rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Resources Available to Utility Regulatory Agencies in DTEs**

This is an issue of which there has been general awareness for some years. Attention has been focused on two different aspects:

- (i) Financial resources; and

- (ii) Human capital resources – particularly the availability of scarce, highly skilled professional staff resources.

The former is a relatively straightforward issue where the willingness to provide and ensure adequate funding is essentially a sign of the commitment (or the lack of it) by governments and/or legislatures to accept independent regulation in practice. It is very rarely if ever a genuine question of resource insufficiency. The human capital resource issue is the more serious problem and the one on which we will focus in this paper.

Warrick Smith’s 1997 World Bank Public Policy for the Private Sector Notes included some discussion of the issue as, in passing, did Levy and Spiller (1994) in their discussion of the role of resource endowments. In particular, Smith’s papers considered the case for having multi-industry or multi-sector regulators as a way of economizing on scarce, specialist staff resources. Noll (2000) also raised the issue in the context of whether countries should combine to provide multi-national regulators.

These papers all discussed the issue from the perspective that an inadequate supply of staff with good regulatory knowledge and experience would lead to poor and longdrawn out decision-making. Inadequate regulatory resources would particularly jeopardize _discretionary_ regulation. Hence, this became an important issue for those who advocated regulation by contract rather than by agency.

Data on the number of regulatory staff in a large sample of DTE agencies was first systematically reported in Stern (2000). Drawing on World Bank and other sources, he reported staff numbers in electricity or energy regulatory agencies for 24 countries. For African countries, staff numbers were clearly low (around 20) and in some cases under 10. However, Argentina, Brazil, Mexico, Malaysia and the Philippines all had over 140 staff, although there was still the question of whether they had adequate numbers of experienced regulatory professional staff (eg accountants, economists and lawyers)<sup>29</sup> .

The discussion of the whole topic was advanced considerably by Domah, Pollitt and Stern (2002) – henceforth DPS (2002). Using a purposed-designed postal and e-mail survey, information was collected for electricity regulators<sup>30</sup> in 60 countries on (a) numbers of staff and (b) numbers of professional staff. Data was also collected on a

> 29 Stern (2000) also reported staff numbers for 13 mainly developed country telecom regulators reported in Buckle (1999).  The staff numbers for telecom regulators did not indicate as serious a problem on staff numbers even for relatively low income countries like Jordan or Peru.

> 30 Including energy and multi-sectoral regulators.

23
evidence on Germany (Bloomberg) shows that leverage for the major banks increased on average from 27 to 45 percent between 1996 and 2007 (Table 4).

**Table 4: Selected indicators of the performance of the banking system in the European Union**

|**Period**|**Financial**<br>**deepening**|**Co**<br>**Costs/Income**|**re Countries**<br>**Interest**<br>**margins**|**ROA**|**Concentration**|**Z-Score**|
|---|---|---|---|---|---|---|
|**1990–1995**|92.5|72.8|2.3|0.3|67.2|15.4|
|**1996–2001**|103.5|67.8|2.5|0.9|67.2|9.4|
|**2002–2007**|108.2|70.2|2.1|1.0|68.2|7.3|
|||**Non-**|**Core Countries**||||
|**Period**|**Financial**<br>**deepening**|**Costs/Income**|**Interest**<br>**margins**|**ROA**|**Concentration**|**Z-Score**|
|**1990–1995**|56.8|67.6|3.9|0.8|72.1|23.9|
|**1996–2001**|73.3|70.8|2.8|0.5|66.7|25.6|
|**2002–2007**|112.8|66.5|2.2|0.4|68.9|15.8|
|||**Euroz**|**one Countries**||||
||**Financial**||**Interest**||||
|**Period**|**deepening**|**Costs/Income**|**margins**|**ROA**|**Concentration**|**Z-Score**|
|**1990–1995**|77.0|65.8|3.1|0.6|73.7|18.1|
|**1996–2001**|77.3|69.9|2.7|0.7|68.8|12.7|
|**2002–2007**|101.3|67.6|2.4|0.8|70.8|10.4|

Note: Core countries include Austria, Belgium, France, Germany, and the Netherlands. Non-Core countries include Greece, Ireland, Italy, Portugal, and Spain. “Financial deepening” is measured by private credit, deposit money banks, and other financial institutions. “Cost/Income” is measured as total costs, as a share of total income of all commercial banks. “Interest margins” equals the accounting value of a bank’s net interest revenue, as a share of its interest-bearing (total earning) assets. ROA equals “rate of return over assets.” “Concentration” refers to assets of three largest banks as a share of assets of all commercial banks. “Z-Score” is estimated as ROA+equity/assets/sd(ROA); the standard deviation of ROA, sd(ROA), is estimated as a 5-year moving average. Source: Beck and Demirgüç-Kunt (2009).

