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26
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28
|Table 10|a: Chan|ge in real v|alue add|ed in 203|0, percent ch<br>Rest of|ange from b<br>Latin|aseline,|ctd.||
|---|---|---|---|---|---|---|---|---|---|
||Kenya|Tanzania|Non-<br>BRI<br>Area|Ethiopia|Sub-<br>Saharan<br>Africa|America<br>and<br>Caribbean|United<br>States|Rest of<br>High-<br>income|Rest of<br>Western<br>Europe|
|Agriculture|1.3|0.7|0.5|2.5|0.3|1.2|1.9|0.8|0.4|
|Minerals n.e.s.|-2.7|-2.0|-0.5|-0.2|-0.7|-0.1|-0.5|-0.8|-0.3|
|Coal||-0.5|0.6|-26.6|1.3|3.2|0.3|-2.7|-2.4|
|Oil|-9.1||0.3||0.2|1.2|-0.8|-0.7|0.5|
|Gas|-4.0|-0.5|0.2||0.4|0.0|0.0|-0.3|0.0|
|Textiles|-3.3|-3.9|0.0|-0.3|-0.3|0.2|-0.4|1.2|-0.4|
|Wearingapparel|-1.5|0.9|0.2|1.1|0.5|0.2|-0.5|-0.2|-0.3|
|Leathergoods|-2.2|6.8|0.6|2.4|0.0|1.0|-0.2|0.3|-0.7|
|Processed foods|-1.3|0.5|0.5|2.2|0.3|0.8|0.6|0.0|0.4|
|Woodproducts|-2.7|-2.8|-0.4|-2.2|-0.5|0.0|-0.2|-0.5|-0.7|
|Paperproducts, publishing|-7.3|-5.8|-0.2|-3.5|-0.7|-0.1|0.2|-0.1|-0.2|
|Petroleum,coalproducts|-4.2|-6.1|-0.8|-5.7|-0.9|-0.5|-0.4|-1.2|-1.0|
|Chemical,rubber, plastic|-11.0|-2.1|-0.8|-5.2|-1.6|-0.6|-0.6|-0.6|-1.0|
|Energyintensive manu.|-7.9|0.3|-0.8|-2.6|-1.0|-0.6|-0.8|-0.7|-0.6|
|Metalproducts|-6.7|-16.9|-1.0|-4.4|-1.5|-0.9|-1.1|0.0|-1.3|
|Electronics|-13.1|-11.4|-6.5|-19.4|-6.3|-8.3|-2.1|-5.1|-8.6|
|Machineryand equipment|-10.0|-11.0|-1.6|-9.5|-2.7|-3.2|-1.2|0.7|-1.4|
|Transport equipment|-3.6|-5.7|-0.7|-6.9|-1.7|-2.4|-0.2|2.6|-0.9|
|Manufactures,n.e.s.|-6.8|-3.3|-1.1|-2.4|1.0|-1.4|0.3|-1.7|-1.3|
|Electricity|-1.9|-0.4|-0.2|-2.1|-0.3|0.0|0.0|0.1|0.0|
|Construction|-1.2|-0.1|-0.1|0.4|-0.2|-0.1|-0.3|0.0|0.0|
|Trade services|-1.4|-4.9|-0.1|-0.8|-0.5|-0.2|-0.3|0.1|0.1|
|Other transport|-2.2|-3.4|-0.3|-1.5|-0.5|-0.2|-0.7|-0.3|-0.2|
|Water transport|0.1|-2.9|0.2|-4.8|-1.0|0.0|-0.6|1.1|0.8|
|Air transport|-0.8|-2.8|-0.6|-2.2|-0.7|-0.4|-0.8|0.1|-0.6|
|Hospitalityservices|-2.1|-1.5|0.2|0.5|0.0|0.1|0.6|0.2|0.4|
|Other business services|-3.1|-2.7|0.0|-1.0|-0.1|0.1|0.0|0.0|0.2|
|Other services|-0.5|-0.3|0.0|0.1|-0.1|0.0|0.1|0.1|0.1|
|**Agriculture**|1.3|0.7|0.5|2.5|0.3|1.2|1.9|0.8|0.4|
|**Manufacturing**|-2.6|-0.6|-0.5|0.3|-0.1|-0.7|-0.5|-0.5|-1.0|
|**Services**|-1.6|-1.5|0.0|-0.1|-0.2|0.0|0.0|0.1|0.1|
|**Other**|-2.9|-0.8|0.0|-0.2|0.0|0.3|-0.4|-0.8|-0.2|
|**Total**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

