rafmacalaba/datause-encoder-data huggingface dataset:
Figure C2: Impacts on Individual Components of Operational Productivity and Commercial Practices Indices <!-- Start of picture text --> Operational Productivity Practices<br>Developed improvement programs of KPIs<br>Measured KPIs on operations<br>Operationalized preventive measures<br>Standardized work sequence<br>Developed continuous improvement methods<br>Developed 5S Methods<br>Used VSM for production processes<br>Identified and managed bottlenecks<br>Evaluated plant lead according to goals<br>Communicated Strategic Goals<br>-.2 -.1 0 .1 .2 .3 .4<br>ITT Treatment Impact<br>Commercial Practices<br>Measured KPIs for Commercial<br>Created incentive schemes for salesmen<br>Sales training courses<br>Defined role of salesmen<br>Advertising and brand improvements<br>Feedback meetings with customers<br>Uses a CRM system<br>Market research on distribution<br>Market research on customers<br>Market segmentation<br>Marketing and sales plan<br>-.2 -.1 0 .1 .2 .3 .4<br>ITT Treatment Impact<br><!-- End of picture text -->

**Notes** : Estimated ITT treatment impacts along with 95 percent confidence intervals are shown.

fair. In technical terms, we want to know your beliefs about the intention-to-treat effect (ITT). This is the effect of BEING OFFERED the full program, regardless of whether or not a firm takes up the program. It is simply the DIFFERENCE IN MEAN OUTCOMES for the 100 firms that get the full intervention compared to the 100 firms that just get the diagnostic and trade fair. For example, suppose we look at the percentage growth in exports. We might imagine the 100 firms in the full intervention group can be broken down into:

- 10 firms that decide not to pay for the program, and see no benefit: 0% export growth

- 10 firms that take up the program, but don’t see any benefit from it: 0% export growth

- 40 firms that take up the program and see small improvements, perhaps a 10% increase in exports: 10% export growth

- And 40 firms that take up the program and see large gains of 50% export growth each

54
8 RENT CONTROL IN KUMASI, GHANA

- (f) While costs and benefits are large relative to rents paid, they are small relative to income. The cost of the subsidy is usually in the order of 2 to 3 percent of consumption. Net tenant benefits are, at best, negligible compared to total consumption.

- (g) These estimates of PmQm are smaller than those taken directly from the cross country model (above) but are of the same order of magnitude. This is not surprising since we also used the cross country estimates to 'calibrate" our estimates of uncontrolled rents due to biases discussed above. While the exact results here would change given different cross country estimates, the qualitative results presented would not change.

The bottom line, then, is that rent control reduces the rents that households pay, but the benefit of this rent reduction is more or less offset by the welfare loss from underconsumption of housing. We estimate that existing units of typical quality would have rented for about twice current rents in 1986, - but that households would typically spend more than three times current rents - implying higher housing consumption -- if supply was elastic.

Nominal prices have risen (roughly doubled) since 1986 while controlled rents remained fixed. In current prices, PmQm and PmQc would be roughly double the above estimates. Also note that while controlled rents are fixed at the same levels for all cities and towns, market rents in Accra would certainly be higher than in Kumasi while those in smaller towns would be less.

# Distributional Issues

Benefits are negatively related to household income, suggesting that rent control may be somewhat progressive; but the benefits are, again, small. When the price elasticity of -.5 is used, only households in the lowest consumption quartile receive positive net benefits on average. Smaller benefits -- or more negative benefits -- for richer tenants come as no surprise since these are exactly those households with the largest a 'fare loss from low consumption of housing.

Long term tenants have the smallest estimated disequilibrium in consumption and the largest benefits. Net benefits are still small compared to consumption. Note that the largest net costs are to recent movers. Even larger unmeasured costs are imposed on households who are constrained from moving at all.

