rafmacalaba/datause-encoder-data huggingface dataset:
4 the averagt. real wage rate for EGS employees over the period April 1987 to February 1990, as well as our estimate of the average wage rate for agriculture, which we will discuss further in section 4.

The adjustment period saw the EGS wage roughly double initially, in line with the increase in the statutory minimum wiage rate. The wage rate then fell almost as dramatically, to end up at about one third higher (in real terms) than it had been prior to May 1988 (Figure 1).

Inflation contributed to this decline, but there are other factors to consider. Wage rates on the EGS are fixed according to the type of w?ork done and its output; for example, rock breaking is paid at a different rate per cubic foot to loading soil on trucks. The whole piece rate schedule doubled after May 1988. The allocation of the available work across these activities - - which we will refer to as the composition of EGS work is determined by the EGS authorities, through their project choices. Under such conditions it may be quite sensible for the authorities to adjust the latter in favor of activities with lower piece rates, in response to the increase in the wage schedule.6 Given that such adjustments are not instantaneous, a better indication of how the scheme responded to the change in wage schedule can be obtained by comparing outcomes across periods 1 and 3. Table 1 gives some data of interest for all three sub-periods, calculated from unpublished monthly EGS records.

By the third sub-period, the impact of doubling the piece rate schedule amounted to about a one-third increase in the average real EGS wage rate; roughly two-thirds of the initial wage increase was absorbed through inflation and changes in work composition. But the more striking observation is that,
# **Table of Contents**

|**1**<br>**Intr**|**oduction**|**4**|
|---|---|---|
|**2**<br>**Assu**|**mptions**|**7**|
|**3**<br>**Mea**|**sures**|**8**|
|3.1|Defnitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>8|
|3.2|Geometric interpretation<br>. . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>10|
|**4**<br>**Com**|**putation**|**13**|
|4.1|Index numbers<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>13|
|4.2|Demand functions . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>13|
|4.3|Elasticity<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>15|
|4.4|Taylor’s approximations<br>. . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>16|
||4.4.1<br>First order . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>19|
||4.4.2<br>Higher orders . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>21|
||4.4.3<br>Convergence and the Cobb-Douglas function . . . . . . .|. . . . . . .<br>24|
|4.5|Vartia’s approximation<br>. . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>24|
|4.6|Breslaw and Smith’s approximation<br>. . . . . . . . . . . . . . . .|. . . . . . .<br>26|
|4.7|The ordinary differential equations methods . . . . . . . . . . . .|. . . . . . .<br>27|
|4.8|Relational approach . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>28|
|**5**<br>**App**|**lications**|**29**|
|5.1|Individual welfare . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>30|
||5.1.1<br>Small price variations<br>. . . . . . . . . . . . . . . . . . .|. . . . . . .<br>30|
||5.1.2<br>Large price variations . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>33|
||5.1.3<br>A choice tool for practitioners . . . . . . . . . . . . . . .|. . . . . . .<br>36|
|5.2|<br>Social welfare . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>41|
||5.2.1<br>Statistical inference (Society)<br>. . . . . . . . . . . . . . .|. . . . . . .<br>42|
||5.2.2<br>Statistical inference (Quantiles)<br>. . . . . . . . . . . . . .|. . . . . . .<br>44|
||5.2.3<br>Pro-poor curves and stochastic dominance . . . . . . . . .|. . . . . . .<br>45|
|**6**<br>**Con**|**clusion**|**47**|
|**Append**|**ices**|**54**|
|**A Dem**|**and Systems**|**54**|
|A.1|Linear Demand (LD) . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>54|
|A.2|Log Linear Demand (LLD) . . . . . . . . . . . . . . . . . . . . .|. . . . . . .<br>54|
|A.3|The Linear Expenditure System (LES) . . . . . . . . . . . . . . .|. . . . . . .<br>54|
|A.4|<br>The Almost Ideal Demand System (AIDS) . . . . . . . . . . . . .|. . . . . . .<br>56|
|A.5|The Quadratic Almost Ideal System (QUAIDS) . . . . . . . . . .|. . . . . . .<br>57|
|A.6|Exact Affne Stone Index (EASI) . . . . . . . . . . . . . . . . . .|. . . . . . .<br>58|
|**B**<br>**Non**|**linear price changes and wellbeing**|**59**|

