rafmacalaba/datause-encoder-data huggingface dataset:
|PPR<br>rle|Working PaperSeries<br>Author|Date|Contact|
|---|---|---|---|
|WPS174 Trade Restrictions<br>with Imported<br>Intermediate<br>Inputs:<br>When Does||||
|the Trade BalanceImprove?|Ramon E.Lopez<br>Dani Rodrik|March1989|M.Ameal<br>61466|
|WPS175 An Integrated Model ofPerennial<br>andAnnual Crop Production<br>for<br>Sub-Saharan<br>Countries|Robert D. Weaver|||
|WPS176 Credit Rationing,<br>Tenancy,<br>Productivity,<br>andthe Dynamics<br>of Inequality|Avishay Braverman<br>Joseph E. Stigiltz|||
|WPS177 Cash-Flow or Income? The Choice of||||
|**Base**forCOmpany Taxation|Jack M. Mintz<br>Jesus Seade|April<br>1989|A. Bhalla<br>60359|
|WPS178TaxHolidaysandInvestment|Jack M. Mintz|April<br>1989|A.ihalla<br>**60359**|
|WPS179 Public Sector Pricing<br>inaFiscal<br>Context|Christopher Heady|April<br>1989|A.Bhalla<br>60359|
|WPSI8OStructural<br>Changes inMetals<br>Consumption:<br>Evidence from||||
|U.S. Data|Boum-Jong Choo|April<br>1989|S.Lipscomb<br>33718|
|WPS181 Public Finance, TradeandDevelopment:||||
|What Have We Learned?|Johannes F.Linn<br>Deborah L. Wetzel|April<br>1989|M. Collnet<br>33490|
|WPS182 The Experience of LatinAmerica<br>WithExport Subsidies|Julio Nogues|April<br>1989|S.Torrivos<br>33709|
|WPS183PrivateInvestment, Macromanagement<br>and TradeLiberalizatic.a:<br>The<br>Caseof Mexico|Alberto R. Musalem|||
|WPS184Women andForestry:<br>Operational<br>Issues|Augusta Molnar|||
|WPS185Uniform TradeTaxes,Devaluation,<br>andthe Real Exchange Rate:<br>A Theoretical<br>Analysis|Stephen A. O'Connell|April<br>1989|A. Oroposa<br>61758|
included both industrial and service sector organizations. The study showed that financial incentives alone improved task performance by 23 percent, whereas financial incentives combined with social recognition and positive feedback from superiors increased task performance by 45 percent.

**Table 1: James Q. Wilson’s classification of job types**

|||**Actions or intern**|**al production process of the job**|
|---|---|---|---|
|||**Observable**|**Not observable**|
||**Relatively**<br>**easily**<br>**measurable**|**_Production job_**: Simple<br>repetitive stable tasks,<br>specialized skills.|**_Craft jobs_**: Application of general sets of<br>skills to unique tasks, but with stable, similar<br>outcomes.|
|**rom the job**||Examples: Manufacturing,<br>sales, simpler municipal<br>services (garbage collection).|Examples: Auditing; revenue collection;<br>teaching; medical practice; Job placement<br>work|
|**Outputs f**|**Not easily**<br>**measurable**|**_Procedural job_**: Specialized<br>skills; stable tasks, but unique<br>outcomes|**_Coping job_**: Application of generic skills to<br>unique tasks, but outcomes cannot be<br>evaluated in absence of alternatives|
|||Examples: Military|Examples: Administration; managerial jobs<br>in large private sector organizations|

A well-known study by (Lazear 2000) uses individual-level worker data from a glass company and estimates a 44% increase in productivity after switching to a piece-rate salary schedule. In a structurally similar setting, the introduction of piece-rate pay in a Canadian tree planting business has also been found to strongly affect worker productivity and profits (Paarsch and Shearer 1999).

