rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: How effects of shocks on revenue and profit vary by enforcement**

||_Change i_|_n Revenue_|_Chang_|_e in Profit_|
|---|---|---|---|---|
||_(1)_|_(2)_|_(3)_|_(4)_|
|_Slump_|‐1,858,934.0<sup>*</sup>||‐371,501.2<sup>**</sup>||
||(1,093,314.0)||(181,618.1)||
|_Surge_|1,281,901.0||142,899.7||
||(814,809.9)||(102,560.0)||
|_Slump*Enforcement_|1,471.9|75,477.9|‐1,368.0|14,851.9|
||(53,349.3)|(62,419.0)|(7,505.7)|(9,648.8)|
|_Surge*Enforcement_|‐20,905.9|‐83,943.4|‐5,730.9|‐13,355.1|
||(32,575.1)|(64,039.2)|(6,555.3)|(8,986.3)|
|_Slump*LagEmp_||‐18,116.5<sup>***</sup>||‐4,615.3<sup>***</sup>|
|||(4,452.0)||(1,559.1)|
|_Surge*LagEmp_||8,887.8<sup>**</sup><br>(3,904.0)||1,355.2<sup>**</sup><br>(597.5)|
|_Slump*Age_||42,150.8<sup>**</sup><br>(19,818.3)||9,029.9<sup>*</sup><br>(5,171.9)|
|_Surge*Age_||‐33,975.7<sup>***</sup><br>(12,723.9)||‐7,968.9<br>(6,779.8)|
|_Slump*log(K/L)_||‐265,468.4<sup>***</sup><br>(98,286.7)||‐40,378.6<sup>**</sup><br>(20,077.9)|
|_Surge*log(K/L)_||215,338.3<sup>*</sup><br>(128,116.4)||9,064.4<br>(18,779.0)|
|_Fixed effects_|Firm + Year|Firm + Sector * Year|Firm + Year|Firm + Sector * Year|
|_Observations_|306,879|306,879|306,879|306,879|
|_R2_|0.2|0.3|0.1|0.2|
|_Adjusted R2_|‐0.1|‐0.1|‐0.2|‐0.2|
|_Ridl Std Errr_|32,438,810.0 (df =|32,026,253.0 (df =|5,509,078.0 (df =|5,372,099.0 (df =|
|_esua . o_|210462)|210043)|210462)|210043)|

Note:<sup>*</sup> p<sup>**</sup> p<sup>***</sup> p<0.01; Errors clustered by Sector. (2) and (4) include lagged employment, not shown for brevity.

25
WPS3539

# **A Model on Knowledge and Endogenous Growth**

## **Derek H. C. Chen* and Hiau Looi Kee****

The World Bank Washington DC 20433

## **Abstract**

This paper presents a model of endogenous growth in which the main engine of economic development is knowledge. Using a two-sector closed economy model that comprises of a conventional goods-producing sector and a research and development sector, our model incorporates two key aspects of knowledge: technology and human capital. Steady-state equilibrium conditions show that the growth rate of per capita income hinges on the growth rate of human capital. While the growth rate of human capital has been previously shown to affect the growth of the economy _in transition_ between steady states or balanced growth paths, this paper is the first to link the growth rate of human capital to the steady-state growth rate of productivity and output per worker. Furthermore, this result does not exhibit scale effects or policy invariance, both of which have been longstanding concerns with the predictions of endogenous growth models developed in the 1990s.

### World Bank Policy Research Working Paper 3539, March 2005

_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_ _<u>http://econ.worldbank.org.</u>_

* The Knowledge for Development Program, World Bank Institute.  ** International Trade Team, Development Research Group.
- 1.25 -

# Tree Crop Investments

The essence of a perennial crop Investment such as cocoa Is to plant tree seedllngs on cleared land set aside for the crop and to apply labor for cultivation, pruning, spraying of pesticides, etc., before and after the maturing trees begii to produce an output, as well as to harvest, ferment, dry and transport to market the output. In order to reallocate the land to another crop, It Is necessary to disirivast by clearing the land. If the prospective returns from alternative crops do not Justify the Investment In clearing, and the return from the crop Is so low that harvestlng and marketing the output Is unprofitable, then the land and the trees may be abandoned. This outcome may occur under some combinations of misguided policies and exogenous shocks. Therefore, we treat clearing and planting as separate Investment decisions and permit the (dis)invectment decision to abandon as well. Of course, the short-run marginal costs of producing from a mature tree are quite low In comparison with the long-run marginal costs of Investment and operations so that Is more likely that misguided policies will deter investment In planting while some level of harvesting remalns profitable. In modeling tree crops, accounting relationships distinguish each perennial variety by vintage (age) and tkie period. The stock of each perennial vintage Is a process variable that has a specific yield and specific Input requirements. Thus, the age yield curve of the perennial variety Is embedded In the model parameters.

