rafmacalaba/datause-encoder-data huggingface dataset:
- 11 -

**_The External Incentive Framework._** The regulatory and supervisory framework, along with accounting rules, disclosure requirements, and the existence of a deposit insurance scheme, play a crucial role in defining the incentive framework in which financial institutions operate. In particular, the extent to which excessive risk taking is curbed by regulation, or penalized by the supervisory authority as well as by the market, greatly influence the behavior of financial institutions. There are three potential groups that can monitor bank managers, namely the owners, the market, and supervisors.

At end 1987, the incentive framework of banks and finance companies appeared relatively weak and may have been ineffective in aligning owners/managers incentives with prudent banking. For example, the disciplinary effect of capital to asset requirements were lirmited due to the level and definition of capital adequacy requirements. While eight percent appears to be in line with the capital adequacy ratio imposed on banks in developed markets, it appears low relative to the high risk operating environment in which Thai financial institutions were. Moreover, despite the fact that the minimum level for capital is based on a narrow definition for capital, BoT permitted 31 exemptions for different classes of assets, including certain categories of risky, priority sector loans. In 1989, total exemptions from capital adequacy computation accounted for approximately 40 percent of total assets. By permitting these exemptions BoT used capital adequacy as a tool of economic regulation to encourage directed credit rather than as a buffer to absorb unusual losses."<sup>9</sup> These capital adequacy guidelines were flrther weakened by prudential norms on asset quality which effectively led to an overstaternent of capital.<sup>20</sup> Furthermore, financial institutions were allowed to accrue uncollected interest income for up to twelve months, thereby overstating income and capital. Finally, regulations aimed at limiting excessive exposure to a single related entity or connected group of entities were weak and ceilings on exposure to particular "risky" sectors (to sector which are prone to boom and bust cycles: i. e., real estate) were nonexistent.

Table 2 contains a summary of prudential regulations that Thai banks and finance companies were subject to. It illustrates one important point: in spite of the fact that finance companies tended to engage in riskier activities due to their regulatory constraints, finance companies were subject to less stringent prudential requirements than banks. For example, while conmmercial banks' capital to asset ratio was set at 'eight percent, finance companies had to hold only six percent of capital against their risky assets.

- 19 World Bank (1990).

> 20 Moreover, the tax treatment of provisions acted as a disincentive to adequate provisioning since an institution had to have exhausted nearly all legal remedies before the tax authorities would consider the loss as a deductible expense. World Bank (1990)-
license, obtaining operating license, and inspections or meetings with tax officials. Based on this information, the ES compiles two separate measures of petty corruption. The first measure is the incidence of petty corruption defined as a dummy variable equal to 1 if a firm experienced a bribe payment or request for one or more of the six transactions listed above ( _Petty Corruption Incidence_ ). The second measure is the depth of petty corruption defined as the percentage of the 6 transactions for which the firm experienced a bribe payment or a request for one ( _Petty Corruption Depth_ ).<sup>6</sup> We use both these measures of petty corruption as our dependent variables. The mean value of _Petty Corruption Incidence_ equals 0.18 and the standard deviation is 0.39; the corresponding figures for _Petty Corruption Depth_ are 14.1 and 32.1, respectively.

# 2.2 _Main explanatory variable_

Our main explanatory variable is a measure of country size. Following the broader literature, we use the (log of) total population of the country as our main measure of the size of the country ( _Population_ ). We use five-year lagged values (from the date of the ES) of the variable to minimize concerns about the reverse causality problem. The mean value of _Population_ in our baseline sample equals 15.7 log points (or 42 million people) and the standard deviation equals 1.96. The smallest country in our sample has a population of 47,971 (St. Kitts and Nevis) while the largest country has a population of about 1.3 billion (China). The data source for the variable is WDI.

Another measure of country size frequently used is the total surface area of the country. For a robustness check, we use (log of) country’s total surface area as a measure of country size ( _Area_ ). The variable is taken from WDI.

> 6 The two measures of petty corruption are available for only those firms that solicited the public services listed above or were inspected by tax officials. For the bribery depth measure and following the ES methodology, a refusal to answer a question on whether bribes were requested or expected is considered as an affirmative answer.

