rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9770

# **Abstract**

This paper exploits a novel dataset covering the universe of transactions in the Colombian Stock Exchange to analyze episodes of additions to and deletions from MSCI equity indexes. The analysis finds that additions and deletions have large price effects: the median cumulative abnormal return in absolute value is 5.5 percent. The paper shows that these price effects are due to large demand shocks by different classes of international investors—not only passive funds and ETFs, but also active mutual funds, pension funds and government funds—which are not absorbed by arbitrageurs. Consistent with recent asset pricing models with limits to arbitrage, stock demand curves are estimated to be very inelastic: the demand elasticity for the median stock in the sample is −0.34, implying that a 1 percent increase in the demand for the stock increases its price by 2.9 percent.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at lescobvi@banrep.gov.co, lorenzo.pandolfi@unina.it, apedrazamorales@worldbank.org, and tomaswilliams@gwu.edu.

_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
HMR, as a first step, derives an equation for the probability of trade at the firm level based on firms’ decisions and use it to estimate effects on the extensive margin. The proportion of firms that are able to export are determined by a country-pair specific fixed cost to export. Only those firms that are productive enough to cover this fixed cost and remain profitable will export.

The selection of firms into export markets is a function of firm-level decisions about the profitability of exporting, which in turn is a function of firm efficiency, fixed and variable costs, trade barriers, demand and the elasticity of substitution between symmetric products. Since the ratio of the profit level relative to the fixed cost at which firms can export is unobserved but is observed positive when trade is positive, the following probit equation can be used to estimate the ρijpt, the probability of positive exports between two countries for product _p_ at time _t_ , (2) ρijpt = Φ(γ0 + ζit + ξjt + ςpt + γdij + ρτip −κφij) ξjt is exporter-time fixed effects, ζit is an importer-time fixed effect, and ςpt is product-time fixed effect. dij is the distance between country _i_ and country _j_ , τip is the importer-product standard regulation, and φij is a country-pair specific fixed trade cost. The predicted ρijpt can be used to estimate controls for firm-level heterogeneity and estimate the inverse Mills ratio that corrects for sample selection bias when used in the trade flow gravity regression.

The trade intensity equation that gives consistent estimates by correcting for firm heterogeneity and selection bias and estimated in this study is: (3) lnMijpt = αit + αjt + αpt + β1STDipt + β2 ln(1 + tariffijpt) + ψGravityij + ijpt + η̅̂ijpt + εijpt where Mijpt is the imports by importer _i_ from exporter _j_ of product _p_ in year _t_ is determined by STDipt, one of the measures of standards set by the importer on product _p_ and the ad-valorem applicable tariff rate (tariffijpt). The importers-time effects, among other things, capture the purchasing power and the market size of the importing country, the demand side effect of the commodity. Exporter-time effects are used to measure the supply side effect. This is an advantage over previous work since applying the logic of the gravity equation to sectoral trade is not entirely straightforward. When looking at sectoral trade flows and particularly agricultural goods, the idea that trade flows between _i_ and _j_ in a certain product _p_ are increasing in GDP is not necessarily warranted. In the monopolistic-competition model, larger countries produce more 9
21 sector differentials, as well as their effect on informal wages, would be a useful supplement to the analysis presented here.

By contrast in Colombia, where minimum wages have a much stronger impact on wvages judging by their proximity to the average wage and the time series estimates, the effects of the minimum wage are far more pronounced. The estimates from firm-level panel strongly confirm the proposition that minimum wages have affected employment in Colombia. Given reasonable assumptions about the distribution of minimum wage labor across firms, the estimates imply a disemployment impact of minimum wages on low paid unskilled labor in the range of 2 to 12 percent over the period 1981-1987 in Colombia. The estimates that exclude the unskilled wage and therefore posit an effect of minimum wages on all unskilled workers imply a disemployment impact of minimum wages on low paid unskilled labor that falls in the lower half of this range.
<!-- Start of picture text --> i ‘Output per<br>|) worker YE<br>_|BEmploymentrate<br>| Ba<br>'|D.Demographic|| change AN<br>2841.9<br>-500.0 0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0<br>Contribution to Growth in Value Added per capita, in 1000's of Birr<br><!-- End of picture text -->
from 58 of Ethiopia’s 60 zones and 30 of the 32 non-MTI zones.<sup>5</sup> The data available for individual women in the DHS include detailed information on birth date, district of residence, education, health, contraceptive use, and employment. To further analyze the main outcome of the study with an alternative sample, data from the 2007 Ethiopian census are also combined with the DHS data. The census data include information on age, schooling, and total number of births. These data can be used to demonstrate that the conclusions of this study are not unique to the DHS sample.