- 49 -
**Figure 1:**

<!-- Start of picture text --> India Regional Divergence in Growth and Income<br>West Bengal Gujarat<br>Karnataka<br>Andhra Pradesh<br>Kerala Tamil Nadu<br>Rajasthan Haryana<br>Average Annual Growth Rate Maharashtra<br>Orissa<br>Madhya Pradesh Punjab<br>Uttar Pradesh<br>Average Real GDP per capita<br>Bihar<br>4000 6000 8000 10000 12000 14000<br>Real per capita GDP (1994)<br>6<br>4<br>2<br>0<br>Average Growth Rate (1994-2003)<br>-2<br><!-- End of picture text -->

**Figure 2: Manufacturing productivity by state groups**

<!-- Start of picture text --> 0.20<br>0.10<br>0.00<br>Q/N K/N TFP W/N<br>-0.10<br>-0.20<br>-0.30<br>-0.40<br>High-income high-grow th Low -income high-grow th Low -income low -grow th<br>Premium over national average<br><!-- End of picture text --> 27
# **_5.3. Estimation results – competition_**

In addition to the above-described firm characteristics, we also explored the effect of the extent of competition in the product market environment in which the firm operates. The effects of competition on wage rigidities are ambiguous. Firms subject to stronger competitive pressure may need more flexible wage-setting practices, which would imply a negative relationship between competition and wage rigidity. On the other hand, in sectors with severe competition rents should be low, and therefore so should wages. In such sectors, unions try to set common wage standards to avoid severe product market competition causing a race to the bottom of wages. As Cardoso and Portugal (2005) argue, in the absence of the wage cushion typical of non-competitive environments, wages are more likely to be rigid, since the leeway firms have for cutting wages in face of a negative shock is reduced. This would imply a positive association between competition and wage rigidity.

`–` **Table 6: Downward nominal and real wage rigidity competition**

||Perceived|competition|Price c|ompetition|
|---|---|---|---|---|
||DNWR/FW|DRWR/FW|DNWR/FW|DRWR/FW|
|Perceived comp=strong|0.674***|1.128|||
||(0.000)|(0.161)|||
|Perceived comp=weak|0.770*|1.255|||
||(0.079)|(0.128)|||
|Perceived comp=none|0.696|0.662|||
||(0.154)|(0.150)|||
|Price comp=likely|||0.920|0.887|
||||(0.423)|(0.161)|
|Price comp=not likely|||0.881|0.851*|
||||(0.261)|(0.089)|
|Price comp=not at all|||1.019|1.039|
||||(0.915)|(0.782)|
|Observations|7|549|9|969|

Notes: The table presents the estimated odds ratios for nominal wage rigidity vs flexible wage and real wage rigidity vs flexible wage. Worker skill groups, % permanent workers, % labour cost, dummy variables for different types of union contracts and sector, size and country fixed effects are added in all specifications. Robust P-values in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

The survey included two questions on the firms’ competitive environment. The price competition variable relates to a question on the likelihood of the firm changing its price in response to a price change by its main competitor; the answers were given on a four-point scale, from very likely to not at all. A second question on perceived competition was also 22
is recorded in the first box of Tables 9 and 10, would be a very misleading indicator of welfare, as indicated in the bottom two boxes. The effects on welfare are much smaller, and – depending on the discount factor – can also be in the opposite direction than the short-run wage effect.

Table 9: Simulation Results - Short Run Effects (Percent Change)

||Change in|Wages, _β_|= 0_._97||
|---|---|---|---|---|
||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|_White Collar_|6.42|-17.00|6.42|12.29|
|_Tech/Sales_|6.42|-17.00|6.42|12.29|
|_Service_|6.42|-17.00|6.42|12.29|
|_Craft_|6.42|-17.00|6.42|12.29|
|_Laborer_|6.42|-17.00|6.42|12.29|

Change in Welfare, No-College, _<u>β</u>_ = 0 _._ 97

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|1.57|0.31|1.53|1.93|
|_Tech/Sales_|1.43|0.41|1.50|1.71|
|_Service_|1.30|0.37|1.39|1.51|
|_Craft_|1.38|-0.22|1.35|1.58|
|_Laborer_|1.31|-0.12|1.28|1.55|

