rafmacalaba/datause-encoder-data huggingface dataset:
**_III.2 Using the monetary survey data to assess the banking system's absorptive_**

**_capacity._**

> Deposit shocks can be severe, as witness the dramatic experiences in Argentina in 1994-95 and again during 2001, when deposits fell by over 20 per cent and 10 per cent respectively, or in Turkey in 1994. The heightened depositor uncertainty which they typically imply can reflect heightened lender uncertainty also, but even if the bank lenders do not have a heightened sense of lending risk, they will have to find alternative sources of funding if they are not to shrink their loan portfolio in response to a withdrawal of deposits. Fluctuations in loans in turn can drive macroeconomic

flctuationsn SO w.e want to know whether the banking syste-m **dnoe in fiAt** t to insulate *e VUIoLL-r **Uk iofar iLumL** cAexogen-ious shocks in dUepsJitL, WIUL **Ule piU.JL11pL1UionU ULd** such1 insulation is socially beneficial.

Even if deposits remain stable, disturbances in loan-loss experience can affect the banks' capitalization. This in turn will lead to other portfolio adjustments including fluctuations in lending impacting the macroeconomy. Here again we want to know if the banks are prone to cutting-back on new loans simply because of loan-losses (as distinct from cuffing-back in a prudent response to heightened risk.) If they do so, this is likely to exacerbate an economic downturn and as such be socially undesirable.

Tn the case bo%th of shocks to deposits and to capita, it is evide.nt that simltaneity and **ifeedback** wil; be a cruciai issue. r or exampie, a poor narvest **wiii** tena to affect Dom credit demand and deposits, without there being any causality from the latter to the former. In what follows we use standard econometric techniques to correct for this problem.

17
35 Indonesia calculated average unit costs in 1986/87 to be Rp 3,593 for an outpatient visit and Rp 12,803 for one inpatient day (Djuhari Wirakartakusumah et al., 1988).> Data on tariffs charged by a number of facilities for specific hospital treatments indicate an average fee of Rp 300 for outpatient and Rp 2,089 for inpatient care (MOH, 1991a). This establishes a subsidy ratio of .307. Based on this information, the hospital outpatient subsidy is estimated to be Rp 4,500 and the inpatient subsidy Rp 14,600.

Tile average subsidy from recurrent expenditures for a visit to a health center is calculated to be Rp 500. Although both sets of estimates of the per unit subsidies must be viewed as very rough, they do permit an idea of the relative orders of magnitude at stake.

# **_The_** _Incidence of Government Health Subsidies in_ **_1987_**

Results of the 1987 analysis of the incidence of health subsidies are presented in Tables 18 and 19. As before, monetary units are expressed in monthly Rupiahs per person and results given for the all Indonesia, all urban, and all rural Indonesia population distributions. Table 18 characterizes the decile specific distribution of public subsidies to hospitals and primary health centers in 1987. The hospital subsidy calculations are made differentiating between inpatient and outpatient visits as described above, and assume that the distribution of public hospital visits across deciles in 2 These are averages for class D and C hospitals. Note also that the SUSENAS does not contain details on the length of hospitalization episodes. The paper assumes each inpatient visit to be worth one subsidy amount.

a A study based on a survey of 42 rural health centers in 5 provinces in 1986/87, found average unit costs for curative care to average around Rp 900 per visit, varying from a low of Rp 526 for MCH to a high of Rp 1,337 for family planning consultations (Gani et al., 1988). Although the official (MOH recommended) fee at the time was Rp 150, it seems that many local governments raised them to somewhere between Rp 300 and Rp 1000 (World Bank, 1991a). Based on a fee of Rp 300, the above average unit cost estimate points to a Rp 600 per visit subsidy, not too far off from the paper's estimate for primary health centers. It is true that some patients pay less, while some are treated gratis if they are in possession of the letter of indigence from the village headman. On the other hand, total visits from the SUSENAS data set include posyandu consultations whose unit costs are low and which were not considered in the above study.
||Production<br>Activities|Factors|Households|Other<br>Institutions|Rest of the<br>World|Total|
|---|---|---|---|---|---|---|
|Production<br>Activities|||||||
|Factors|||||||
|Households|||||||
|Other<br>Institutions|||||||
|Rest of the<br>World|||||||
|Total|||||||
|Fig|ure 3-1. Stru|cture of S|AM for the Gl|obal Aggreg|ate Estimatio|n<sup>~~18~~</sup>|

# **4. Analysis of Global Aggregate Disaster Damages, Losses, and Higher-Order Effects**

The economic impact of 184 disasters for the last 50 years are estimated and analyzed in this section. As described in the previous section, the higher-order effects of these disasters are derived based on the data from EM-DAT and NatCat and the constructed SAMs for this study. The historical trends, differences in types of disaster, and the relationship between disaster impact and development level are investigated below.