2
party affiliation and by membership in local Congress or Consultative Conference. These findings thus show that private firms have followed dual-track routes, one external and one internal, for gaining commercial advantages: political connection and human capital of owners. Whereas the external route relies more on the power of the government, most likely originating from preferential treatment, the internal route resorts to the strengthening of the firm's own competitiveness to win in the market system. The importance of both routes in determining profit rates of private firms highlights the complexity of the BE in China.

_Evolution of firm governance mechanisms_ **.** To measure Chinese firms’ reliance on formal legal institutions, we use two indicators on the efficiency of local courts in solving business disputes. The first is the proportion of local firms resorting to the legal system in the event of a business dispute; the second is the percentage of firms being satisfied with the legal system's resolution. Both measures are based on the private firm surveys. These measures are chosen because firms will resort to courts to resolve business problems or commercial disputes only when they have sufficient confidence in the quality of the court system (Long 2010; Li et al. 2008).

Chinese private firms have increasingly relied on formal mechanisms such as courts to resolve business disputes instead of resorting to private negotiations or suffering in silence, and they are more satisfied with court resolutions over time, implying more confidence in the legal system (see panels A and B in table 9). Strikingly, the share of firms using the court system to resolve disputes more than tripled, rising from 9 percent in 1995 to 32 percent in 2006. Equally surprisingly, the share of disputes resolved via the route of “appealing to the local government” rose from 2 percent to 21 percent, an increase by 10 times. Interestingly, the ratio of being satisfied with dispute resolutions by courts has increased from 54 percent in 1995 to 83 percent in 2006; and the corresponding satisfaction rate via appeals to local governments has also risen from 63 percent in 1995 to 91 percent in 2006. Thus, we witness a dual-track system of conflict resolutions: the increasing reliance on formal market-supporting institutions and also an everstronger clinging to the government, with satisfactory resolution outcomes in both cases.

Politically-connected firms use the formal court system to a greater extent (see Panel C of Table 9 and Figure 5). Firms with state origin (i.e., privatized firms) or government connections 24
18