Data on Initial tree stocks by variety, production, crop ylelds, technology, input use and prices are mostly taken from the 700 farm household Survey for Cocoa Producer Pricing (1987), although data from Ghana Cocoa Board, Ghana Ministry of Agriculture, Ghana Statistical Services and persons knowledgeable concerning crop agronomy were also used In estimating parameters related to crops. Typically, sample means and proportions were used as point estknates of population parametars. Mapping of task requirements Into Input requirements by months was done using crop calendars and agronomic consultation. While perennial stock proportions were taken from the survey, estimation
UNICEF (2019))<sup>23</sup> . The estimates based on the MICS 2016/17 data presented here are however close to those reported in the MICS official survey report restricted to a slightly older age group (see NBS and UNICEF (2017)). Therefore, the regional health deprivation analysis is based on the GHS survey findings. Figure 21 again shows higher deprivation rates for children living in rural areas, whereby, as Figure 22 displays, deprivation rates in both sectors are driven by much higher rates in the North-East and NorthWest compared to the rest of the country. Also, these findings coincide with those of DHS (2018).

Figure 21: National Level Health Deprivation

# **AGGREGATE HEALTH DEPRIVATION -GHS AND MICS**

<!-- Start of picture text --> child child north child south child rural child urban<br>W A VE 1 W A VE 2 MIC S 2 0 1 6 / 2 0 1 7<br>22% 25% 15% 27% 11% 19% 25% 8% 23% 9% 10% 3% 1% 13% 5%<br><!-- End of picture text --> Source: Authors’ estimates based on GHS wave 1-2 and MICS 2016/17. Note: Individuals with missing age were dropped from the analysis.

Figure 22: Regional – Level Health Deprivation by Sector a) b) <!-- Start of picture text --> Urban Health Deprivation - by Geopolitical Zone - Rural Health Deprivation - by Geopolitical Zone -<br>GHS GHS<br>80% 80%<br>70%<br>70%<br>60%<br>60% 50%<br>50% 40%<br>30%<br>40% 20%<br>30% 10%<br>20% 0%<br>child 0-1<br>10%<br>0%<br>child 0-1<br>Wave 1_NC Wave 2_NC Wave 4_NC<br>Wave 1_NE Wave 3_NE Wave 4_NE<br>Wave 1_NC Wave 2_NC Wave 1_NE Wave 2_NE Wave 2_NW Wave 3_NW<br>Wave 1_SE Wave 2_SE Wave 4_SE<br>Wave 1_NW Wave 2_NW Wave 1_SE<br>Wave 2_SE Wave 1_SS Wave 2_SS<br>Wave 1_SW Wave 2_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-2 Note: Individuals with missing age were dropped from the analysis.

23 The estimates in the official report on the MICS 2016/2017 data and DHS 2018 data are however based on children aged 1223 months, whereas those presented here based on GHS and MICS data refer to children 0-1 year thus 0-23 months.

34
# **Figure 8.  Probabilistic Benefit-cost Ratio of Structural Investment, Exceedance Probability Curve**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 27 29<br>B/C ratio<br>Exceedance probability (percent)<br><!-- End of picture text -->

# **4.  Conclusion**

The World Bank is becoming increasingly involved in mitigation investments related to natural disasters, including those resulting from earthquakes and hurricanes. The World Bank requires from its Borrowers that they conduct a sophisticated cost-benefit analysis to assess whether the proposed investments yield a sufficiently high rate of return.

The cost-benefit analysis of projects exposed to variable benefits has usually been performed using average values. However, such a methodology fails to capture much of the information available and may lead to inappropriate decisions. This paper has presented a probabilistic cost-benefit analysis where economic indicators, such as benefit-cost ratios, are analyzed using risk metrics, including exceedance probability curves. The analysis relies on sophisticated catastrophic risk models that simulate the loss impact of natural disasters on (retrofitted and unretrofitted) buildings.