Change in Welfare, College, _<u>β</u>_ = 0 _._ 97

||**_Aggr/Cons_**|**_Manuf_**|**_NonTraded_**|**_Traded_**|
|---|---|---|---|---|
|_White Collar_|1.60|-0.10|1.60|1.94|
|_Tech/Sales_|1.55|0.14|1.57|1.86|
|_Service_|1.46|0.27|1.50|1.59|
|_Craft_|1.53|0.37|1.53|1.67|
|_Laborer_|1.51|0.76|1.51|1.63|

29
Table 4. Dynamic Single Price Models in Ln with Fixed Entity and Time Effects

|Emission Intensity in ln<br>|Average<br>|Single Pric<br>Gasoline<br>|e Models<br>Diesel<br>|Ethanol<br>|
|---|---|---|---|---|
|(Dep. Var)|Fuel Price|Price|Price|Price|
|Price in ln|-0.51*|-0.27**|-0.74**|-0.04|
|Emission Intensity in ln (t-<br>|1) 0.70***<br><br>***<br>|0.71***<br><br>***<br>|0.64***<br><br>***<br>|0.71***<br>***|
|_cons|1.61<br><br>|1.40<br><br>legend: * p<0|1.89<br><br>.1; ** p<0.05|.17<br>; *** p<0.01|
|N|270|270|270|270|
|F|50.23|58.45|56.37|57.59|
|p|0|0|0|0|
|r2_within|0.65|0.64|0.67|0.64|
|r2_adjusted|0.63|0.63|0.66|0.62|
|r2_between|0.99|1|0.97|1|
|r2_overall|0.97|0.98|0.95|0.98|
|Year|||||
|2012|0.03|0.05|0.03|0.06|
|2013|-0.02|-0.01|0.01|0.01|
|2014|-0.02|-0.01|0.03|0.02|
|2015|-0.05|-0.04|0.02|-0.02|
|2016|0|0|0.04|0.01|
|2017|-0.02|-0.03|0.02|0|
|2018|-0.04|-0.06|0.01|-0.06|
|2019|0|-0.01|0.06|0|
|2020|-0.08|-0.07|-0.05|-0.04|
|FU|||||
|AL|-0.07|-0.03|-0.16|-0.01|
|AM|-0.18|-0.13|-0.27|-0.11|
|AP|-0.01|0|-0.04|0.05|
|BA|-0.01<br>|0.04<br>|-0.09<br>|0.06<br>|
|CE|-0.06|-0.02|-0.13|0|
|DF|-0.37|-0.31|-0.48|-0.28|
|ES|-0.09|-0.05|-0.19|-0.02|
|GO|0.01|0.07|-0.04|0.08|
|MA|0.01|0.05|-0.06|0.08|
|MG|-0.05|0.01|-0.11|0.03|
|MS|0|0.04|-0.02|0.07|
|MT|0.07|0.12|0.09|0.13|
|PA|0.03|0.05|-0.02|0.07|
|PB|-0.06|-0.01|-0.13|0.03|
|PE|-0.08|-0.03|-0.16|0|
|PI|0.03|0.07|-0.01|0.1|
|PR|-0.05|0.02|-0.12|0.05|
|RJ|-0.26|-0.2|-0.39|-0.19|
|RN|-0.04|0|-0.12|0.02|
|RO|0.13|0.14|0.1|0.16|
|RR|-0.01|0|-0.07|0.03|
|RS|-0.04|-0.01|-0.14|0.01|
|SC|-0.04|0|-0.12|0.04|
|SE|-0.04|0|-0.12|0.03|
|SP|-0.27|-0.18|-0.34|-0.15|
|TO|0.22|0.25|0.17|_Note: Significance based on robust error._<br>0.26|

18
- 35 -

**Table 9. Performance Indicators of Commercial Banks and Finance Companies**<sup>**5 0**</sup>

||Commercial|Banks|Fincos||
|---|---|---|---|---|
||1995|1996|1995|1996|
|ROAE|30.6|21.0|20.9|16.7|
|ROAA|2.4|1.7|2.0|1.4|
|NPL/ Total Loans|||3.5|_5.9_|
|Provision for Loan|2.1|2.8|6.2|9.0|
|losses/Total Loans|||||
|Increase in accrued interest|10.0|16.9|14.6|23.5|
|receivable/pre-tax profit|||||
|Capital/Asset Ratio|8.0|8.3|11.3|_10.5_|
|Loan/Deposit Ratio*|108.7|110.8|137.1|138.2|

_Source:_ Bank of Thailand.