|Country,<br>ta<br>economy, or<br>territory|stclubtis<br>P<br>Date|hedcensus<br>opulation<br>(1000) *|Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless irdicated)|
|---|---|---|---|---|---|
|British<br>Virgin|May80|12 F|UNPVSR<br>1/88 (official<br>est.)|--||
|islands||||||
|Brunei|Aug81|193 F|UNPVSR<br>4/87 (official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|Bulgaria|Dec 85|8948 F|UNPVSR<br>7/86 (official<br>est.)|Based onINED10/87|Based on UNPVSR<br>4/87|
|Burkina Faso|Dec 85|7747 F|Bankprojection from census|Bank est.|U.N. 1988 revision (prelim.)|
|Burma|Mar83|35308 F|U.N. 1988 revision (prelim.)|Based on U.N. 1988 revision<br>(pretim.)|Based on U.N. 1984<br>assessment|
|Bururdi|Aug79|4114 F|Bank projection from census|Bank assessment of 1965,<br>1970-71, and 1979 censuses<br>and 1979 postcensal<br>|Bank est.|
|||||demographic survey||
|Cameroon|Apr 76|7090 F|Bank projection<br>from census,<br>adjusted for 7.4X undercount|1980-85, world Fertility<br>Survey; 1985-90, Bank<br>projection assuming<br>reduced sterility|Bank assessment of Uorld<br>Fertility<br>Survey child<br>survival<br>rates|
|Canada|Jun 86|25309 J|UNPVSR<br>1/87 (official<br>est.)|Official<br>est.,<br>Population<br>Projections for Canada,<br>1984-2006|Official<br>est. (population<br>projections<br>for Canada,<br>1984-2006)|
|Cape Verde<br>|Jun 80<br>|296 F<br>|Bank projection<br>from census<br> <br> <br>|Bank assessment of official<br>CBR est.|Bank assessment of official<br>CDR est.|
|Cayman Islands|Oct 79|17 F|UNPVSR<br>1/88 (official<br>est.)|--|--|
|Central African<br>Rep.|Dec 75|2055 F|Bank projection<br>from census,<br>adjusted for 11.3XLradercount|Bank est.|Bank assessment of census<br>data on child survival|
|Chad|--|--<br>-|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|Chamnel Islards|Mar86|136 F|UNPVSR<br>4/87|Based on UNPVSR<br>7/86|Based on UNPVSR<br>7/86|
|Chile|Apr 82|11330 F|UNPVSR<br>7/86 (official<br>est.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|China (excl.<br>Taiwan)|Jul 82|1031883 F|Bank projection<br>from official<br>1984 est. using official<br>grouth rate of 1.08X|Bank est.|Bank est.|
|Colarbia|Oct 85|27867 J|Official<br>est.|Bank est.|Bank est.|
|Camoros|Sep 80|335 F|Bank projection from census,<br>excluding Mayotte Island<br>(population 50.74 thousand)|Bank sector report|Bank sxctor report|
|Congo, People's<br>Rep. of the|Jan 85|1854 F|Bank projection from census|Bank est.|Bank est.|
|Cook Islands|Dec 81|18 F|USBOC<br>1985|||
|Costa Rica|Jun 84|2417 J|UNPVSR<br>1/87 (official<br>est.)|U.N. 1988 revision (prelim.)|1980-85, Celade printout<br>2/87; 1985-90, Bank<br>projection|
|C6te dlvoire|Apr 75|6710 F|U.N. 1988 revision (prelim.)|1980-85, U.N. 1988 revision<br>(prelim.);<br>1985-90, Bank<br>projection|U.N. 1988 revision (prelim.)|
|CLba|Sep 81|9724 F|UNPVSR<br>7/86 (official<br>est.)|Based on official<br>CBR|U.N. 1988 revision (prelim.)|
|Cyprus|Sep 76|613 J|UNPVSR<br>7/86 (official<br>est.)|Based on U.N. 1984|Based on U.N. 1984|
||||<br>|assessment|assessment|
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# ANNEX TWO: Some Comparisons of Cote d’Ivoire’s Growth Record

## **Cross Country Comparisons**

How did Cote d’Ivoire’s growth compare with that of other developing countries? This section compares recent per capita real growth in GDP<sup>33</sup> for several peer groups of countries versus Cote d’Ivoire for the most recent, ten year period 1993 to 2002, and the ten-year average. First, we compare Cote d’Ivoire to other African and poor countries. Then we more on to compare her with other CFA countries, and other countries in Africa experiencing conflict. Cote d’Ivoire grew more slowly than any of these benchmark peer groups.

## **Other Developing Countries**

Summary benchmarks and comparisons: • **_<u>Sub-Saharan Africa</u>_** (48 countries) The Sub-Saharan Countries as a group averaged 2.90% growth over the decade, more than half a percent more than Cote d’Ivoire.<sup>34</sup> • **_<u>West Africa</u>_** (23 countries) Cote D’Ivoire ranked 18<sup>th</sup> in this group for the decade. West Africa grew at a 4.6% average annual rate for the decade, more than 2.2% faster than Cote d’Ivoire • **_<u>Poor Countries</u>_** (63 countries with 2002 per capita GDI less than $735) Measured by average growth over the decade of 1993-2002, Cote d’Ivoire ranked 39<sup>th</sup> out of the 61 poor countries for which statistics are available. The poor countries as a group averaged 3.52% growth for the decade versus 2.36% for Cote d’Ivoire.

Figure A-7 shows plots West African average annual growth against that of Cote d’Ivoire. Both Cote d’Ivoire and West Africa emerged from a slump in 1993 to grow strongly in the post-devaluation period. This is in part due to the fact that many West African countries are CFA countries, which suffered together from currency overvaluation until 1994, and then benefited from the large and long-awaited devaluation in January 1994. But West Africa as a whole posted even stronger growth in 1997, while Cote d’Ivoire experienced a downturn. From 1998 to 2002, West Africa grew at about 5% while Cote d’Ivoire did not grow at all, posting negative growth in 2000 and 2002. While West Africa as a whole turned around in the mid 1990s and began to grow again, Cote d’Ivoire experienced an ephemeral upturn and slipped back into decline.