Landlords are not all that much better off than cenants. The median of landlord consumption is about 36 percent more tha- the median consumption of 725 controlled renters (11,563) and roughly the same as iO5 uncontrolled renters. Thus, while landlords are richer than tenants "on average" there is quite a lot of overlap. A quarter of controlled renters consume more than the median resident landlord; a quarter of resident landlords consume less than the median tenant.
**Table 4** <u>: Percent distribution of households by number of income earners (M)</u>

|**Number of Income**|**Ru**|**ral**|**Ur**|**ban**|
|---|---|---|---|---|
|**Earners**|1990|1995|1990|1995|
||(%)|(%)|(%)|(%)|
|1|1.1|1.6|4.0|9.6|
|2|33.0|30.7|36.0|33.5|
|3|39.1|38.8|43.8|42.3|
|4|15.7|15.8|12.3|10.1|
|5|6.4|7.4|3.1|2.9|
|6|2.7|3.3|0.7|1.0|
|>6|1.1|1.5|0.2|0.3|
|Total|100|100|100|100|

Source: Zimbabwe ICES 1990/91 and 1995/96

**Table** **<u>5</u>** <u>: Percent distribution of households by number of income source-earner combination (G)</u>

|**Number of income**|**Ru**|**ral**|**Ur**|**ban**|
|---|---|---|---|---|
|**sources**|1990/91|1995/96|1990/91|1995/96|
|1|9.8|16.7|34.9|38.1|
|2|17.1|25.8|34.1|35.1|
|3|20.4|25.4|20.4|18.2|
|4|21.6|18.9|7.2|6.1|
|5|17.8|9.1|2.5|2.0|
|6|9.4|3.3|0.7|0.5|
|>6|4.1|0.8|0.2|0.1|
|**_Total_**|**_100_**|**_100_**|**_100_**|**_100_**|

Source: Zimbabwe ICES 1990/91 and 1995/96 18
This is because students are more likely to reside outside of the municipality where the university is located at the university level. Thus, the outcome variable (number of students enrolled in the municipality) may not be indicative of the schooling decisions of the population in question. However, the analysis using the Census and ENOE data do allow us to look at the effects on the education decisions of college-age individuals.

These data were merged with the homicides database, resulting in a panel with 2,457 counties followed over the 1994-2010 period, yielding a total of 41,769 observations.

The first data set we constructed is a large panel data set at the municipality level covering the period from 1994 to 2010. _Municipios_ are the third-level administrative division in Mexico (the secondlevel being _Estados_ , or States, and the first-level the Federal Government). There are 2,457 municipalities in the Mexican territory. This original data set was constructed from SIMBAD produced by INEGI and includes yearly education variables (from the official records of the Ministry of Public Education) as well as monthly crime variables and demographic characteristics for census years.

The second data set was constructed using data from the records of a standardized test applied to all students nationwide. These records allow us to follow schools over the 2009-2011 period. By merging these data with information from the administrative school census we are able to locate schools at the municipality level, and thus combine the education variables with the municipality violence data.

Our second data set allows us to look at student performance and school size. These data come from the ENLACE, _Evaluación Nacional del Logro Académico en Centros Escolares_ (National Evaluation of Academic Achievement in School Centers) results. ENLACE is a nationwide test applied to all students in _educación básica_ (basic education which is composed of primary and lower secondary education, grades 1-9). We use the results from ENLACE 2009-2011.<sup>3</sup> The data from these tests is reported at the student level, but due to the nature of the data it is impossible to follow a particular student through time. However, using administrative data from the School Census 2009 ( _Formato 911_ ) we are able to match students to their schools and locate the municipality where these are located. This allows us to create a novel panel at the school and municipal level and use the aggregate test scores through time. Additionally, these data provide us with information about the school size and mean test scores. The database follows 78,830 primary schools and 25,989 lower secondary schools over three years.

# **5. Identification and Econometric Specification**

> 3 The database constructed includes ENLACE 2008. However, the results from this year have been greatly questioned and invalidated. The results presented therefore only include ENLACE 2009-2011. Regressions including 2008 were also run, and results do not differ significantly

10
Policy Research Working Paper 7663

# **Abstract**

This paper presents a model to assess the socioeconomic resilience to natural disasters of an economy, defined as its capacity to mitigate the impact of disaster-related asset losses on welfare, and a tool to help decision makers identify the most promising policy options to reduce welfare losses due to floods. Calibrated with household surveys, the model suggests that welfare losses from the July 2005 floods in Mumbai were almost double the asset losses, because losses were concentrated on poor and vulnerable populations. Applied to river floods in 90 countries, the model provides estimates of country-level socioeconomic resilience. Because floods disproportionally affect poor people, each $1 of global flood asset loss is equivalent to a $1.6 reduction in the affected country’s national income, on average.