15. Where did the increase in lending go? The sharp fall in interest rates led to a very significant increase in consumer lending; and in Ireland and Spain, and to an extent in Greece, much of it was channeled into real estate (Figure 4). As a result, from 1997 to 2007, housing prices rose at an average annual rate of 12.5 percent in Ireland and 8 percent in Spain, compared to 4.6 percent in the United States during its bubble. Over the same period, construction as a share of gross output rose from 9.8 to 13.8 percent in Spain, and from 7.9 to 10.4 percent in Ireland. In the United States, the same figure only increased from 4.6 to 4.9 percent.<sup>6</sup> Yet, the channeling of funds into real estate allowed bubbles to emerge in Ireland and Spain whose burst was at the core of the economic crisis.

> 6 Dadush (2010) p.13.

8
production function approach), on employment, on capital and on investment. The variable “NRP” denotes annual average nominal rate of protection at the two-digit industry level. The variable “FLEX” is a measure of labor market flexibility of a state. As explained in the subsection on measuring labor market flexibility, this is based on the Besley-Burgess measure of labor market flexibility combined with David Dollar’s survey. This is a binary variable where a value of 0 represents a state that has a rigid labor market while a value of 1 represents a state that has a flexible labor market. There seems to be no variation in this variable over time for the period we are looking at, i.e., it varies only across states. In order to control for the export oriented industries, we determined 2-digit industries with exports greater than imports in each state in 1991, which is called “Export Dummy” in the regressions.

Our regressions are run on a three-dimensional panel. The data are by two-digit industry across the fifteen major states of India over the period 1989-2000. Table 1 summarizes the key variables in our analysis.

# **_The Effects of Protection, Labor Market Flexibility and Development Expenditure on Labor Productivity_**

Starting with Table 3, we see that the real net value added per worker is higher in less protected industries, as seen by the statistically significant, negative sign of the coefficient of the NRP variable in column 1 and 2. Since the labor productivity variable is in logarithms, the magnitude of the coefficient indicates that a percentage point reduction in the tariff rate leads to a 0.2 to 0.5 percent increase in labor productivity in a state with a rigid labor market. The strength of this effect is stronger in the case of a flexible labor market, as seen by the statistically significant negative sign on the coefficient of the interaction term between NRP and FLEX. A percentage point reduction in the tariff rate leads, in states with flexible labor markets, to a 0.3 - 0.65 percent increase in labor productivity. In the period 1980-91, the average NRP across all two-digit sectors was 153 percent, while it was 65 percent in the post 1991 period. This reduction, of 88 percentage points, according to our regressions, could have led to a 60 percent increase in average labor productivity in the flexible states and a 45 percent increase in average labor productivity in the rigid states. This estimate of an increase in productivity, attributable to tariff 18
replacing already more non-routine and abstract tasks, is leading to a significant expansion of jobs among administrative and unskilled production workers. Furthermore, it also reallocates employment within firms away from skilled production workers . Third, consistent with these employment shifts, the adoption of complex software is associated with an increase in firms’ use of routine and manual tasks, and with a reduction in firms’ use of abstract tasks which are now arguably being performed by technology. Finally, our findings are mainly driven by advanced technology adoption in sectors with a relatively low-educated workforce and low-productivity, where most of the unskilled workers are employed (e.g., wholesale and retail trade and manufacturing sectors). In sum, taken together these findings suggest that the adoption of more advanced technologies, such as complex software, has the potential to be inclusive among the lower skilled workers. These results shed new light on an important policy debate and contribute to a better understanding of the medium-term impacts of advanced technological adoption on firmlevel employment decisions and the task content of occupations.