> 33 From SIMA, in constant 1995 US dollars.

> 34 There are simple arithmetic averages of  reported growth rates; they are not weighted by population.

37
# **Toward Successful Development Policies: Insights from Research in Development Economics**

Erhan Artuc, Robert Cull, Susmita Dasgupta, Roberto Fattal Jaef, Deon Filmer, Xavier Giné, Hanan Jacoby, Dean Jolliffe, Hiau Looi Kee, Leora Klapper, Aart Kraay, Norman Loayza, David McKenzie, Berk Özler, Vijayendra Rao, Bob Rijkers, Sergio Schmukler, Michael Toman, Adam Wagstaff, and Michael Woolcock World Bank Development Research Group

## **JEL codes:**

D0 (Microeconomics), E0 (Macroeconomics and Monetary Economics), F0 (International Economics), G0 (Financial Economics), H0 (Public Economics), I0 (Health Education and Welfare), O0 (Economic Development, Technical Change and Growth), Q0 (Agriculture, Natural Resource, and Environmental Economics) ______________________

> 1818 H Street NW, Washington DC 20433.  We are grateful to Simeon Djankov and Pinelopi Goldberg for helpful discussions. The views expressed here are the authors’, and do not necessarily reflect the official views of the World Bank, its Executive Directors, or the countries they represent.
Policy ReseaRch WoRking PaPeR 4783

# **Abstract**

In 1993, in response to persistent unemployment, and rising poverty and social unrest, the government of Albania introduced an anti-poverty program, namely Ndihma Ekonomike; in 1995 it was extended to all poor households. This paper estimates the separate effects of participation in this income support program and the old-age pension program on objective and subjective measures of household poverty. The analysis uses the nationally representative Albanian Living Standards Measurement Surveys carried out in 2002 and 2005. Using propensity score matching methods, the paper finds that Ndihma Ekonomike households, particularly urban residents, have lower per capita consumption and are more likely to be discontented with their lives, financial situation, and consumption levels than their matched comparators. In contrast, households receiving pensions are not significantly different from their matched comparators in reference to the same set of outcomes. The paper finds that the negative impact of Ndihma Ekonomike participation on welfare is driven by a negative labor supply response among work-eligible individuals. This negative labor response is larger among women and urban residents. In contrast to Ndihma Ekonomike, the receipt of old-age pension income transfers does not significantly impact the labor supply of prime-age individuals living in pension households This paper—a product of the Poverty Reduction and Economic Management, Europe and Central Asia Region, and Human Development and Public Services Team, Development Research Group—is part of a larger effort in the departments to understand the impact of social protection programs on the labor supply of by prime-age individuals and poverty reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at adabalen@worldbank.org, tkilic@worldbank.org, and wwane@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
**Table** A1.10 - Ranking of It _em._ Brazil-MNxico, Grain Mill Products. (1975'

|--------------------------------<br>**rank**<br>of item<br>a) Items S<br>contribu<br>|----<br>value**as**%<br>**ot**total valu-<br>of output<br>pecified by Quanti<br>ting more then 1<br>|___-----------<br>BRAZIL<br>cumulative<br> <br>%**of**total<br>**value**of outpu<br>ty and Value.<br>per cent to O<br>|--,-------__---------------__---------_-----<br>matched<br>with**MEXICO**<br>t<br> <br>utput:<br>|------------._---<br>rank<br>of it"e<br>a) Itemz Sp<br>contribu|**valueas% **<br>of total value<br>of output<br>ecified by Quant<br>ting more than 1|MEXICO<br>cumuletive<br> <br>'kof total<br>value of outp<br>ity end Value.<br> **per**cant to O|<br>matched<br> <br>with BRAZIL<br>ut<br> <br>utput:|
|---|---|---|---|---|---|---|---|
|1|59.51|59.51|**Yes**|1|26.89|26.89|Yes|
|2|7.26|66.77|You|2|20.08|46.97|Yes|
|3|7.07|73.85|**Yes**|3|14.31|61.28|Yes|
|4|0.55|74.40|No|4|3.49|64.77|No|
|5|5.52|79.92|Yes|5|2.94|67.71|No|
|6|3.82|83.74|No|6|1.95|69.67|No|
|7|2.86|86.60|No|7|1.70|71.37|No|
|8|2.58|89.18|Yes|8|1.32|72.69|No|
|9|1.79|90.97|No|9|1.23|73.92|No|
|10|1.22|92.20|No|10|1.20|75.12|Yes|
|||||11|1.17|76.29|No|
|||||12|1.08|77.37|No|
|Percenta|ge matched:||81.95|Percenta|ge matched:||62.49|