However, production jobs, as well as procedural jobs, are of limited relevance to the public sector. Rather, given that the loci for incentive schemes, following the principal-agent literature, are mostly in contexts where actions are not easily observable but outcomes may or may not be, the focus of this paper will be on jobs in the right hand column of Table 1. Note that some of the studies of the private sector that have focused on incentives for rank-and-file ―knowledge workers‖ in large organizations can also be classified in this right hand column and will be included in the discussion.

# **Methodological approaches**

The vast empirical literature on PRP utilizes a range of methodological approaches. Early studies on performance pay in the public sector were largely qualitative case studies. As elements of civil service reform and incentive schemes were introduced in OECD countries throughout the 1980s and 1990s, academics and policy experts wrote initial studies and reports that summarized the main descriptive feature of reform attempts, and chronicled the reform process and implementation. Overall evaluation of the success of incentive schemes was based on qualitative impressions of practitioners. Later studies 11
# **Conclusion**

In sum, we benchmarked Lesotho’s infrastructure performance using available comparative indicators of various dimensions of performance. The indicators come from the recent World Bank research data base specifically developed for this purpose. The results are revealing of the several major, comparative deficiencies in infrastructure performance in Lesotho: (i) extremely low access to electricity and its affordability; (ii) poor coverage, quality and the cost of local (non-cellular) telephony; (iii) poor quality of roads, and (iv) very low access of the population to improved sanitation. Infrastructure service delivery in these areas is clearly well below what would be expected on average for a country in Lesotho’s income group.

These infrastructure shortfalls are likely to adversely affect not only the welfare of the poor but also the cost competitiveness and growth prospects of a range of economic sectors (e.g., tourism, trade) that depend critically on a stable and competitive supply of basic infrastructure service. As such, these shortfalls are also indicative of the infrastructure sectors and performance dimensions that need improvement if Lesotho is to catch up with competitive performance of its peer group of countries. Within the SACU sub-region, Lesotho’s infrastructure shortfalls are also likely to constrain its regional economic integration in several areas (e.g., Southern Africa power pool, road network) that are important for long-term economic prosperity. Finally, this simple, international comparison of Lesotho’s infrastructure performance should, of course, be combined with more in-depth, sector analyses for a more complete picture of the overall infrastructure performance. Taken together, such analyses could provide policymakers in Lesotho a useful guide to the areas of infrastructure performance requiring urgent attention.

46
|Energy|Lesothy|Pemclmark<br>Lowlicome|coy|Middle<br>Fastand <br>Noth <br>Africa|Sou<br>= Asia|Fast<br>ths <br>Pacific|Latin<br>,<br> America <br>&<br>Caribhea|Europe&<br> central <br>Asia|MMe<br>Income|Lower <br>rsadle<br> Income|— Upper<br>Middle <br>Income||World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access to ElectticityNetwork(% ofPopulation)|i|31|15|88|31|id|9|9|85|82|a7|é0|
|NEENGL.End-UserPrices (03cents/k/h)<br>-(Residential/Non<br>Residentia|8/3|ar|ar|;|;|5/5|10/8|B|OR|E|T|g|

Nowa: Data used isfrom the latest observation available between 1997 and 2603.

Source: A "Research" Database of Infrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
households if an original household member became a new household head and lives in the same village (called split households). Household members who moved outside the original village or joined other households within the village (both called out-migrants) are excluded from our analysis.<sup>5</sup> We aggregate incomes from both original and split households in 2010 to be comparable with the 2007 original households. The food price crisis of 2008-10 may be correlated with split (and migration) decisions, which raises an additional concern that supports the aggregation of incomes from the 2007 original household and its splits in the period 2007 – 2010.

# **3.2 Agricultural Production, Landholding and Machines**

Agricultural production was recorded with detailed information but rather differently in the two rounds. In 2007, the survey collected information on outputs and inputs for each crop by crop season, using management units defined by irrigated and un-irrigated lands. The 2010 survey collected plot-level information for each crop and crop season. In the analysis, I use crop income deducting input costs, i.e., crop profit including family labor costs. The estimation uses the sample of farmers who had positive crop profits in the two rounds.