We exploit three micro data sets. First, we <mark>use a novel firm-level survey of formal private firms in the Chilean economy across all sectors of activity,</mark> _<mark>Encuesta Longitudinal de Empresas</mark>_ <mark>(henceforth ELE)</mark> for the period 2007-201 <mark>3. This data set is rich in capturing</mark> _<mark>direct</mark>_ <mark>measures of technology adoption and the human capital of the workforce. Second, we use Chilean</mark> data on the task content of each occupation from the Programme for the International Assessment of Adult Competencies (PIAAC) survey (henceforth PIAAC survey) as of 2014. Third, <mark>we explore the Chilean national household survey (CASEN), for 2006 and 2013, to obtain information on ICT use at the sub-national level which is used in our instrumental variable approach.</mark> This combination of data sources is unique. First, it allows us to assess and differentiate impacts of complex software adoption across different employment/occupation categories (managers, administrative workers, skilled and unskilled production workers) as well as across the task content of occupations, and thus identify which groups disproportionately benefit and which ones bear the cost of this technology adoption. Second, Chile is a particularly interesting setting to study the impacts of ICT adoption on the demand for skills and jobs due to its high and persistent degree of income inequality. Despite strong poverty reduction and economic growth supported by the commodity boom and several market-oriented structural reforms over the past decades, Chile’s degree of income inequality has remained persistently high, and is still today among the highest in the world. The Chilean economy is currently looking for sources of diversification and for a model of growth 3
The automatic system is responsive to clues from the environment in ways we are often not conscious of. For example, we know that social norm compliance often requires that our attention is drawn to the particular social norm in question and to how it applies in the context at hand. A crucial factor in influencing behavior is the direction of attention. Since social situations don’t come with an explicit interpretation sign attached to them, and we are constantly flooded by stimuli that we have to attend to, drawing attention to a particular interpretation and the norms associated with it can have a significant impact. And even if it is generally accepted that a particular norm applies, people’s attention to that norm and its requirements will vary, because they have so much else on their mind. Consequently, experiments show that if people are primed to focus on a particular social norm generally accepted in their society, they are much more likely to behave in accordance with it. The famous littering experiments by Cialdini et al., for example, show among other things that most people are sensitive to what others do and refrain from littering in a clean environment but not in one which is already dirty, but if they were primed to focus on the social norm against littering by handbills that drew attention to the normative requirement, they were even more likely to refrain from littering. Further, observing behavior that drew attention to the strong social disapproval of littering had a very strong effect: watching someone (an experiment confederate) pick up someone else’s litter and put it in the bin made almost everyone refrain from littering, even in an already very littered one (Cialdini et al 1990).

In contrast, the strategies discussed below are more about the reflective decisions that people make, although some of their impact probably stems from how they affect our automatic responses too. The strategies can be grouped together into different categories, associated with some relevant feature(s) of social norms. I devote a separate sub‐section to each category.

# _Information about what others do_

One important feature of social norms is that they are based on expectations and beliefs about what others do. Therefore, information about what others do tend to be important.

The most common strategy is so far probably social norms marketing, which is based on the fact that information about what others do tend to have a great effect on behavior. This strategy has been used to decrease problematic behaviors like smoking (Zhang et al 2010), binge‐drinking (De Jong et al 2006, Haines and Barker 2003, Moreira et al 2009, Turner et al 2008, although see also De Jong et al 2009), and domestic violence (WHO 2009). The strategy uses general marketing principles, through posters, flyers, and advertisements in newspapers, magazines and TV. But sometimes more 22
errors are adjusted for clustering.<sup>8</sup> Appendix Tables A3, A4, and A5 report results for the 20 individual symptoms and 12 activities of daily living used to construct the summary measures in Table 3.

Columns 1 through 3 report impacts on acute symptoms for older children. The results suggest there are no precisely estimated links between the _Mirt_ intervention and one’s symptom burden over the prior 4-week period. There is no evidence of an ITT effect for the total number of symptoms in Column 1, an indicator for any symptoms in Column 2, or for cough specific symptoms in Column 3. There is an imprecisely estimated -5.3 percentage point use estimate for reporting any symptoms in Column 2 (p-value=0.097). Given a control group mean of 51.6 percent, this represents an approximately 10 percent reduction but is on the knife-edge of statistical significance.

While many of the point estimates are negative and suggest reduced symptom loads for children in treatment households, the results should be interpreted as suggestive at best. For the 20 specific symptoms in Appendix Table A3, we estimate one reduction at the 5 percent significance level on headaches and one on effects at the 10 percent level—effects which are balanced out by estimated increases in wheezing and shortness of breath. Such a pattern is consistent with pure chance given the expected error rates and the number of outcomes. Adjusting statistical power for multiple comparisons only further weakens the results. The null findings also hold true when examining sub groups of the population based on specific 5-year age groups (ages 5 and under, 6–10, and 11–15), regions, and baseline wealth quantiles.