9
_Figure 7: Estimation Results - Additional Employment Probability (%) Based on the Various DTS Implementation Aspects_ <!-- Start of picture text --> Times visited by TVI teacher during in-company 0.6<br>training (1=yes) 0.1<br>Reported to TVI during in-company training 2.1<br>(1=yes) -1.5<br>7.3<br>How to learn work process - from all -1.9<br>0.9<br>How to learn work process - by yourself -5.2<br>-13.7<br>How to learn work process - from peers -7.4<br>8.6<br>How to learn work process - from supervisors -4.8<br>3.2<br>Number of hours per week for in-company training 2.1<br>0.3<br>Weeks spent for in-company training 0.1<br>0.1<br>Weeks spent for in-school learning 0.1<br>-3.4<br>Given training plan from TVI (1=yes) 3.1<br>DTS implementation scheme dummy (1=Block 7.1<br>release) -5<br>-15 -10 -5 0 5 10<br>Percentage point change in employment probab<br>Employment at the survey Employment after TESDA training<br>Monitoring<br>In-company training<br>Time allocation<br>Training strategy<br><!-- End of picture text --> Source: _DTS Tracking Survey Data, 2015, World Bank; World Bank calculations_ . Note: _Marginal effects on the employment probability, percentage point increase or decrease to the probability of being employed with one unit change to the listed controls, are computed from a Probit estimation. The estimation controls for gender, years of work experience and squared term, maternal education level, TVI dummies, and enrollment year dummies. (See Table 1.4 for regression results in detail)._ 23
oping countries (for details, see Agénor (2003)). As an indicator of living standards, we consider in what follows only disposable household income per capita.

# 5.1 Reduction in the Minimum Wage

The simulation results associated with a permanent 7 percent reduction in the minimum wage are illustrated in Tables 1 and 2 for the first 10 periods after the shock.<sup>13</sup> This time period is referred to below as the “adjustment period.” Table 1 provides data on the most important macroeconomic indicators, the government budget balance, and the labor market. Table 2 presents data on consumer prices, disposable income for each household group, and poverty and distributional indicators for the income variable, all in absolute deviations from the baseline solution. The experiment assumes that the government borrows domestically to finance its deficit–implying therefore (as discussed earlier) an offsetting adjustment in the investment of private firms, in order to maintain the aggregate balance between savings and investment. We first comment on the general results of this simulation and then compare more specifically the effects on poverty and inequality, as measured by the three methods.

The impact effect of the reduction in the minimum wage is an increase in the demand for unskilled labor in the private sector of almost 3.4 percent in the first year and approximately 7 to 9 percent in the following years. The increase in demand is met by the existing pool of unskilled workers seeking employment in the urban sector. As a result, the unskilled unemployment rate drops significantly, by 2.2 percentage points in the first year and by more than 8 percentage points in the following years, which reduces unemployment for this segment of workers to almost zero. The cut in the minimum wage, by reducing the relative cost of unskilled labor, leads to substitution among production factors not only on impact but also over time. Because unskilled labor has a relatively high elasticity of substitution with respect to the composite factor consisting of skilled labor and physical capital, the lower cost of that category of labor gives private firms in the formal sector an incentive to substitute away from skilled labor and physical capital. In turn, the fall in the demand for that category of labor puts downward pressure on skilled

> 13This reduction means that the minimum wage decreases from 1.23 times the poverty line to 1.14 times the poverty line.

21
handwashing exist just at very high rates of improved sanitation coverage. In this case it is shown that improved sanitation access may drive much of the benefits of handwashing, but just at very high rates of access. Information campaigns may have externalities for those sets of household with different sanitation coverage levels and for different wealth cohorts. This may well be an area of further research.

# _Estimates for lowest-income quintiles_

Final estimations were conducted in survey subsamples based on income quintiles. Panel data on children below 5 years old was used to determine whether low income versus higher income cohorts showed differentiated effects of all handwashing promotion programs and among different cognitive outcomes.

As explained earlier, because of contamination issues in some components of the program (treatment status) there could be potential bias driving results. However, the relative size of the effects of the interaction term between treatment exposure and asset tenure may highlight if effects are indeed differentiated according to the income cohort where households belong to.

Table 11 shows the results of these estimations<sup>12</sup> . Results for the effects variables and interaction were only statistically significant for communications and motor skills of children. In terms of communication skills, the interaction of the treatment status and the communications asset tenure in the household showed around 1/3 increase of one standard deviation in the communication scores of children for the lowest quintiles of income (I & II), and yet for higher income cohorts the interaction coefficients showed a progressive reduction in the effects (0.13 of one standard deviation for quintiles III and IV; and -0.008 of one standard deviation for quintile V). Although the magnitude of effects may contain some bias, there are indeed differences between income cohorts of the effects that program exposure and communication assets in the household have on developing communications cognitive skills in children.