Machine investments in the period from 2007 to 2010 were captured in the 2010 survey. The analysis uses total values of investment such as tractors, threshers, etc., which function to substitute for farm labor.

Land is aggregated at the household level, but grouped into three types: self-cultivated own land, self-cultivated rented-in land and rented-out land. In the analysis, a focus is on the first type as the farm cultivation decision is the main issue of this paper. Whether owned or rented-in does not matter in the analysis.

> 5 Potential attrition-related bias in the current analysis is small since the dynamics of crop income is its focus, not household income in general, which includes non-agricultural employment and remittance incomes.

8
caseload around these multiple cutoffs in the event study specification (Panel A, Figure 5). We obtain similar results estimating (3), as reported in col 1, Table A12.

Second, we look for changes in the composition of the caseload across the implementation cutoffs. Even though we verify that the president of the court did not assign fewer cases to the chambers that just started applying the reform, she could have assigned different types. Alternatively, plaintiffs may have decided to use the court for different types of cases as a result of the decree. We estimate (1) on the size of the claims, number of parties (having more than one party involved on either side of the dispute), and the type of dispute (commercial case). The results corroborate the notion that those case characteristics are not affected by the introduction of the reform (Panels B, C, and D, Figure 5). Again, estimating (3) lead to the same conclusion (cols 2–4, Table A12).

Another potential source of bias is certain types of plaintiffs may have anticipated the enactment of the decree and may have fast-tracked/delayed their case submission just before/after the application in any of the chambers. Inversely, certain types of plaintiffs may have waited for the decree to be applied in all chambers to file their cases. Alternatively, plaintiffs may have delayed or fast tracked certain types of cases (e.g., against certain kind of defendants). We note that such a behavior would likely have resulted in bunching in the chamber-level incoming caseload either over the months before the decree application cutoffs or over the months following it. The event study graph on chamber-level incoming caseload shows no such pattern (Panel A, Figure 5). We use tax administration data to show that there is no jump in the size of firms (measured by 2012 revenues) involved in cases at the cutoff, neither overall, nor for plaintiffs and defendants separately (Panels E, F, and G, Figure 5). This indicates that the effect is neither driven by different firms bringing cases to court after the reform, nor by different firms being sued. Running (3) on these variables returns the same conclusions (cols 5–7, Table A12).

One scenario that our robustness checks cannot fully rule out is that anticipation effects may have caused sorting along unobserved case characteristics across the decree introduction cutoffs. For instance, plaintiffs whose cases have weak or incomplete evidence may have rushed them to court just before the decree introduction to avoid the threat of desk-rejection under decree application. Typically, such cases would tend to take longer in pre-trial and demand more hearings, which would 68
**Table 9 : Earnings and Selection into the Private and Public Sectors of Wage Employment: 1997**

