rafmacalaba/datause-encoder-data huggingface dataset:
34 <!-- Start of picture text --> Manageriol Bonuses<br>1990<br>4<br>6 2.5<br>f<br>§2<br>1.5~~~~~~~~~~~~~~~~~~~~~~~.<br>0.5 ~ ~ ~ ~ ~ ~ ~<br>0.0 0.1 . 0.3 0.9 2. 3.4 4.6 8.4 18.9 31.6 63.2 94.7<br>Prof"t USO 10'6<br><!-- End of picture text --> <!-- Start of picture text --> Managerial Bonuses<br>1991<br>3<br>o 2.<br>~ ~ -<br>0.0 0.1i 0..3 0.9. 1.9 . 3.0 4.2 7.6 17.0 28.4 56.8 85.2<br>Profits USO 10'6<br><!-- End of picture text -->

# F%m13

When managers who had clearly engaged in deep resruturing (new products, new markets, firms brought back from the verge of liquidation) were asked what mouivated them to take a long-um view given the compensation system, they mentoned emotional reasons, patriotism, personal ambition and the like. However, a few candidly admitted that they expected to gain from privatization, hoping to acquire part of the shares at below-market prices. This would he their deferred compensation. Managers were secure about keeping thei jobs after privatization; their reasoning is that they are the best repository of restructuring talent in this economy and even if fired, would easily find new jobs.

# MI.3.F _S&I A_

The formal compensation package for managers, which emphasizes the short-run at the expense of maximizng the long-nm value of the firm, needs to be remedied. First, there should be transparency of rules, not a vague hope that the Supervisory Board or Founding Organ wffl deem it fit to reward a good manager. Second, the need for a suitably designed management contrdac is of paramount importanc, clarPifyn statu after privauiaton and mode of compensation. In fact, privatization, the long-run health of the firm and managerial reward can be made congruent through such clarification and the increasing adoption of decenrali approaches to transformation such as MBOs and privatizaon thrugh management conacts.

This view is reinforced by the observation that manager **s** upport privatization, perceiving it as an opportunty for themselves. Of course, the behavior of managers prior to privatization wil depen on what they expec **t** o gain from ownership change. There can be a positive impact if incentives are clear and rules of the gawe stable; buit a negative inmpact when uncertinty prevails.
Bangladesh is nine years; World Bank, 1996.) We find that 74% of boys in the sample were recorded as "currently attending school", and 75% of girls. Of the 1295 children not at school (685 boys), 704 (378 boys) were not classified as being at work in the "normal activity" question either. So the data do not suggest that the majority of those children not at school are normally working instead.

The extent of household participation in the FFE program is measured by the actual quantity of foodgrains received under the program. Apart from program participation, the explanatory variables in the regressions include household size and family structure variables, the education levels of the father and the mother, the land ownership, the age of the kid and the religion, and a number of village level variables on school access and quality.

Household participation in the program is treated as endogenous. There are two levels of purposive targeting: selection of the village, and selection of the household. The community module provides independent information on whether the village participates in FFE. Given that geographic placement is based on explicit targeting criteria, it is reasonable to treat this aspect of the purposive targeting as a problem of selection-on-observables. However, that is not plausible for individual placement, given that the program is designed to exploit idiosyncratic local information. Our estimation method assumes that household-level participation depends on unobservables (notably the idiosyncratic information on household-level circumstances held by those allocating the program within villages) but village level placement can be accounted for adequately by a set of observed control variables at village level. This is consistent with what we know about how the program was targeted in practice (section 2).

In addition to the common vector of explanatory variables X (which includes the FFE stipend), the probits for schooling and child labor included the residuals from a first stage tobit 12
<!-- Start of picture text --> 15.0% Changes in sample composition based on<br>release of new census data and field level<br>challenges<br>Expansion of the rural<br>10.0% sample<br>5.0%<br>0.0%<br>-5.0%<br>-10.0%<br>% addition % deletion<br>-15.0%<br>Percentage of samples added or deleted<br><!-- End of picture text --> Figure 1: Percentage of samples added and deleted over survey waves. Notes: Based on Vyas (2020).

observe changes in socioeconomic variables since 2011. These are: (i) periodic labor force surveys (PLFS) of 2017-18, 2018-19 and 2019-20; (ii) the situation assessment of agricultural households (SAAH) of 2013 and 2019; and, (iii) the all-India Debt and Investment Surveys (AIDIS) of 2013 and 2019. The PLFS provides estimates of wage growth for casual and salaried wage workers, while AIDIS surveys track the evolution of physical and financial assets ownership overtime. The SAAH surveys allow us to study income inequality across agricultural (and predominantly rural) households. Following Himanshu (2019), we use these surveys to construct updated estimates of consumption, earnings, income and asset inequality.