b) Items Specitied by Quantity and Value. contributing lee- than 1 per cent to Output: 11-21 2.07 94.26 b) Items Specified by quantity **and Value.** contributing lees then 1 per cent to Output: 13-55 8.73 86.10 Percentage matched: 0.00 Percentage matched: Percentage matched: 0.00 Percentage matched: 2.84 c) Output not Specified by Quantity: c) Output not Specified by Quantity: 5.74 100.00 13.90 100.00 Percentage matc)ed: 0.00 Percentage matched: 0.00 --------------------------------------------------------------__-------_-_-___-------------------_-----------Total percentage matched: 81.95 | Totel percentage matched: 65.33

Source: Brazil figures derived from tables A1.3 and Al.?; Mexican figures derived from tables A1.4 and A1.7;
liquidity of a stock would be determined to some extent by how much the stock‟s current price is above the investors‟ weighted average purchase price for that stock. Given the robust and significant relationship that has been established in the literature between trading decisions and the disposition effect, and given its close relation to liquidity, I use the disposition effect as a control in the hazard regressions. To do this, as mentioned earlier, I create a time-varying covariate to capture the disposition effect. A dummy variable ( _Disp Dum_ ) is set to one for each day a stock in an investor‟s portfolio trades above its purchase price. I run the same hazard model as before, but now I include the _disp_ variable as a time-varying covariate. The results are provided in Table 5. Using household level controls, I find that an individual is 1.8 times more likely to sell a stock when it is trading above its purchase price than when it is not. The transaction costs variable is significant after controlling for the disposition effect, but is not able to explain away this effect. It is also worth noting that the interaction term is positive, indicating that the disposition effect is stronger among less liquid stocks. Households are more likely to sell an illiquid stock that is trading above the purchase price than one that is not illiquid.

Existence of asymmetric information complicates the analysis. It is not entirely clear how aggregate asymmetric information for a given security would affect its average holding period. On the one hand, one can think of asymmetric information as a component of transaction costs, which investors take into account in selecting which securities to hold. On the other hand, if investors trade for both liquidity and information reasons, allocational inefficiencies could reduce the correlation between holding periods and liquidity (Garleanu and Pedersen 2007). I control for aggregate asymmetric information in a given security by including the probability of information based trading (PIN) measure (Easley et al. 1997) calculated from intra-day data.<sup>19</sup> As before, I compute an annual PIN dummy variable for each stock in the dataset. _PIN Dum_ takes on a value of one if the stock is in the highest PIN group. The results appear in Table 5 under Model V. The PIN measure significantly reduces the holding period of investors.

> 19 A detailed description is contained in Easley, Hvidkjaer and O‟Hara (2004).  The data is provided by Soeren Hvidkjaer at http://www.smith.umd.edu/faculty/hvidkjaer/pin1983-2001.zip.

18
25

- Figlio, D. and U. Ozek<sup> ̈</sup> (2017): “Unwelcome Guests? The Effects of Refugees on the Educational Outcomes of Incumbent Students,” .

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- NPR (2018): “Venezuela’s Deepening Crisis Triggers Mass Migration Into Colombia,” Accessed from: https://www.npr.org/sections/parallels/2018/02/20/587242391/venezuelas-deepeningcrisis-triggers-mass-migration-into-colombia.

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