> 12 Only effects coefficients reported from quintile regressions of the full specification model. This full specification model included independent variables that control for parents education, other sources of income, and household basic characteristics.

26
<!-- Start of picture text --> 100%<br>90%<br>B57om57omom<br>=ie)ie) 60% TM Rudimentary..<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>B57om57omom<br>=ie)ie) 60% TM Rudimentary..<br>S so% Informality<br>2 40% ® Modern<br>—2 30% Informality.<br>XS 20% © Formality<br>10% |<br>0% L _<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>Oo .<br>= 60% # Rudimentary<br>S so% Informality<br>a 40% TM Modern<br>2 30% Informality<br>S 20% ® Formality<br>10% |<br>0% L — Laas _ _ _ _ _<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
# TABLE OF CONTENTS

||Introduction|1|
|---|---|---|
||Rationale for Bank Involvement in Population|4|
||The Macroeconomic Consequences of Rapid Population Growth|4|
||The Role of Governrnent|6|
||Conclusion|6|
|II.|Resources and the Demand for Fam;ly Planning|8|
||Table 1. Contraceptive Prevalence in Developing Countries by<br>11c5 Socioeconomic Setting and 1989 Family Planning Program Effort|12|
||Lassons Learned from Family Planning Program Experience|13|
|Ill.|Implications for the Bank|15|
||Incorporating Population More Fully into Economic Work and<br>Policy Dialogue|15|
||Resource Requirements and the Future Role of the Bank|17|
||Specific Proposals for Immediate Action|18|
||Recapitulation of Recommendations|19|
||Figure 1. Population Growth Rate, 1990<br>|21|
||Figure 2. Crude Birth Rates, 1990|21|
||Figure 3. Total Fertility Rates, 1990<br>|22|
||Figure 4. Contraceptive Prevalence Rate, 1980<br>|22|
||Figure 5. Trends in Contraceptive Prevalence, by Region|23|
||References|24|
||Annex: Overview of the Bank's Record in the Population Sector<br>from 1970 to the Present|26|
||Table A-1: Bank Lending for Population, Health and<br>Nutrition (PHN) Projects|31|
||Table A-2: Lending for Population by Region|34|
WPS3891

# **Protection, Openness and Factor Adjustment: Evidence from the manufacturing sector in Uruguay**

Carlos Casacuberta (Universidad de la República) Néstor Gandelman (Universidad ORT)<sup>*</sup>

## _Abstract_

_This paper uses a panel of manufacturing firms to analyze the adjustment process in capital, blue collar and white collar employment in Uruguay during a period of trade liberalization when average tariff protection fell from 43 to 14 percent. Desired factor levels arising from a counterfactual profit maximization in the absence of adjustment costs are calculated, generating a measure of factor shortages or surpluses. The average estimated output gap for 1982-1995 is 2%. Our policy analysis shows that trade openness affected the adjustment functions of all three factors of production. Highly protected sectors adjust less when creating jobs (reducing labor shortages) than sectors with low protection. This may be due to fears of policy reversal in highly protected sectors. Also, highly protected sectors adjust more easily (than low protection sectors) when destroying jobs (reducing labor surpluses), especially in the case of blue collar labor. This suggests that trade protection may in fact destroy rather than create jobs within industries, as firms in highly protected sectors are more reluctant to hire and more ready to fire than firms in sectors with low protection. The results for capital are qualitatively similar but quantitatively smaller, suggesting that trade protection plays less of a role in explaining adjustment costs for capital. Interestingly, export-oriented sectors have lower adjustment costs for blue collar labor, but not for white collar employment or capital, suggesting that export-led growth may be successful in reducing blue collar unemployment._

### World Bank Policy Research Working Paper 3891, April 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors thank Marcelo Olarreaga, Gabriela Fachola and participants at the 2005 Annual Meeting of the BCU for valuable comments. Fachola also helped with the database and the construction of the capital series. Financial support from the Word Bank Research Support budget is also gratefully acknowledged.