The PLFS furthermore contains a single self-reported expenditure variable referred to as “usual household consumption expenditure”, which may serve as a proxy for the respondent’s monthly consumption. Mehrotra and Parida (2021) have used this “usual consumption expenditure” variable to document a large increase in headcount poverty in 2019-20. In Appendix 5, we examine this welfare aggregate and detect the presence of significant bunching of consumption around multiples of Rs. 1000 - consistent with theory of satisficing documented in Krosnick (2018). Our simulations suggest that these rounding off errors can have a considerable impact on estimates of poverty and 7
# **Abbreviations and Acronyms**

|ABS|Asset Backed Security|
|---|---|
|BCBS|Basel Committee on Banking Supervision|
|Basel II|Revised International Capital Framework|
|Basel III|International Regulatory Framework for Banks|
|CDO|Collateralized Debt Obligation|
|CEMLA|_Centro de Estudios Monetarios Latinoamericanos_|
|CRS|Credit Reporting System|
|FSA|Financial Services Authority|
|GDP|Gross Domestic Product|
|G10|Group of Ten Finance Ministers and Central Bank Governors|
|G20|Group of Twenty Finance Ministers and Central Bank Governors|
|ID|Personal Identification|
|IRB|Internal Rating Based|
|LGD|Loss Given Default|
|OTC|Over-the-Counter|
|PCR|Public Credit Registry|
|PD|Probability of Default|
|PIT|Point-in-Time|
|QIS|Quantitative Impact Study|
|ROC|Receiver Operating Characteristic|
|RWA|Risk Weighted Asset|
|SIFI|Systemically Important Financial Institution|
|SME|Small and Medium Enterprise|
|TTC|Through-the-Cycle|
|VaR|Value at Risk|
|WB|World Bank|
|WHCRI|Western Hemisphere Credit and Loan Reporting Initiative|

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implementation. Other important training steps could include setting up a help desk, conducting hand-holding clinics, and thoroughly training a group of power users to be trainers or technical resources for other staff. Experience suggests that operational end user staff can pick up automated systems with a well-managed training program, as was the case in Pakistan.

3.54 **Training managers to use the system helps them understand the system’s capabilities and use them adequately.** This training category is important because government senior finance managers can be unacquainted with using computer-based systems and are reluctant to learn from junior staff. They thus depend on junior staff to produce the required information, which imposes a limitation. At the least, managers should be familiar with what the system can and cannot do, so they become sovereign users and apply the system to its full potential.

3.55 **Effective change management programs, as noted in many World Bank ICRs, are important.**<sup>**46**</sup> There is voluminous literature available on change management and its application to organizational change in public sector organizations in general. Central to this literature is the role of political leadership, outreach, and communication to mitigate opposition to reform. The interested reader is referred to a 2015 guide for practitioners, which also includes a comprehensive literature review (World Bank 2015).<sup>47</sup> The study identifies the following twelve key challenges: high level political support; developing adaptive leadership skills; stakeholder mapping; development of a shared vision; an effective communications strategy; readiness of reform teams and project leaders; a well-defined deployment plan; a capacity building plan and appropriate institutional framework; system integration and transition capacity; managing resistance; assessing risks; and taking into account the political context.

3.56 With respect to FMIS, a recent World Bank study derives important lessons from the experience in Cambodia and Indonesia, among others, also on the role of political economy (World Bank 2017). Lessons from Chile’s FMIS reform are carefully documented by Dorotinsky and Watkins (2013) and stress the importance of internal capacity to developing a conceptual model; establishing user committees to validate a preliminary design; and giving priority in the development phase to the core functions (based on a 2011 presentation by Gerardo Uña). However, while an in-depth review of change management is beyond the scope of this study, the following aspect was identified as a common thread across countries: that staff in implementing agencies

> 46 See Chêne (2009), World Bank (2017), World Bank (2015), and project documentation for FMIS operations in Cambodia, Chile, El Salvador, Kenya, Kyrgyz Republic, Malawi, Moldova, Nigeria, Russia, Slovak Republic, Tanzania, and Vietnam.

> 47 The experience of six OECD countries is presented by Huerta Melchor (2008).

**42**
mentioned international activities are associated with higher relative wages for skilled workers. However, the effects on relative skilled employment are negative and signi fi cant for imported inputs and export shares and are not signi fi cant for FDI shares.