* Gross loans/notes payable for finance companies. Gross loans to total borrowing (which included overdrafts, rediscounts and other borrowings) reached 117.4 in 1995 and 116.5 percent in 1996.

While finance companies showed strong profits in the early nineties, the quality of their loan book continued to be problematic (Figure 12). The portfolio deteriorated sharply in 1995 with rising NPLs and a significant increase in interest accrued. This deterioration can mainly be attributed to three factors: (i) the collapse of the property market with its repercussion on the construction sector and the slump in the stock market; (ii) a slowdown in the economy and high and rising interest rates that adversely affected both the consumer portfolio and interest rates margins since finance companies were highly exposed to interest rate risk (see above); and (iii) deficient risk and liquidity management practices coupled with poor prudential regulation, deficient supervision and weak enforcement.

**Figure 12. Finance companies NPLs** (Baht million)

<!-- Start of picture text --> 80,000.00<br>70,000.00<br>60,000.00 _1__hr_<br>50,000.00 l Trading<br>40,000.00 Servi'ces<br>E- Consumer loans<br>30,000.00 U Manufacturinig<br>20,000.00 U Construction<br>10,000.00<br>0.00<br>1993 1994 1995 1996<br><!-- End of picture text -->

- 50 Data for finance companies reported refer to 30 largest companies in the market. Data cited after Wotld Bank, The Finance Company Sector, World Bank Mimeo, 1998.
<!-- Start of picture text --> Cost of increasing the RAI by 1% for CRI Cost of increasing the RAI by 1% for MLI<br>120 e, .<br>— e 600<br>100<br>= — 500<br>s % ° {8<br>é s E °<br>60 400 .<br>33<br>S<br>40 e.2 o%*" So °*<br>20 oe ° 200 e e<br>«re<br>50 55 60 65 70 75 30 32 4 %6 38 40<br>RAI (%) RAI (%)<br>Cost of increasing the RAI by 1% for CMR Cost of increasing the RAI by 1% for GEO<br>350<br>° @<br>500<br>300 e<br>400 250<br><!-- End of picture text --> <!-- Start of picture text --> ec =<br>2 = 200<br>= 300 3 ©<br>bs Ss<br>2 e + 150 e<br>° fo}<br>Y 200 e o<br>°/e 100 Ae<br>e °<br>100 ° 50 *eoo<br>e 2&<br>35 40 45 40 45 50 55 60 65<br>RAI (%) RAI (%)<br><!-- End of picture text -->
Policy Research Working Paper 7209

# **Abstract**

This article examines how economic shocks affect individual well-being in developing countries. Using the case of a sudden and unanticipated currency devaluation in Botswana as a quasi-experiment, the article examines how this monetary shock affects individuals’ evaluations of well-being. This is done by using microlevel survey data, which—incidentally— were collected in the days surrounding the devaluation. The chance occurrence of the devaluation during the time of the survey enables us to use pretreatment respondents, surveyed before the devaluation, as approximate counterfactuals for post-treatment respondents, surveyed after the devaluation. Estimates show that the devaluation had a large and significantly negative effect on individuals’ evaluations of subjective well-being. These results suggest that macroeconomic shocks, such as unanticipated currency devaluations, may have significant short-term costs in the form of reductions in people’s sense of well-being.

This paper is a product of the Development Policy Department, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at JGH@ifs.ku.dk, ChBj@econ.au.dk, and mkj.dbp@cbs.dk.