This analysis has been illustrated using the earthquake vulnerability reduction project of Bogotá District, implemented with the technical and financial assistance of the World Bank. The model has shown that the proposed investments are economically viable, given the country’s exposure to earthquakes.

While this methodology relies heavily on the availability of probabilistic catastrophe risk models, it offers a new framework that could be used in a variety of risk mitigation projects financed by the World Bank and other multilateral finance institutions.

18
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Power indicators, benchmarked against select groups of countries**

|||**CAR**|Fragile states|Low-income<br>countries|Middle-<br>income|
|---|---|---|---|---|---|
|Access(national)|%ofpopulation|**1**|15|33|50|
|Access(urban)|%ofpopulation|**10***|58|86|101|
|Access(rural)|%ofpopulation|**0**|4|4|32|
|Installed generation<br>capacity|MWper millionpeople|**10**|46|20|799|
|Electricityconsumption|kWh/capita|**27**|165|107|4,479|
|Poweroutages|Days/year|**40**|11|10|6|
|Collection rate|% ofbilling|**69**|34|92|91|
|Cost-recoveryratio|%of total costs|**75**|100|89|85|
|Revenueper unit|U.S. centsper kWh|**13**|3|14|13|
|System losses|% generation|**48**|40|24|20|
|Total hidden costs|% revenue|**167**|443|69|0.1|
|**U.S. cents**||CAR|Predominantly<br>hydro|Other developi|ngregions|
|Effective power tariff|Residential at 100 kWh|15|<br>10.27|||
|Effective power tariff|Commercial at 100 kWh||11.73|5.0–10|.0|
|Effective power tariff|Industrial at 50,000 kWh||11.39|||

_Sources:_ Fragile and nonfragile countries’ figures as of 2005, calculated using the AICD power database (http://www.infrastructureafrica.org/aicd/tools/data), which contains household data. For the CAR, national access is for 2009 (World Bank 2009g), electricity consumption is for 2007 (CIA World Fact Book 2010 **)** ; power outages are for 2005 (Rosnes and Vennemo 2009); collection ratio is for 2010 (World Bank 2010c); cost-recovery ratio is for 2009 (World Bank 2009f); system losses are for 2009 (ENERCA 2010); effective tariff is for 2008 (World Bank 2009f).

_Note:_ Urban access for the CAR represents access to electricity in Bangui.

kWh = kilowatt-hour; MW = megawatts.

Thermal generation is curtailed by the expensive price of diesel in the CAR. The routes used to transport fuel are expensive,<sup>8</sup> increasing the price of oil in the CAR. At 144 cents per liter of diesel, in 2008 the CAR paid 40 cents more for fuel than Cameroon and 23 cents more than the Democratic Republic of Congo (GTZ 2008). This situation has affected secondary centers that rely on thermal sources for power. Further, Bangui’s diesel plant is often strapped for funds and finds itself unable to pay for fuel, limiting power availability.

The limited availability of power translates into poor access to electricity. As of 2009 only 1 percent of the CAR’s population had access to electricity compared to 33 percent in low-income countries (table 9). Urban access, at 10 percent and mainly concentrated in Bangui, is just one-ninth of the average access in urban areas found in other low-income countries. No one in rural areas benefits from electrification. Households in the CAR rely on firewood or fuel oil as the main source of energy for domestic

> 8 There are two main routes used to transport fuel. The first is a more costly and poor-quality overland route from Cameroon. The second is from Kinshasa. One-fifth of CAR’s oil is supplied from the Democratic Republic of Congo via the Oubangui River. River transit was also affected by tensions along the border that disrupted the supply of oil (EIU 2007).

25
Table E.2 reports ITT estimates under these attrition scenarios. Our results are generally robust to these alternate specifications. When X = 0.25 SD, we still observe large and statistically significant changes in antisocial behaviors, self control, time preferences, and antiviolent and anticriminal values in both the short- and long-term. When X = 1 SD, our estimates of treatment effects lose significance but generally point in the correct direction. Meanwhile, the Manski bound brings us closer to having no treatment effects in the long term.