> 8 We cluster at the village level for adults and, to account for multiple children per household, at the household level for child outcomes.

17
<mark>land under permanent crops) increased slightly, from 1,523 million hectares to 1,562 million hectares (FAOSTAT). While arable land use declined in the high-income group and the ECA region, arable land use increased in SSA, LAC and EA (other than China) regions. Between 1992 and 2012, the EA, LAC and SSA regions added 25 million hectares, 38 million hectares and 57 million hectares of arable land respectively.</mark><sup>11</sup> <mark>During the same period, however, arable land per capita declined in all regions due to a combination of population growth and higher productivity of the land in use.</mark> Figure 6 shows the evolution of arable land, which declined <mark>from an average of 0.28 hectares per person globally in 1992/1993 to 0.22 hectares in 2011/2012. The last two columns of the table show the average per capita arable land endowment during the period 1992-2012 and the percentage decline to 2011/12. In the ECA region and the high-income countries as a group arable land per person declined by 15 percent and 21.5 percent between 1992 and 2012 respectively. The LAC and SSA regions have been relatively land-abundant with 0.32 ha and 0.30 ha of arable land endowment on average. However, with high population growth, the land endowment per capita in the SSA region declined by 20.2 percent over the same period. In the MENA and SA regions, with relatively high population growth, arable land per capita fell by 31.6 percent and 29.0 percent respectively. China was relatively land-scarce throughout the period with 0.1 ha of per capita arable land endowment on average, which declined by 19.2 percent during the same period.</mark> While arable land per capita has declined, the world has been successful in producing more food per person on average, primarily because of agricultural productivity growth. However, some observers are concerned by a decline in yield growth in recent years. Figure 7 plots the evolution of cereal yield per hectare<sup>12</sup> by region. <mark>The figure reveals that cereal yields increased by about onethird between 1992/1993 and 2012/2013 on average, but that yield levels and growth rates vary widely across regions. The high-income countries as a group had high productivity throughout the period. L</mark> and productivity in China has reached the average level of the high-income country

> 11 <mark>The expansion of arable land for the LAC and EA regions appears to have been motivated in large measure by opportunities for exports, for instance, of soybeans from South America and of oil palm from Southeast Asia. In contrast,</mark> land expansion in the SSA region has been driven largely by <mark>growing needs for food and employment (Alexandratos and Bruinsma 2012).</mark>

> 12 Fukase and Martin (2014) show a generally positive relationship between income and land productivity and between income and labor productivity (Figure 7ab). Fuglie (2012) reports that agricultural production growth of ‘developed’ countries is primarily attributable to total factor productivity (TFP) growth while their agricultural input growth has been negative since the 1980s.

18
**Figure 2.  Overall Adjustments among Labor Force: Unemployment rate and employment status**

<!-- Start of picture text --> Average Adjustment among Labor Force<br>Unemployment rate Wage employment share Self Employment share<br>LV<br>LT<br>PL<br>BG CR<br>TR<br>RO ME AR CH EG LT SA CH<br>SA MK JOBR PH MK EGBR ID LV RO ME BG ARMK JOTRCR BR PHPL ID<br>ID ME ARJO PH<br>EG<br>RO BG SA CHTR PL<br>CR<br>LT<br>LV<br>-20 -15 -10 -5 0 -20 -15 -10 -5 0 -20 -15 -10 -5 0<br>GDP slowdown<br>12 12 12<br>10 10 10<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>-6 -6 -6<br>-8 -8 -8<br>-10 -10 -10<br>-12 -12 -12<br><!-- End of picture text --> Note: See note for figure 1. All four indicators are measured as a share of the active labor force. Self-employment includes self-employed and unpaid family workers.

32
# **Reference List**

Alcázar, L., Andrade, R., Jaramillo, M. (2010): “Panel/tracer study on the impact of business facilitation processes on enterprises and identification of priorities for future business enabling environment projects in Lima, Peru – Report 5: impact evaluation after the third round”, Report to the International Finance Corporation, Mimeo.

Andrade, G. H., Bruhn, M., McKenzie, D. (2013): “A helping hand or the long arm of the law? Experimental evidence on what governments can do to formalize firms”, _World Bank Policy Research Working Paper_ no. 6435.