The model also assesses and ranks policy levers to reduce flood losses in each country. It shows that considering asset losses is insufficient to assess disaster risk management policies. The same reduction in asset losses results in different welfare gains depending on who benefits. And some policies, such as adaptive social protection, do not reduce asset losses, but still reduce welfare losses. Asset and welfare losses can even move in opposite directions: increasing by one percentage point the share of income of the bottom 20 percent in the 90 countries would increase asset losses by 0.6 percent, since more wealth would be at risk. But it would also reduce the impact of income losses on wellbeing, and ultimately reduce welfare losses by 3.4 percent.

This paper is a product of the Climate Change Cross-Cutting Solutions Area. 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 shallegatte@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
employs the combination that minimizes the AIC.<sup>23</sup> Table 5 reports the parsimonious specification chosen by the AIC.

|Table 5:In-sampleEstimation Results|forthePhillips Curve<sup>a</sup>||
|---|---|---|
|Regressor|Coefficient|T-ratio<sup>a</sup>|
|Constant|0.017|5.26|
|πt-1|0.364|6.91|
|gapt|0.076|2.20|
|∆st|0.108|3.38|
|∆st-6|0.068|2.92|
|∆st-8|0.054|2.30|
|1994.4 dummy|0.123|6.23|
|2001.4 dummy|0.039|2.42|
|**_Other Statistics_**<br>|||
|R<sup>2</sup>= 0.622<br>|||
|Adj R<sup>2</sup>= 0.603|||
|S.E.E.= 0.015|||
|Jarque-Bera = 2.03|||
|LM AR (12) = 31.29|||
|LM ARCH (12) = 23.46|||
|White-Heteroskedasticity = 3.87|||
|Reset= 2.77|||

a: Figures are the absolute value of the t-ratios.

The coefficient estimates of the Phillips curve are consistent with our priors. The coefficient on lagged inflation is roughly 0.4. This would imply that a 1 percent increase in inflation in a given month would translate into a 0.4 percent increase in inflation in the next month. The coefficient on the output gap is positive and equal to roughly 0.08, implying that a 1 percent rise in the output gap would increase monthly inflation by 0.08 percent. The impact of a depreciation of the nominal exchange rate on inflation is estimated at around 0.23 percent (including both the lagged and contemporaneous coefficients).

> 23 This exercise involves running 66,585,855 regressions.

26
## constant.

For the rm-level shocks we estimate a correlation coecient of _ρa_ = 0 _._ 902 and a variance for the innovation term of _σa_<sup>2= 0</sup><sup>_._118.For the sector-level shocks we estimate a constant of</sup> _b_ = 0 _._ 318 , a correlation coecient of _ρb_ = 0 _._ 777 , and variance of _σb_<sup>2= 0</sup><sup>_._044.Once we have the AR(1) parameters</sup> we discretize the state space for _Aijt_ and _bjt_ and compute their Markov transition matrices following the approximation of Tauchen-Hussey. We dene an 8-point grid for _Aijt_ and two values for _bjt_ , high and low. The estimated probability of staying in the same state is 0.827 and the probability of switching is 0.172 (Table 1). More details about the estimation of the production function and stochastic process parameters are in on-line Appendix A.

Note that the rm survey covers only the manufacturing sector. For the non-manufacturing sector, which we want to include in the analysis given its importance in the overall economy, we use the production function and shock processes parameters estimated for the manufacturing sector.

# 3.2 Adjustment cost parameters

We now turn to the estimation of the vector of adjustment cost parameters Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) . Recall that _γ_ 1 , _γ_ 2 and _γ_ 3 are the capital adjustment parameters: the xed cost, the convex cost and the irreversibility cost. On the worker side, _Ce_ and _Cu_ are the costs of moving between production sectors and in or out of non-employment.

The SMM is based on comparing a vector of empirical moments computed from rm and worker actual data, with moments computed from rm and worker simulated data. The simulated moments depend on the choice of Γ through optimal investment _Iijt_ and equilibrium labor allocation _N_<sup>_jt_</sup> . The estimator for the adjustment costs minimizes the weighted distance between the empirical and simulated moments.<sup>10</sup> Within the SMM algorithm we further estimate the worker utility function parameters _η_ (sector quality shifters) and _ν_ (worker shock variance) using a linear regression derived from the workers' Bellman equation. This step is done within the SMM because the Bellman equation is a function