# **E.2 Both versus just one treatment**

In this section, we compare the effects of receiving one treatment versus receiving both therapy and cash. Specifically, we test whether the coefficients on either therapy only or cash only in Section 6 are statistically different from the coefficients on therapy and cash. Table E.3 displays the mean difference between treatment effects and corresponding p-value for each of our five main outcome variables.

Our results indicate that cash and therapy compliment each other in reducing antisocial behaviors in the long-run, while therapy compliments cash in improving self control skills and reducing violent attitudes in the long-run.

# **E.3 Short versus long term treatment effects**

In discussing our results, we emphasize differences between outcomes 2–5 weeks after the intervention and outcomes 12–13 months after the intervention. In this section, we test whether short- and longterm impacts are the same. We pool our short-term results with our long-term results and run the following OLS regression: where _ShortTerm_ is an indicator for outcomes measured in weeks 2 or 5, and _T_ is an indicator for treatment group assignment. In our application, we have three treatment groups (therapy only, cash only, and therapy and cash), include baseline controls and block fixed effects, and cluster our standard errors at the individual level _i_ . The size and direction of _β_ 3 determine whether the treatment effects we observe in the short-term are the same as those observed in the long-term.

Table E.4 reports these estimates for our five main family indexes. For many outcomes, we cannot reject that _β_ 3 is zero. In particular, the short- versus long-term effects of both therapy and cash are not statistically distinguishable. There are two exceptions worth noting. First, while the cashonly group experienced the largest increase in the composite income measure 2–5 weeks after the intervention, these effects diminished a year later. Second, while all three treatment groups saw decreases in antisocial behaviors in the short term, the effects of cash alone and therapy alone subsided 12–13 months later.

# **E.4 Crime: Disaggregated and annualized impacts**

Table E.5 reports the incidence of specific crimes reported in the two weeks prior to the long run survey, breaking down the total number of crimes into the type of crime reported. For consistency, xli
Table A.9: Baseline balance, restricted to sample with midline student assessment data

|||(1)||(2)||(3)|T-|test|
|---|---|---|---|---|---|---|---|---|
||Co|ntrol|In-|Kind|Reco|gnition|Dif|erence|
|Variable|N|Mean|N|Mean|N|Mean|(1)-(2)|(1)-(3)|
|_Student-level (grade 2)_|||||||||
|Math|5528<br>[84]|0.049<br>(0.084)|5771<br>[80]|0.093<br>(0.075)|5409<br>[86]|-0.100<br>(0.088)|-0.044|0.149|
|Language|5528<br>[84]|0.050<br>(0.076)|5771<br>[80]|0.063<br>(0.074)|5409<br>[86]|-0.025<br>(0.074)|-0.013|0.075|
|Female|5517<br>[84]|0.482<br>(0.008)|5767<br>[80]|0.484<br>(0.010)|5400<br>[86]|0.479<br>(0.009)|-0.003|0.003|
|Age<br>_Student-level (grade 3)_|5349<br>[84]|9.485<br>(0.071)|5549<br>[80]|9.618<br>(0.101)|5255<br>[86]|9.644<br>(0.085)|-0.133*|-0.159**|
|Math|5473<br>[96]|0.048<br>(0.082)|5642<br>[93]|-0.032<br>(0.071)|5482<br>[91]|-0.122<br>(0.085)|0.080|0.171|
|Language|5473<br>[96]|0.067<br>(0.083)|5642<br>[93]|0.017<br>(0.067)|5482<br>[91]|-0.098<br>(0.094)|0.050|0.165|
|Female|5468<br>[96]|0.479<br>(0.009)|5633<br>[93]|0.477<br>(0.010)|5478<br>[91]|0.478<br>(0.009)|0.003|0.001|
|Age|5364<br>[95]|10.977<br>(0.075)|5545<br>[93]|11.112<br>(0.082)|5373<br>[91]|11.096<br>(0.069)|-0.136*|-0.119|
|_School-level_|||||||||
|No. grade 2 students|112|57.732<br>(3.545)|109|60.945<br>(4.444)|115|56.626<br>(3.600)|-3.213|1.106|
|No. grade 3 students|119|51.168<br>(3.130)|118|52.907<br>(4.251)|116|54.112<br>(3.832)|-1.739|-2.944|
|Headteacher female|138|0.181<br>(0.033)|137|0.168<br>(0.032)|138|0.232<br>(0.036)|0.013|-0.051|
|Headteacher age|132|48.455<br>(0.782)|134|47.754<br>(0.854)|134|46.388<br>(0.913)|0.701|2.066**|
|No. deaths, Ebola|107|14.523<br>(4.002)|105|11.638<br>(3.497)|108|10.787<br>(3.111)|2.885|3.736|