# 5.2. Industries with Comparative Advantage

The effect of international economic activities on the demand for skilled labor may depend on whether or not the plant belongs to an industry with comparative advantage. We follow Yeats (1998) in calculating revealed comparative advantage indexes for each 4-digit ISIC revision 3 industry in Brazil (for 2001) and in China (for 1999) and based on these indexes, we construct dummy variables identifying the industries with comparative advantage in each country.<sup>45</sup> We estimate a modi fi ed version of Eq. (5), where the international activity Tijr<sup>2enters separately and</sup> interacted with the dummy variable for comparative advantage industries and show the results in Tables 16-18.<sup>46</sup> In Table 16, the coefficients on imported inputs shares for Brazil are positive and close to those in Table 6. The coefficients on the interaction with the comparative advantage dummy are negative in three cases but never signi fi cant. The total effect of imported inputs on the demand for skilled labor is positive suggesting that our earlier fi nding that imported inputs allow Brazilian plants access to skill-biased technology fl ows is not counteracted by the fact that plants importing inputs in comparative advantage industries may be engaging in production activities intensive in unskilled labor. For China, the effect of imported inputs shares per se on the demand for skilled labor is negative, except for education-based skills measures. Chinese plants that import higher shares of inputs in industries with comparative advantage have an even lower demand for skilled workers. This provides support to our interpretation of the negative

> 45We use detailed trade fl ows for Brazil, China and the world taken from World Integrated Trade Solution (WITS) to compute the revealed comparative advantage indexes as detailed in Yeats (1998). Industries with values for those indexes larger than one have revealed comparative advantage.

> 46The results with imported inputs shares, export shares and FDI shares are qualitatively similar to those with the corresponding dummy variables, hence we discuss here only the results with shares.

22
The key difference with the other poverty measures is that there is no longer a discontinuity at the poverty line – growth in all parts of the income distribution matters for poverty reduction, with growth among the poorest mattering most.

To reiterate, the important point of Figure 2 is that poverty measures differ in their sensitivity to growth in different percentiles of the income distribution. As a result, a given pattern of relative income growth might be pro-poor (in the sense that the poverty measure falls) for some poverty measures, but not for others. Moreover, if we take seriously the objective of pro-poor growth with respect to a particular poverty measure, then this requires a growth strategy focusing on particular parts of the income distribution. For example, pro-poor growth with respect to the headcount requires an emphasis on those just below the headcount, while pro-poor growth with respect to EDEI with strong inequality-aversion requires interventions targetted to reaching the poorest of the poor.

Finally consider the average across all percentiles of the sensitivity ηt(p) of poverty to growth in incomes of percentile p. Recall from Equation (3) that this average sensitivity measures the effect of growth in average incomes on the poverty measure. We have been referring to high values of this average sensitivity of poverty to growth in average incomes as one of the three potential sources of pro-poor growth. For the Foster-Greer-Thorbecke class of poverty measures, this average sensitivity can be expressed in terms of the poverty measure itself when θ is not equal to zero, 1 ∫ ηt (p) ⋅ dp = − (Pt (θ) −Pt (θ − )1), where Pt(θ) denotes the FGT measure with parameter θ.<sup>5</sup> 0 In the case where θ is zero, the sensitivity of the headcount to growth in average of income at the poverty line. For the Watts measure, the average elasticity is simply minus one times the headcount. For EDEI, the sensitivity to average growth is EDEI itself, implying that the elasticity of EDEI with respect to growth in average incomes is one. While these results are useful for analytically characterizing the sensitivity of the different poverty measures to growth in average incomes, we will see shortly that cross-

> 5 This result can be found in Kakwani (1993).

10
index and our main (baseline) controls. Corresponding results using the _Law Book_ index are reported in columns 3 and 4 of the same table.

These results show that the effect of labor regulation on permanent employment is roughly similar to what we found above for total employment. For example, a unit increase in the _Perception_ index changes permanent employment by -4.76 (p-value of .024, column 1, Table 5). The corresponding figures with total and temporary employment as dependent variables are -5.33 (p-value of .030; column 2, Table 3) and -.734 (p-value of .352; column 2, Table 5), respectively. Adding all the controls used for the robustness analysis (listed in column 4, Table 3) yields an estimated coefficient value of -6.48 (p-value of .033) for the _Perception_ index with permanent employment as the dependent variable. The corresponding values with total and temporary employment as dependent variables are -6.84 (p-value of .040) and -.360 (p-value of .585), respectively.