# _4.1. Historical Trends of Impact_

During 1960 to 2007, based on the sampled 184 disasters, total damages on capital stock were about US$ 742 billion (in 2007 constant value). Estimated losses and total impacts in this period were US$ 360 billion and US$ 678 billion, respectively. The impact multiplier from these figures becomes 1.88 (ratio between total impacts and losses), implying that on average losses from a disaster can be nearly doubled via

> 18 The highlighted cells are treated as endogenous, and other cells are set as exogenous in this paper.

13
of Cameroon, we have that the actual aggregate collected duties were equal to 20.9 percent of the value of imports in fiscal year 1992-1993; and collected duties were equal to 10.7 percent of imports in fiscal year 1996-1997. This shows that the CEMAC reforms have led to a reduction in collected duties. In our view, the collected rates present a more accurate view of the protection provided to industries and the costs to consumers and finns of import protection. Thus, it is important to adjust the legal rates to reflect the collections. Since we do not have data on collected rates by intermediates versus consumption goods, in order to obtain our pre-reform (or benchmark) tariff rates of table 3, we scaled down the structure of the legal tariff rates proportionately such that the weighted average tariff rates equal to the collected rate of 20.9 percent. For the CEMAC compliance scenarios, we set all regional tariffs equal to zero and then scaled down the legal rates on rest of world imports such that the weighted average tariff rate was equal to 10.7 percent, the post-reform collected rate. For the pre-reform tariff rates that applied on exports of Cameroon to its regional partners, we employed the estimates of a World Bank study of UDEAC; it indicates that on average, Cameroon's exports faced a tariff of 22 percent in the region. If Cameroon's regional partners conform to the arrangements of CEMAC, Cameroon will obtain tariff free access to the CEMAC markets, i.e., there will be a reduction of tariffs facing Cameroonian exporters by 22 percent. Cameroonian exporters cannot expect, however, to receive a price increase of the full 22 percent on their regional exports, since the common extemal tariff of CEMAC is also being reduced. The reduction in the external tariff by Cameroon's partners in CEMAC implies that the increase in demand that Cameroon will obtain from the elimination of the 22 percent tariff against its exports, will be eroded due to increased competition in the regional markets from rest of world imports, i.e., there will be a partially offsetting shift down in the demand curve for the regional exports of

-17-
**Table 1: Summary Statistics of Variables (all countries, 1995-2010 period)**

|**Variables**|**N**|**Mean **|**Std.**<br>**Dev**|**25**<sup>**th**</sup><br>**pctile**|**75**<sup>**th**</sup><br>**pctile**|
|---|---|---|---|---|---|
|∆lnLP|450|0.05|0.07|0.02|0.08|
|∆lnLP<sup>leader</sup>|450|0.03|0.04|0.01|0.06|
|prodgap|480|-0.96|0.80|-1.59|-0.38|
|Reg|480|1.23|0.43|0.95|1.60|
|humanK|480|47.74|16.85|38.60|60.25|
|Open_trade|480|101.95|48.16|66.00|133.00|
|Open_trade_EU|480|52.96|26.09|33.00|70.00|

**Table 2: Summary Statistics of Variables (Romania, 1995-2010 period)**

|**Variables**|**N**|**Mean**|**Std.**<br>**Dev**|**25**<sup>**th**</sup><br>**pctile**|**75**<sup>**th**</sup><br>**pctile**|
|---|---|---|---|---|---|
|∆lnLP|15|0.08|0.16|0.01|0.17|
|∆lnLP<sup>leader</sup>|15|0.03|0.04|0.01|0.06|
|prodgap|16|-2.38|0.35|-2.65|-2.12|
|Reg|16|0.23|0.27|0.03|0.50|
|humanK|16|57.26|4.72|52.20|61.13|
|Open_trade|16|69.75|8.27|62.50|76.00|
|Open_trade_EU|16|42.63|7.41|35.00|48.00|

Productivity growth in the leader country is found to have a positive and significant impact on productivity growth in the follower countries. The coefficient of the productivity gap term is negative and significant, suggesting that the more distant a country is from the technological frontier the greater is the scope for productivity improvements arising from the catching-up process. The coefficient of human capital (measured as education achievement but not necessarily representing the skills of the labor force) does not appear to be statistically significant, suggesting that the effect of quality of human capital -- at least in the measure used here -- is fully captured by the productivity gap term.<sup>18</sup>

> 18 In principle any effect of quality of human capital (or of any other input) on productivity growth by country should be captured by the productivity gap term. Human capital is included as a distinct variable in the model to check whether it has a _direct_ effect on the ability of the country to catch up with the productivity frontier.