> 10 We use six moments: the serial correlation of the investment rate, _corr_ ( _Iijt, Iijt−_ 1) ; the correlation between the investment rate and the protability shocks, _corr_ ( _Iijt, Aijt_ ) ; the negative spikes rates, dened as the percentage of rms with investment below negative 10 percent; the correlation between sector deviations in wages and employment with respect to the sector mean, _corr_ (( _wjt −_ _<u>wj</u>_ ) _,_ ( _Njt − N j_ ) ; the serial correlation in non-employment, _corr_ ( _N_ 0 _t, N_ 0 _t−_ 1) ; and the correlation between the change in non-employment and the change in the average wage across sectors, _corr_ (( _N_ 0 _t − N_ 0 _t−_ 1) _,_ <u>(</u> _<u>wt − wt−</u>_ 1)) . We search over values of Γ using a combination of ne grid search and coordinate descent search, which works better than second-derivative Newton-Raphson or quasi-Newton methods in a case like ours with a discretized state space.

16
Household-farm Economy”, _Journal of Development Studies_ , 32(6), 899-912.

- The World Bank (2000), _Can Africa Claim the Twenty-First Century_ (Washington D. C., The World Bank).

- The World Bank (2005), _China: Integration of national product and factor markets – economic benefits and policy recommendations_ (Washington D. C., The World Bank).

- The World Bank (2007), _World Development Report 2008: Agriculture for Development_ (Washington D. C., The World Bank).

- Wade, R.H. (2004), “Is Globalization Reducing Poverty and Inequality?”, _World Development_ , 32(4), 567589.

- Wagstaff, A. (2005), “Decomposing Changes in Income Inequality into Vertical and Horizontal Redistribution and Reranking, with Applications to China and Vietnam”, The World Bank, Development Research Group.

- Wan, G. (2004), “Accounting for income inequality in rural China: a regression-based approach”, _Journal of Comparative Economics_ , 32(2004), 348-363.

- Wan, G., Z. Zhou (2005), “Income Inequality in Rural China: Regression-based Decomposition Using Household Data”, _Review of Development Economics_ , 9(1), 107-120.

- Yao, S. (1999), Economic Growth, Income Inequality and Poverty in China under Economic Reforms, _Journal of Development Studies_ , 35(6), 103-130.

- Zhao, Y. (1999), “Leaving the Countryside: Rural-to-Urban Migration Decision in China”, _The American Economic Review_ , 89(2), 281-286.

- Zhou, Q. (1994), _Rural reforms and Development in China_ (Hong Kong, Oxford University Press).

- Zhu, L. (1991), _Rural Reform and Peasant Income in China_ (London, Macmillan).

- Zhu, L., Z. Jiang (1993), “From brigade to village community: the land tenure system and rural development in China”, _Cambridge Journal of Economics_ , 17(4), 441-461.

- Zhu, N. (2002a), _Analyse des migrations en Chine: mobilité spatiale et mobilité professionnelle_ , Thèse pour le Doctorat en Sciences Economiques, présentée et soutenue publiquement le 26 novembre 2002.

- Zhu, N. (2002b), “Pauvreté, inégalité et croissance du secteur non-agricole rural en Chine”, in J-M. Dupuis, C. El Moudden, F. Gavrel, I. Lebon, G. Maurau, N. Ogier, _Politiques sociales et croissance économique_ (Paris/Budapest/Torino, L’Harmattan).

- Zhu, N., X. Luo (2006), “Non-farm activity and rural income inequality: a case study of two provinces in China”, Policy Research Working Paper No. 3811, The World Bank.

30
coding principle is that the first successor state government must be a new ruler in its republic to be counted as a change. If such a new institutional ruler did come to power, the change is coded as occurring in the year that he began to wield effective authority in the republic—either 1990 or 1991. The collapse of the Soviet Union did not involve an alternation in the holder of the seat of power in Azerbaijan, Belarus, Kazakhstan, Tajikistan, Turkmenistan, and Uzbekistan. For the same reason, no alternation is counted as having occurred in 1991 in Macedonia, Montenegro, and Serbia with the break-up of Yugoslavia. In Appendix Table A-1, we list the 27 post-communist countries in our data, along with their cumulative alternation scores by year 2006.