||(3)|(4)|(5)|(6)|
|---|---|---|---|---|
||Public<br>sector log<br>monthly<br>earnings|Private<br>sector log<br>monthly<br>earnings|Log<br>monthly<br>earnings<br>in 1997|Selection<br>into public<br>sector<br>in 1997|
||in 1997|in 1997||(Probit)|
|Female|-0.245|-0.508|-0.375|0.492|
||(3.50)**|(4.31)**|(5.82)**|(2.49)*|
|Married||||0.367<br>|
|||||(2.96)**|
|Log age|6.035|8.117|8.072|16.900|
||(2.04)*|(2.89)**|(4.12)**|(4.39)**|
|Log age squared|-0.767|-1.089|-1.069|-2.251|
||(1.85)|(2.73)**|(3.86)**|(4.12)**|
|**Education (reference=no formal schooling):**|||||
|Grades 1 to 10|-0.033|0.141|0.096|0.402|
||(0.25)|(0.88)|(0.94)|(1.95)|
|Preparatory school|0.322|0.597|0.518|0.564|
||(2.22)*|(3.12)**|(4.40)**|(2.11)*|
|Some tertiary education|0.585|1.084|0.782|1.024|
||(3.89)**|(3.77)**|(5.77)**|(2.69)**|
|**Own occupation (reference=unskilled workers):**|||||
|Professional or technical|0.530|0.799|0.584|0.519|
||(5.27)**|(3.69)**|(5.91)**|(2.07)*|
|Admin or clerical|0.384|0.439|0.430|0.088|
||(3.72)**|(2.58)*|(4.55)**|(0.53)|
|Skilled production worker|0.322|0.461|0.409|-0.130|
||(2.98)**|(3.16)**|(4.56)**|(0.73)|
|Public1997|||0.320||
||||(4.46)**||
|Genresidualpublic1997|||-0.228||
||||(2.41)*||
|public sector wage premium1997||||-0.268|
|||||(0.44)|
|Selectivity term 1|-0.200||||
||(2.09)*||||
|Selectivity term 2||0.164|||
|||(0.80)|||
|Constant|-6.299|-10.006||-32.604|
||(1.20)|(2.05)*||(4.85)**|
|city dummies|Yes|Yes|Yes|Yes|
|ethnicity dummies|Yes|Yes|Yes|Yes|
|religion dummies|Yes|Yes|Yes|Yes|
|Observations|402|328|7|30 730|
|R-squared|0.35|0.37|0.|39|
|L liklihd||||402|
|og eoo||||-|
|Pseudo R-squared||||0.2|
|Absolute value of z statistics in parentheses|||||
|* significant at 5%; ** significant at 1%|||||

37
6

**Figure 3:  Indicators of the Balance Sheet Strength of Chilean Firms, Average over 2001-2003**

<!-- Start of picture text --> Leverage Total liabilities / shareholders funds Interest cover EBITDA / Interest charges<br>LT liabilities / shareholders funds EBITDA / (interest charges + LT maturing within a year)<br>LT financial liabilities / shareholders funds<br>1.5 1.3 250 227<br>200<br>145<br>1.0 0.7 150<br>0.4 100<br>0.5<br>50<br>- -<br>Average 2001-3 Average 2001-3<br>Current ratio Profitability RoE<br>Cost of capital<br>80 12% 10%<br>59.2 10%<br>60<br>8%<br>6%<br>40 6%<br>4%<br>20<br>2%<br>- 0%<br>Av e r age 2 00 1- 3 Av e r age 2 00 1- 3<br>Source: Author’s calculations based on FECUs<br><!-- End of picture text --> Compared to other countries and to rating agency benchmarks, the financial situation of Chilean firms remains sound, although profitability is low. A comparison with US firms by sector (see Figure 4) highlights that leverages are smaller in Chile than in the US in all sectors, while the profitability of Chilean firms is much lower than that of their US peers in all sectors, except in mining (and in the services sector where the difference in profitability is small). Compared to rating agencies’ benchmarks, this conclusion remains valid (see Figure 5): on average Chilean firms have a low leverage (equivalent to that of AA / A rated firms), an excellent interest cover (well above AAA firms) but a low profitability (below B firms).

**Figure 4:  Comparison of Leverage and Profitability by Sector, Chile and the USA, 2003**

<!-- Start of picture text --> Leverage Chilean average USA average Profitability Chilean average USA average<br>3.0 2.4 2.3 2.4 25% 21% 22% 22% 22%<br>2.52.01.5 1.31.9 1.0 0.91.8 1.51.8 1.3 2.1 1.1 1.32.1 0.9 20%15% 11% 15% 15% 18% 14% 15%<br>1.00.5 0.1 0.4 10% 3% 6% 5% 4% 3% 6%<br>5%<br>- 0%<br>-5% -2% 0%<br>Sources: FECUs, Bizstats<br>Agriculture Mining IndustryUtilities CommerceReal estateServices Mining IndustryUtilities Services<br>Manufacturing Construction Agriculture Manufacturing ConstructionCommerceReal estate<br><!-- End of picture text --> income to total shareholders’ funds; and the cost to income ratio is defined as the ratio of total operating costs (cost of sales, operating and administrative costs) to operating revenues (sales and other operating revenues).
<!-- Start of picture text --> c 3.50% —_———s_S—[P————s$=—s——[$——[_——_——_—__—————<br>ae) i<br>500% me<br>vy<br>c<br>°<br>Co<br>z 2.50%<br>2.<br>3 S 2.00%<br>=s<br>29<br>% = 150%<br>ive)c 2 Orocco<br>ae | @ Philippines<br>—E 1.00%<br>vi |<br>5 0.50%<br>°<br>1)<br>0.00%<br>0% 5% 10% 15% 20% 25%<br>% of monthly GNI spent on consuming avg consumption level<br><!-- End of picture text -->
In (12) we construct domestic reference prices for the final goods, and in (13) and (14) we construct reference prices for exports and imports. These are used to construct purchasing-powerparity PPP in (15), which is the ratio of nominal GDP and real jo GDPjo . In order for these definitions to make sense, we assume:

# **<u>Assumption 1</u>**

Summing up, we have shown that the augmented-GK system (12)- (15) can be used to obtain a cross-country measure of the GDP price deflator on the output-side, which is PPPjo . We have therefore achieved our goal of demonstrating that final goods data, in conjunction with export or import data, can be used to construct real GDP on the output side. However, it jo remains to be shown that this system has a solution. This task is complicated by the fact that real GDPjo , appearing in (11) and the denominator of (15), is not guaranteed to be positive for all possible reference prices. This can be ruled out by some additional assumptions, as follows.

First, define the _budget shares_ for each final, export and import goods as: where i=1,...,M for v = q; i= M0+1,...,M+N for v = x, m; and j=1,...,C. Notice that these budget shares are measured relative to nominal GDP. In addition, define the _market shares_ for each good as: where i=1,...,M for v = q; and i=M0+1,...,M+N for v = x, m. The market shares are measured relative to the world quantity of final demand, exports or imports for each good. Denote the column vectors of budget and market shares by θ vj and μ vj for v = q, x, m and country j. Then our second assumption is: 11
as in th.e CES case. O:therwhe, the interest rate elasticity would,, also be affected ~yJinancial innovation .. As a starting point we prefer .to concentrate in financial innovation affecting money demand independently from the other variables.

Scale economies in transactions Note that until now equation (4) a],.lows only for a scale (consumption) elastkity equal to one. Of course this is a r.estrictive .;issumption. We know from traditional models a la Baumol-Tobin or Miller-Orr that transaction. technologies may involve increasing returns to scale, and therefore, the scale elasticity could be less than one. In fact, the Baumol-Tobin model suggests that this elasticity is equal to 1/2, apd the Miller-Orr model ,implies an elasticity equal to 1/3.