6
Policy Research Working Paper 8053

# **Abstract**

Many employers and employees believe that non-cognitive skills are an important contributor to labor market success. This study has assessed the empirical evidence for such a claim in the case of Bangladesh by evaluating unique employer-employee matched labor market data. The analysis is based on data collected from 6,981 workers in 500 formal sector firms in Bangladesh’s five largest formal economic sectors. Using ordinary least squares and firm fixed-effect models, the study assesses correlations between wages and the so-called “big five” personality traits, and augments the analysis with the latent personality scores captured by the Rasch model. Comparing the ordinary least squares and fixed-effect models reveals statistically significant correlations between personality traits and wages, within and across firms. The results appear to indicate that non-cognitive skills are correlated with a worker’s likelihood of achieving success in the labor market. Although many of the findings are consistent with the literature, the analysis reveals specific patterns that appear to be unique to Bangladesh, including a positive correlation between “emotional stability” and wages and a negative correlation between “grit” and wages, especially among manufacturing workers. Differences across firms could indicate that firms that offer higher wages may tend to attract workers with distinct types of non-cognitive skills, whereas differences within firms may indicate that variations in non-cognitive skills are associated with disparities in firm-level wage structures. Correlations between wages and personality traits are more prominent among large firms than among small or medium-sized firms.

This paper is a product of the Education Global Practice Group. 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 snomura@ 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
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics positive regional tariff changes and slower in regions facing more negative shocks, consistent with our main results in Tables 1 and 2. Liberalization drove a convergence in earnings across regions by accelerating earnings growth in low-earnings locations relative to those in high-earnings regions. Thus, liberalization seems to have spurred growth in formerly marginal cities and towns as compared to the country’s more traditional economic engines in large high-wage cities.

# **6 Individual Analysis**

## **6.1 Empirical Specification**

The RAIS data make it possible to follow individual workers over time, so we can observe the medium- to long-run effects of liberalization on individual workers based on their initial region of employment. Our main analysis focuses on a panel of workers who were initially employed in the tradables sector in December 1989, just before trade liberalization began.<sup>33</sup> Our population of interest includes all workers aged 25-44 in December 1989, whose highest paying job was in the tradable sector. For computational tractability, we take a 15% sample of individuals meeting these criteria in regions with more than 2,000 tradable sector workers in 1989 and include all relevant workers from smaller regions, weighting appropriately in subsequent analyses. This process yields 587,575 individuals in our main tradable sector sample. In Section 6.6, we also consider an alternate population of workers initially employed in the nontradables sector, in order to investigate the transmission of the trade shock into this sector. All other restrictions and sampling procedures are the same, yielding a sample of 973,048 nontradable sector workers. Table 5 provides summary statistics for the tradables sector and nontradables sector samples. Finally, we use alternate samples to study in-migration and the length of non-formal spells, described in Sections 6.2 and 6.5, respectively.

The individual-level analysis estimates equations of the following form, separately for each year in 1990-2009: where _i_ indexes individuals, _j_ is the worker’s initial industry of employment in December 1989, _r_ is the worker’s initial region of employment in December 1989, and _t_ indexes years following the start of liberalization (1990 to 2009).<sup>34</sup> _yijrt_ represents various worker-level post-liberalization outcomes, which we define below. _Xijr,_ 1989 is a rich set of worker-level controls including demographics (9 education category indicators, age, age-squared), initial job characteristics for the highest-paying job in December 1989 (84 occupation category indicators, 14 tradable industry indicators, 12 non-

> 33In the regional analysis above, we use 1991 as the base period to maintain comparability with the Census. Regional results are unchanged when using 1989 as the base year.

> 34The 2010 RAIS data use a different individual identifier than the previous years, so we are only able to implement the individual analysis through 2009.

23
|Share agri. pop.<br>Transport cost||0.04***<br>[3.57]<br>0.00***<br>[3.21]|
|---|---|---|
|National road||0.23|
|||[1.19]|
|vanilla region||3.23***|
|||[6.26]|
|1997|1.01***|0.66**|
||[3.57]|[2.12]|
|1999|0.61**|0.81***|
||[2.13]|[2.63]|
|2001|0.65**|0.19|
||[2.51]|[0.57]|
|Constant|7.50*|-4.06|
||[1.83]|[1.02]|
|Observations||7'674|
|ρ|-0.485||
||(0.134)||
|σ|0.952<br>(0.067)||
|λ|-0.462<br>(0.148)||

Notes a/ Omitted category: landless farmers tilling community land b/ Preceding 3-year change in output-weighted Laspeyres price index c/ Preceding 3-year standard deviation in output-weighted Laspeyres price index d/ Residual of unit-value regression on individual/local characteristics (see Table 6 below) As expected, the output equation gives positive and significant estimates for labor and land. However as the equation’s “land” variable is the farm’s overall land holdings rather than vanilla acreage, the estimated coefficient cannot be used to retrieve the marginal product of land. Education seems to raise the productivity of factors, and the effect is estimated precisely. Aggregate price volatility has a negative effect, possibly because with nonexistent capital markets price shocks impair a farmers’ ability to save and invest.<sup>8</sup>

> 8 The EPM reports very little access to credit. However, as noted elsewhere in this paper, this should be taken cautiously as farmers may fail to report pre-financing of the harvest as credit. This said, pre-financing of a harvest alleviates short-run liquidity problems but is not a substitute for investment credit to buy tools or seeds.