Bruhn, M. (2011): “License to sell: the effect of business registration reform on entrepreneurial activity in Mexico” _Review of Economics and Statistics_ , 93(1): 382-86.

Bruhn, M. (2013): “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal Business Owners in Mexico” _Journal of Development Economics_ 103: 275-83.

Bruhn, M., McKenzie, D. (2013): ““Using Administrative Data to Evaluate Municipal Reforms: An Evaluation of the Impact of Minas Fácil Expresso” _World Bank Policy Research Working Paper_ no. 6358 Cárdenas, Mauricio, and Sandra Rozo (2007): “La informalidad empresarial y sus consecuencias: ¿Son los CAE una solución?” Documento de Trabajo 38, Fedesarrollo, Bogota, Colombia.

De Giorgi, G., Rahman, R. (2013): “SME’s Registration: Evidence from an RCT in Bangladesh”, Mimeographed. Stanford University and World Bank.

De Mel, S., McKenzie, D., Woodruff , C. (2013): “The demand for, and consequences of, formalization among informal firms in Sri Lanka”, _American Economic Journal: Applied Economics_ 5(2): 122-50 De Mel, S., McKenzie, D., Woodruff , C. (2010): “Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto”, pp.63-87 in J.Lerner and A. Schoar (eds.) _International Differences in Entrepreneurship_ , National Bureau of Economic Research: Boston, MA.

De Soto, H. (1989): _The Other Path_ , New York: Harper and Row Publishers.

Farrell, D. (2004): “The hidden dangers of the informal economy.” _The McKinsey Quarterly_ (Number 3). <u>http://www.mckinseyquarterly.com/article_page.aspx?ar=1448&L2=7&L3=10&srid=6&gp=1</u> 15
RENT CONTROL IN KUMASI, GHANA 88

**Table** 5.8: Cost-Benefit Measures From Survey Ccmand **and Hedonic** Models

||Current<br>Cont-<br>rolled<br>**Rent**<br>PcQc|Market<br>Rent<br>for<br>Current<br>Unit<br>PmQc|Est.d.<br>Rent<br>with no<br>Controls<br>PMQm|Cost<br>of<br>Rent Control<br>Subsidy<br>(PmQC-PcQc)|Tenant<br>Benefits<br>1<br>2<br>**(Ep--i)**(Ep--0.5)|Median<br>EffLciency,<br>Ep--l<br>(Beneflt/Cost)|
|---|---|---|---|---|---|---|
|Tenement Samole|||||||
|Mean|290|613|1094|**.32**|106<br>-264||
|Q3|300|**580**|1220|287|221<br>125||
|Median<br>Q1|300<br>**300**|574<br>570|1040<br>909|274<br>270|135<br>-76<br>14<br>-415|0.50|
|N|358|343|328|343|328<br>328||
|Representative<br>Consumer|300|574|1040|274|122<br>-105|C.45|
|Indigenous Sample|||||||
|Mean|244|563|1105|319|72<br>-382||
|Q3|300|580|1205|370|221<br>80||
|Median|250|574|1044|291|123<br>-127|0.41|
|Ql|200|513|910|275|-17<br>-542||
|N|322|319|311|319|310<br>310||
|Representative<br>Consumer|250|574|1044|324|169<br>-61|0.52|

The key results from Table 5.8 are:

- (a) Renters pay a fraction of the estimated market rents for their units. The rent paid is only 43 percent of the estimated market rent in the indigenous sector, and 52 percent in the tenement sector. Furthermore, while the controlled rents PcQc hardly vary, the estimated market rents PmQc vary with size and type of unit. Market demand PmQm varies even more.

- (b) The median cost of the subsidy implied by these rent reductions is estimated to be about 274 cedis per month in the tenement and 291 in indigenous sector.

- (c) But household would (we estimate) spend even more on housing in the absence of controls. Median PmQm is over 1000 cedis in both sectors.

- (d) Comparing PmQc and PmQm it appears that while units rent for less because of controls, households would spend even more at market prices; that is, consumption of housizlb services has been greatly reduced under controls.

- (e) Rent control imposes a landlord cost (PmQc-PcQc), which exceeds the net benefit to tenants in both sectors. This cost to landlords approximates only the static cost of rent control. Dynamic costs, which can also be large, are discu_.sed in the next chapter.