TABLE 1. SUMMARY STATISTICS FOR TWO MEASURES OF ALTERNATION IN THE TRANSITION ECONOMIES, UP TO AND INCLUDING YEAR 2006

||Cumulative<br>political<br>alternation|Cumulative<br>ideological<br>alternation|
|---|---|---|
|Mean|2.71|1.93|
|Median|3|1.5<br>|
|Minimum|0<sup>a</sup>|0<sup>b</sup>|
|Maximum|5|4|
|Countries with<br>the maximum|Lithuania<br>Slovakia|Lithuania<br>Slovakia<br>Bulgaria,<br>Hungary<br>Macedonia|
|Correlation with<br>cumulative<br>political<br>alternation|1|0.84<br>(0.00)|

_Note_ :

- a. Countries are Kazakhstan, Turkmenistan, and Uzbekistan.

- b. Countries are those in note a. plus Belarus and  Tajikistan.

- c. Correlation is calculated across all years _.  p-_ value is in parentheses.

An obvious refinement, which we call _ideological alternation,_ is to count only those political alternations in which the shift of power also entails a shift of ideology. In the latter case, alternation may be more likely than in the form case to entail an end to the privileged government treatment that select groups of businesses enjoyed under the predecessor regime. In order to define what constitutes an ideological alternation, we use a new database on political institutions, the Post18
**_Nrbm_ ax** Averag Ilthly ERurV of **atdmBd** aff **Nm-Matdied Rers** by hbject of **StLdy** Rill Timn Male _Salrd_ Wkrs, Ckmpl.tn of Vocativul Sdtols - **Tsrali** Cmsus, 1983

||SChol|CD pleters|Direct Trai|ning- OzWation|<br>Matchig|Wider Training-|Ocuzation|Matchina|
|---|---|---|---|---|---|---|---|---|
|Area of<br>Study|Nluber<br>of<br>Workers|Average<br>NImhily<br>Earnirgs*|Percent<br>of Workers<br>in Matched<br>Occupatiark*|Average<br>Monthly<br>Earni*V of<br>Matched<br>Workers*|Average<br>Mointly<br>Earnigs<br>of<br>Non-Matched<br>Workers*|Percent<br>of Workers<br>in Matched<br>Occupstions**|Average<br>Monthly<br>Earninr<br>of<br>Matched<br>Workers*|Average<br>Monthly<br>Earrnigs<br>of non-<br>Matched<br>Workers*|
|Agriculture|1002|37,898<br>(28,782)|6.09<br>(61)|29,324<br>(11,861)|38,448<br>(29,453)|14.47<br>(145)|45,849<br>(28,814)|36,571<br>(28,408)|
|Electricity|1357|37,602<br>(27,694)|42.08<br>(571)|37,768<br>(18,918)|37,482<br>(32,639)|51.14<br>(694)|41,217<br>(34,151)|33,824<br>(17,694)|
|Electronics|691|43,759<br>(29,633)|49.06<br>(339)|51,215<br>(35,915)|36,579<br>(19,461)|60.35<br>(417)|50,212<br>(34,142)|33,938<br>(16,814)|
|Metal work|4337|36,834<br>(28,530)|37.91<br>(1644)|36,944<br>(26,525)|36,767<br>(29,693)|45.35<br>(1967)|39,996<br>(37,134)|34,210<br>(18,170)|
|Autceechardes|1967|36,341<br>(22,450)|42.76<br>(841)|37,122<br>(23,655)|35,757<br>(21,497)|56.58<br>(1113)|38,556<br>(25,517)|33,451<br>(17,258)|
|Bookkeping,<br>Secretarial<br>& Cleri|331<br>cal|38,007<br>(22,045)|51.06<br>(169)|37,990<br>(19,139)|38,025<br>(24,777)|58.31<br>(193)|40,945<br>(22,257)|33,899<br>(21,148)|
|Sewirg<br>Fashion|20|30,213<br>(16,522)|20.00<br>(4)|17,827<br>(3,175)|33,516<br>(17,126)|20.00<br>(4)|17,827<br>(3,175)|33,516<br>(17,126)|
|Hotel Mmagemnt|93|29,433<br>(10,893)|40.86<br>(38)|31,009<br>(10.112)|28,344<br>(11,364)|40.86<br>(38)|31,009<br>(10,112)|28,344<br>(11,364)|
|IUrAL|9798|37,396|37.4|38,274|36,870|46.7|40,892|34,338|
|||(27,129)|(3667)|(25,638)|(27,973)|(4571)|(33,104)|(20,037)|

***** Stamiard deviations in parentbesis

****** absolute ruiers in parentheses

- 28 -