13
escaping poverty and changes during 1992-2002. This section also addresses the extent to which the rural South region is different from rural Mexico as a whole. Finally, Section 6 presents a three-pronged rural poverty reduction strategy guided by the findings from the poverty profile and determinants of poverty analysis. The strategic principles for reducing poverty involve seeking to strengthen the key assets of the poor, taking into account geographic differences in the poverty situation and priorities.

# **2. Populations and Land**

The degree of poverty a society might experience depends on the volume and distribution of resources and on the size and distribution of the population among households. These two basic determinants of poverty, however, are not independently determined. On one hand, the size and age structure of a population are consequences of fertility decisions taken over past decades that were influenced by economic conditions. On the other hand, the volume of resources available today is influenced by the size and age composition of the labor force. This section analyzes recent changes in demographics in Mexico’s rural areas. The following section very briefly addresses agriculture, land, and rural living in Mexico.

# **2.1 Population**

## **The importance of demographic factors**

Demographic factors have direct and indirect impacts on prices and poverty. As the size and age composition of the population changes, the relative size of the labor force and the number of dependents also change, modifying the dependency ratio of families, and therefore their level of poverty. This is the direct effect of demographic changes. It captures the effect that demographic changes have on quantities: number of children, size of the labor force, and the number of elderly people. These changes in quantities, however, will, in general, influence prices in the economy. In particular, changes in the rate of growth of the population and in the age structure may have important impacts on labor supplies, savings, and household production decisions and migration. As a consequence, demographic changes may have considerable impact on the level of wages and on interest rates. Since these prices are important determinants of family income, they are bound to have a profound influence on the level of poverty. These are the indirect impacts of demographic changes on poverty, which occur through the effects of demographic changes on savings, wages, production decisions and interest rates.

Changing demographics can also have important impacts on the demand for public sector investments and public services, on incentives for private sector investments, on political power, and on labor markets. As a result, it is important to look at recent changes in demographic patterns in rural Mexico. The following overview describes demographic changes between rural and urban areas that have taken place from 1990 to 2000 followed by Section 3 on rural labor markets.

2
2 short-term in the firms divested, and then in the longer-run and in the economy at large. Third, there is a widespread belief that even if privatization enhances efficiency, the bulk of its benefits accrue to a privileged few-shareholders, managers, domestic or foreign business interests, those connected to the political elite-while the costs are borne by the many, particularly workers and consumers. In addition, many are concerned that lack of transparency and corruption in the privatization process itself has minimized the intended gains and led to or deepened broader problems of governance.

5. This paper takes stock of the empirical evidence on privatization outcomes, and-on the basis of this evidence-highlights the conditions for success. The paper deals mainly with traditional privatization efforts involving enterprises operating in competitive markets; it does not cover infrastructure enterprises (which are the subject of a separate background paper),' though a number of general issues arising from infrastructure privatization are mentioned. Section 2 provides a brief review of overall privatization trends over the past ten years. Section 3 reviews the literature on the micro- and macroeconomic impacts of privatization, and also examines the employment and broader distributional impact. Section 4 highlights the key factors for successful privatization.

# **2. PRrVATIZATION TRENDS**<sup>**2**</sup>

6. Privatization activity has grown in the past ten years, both in terms of number and value of transactions. In the 1980s there were only a few transactions on average per year, but by the late 1990s the annual average rose to about 500. Between 1990 and 1999, total global proceeds amounted to US$850 billion, growing from $30 billion in 1990 to $145 billion in 1999 (Figure 1). Developed countries account for the bulk of the proceeds, mainly from public offerings of large firms in countries of the European Union (Mahboobi, 2000).

> ' Gray 2001.

> 2 Global data on numbers of enterprises privatized are not readily available. Compiling this information from country level data is a difficult task given problems of consistency and comparability; for example, some countries include the privatization of small retail outlets in their figures while others exclude them. Data on global privatization proceeds are available from the World Bank's Global Development Finance, 2001, on which this section is based. The data is on an announcement basis rather than on the basis of actual flows of receipts, which means that privatization commitments do not reflect receipts in a particular year, as transactions may be paid for over several years. Data on privatization revenues should be viewed cautiously as they can be concentrated in a few large public offerings and thus do not reflect the scope or progress of a country's overall program. The discussion here is mainly intended to provide an overview of broad trends rather than comprehensive coverage of privatization activities to date.