Regression results using the _Law Book_ index show a similar pattern (columns 3 and 4, Table 5). The only difference here is the negative effect of the index on temporary employment in the main specification is significant at less than 10% level (column 4, Table 5) although it is much lower (less negative) in value than for permanent employment (column 3, Table 5). However, even this weak negative effect becomes insignificant (at 10% or less) as we add some of the controls used in the robustness checks (listed in column 4 of Table 3).<sup>15</sup>

> 15 Using the _Law Book_ index and controlling for all the variables listed in column 4 of Table 3 (except for the _Perception_ index) yielded an estimated coefficient value of -.163 with a p-value of .127 for the _Law Book_ index with temporary employment as the dependent variable. Corresponding values with permanent and total employment as dependent variables are -.816 (p-value of .013) and -.979 (p-value of .005), respectively.

17
32 regressions being based on an explicit theoretical foundation, and is thus relatively less ad hoc than a fully reduced-form growth regression. Another important reason for choosing the semi-reduced form specification is the documented robustness of the highly statistically significant positive effects of the growth rate of capital on economic growth. This is compared to that of the investment rate, which does not generate such robust results. Given that capital is one of the two inputs into aggregate output, it is important to properly account for its role in the production process before we are able to correctly examine the roles played by the knowledge economy in economic development.

If we are estimating equation (7), we see that the estimated coefficient of the growth rate of capital will simply be the elasticity of aggregate output with respect to capital. On the other hand, if we were estimating the per-worker specification as seen in equation (8) then the estimated coefficient of the growth of capital per worker would represent the elasticity of output per worker to the capital-labor ratio. A detailed account for the generation of the capital stock data is given in Appendix A.

# **Growth Rate of Labor**

The growth rate of labor only appears as a regressor when we are estimating equation (7). As in the case of capital, the estimated coefficient of the growth rate of labor is the elasticity of aggregate output with respect to labor.

Data for the total labor force used in this paper are comprised of people who meet the International Labour Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labor force includes the armed forces, the unemployed, and first-time jobseekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector.

# **Initial Income**

One of the key implications of the neoclassical growth model is that, all else being equal, poorer countries should grow faster than richer countries. For example, if two countries have the same long-run potential level of income, the country with lower current income should grow faster than the richer country. Poor countries tend to have a smaller capital stock (i.e. fewer machines, factories and roads) than rich countries. Since capital is relatively scarce, the rate of return on new investments tends to be higher, leading to faster growth. Poor countries also have the advantage of being able to borrow new technologies and best management practices from richer countries without paying
these flash floods and subsequent recurrent floods while assessing impacts on households and their ability to respond.

# 3. Methodology and data sources

The aim of the present study is to examine the exposure and vulnerability of the households in Colombo city (the metro Colombo area under the jurisdiction of the CMC) to heavy precipitation and resultant floods. The study particularly focuses on vulnerability and impacts of the November 2010 flood event and subsequent recurrent floods and the ability of households to respond. There is specific emphasis on poor households, their vulnerability and ability to respond vis-à-vis non-poor households. Table 2 gives details of the methodology and sources of primary and secondary data to understand distinct aspects focused upon in this study – hazard, exposure, vulnerability and impacts and ability to respond. This framework is similar to the one used in the World Bank report on building the resilience of the poor in the face of natural disasters.<sup>28</sup> The framework looks at monetary losses from damages to assets due to floods and other disasters but goes beyond this to measure well-being losses for people in the face of disasters. Risk assessment is done by understanding hazard, exposure, vulnerability and socioeconomic resilience. Similar methodology is used in this study to analyze the exposure, vulnerability and impacts of floods on Colombo residents and their ability to respond based on socioeconomic factors that determine resilience.

In order to understand the flood hazard that households are exposed to, information has been obtained from the CMC on the location of flooding spots in the metro Colombo area. CMC has identified 43 flooding spots (given as Annex 1 to this paper) on the basis of the extent of flooding experienced during the November 2010 floods. These flood-prone locations have further been combined into 15 intervention areas where CMC has planned specific flood control measures. Maps have been created as a part of this study to identify the flooding spots to understand the extent of flooding across the metro Colombo area. To assess the exposure of households to recurrent floods, locations selected for the primary survey have also been identified based on the flooding spots.

_Table 2: Methodology and data sources for the study_

||**Methodology**<br>|**Data Sources**<br>|
|---|---|---|
|**Hazard**| Examine the flood-prone<br>locations within Colombo city| Data obtained from Colombo<br>Municipal Council on location of<br>flooding spots identified after<br>the November 2010 floods|

> 28 Hallegatte et. al. (2017), Unbreakable: Building the resilience of the poor in the face of natural disasters, Climate Change and Development Series, Washington DC, World Bank, doi:10.1596/978-1-4648-1003-9.

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