To obtain a scale elasticity less than one in the context of, our framework, it is enough to assume that the transactions tech?ology not only depends on velocity, but also on the level of consumption. Hence, high levels .of consumption would require. a lower ratio m/c for the same unitary cost of transactions .. A technology involving this assumption would be: where in this case the scale elasticity of the money demand is eql,lal t 0 _¢>._ Frain the above discussion, we can summarize the general form of the money demand equation which will be the focus of our empirical application. The equation, in a regression form, can be written as lit is a white noise distributed normally w:itli. variance _av2_ The error _llt_ can 8
|St. Dev.|1.14E+07<br>0.029<br>0.031<br>0.251<br>2.493<br>2.096<br>0.376<br>0.437<br>0.214<br>0.734<br>1.592<br>0.798<br>0.370<br>0.341|
|---|---|
|Mean|652969.4<br>0.000<br>0.001<br>9.994<br>10.837<br>3.220<br>0.170<br>0.742<br>0.048<br>13.072<br>16.554<br>3.257<br>1.744<br>1.176|
|Source|Bank of Italy<br>ISTAT and Istituto<br>Tagliacarne<br>WEO<br>ISTAT and Istituto<br>Tagliacarne<br>WEO<br>ISTAT<br>UCDP/PRIO Armed<br>Confict Dataset<br>EM-DAT, CRED<br>WEO<br>ISTAT<br>WDI<br>WDI<br>Bank of Italy and IS-<br>TAT<br>Bank of Italy|
|Defnition|Total ofcial remittances at constant prices from province _i_ to country<br>_j_ in year _t_<br>Logarithm of actual GDP over potential GDP in province _i_ in year<br>_t_; potential GDP is calculated by applying the H-P flter to the GDP<br>series at constant prices<br>Logarithm of actual GDP over potential GDP in country _j_ in year _t_;<br>potential GDP is calculated by applying the H-P flter to the GDP<br>series at constant prices<br>Logarithm of potential GDP in province _i_ in year _t_, calculated by ap-<br>plying the H-P flter to the GDP series at constant prices<br>Logarithm of potential GDP in country _j_ in year _t_, calculated by ap-<br>plying the H-P flter to the GDP series at constant prices<br>Logarithm of 1 + the stock of migrants living in province _i_ and coming<br>from country _j_ in year _t_<br>Indicator = 1 if country _j_ experienced armed conficts in year _t_; both<br>interstate and intrastate conficts are considered, in which the govern-<br>ment of country _j_ represents one of the warring parties<br>Indicator = 1 if country _j_ experienced natural disasters in year _t_<br>Indicator = 1 if country _j_ experienced a large negative shock to the<br>terms of trade, defned as an observation falling in the lowest 5 percent<br>of the distribution of the annual variation in the terms-of-trade index<br>Logarithm of population in province _i_ in year _t_<br>Logarithm of population in country _j_ in year _t_<br>Logarithm of the ratio of domestic credit to the private sector over<br>GDP in country _j_ in year _t_<br>Logarithm of the number of bank branches per 10,000 inhabitant in<br>province _i_ in year _t_<br>Logarithm of the ratio of the number of branches in province_i_weighted<br>by the logarithm of 1 plus the kilometric distance between the province<br>of the branch and the province where the parent bank is headquartered,<br>over total branches in the province _i_ in year _t_.|
|Variable|_REMi,j,t_<br>_CY CLEi,t_<br>_CY CLEj,t_<br>_TRENDi,t_<br>_TRENDj,t_<br>_MIGRANTSi,j,t_<br>_WARi,t_<br>_DISASTERi,t_<br>_TT SHOCKi, t_<br>_POPi,t_<br>_POPj,t_<br>_FINDEVi,t_<br>_BANKi,t_<br>_FUNCTIONAL_<br>_DISTANCEi,t_|

22
6 broadly, measured in the distribution of household per capita incomes. Section 6 concludes.

# **2. A Brief Literature Review**

The literature on the relationship between “globalization” and distribution is now so extensive that Goldberg and Pavcnik (2004) open their recent survey of the subject by noting that “the number of literature reviews alone is so large by now, that it seems that a review of literature reviews would be appropriate” (p.1). Given the existence of two excellent recent surveys – Winters, McCulloch and McKay (2004) and Goldberg and Pavcnik (2004) – we make no attempt at an exhaustive review here. Instead, this section briefly reviews two sub-themes which are of particular importance for our analysis in this paper: the channels through which trade reforms affect the distribution of income (and hence inequality and poverty); and the recent evidence on the distributional effects of trade reform in Latin America in general, and in Brazil in particular.<sup>6</sup>

# _2.1. Channels of Impact from Trade Liberalization to Household Incomes_

If openness to the international economy brings persistent gains in terms of access to new knowledge and technology; or sustained gains in the growth rate of total factor productivity, then it is possible that trade liberalization leads to faster long-run economic growth. Whether or not this is in fact the case has been the subject of a debate, with Sachs and Warner (1995) and Edwards (1998) among the proponents, and Rodriguez and Rodrik (2001) leading the skeptics. The current balance of opinion seems to be that “despite the econometric and conceptual difficulties of establishing beyond doubt that openness enhances income levels, the weight of experience and evidence seems strongly in that direction” (Winters et al., 2004, p.78).

> 6 See also Nissanke and Thorbecke (2006) for a thoughtful discussion of the links between globalization and distribution.