1
**Table (A.3)** Statistically Significant Impacts of the Program at Endline and Follow-up (with Lee bounds)

|**Outcome**|**TOT**|**(se)**|**FDR**<br>**q-values**|**Lower**<br>**Bound**|**Upper**<br>**Bound**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|---|---|
|**Panel A: Endline**|||||||||
|Focus Respondent Childcare & Chores (Hours)|-6.91***|(2.02)|[0.01]|-13.27***|-5.98***|17.76|(18.39)|1,418|
|At least 30 JD (95 USD PPP) in savings (=1)|0.14***|(0.05)|[0.02]|-0.07|0.18***|0.12|(0.32)|1,422|
|Applied but Did Not Take Loan (=1)|0.06***|(0.02)|[0.00]|-0.02***|0.06***|0.01|(0.10)|1,422|
|Total Labor and Chores Hours Last Week|-10.62***|(3.24)|[0.01]|-22.95***|-6.61**|25.54|(23.39)|1,422|
|IP Assistance in Last 12 Months (=1)|0.08**|(0.03)|[0.14]|-0.11***|0.09***|0.04|(0.19)|1,422|
|IP Assistance in Last 12 Months (USD PPP)|128.25***|(46.50)|[0.10]|-88.67***|145.45***|28.74|(198.87)|1,422|
|General Happiness (Scale 1-3)|-0.22***|(0.08)|[0.02]|-0.51***|-0.04|1.76|(0.61)|1,422|
|**Panel B: Follow-up**|||||||||
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.72***|-0.33*|-0.01|(1.00)|859|

_Notes:_ The table shows the regression results on statistically significant results from the endline survey. Each row is its own dependent variable. Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have month-by-year fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to various families in the pre-analysis plan. The lower and upper bounds refer to the ones proposed in Lee (2009) to correct for attrition. The lower bounds marked by NA are not possible to report because trimming the lower tail would result in no variation in the variable, making the regression impossible to run as it becomes a column of zeros. The outcome "Focus Respondent Childcare Chores" was pre-specified in two outcome families; the table reports the lower of the two q-values; the higher q-value is 0.02 A-4
# 2.3.1 Rainfall Shocks

Figure 10a shows rainfall recorded in 2014 along with average rainfall between 2003 and 2015 at the state level. Rainfall in 2014 was higher than the historical average in all states in Sudan. For most states, this was a significant improvement from the amount of rainfall recorded in 2013. In states such as Central Darfur, West Kordofan, While Nile, and Sennar, the 2014 rains were 200 mm higher than the historical average. The variation in the deviation of rainfall across states can illustrate the extent to which the welfare of households is potentially affected. A large deviation in rainfall may indicate higher possibility of floods, thereby negatively affecting household welfare, whereas a slight deviation may indicate better chance of improved productivity for rain-dependent farmers.

# 2.3.2 Conflict

Figure 10b shows the number of conflict events along with the average number of conflict events between 2012 and 2015 **.** For most states, the number of conflict events increased in 2014 relative to recent averages. In the Darfur region and parts of the Kordofan region as well as in Khartoum, the number of conflict events increased in 2014 reaching more than 400 events in North Darfur. However, some degree of heterogeneity exists in the type of events that are popular across states. For instance, in the Darfur region, most of these events occurred in the form of ‘violence against civilians’ whereas in Khartoum, ‘protests/riots’ dominated conflict events.<sup>11</sup>

> 11 The ACLED provides information on the type of conflict which is used to construct these categorizations.

25
**Table 2 – Descriptive Statistics**

||**Variable**|**Mean**|**Std Dev**|**Min**|**Max**|**80/20th Pct**|
|---|---|---|---|---|---|---|
||**Distance**|570.87|587.84|0.00|2381.13|2.82|
||**Remoteness**|0.071|0.092|0.000|0.341|13.78|
||**Infrastructure**|0.53|0.24|0.15|0.98|2.46|
||**Population**|6,538|9,462|1,520|35,000|3.72|
||**PCI**|1,998|1,762|682|7,915|2.47|
||**Output PC**|229.06|173.83|1.74|1442.26|3.37|
|**e**|**Productivity**|40|8|25|55|1.49|
|**Ric**|**Trace Stat**|19.51|5.47|9.60|42.82|1.54|
||**Price Diff**|259|186|6|870|4.54|
||**Price**|2,520|221|2,174|3,044|1.17|
||**Output PC**|2.95|4.08|0.55|22.88|3.23|
|**ans**|**Productivity**|11.96|1.53|8.48|14.96|1.25|
|**ybe**|**Trace Stat**|12.14|5.04|3.81|28.51|2.20|
|**So**|**Price Diff**|850|725|4|2,555|11.76|
||**Price**|2,664|770|1,872|4,427|1.65|
||**Output PC**|36.55|42.35|0.01|170.78|11.98|
|**ze**|**Productivity**|26.14|6.45|16.00|45.00|1.42|
|**Mai**|**Trace Stat**|13.85|9.13|2.83|51.65|2.36|
||**Price Diff**|359|284|3|1,298|5.93|
||**Price**|973|316|478|1,776|1.73|
|**r**|**Trace Stat**|26.69|11.99|7.59|65.19|2.33|
|**uga**|**Price Diff**|191|173|0|720|5.89|
|**S**|**Price**|3,369|179|3,161|3,880|1.07|
|**il**|**Trace Stat**|13.74|8.54|3.71|74.19|2.26|
|**C. O**|**Price Diff**|565|422|2|1,929|4.54|
||**Price**|4,192|489|2,958|4,887|1.16|