_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
<!-- Start of picture text --> 40<br>35<br>30<br>95 —e Endesa GWh<br>20 generated/employee<br>15 —s— Chilectra GWh<br>sales/employee<br>10<br>5<br>0<br>SFrSoA &D&D B@S Tr&© VFGB© FM&MA wvGB& eS<br><!-- End of picture text --> <!-- Start of picture text --> Chilectra Energy Losses %<br>20<br>20<br>15 — Chilectra<br>Energy Losses<br>10 Yo<br>a<br>0<br>coht Lea)a go—_— or}Oo ur?Oo Oohh aoO —_—Qo<br>S22 22 2 2 &<br><!-- End of picture text -->
where ∆ _Ni,t−_ 1991 denotes overall population growth in ward _i_ between 1991 and _t_ and ∆ _Ni,t_<sup>_B_</sup> _−_ 1991<sup>denotesblackpopulationgrowth.14Ourcontrolsinclude:populationgroups,</sup> population density, education, the gender ratio, employment and income ( **_Xi,_ 1991** ) and province-level fixed effects ( _δp_ ) (see Table 2). The errors are clustered at the municipality level.<sup>15</sup> The ward level is the lowest geographical level that can be tracked consistently over time and municipalities are the lowest level of local government. Distance is defined as the distance to the nearest homeland measured from centroid to centroid. Since no measure of domestic migration is available in the census data, black population growth conditional on fixed effects and covariates is used as a proxy for black migration.<sup>16</sup> A dummy variable for Cape Town is also included as the municipality is a special case in terms of location, politics and demographics and hence migration patterns. The Western Cape was the only province where the African National Congress did not come first in the general elections in 1994. Until today, it has not achieved the political dominance in the province or the municipality of Cape Town that it has in the rest of the country. In terms of demographics, there is a much higher white and especially colored population in Cape Town, more than anywhere else in the country. Most importantly, there is a

> 14∆ _Ni,t B −_ 1991<sup>isdefinedasabsolutegrowthoftheblackpopulationfrom1991toyeartdividedbythe</sup> overall population in t, where t corresponds to 1996, 2001 or 2011. ∆ _Ni,t−_ 1991 is defined similarly using overall instead of black absolute population growth.

> 15Using Conley (1999) standard errors to account for spatial correlation yields similar results.

> 16As has been used previously as a proxy for migration status, see for example Czaika and Kis-Katos (2009).

20
# **Estimation of Oil Rents**

2.21 Oil production, in particular when sold on foreign markets, has recorded sharp increases in profitability since 1998, as illustrated in Table 7. As a share of sales, net profitability rose from 1% in 1998 to 37-55% in 1999. The increase continued in 2000.

2.22 How much of that is normal entrepreneurial profit, how much is rent? Large rents have been claimed to exist in the oil and gas sector. For example, estimated oil rents were estimated at over US$ 20 billion _per annum_ in the early to mid-1990s (Lvov 1994; Markandya and Averchenkova 2000). The paragraphs below renew the estimation of the current oil rent for the years 1999 and 2000.

Table 7. Financial Breakdown of Russia's Oil Exports (US$/ton)

||1998 a|1999 a|1 9 9 9 <sup>b</sup>|1999c|
|---|---|---|---|---|
|Export crudeoilprice|77.9|144.9|120.7|104.8|
|Excisetax|9.1|2.2|2.2|--|
|Royalty|6.0|11.6|2.2|--|
|Mineralbasereplacement|4.6|8.7|2.6|--|
|Road fund|1.1|2.9|0.6|--|
|Propertytax|1.7|0.7|0.6|--|
|Socialinsurance|2.5|0.9|1.1|--|
|Local andothertaxes|0.5|0.2|0.5|--|
|Transport, customsfees,portcharges,handling|19.0|17.4|18.7|--|
|Directproductioncost|15.6|12.8|6.0|--|
|Salaries|6.6|1.7|2.9|--|
|Depreciation,amortization|9.5|2.9|2.5|--|
|Other costs|0.6|0.4|0.0|--|
|Management|0.4|0.2|0.0|--|
|Pre-tax income|0.8|82.4|80.8|--|
|Net income|0.5|53.6|_56.5_|58.4|
|Netmargin**(%)**|1.0%|37.0%|46.8%|55.4%|

Sources: a) Nikoil (2000); b) IFEI (2000); c) _Institut narodnokhozyaistvennogo prognozirovaniya Rossiiskoy akademii nauyk._ 2.23 Rent should theoretically be estimated in an intertemporal framework but the data necessary to carry out such an analysis, in particular reliable discount and inflation rates and marginal cost and revenue stream estimates, were not available. Oil auction data, which could help measure and collect the rent, are equally absent. The analysis is thus limited to average current rents using a net-back approach.

2.24 As per the schematic in Figure 1, potential rent consists of realized and unrealized rent. Unrealized rent is due to allocative inefficiencies. In this case, the inefficiencies can take the form of sales on the domestic market below the international price, leading to large revenue losses. The unit unrealized rent (implicit subsidy) to the national economy and those 16