide any benefits. In Ukraine, the 1994 centrally-decreed increase in allowance for War veterans was never implemented due to shortage of funds on the loca] level. The twin pressures of people and lack of resources mean that the current systems ought to be reformed with two objectives in mind: © How to reduce the number of people who are eligible or who think they may be eligible for social assistance, that is, how to "pre-screen" the candidates before they come to social assistance offices; © How to better use the money, that is how to improve targeting.

> “See World Bank, Poverty in Poland, Chapter 4.

> Note also that the official poverty line at the time was Lats 36! And this was not the “accounting” social minimum line (meaning that it is derived by the "armchair" economists never to be implemented), but the “minimum crisis basket", explicitly worked out to serve as a poverty line during the transition.