**Notes:** (1) Population and PCI are expressed in thousands. **Source:** Own Elaboration, based on data from BPS and Bulog

25
Table 11. SMEs VS. Non-SMEs

||S|ME Interactio|n||SMEs|||Non-SMEs||
|---|---|---|---|---|---|---|---|---|---|
||||LP|||LP|||LP|
|Dep. Variables|TFP<br>(1)|Log LP<br>(2)|<br>Growth<br>(3)|TFP<br>(4)|Log LP<br>(5)|<br>Growth<br>(6)|TFP<br>(7)|Log LP<br>(8)|<br>Growth<br>(9)|
|FemHead|-0.044|-0.108**|-0.002|-0.087**|-0.184***|-0.022***|-0.053|-0.050|0.005|
||(0.064)|(0.053)|(0.010)|(0.039)|(0.035)|(0.008)|(0.062)|(0.051)|(0.009)|
|FemHead*SMEs|-0.060|-0.062|-0.018*|||||||
||(0.084)|(0.058)|(0.011)|||||||
|Foreign|0.388***|0.488***|0.051***|0.401***|0.518***|0.058***|0.367***|0.437***|0.032***|
||(0.049)|(0.049)|(0.012)|(0.075)|(0.055)|(0.015)|(0.059)|(0.052)|(0.011)|
|OwnLargest|-0.113|-0.347***|-0.035***|-0.137**|-0.380***|-0.047***|-0.075|-0.262*|-0.009|
||(0.070)|(0.086)|(0.010)|(0.058)|(0.066)|(0.010)|(0.107)|(0.137)|(0.011)|
|Small|0.020|-0.103***|-0.007|||||||
||(0.031)|(0.031)|(0.004)|||||||
|Middle|-0.044*|0.141***|0.029***|-0.056**|0.140***|0.029***|0.091**|-0.021|-0.008**|
||(0.026)|(0.027)|(0.005)|(0.025)|(0.025)|(0.005)|(0.037)|(0.023)|(0.004)|
|Large|-0.143***|0.180***|0.038***|||||||
||(0.047)|(0.044)|(0.008)|||||||
|Age6_10|0.053|0.089***|-0.009|0.047|0.088***|-0.008|0.090|0.117*|-0.013|
||(0.047)|(0.022)|(0.007)|(0.049)|(0.023)|(0.008)|(0.082)|(0.060)|(0.011)|
|Age10plus|0.014|0.088***|-0.015**|0.027|0.088***|-0.013|0.004|0.108|-0.023*|
||(0.041)|(0.028)|(0.008)|(0.048)|(0.027)|(0.008)|(0.075)|(0.084)|(0.013)|
|Exporter|0.198***|0.222***|0.028***|0.240***|0.201***|0.025***|0.129**|0.216***|0.026***|
||(0.039)|(0.045)|(0.006)|(0.044)|(0.058)|(0.006)|(0.050)|(0.041)|(0.005)|
|Initial LP|||-0.104***|||-0.111***|||-0.085***|
||||(0.017)|||(0.018)|||(0.013)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,157|51,079|45,711|13,385|36,900|32,639|6,772|14,179|13,072|
|Adjusted R<sup>2</sup>|0.283|0.389|0.372|0.298|0.405|0.407|0.276|0.327|0.267|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

SMEs is defined as the permanent employees three years ago is less than or equals to 50, and Non-SMEs above 50.

The share of female-headed firm is higher amongst small firms (12.4 percent) than large firms (7.3 percent).

41