_Notes_ : The value displayed for t-tests are the differences in the means across the groups. Standard errors, in parenthesis are clustered at the school level. Observations with square brackets indicate number of clusters. Strata fixed effects included in all estimation regressions. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

10
**Figure 4.28. House price to income ratios in capital cities**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Belgrade Budapest Moscow Riga Sofia Tblisi Vilnius Yerevan Zagreb<br>House price to income ratios<br><!-- End of picture text --> Source: 1998 UN Habitat Global Urban Indicators

**Figure 4.29. House price to income ratios in secondary cities**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Polish cities Russian cities Kazakh cities Bulgarian cities<br>Gdansk PoznanAstrakhan BelgorodKostromaNiz. NovgoNowomos Omsk SurgutV. Novgo Astana BourgasV. Tarnovo<br>House price to income ratios<br><!-- End of picture text --> Source: 1998 UN Habitat Global Urban Indicators

# **4.2.7. Low mobility rates**

Studies of residential mobility in the developed countries describe it as a mechanism by which households adjust their changing housing needs and improve labor opportunities. The major function of mobility is the process by which families adjust their housing to the housing needs that are generated by the shifts in family composition that accompany lifecycle changes (Rossi 1955). In addition to changes in family composition and consequent changes in housing needs, job changes are another significant determinant of mobility. Several studies indicate that labor market efficiency is enhanced through improved household mobility (Blanchard et.al. 1991; and WB 1993).

47
# _4.4.3 Attrition in the sample_

While household level attrition was only 8 percent, attrition at the individual level for the age groups we analyze was substantially higher, treating as attrited both individuals entirely lost to follow-up as well as those without valid anthropometric measurements in 2012.<sup>xxv</sup> For children under three years old, attrition over the four years was 20 percent and for children 3–6 years old, it was 16 percent. For both age groups, however, the difference in attrition between the children of migrant and non-migrant households, which would be particularly concerning for the differential effects we examine, is less than two percentage points and insignificant.

To assess the possibility of attrition influencing the results, we examined the 2008 household and individual characteristics in Table 1 by attrition in 2012 (Appendix Tables 5a and 5b). Of the more than two dozen indicators, only a handful are statistically different. In addition, and possibly more importantly for our analyses, we examined whether differences for those who attrited versus those who did not were different between migrant and non-migrant households. Similarly, only a small number were statistically different and nearly all were modest in size.

Finally, for the cohort panel analysis, we constructed inverse probability weights (IPW) estimating the probability of re-survey in 2012, allowing weights to vary for each age cohort and migration status. In addition to an array of household and individual characteristics in 2008 summarized in the tables already presented, the prediction model incorporated indicators from the baseline survey likely to suggest stronger ties to the community and increase the chance of re-interview. These included additional household characteristics, as well as whether the household provided a telephone number, whether information on neighbors was collected, and whether household members were engaged in local social or other organizations. To account for collinearity between predictors, the baseline predictor set was limited by conducting stepwise selection of variables with backward elimination and using the adjusted R<sup>2</sup> as the information criterion; the methodology is described in the appendix. Results for IPW weighted DD and DDD estimates of the effect of the economic crisis on HAZ were marginally smaller than the main
<!-- Start of picture text -->
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* Baseline # 3month-lockdown 4 5 month-lockdown<br>UGA ae +<br>ETH ae .<br>RWA Ae<br>SEN a ‘<br>ESW Ae<br>GTM ae ?<br>ALB a 4<br>ECU ae :<br>MNE ae ‘<br>CRI a ‘<br>0 25 50 75 100<br>Share of profitable firms (%)<br><!-- End of picture text -->

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** Baseline — 3 month-lockdown — 5 month-lockdown<br>:<br>J<br>\<br>: :<br>i \ :<br>fi 1 ‘<br>Uy 1 i<br>a7! Wo<br>—--/f-" ! \<br>Lee LEE<br>-50 -25 0 25 50<br>Profit margin (%)<br><!-- End of picture text -->