## **4.2 Summary Statistics**

The summary statistics for the DHS data used in this article are presented in table 1. The table shows information for women in the last three fully pre-reform cohorts, 1968 to 1970, and the first three entirely post-reform cohorts, 1988 to 1990. Although later cohorts are younger, they have higher levels of schooling and literacy and far fewer births. The extremely low number of births in the post-reform sample is likely due to women in this sample being no older than 26. For this reason, it can be informative to examine whether the reduction in fertility is also seen at specific ages; these samples include only women older than the stated age, and allow for a more direct comparison. The number of births to women at the ages of 20 and 25 are also found to decline by over 60 percent between the two cohort groups. This magnitude is consistent with the observed decline in ideal family size. Younger women are significantly less likely to work in the unskilled manual or agricultural sectors, and are slightly more likely to work in either of the other two employment categories, skilled manual or professional and service or sales. Finally, probably because of the increased literacy rate, younger cohorts are more likely to have recently read about family planning, although they are no more likely to report knowledge of modern contraceptive methods.

# **5 Estimation Strategy**

The central estimating model is a 2SLS model. The first stage is defined by the equation The dependent variable is YrsSchl _izy_ , the years of schooling for person _i_ from zone _z_ born in year _y_ ; _Izy_<sup>FPE</sup> is the zone- and birth-year-specific estimated intensity of FPE, as described in Section 3. The first-stage estimate of _θ_ 1 can be interpreted as the impact of providing an additional fee-free year of school. A third-

> 5DHS geocodes and administrative district data are cross-referenced with administrative boundaries using two sources: IPUMS International (2017) and the Food and Agriculture Organization GeoNetwork’s Global Administrative Unit Layers (GAUL) maps (2015).

8
We can now compute the local derivative of _f_ with respect to _ρ_ : The second and third terms of the equation are always positive, since _f >_ 1 _._ And by virtue of (16), the first term<sup><u>(1</u></sup><sup>_−_</sup> _ρ_<sup>_<u>η</u>_</sup> _−_<sup><u>)</u></sup><sup>_<u>f</u>_</sup> 1<sup>_−_1</sup> _>_ 0 _._ We thus have Q.E.D.■

# **B Instrumentation Strategy**

This Appendix describes the steps necessary to build the instrument for the female content of exports. The construction of the instrument follows Do and Levchenko (2007), and exploits exogenous geographic characteristics of countries together with the empirically observed regularity that trade responds differentially to the standard gravity forces across sectors. For each industry _i_ , we estimate the Frankel and Romer (1999) gravity specification, which relates observed trade flows to exogenous geographic variables: where _LogXicd_ is the log of exports as a share of GDP in industry _i_ , from country _c_ to country _d_ . The right-hand side consists of the geographical variables. In particular, _ldistcd_ is the log of distance between the two countries, defined as distance between the major cities in the two countries, _lpopc_ is the log of population of country _c_ , _lareac_ log of land area, _landlockedcd_ takes the value of 0, 1, or 2 depending on whether none, one, or both of the trading countries are landlocked, and _bordercd_ is the dummy variable for common border. The right-hand side of the specification is identical to the one Frankel and Romer (1999) use. We use bilateral trade flows from the COMTRADE database, converted to the 3-digit ISIC Revision 3 classification. To estimate the gravity equation, the bilateral trade flows _Xicd_ are averaged over the period 1980-2007. This allows to smooth out 24
WEST AFRICA

# 1. Estimates and Projections

|_Yvo_|pqnlotlon|**Sep.**<br>ratio|Period|**Growth**<br>rate|<br>cm|CDR|rn|**00**<br>_maim_|**Famtm**|**in**|is<br>xlooo|
|---|---|---|---|---|---|---|---|---|---|---|---|
|196S|202094|97.9|l'85-89|3.15|47.7|16.3|6.63|48.2|51.4|112|180|
|1990<br>19|236516<br>274997|98.9<br>97.3|1990-94<br>1995-99|3.01<br>3.04|44.7<br>43.4|14.6<br>13.0|6.34<br>6.03|49.6<br>51.4|53.8<br>56.4|101<br>90|161<br>141|
|2000<br>2005<br>2010|320053<br>37182<br>426579|94.5<br>91.T<br>87.6|2000-04<br>2005-09<br>2010-14|3.00<br>2.84<br>2.66|41.5<br>38.8<br>35.9|11.5<br>10.3<br>9.2|5.63<br>5.12<br>4.61|53.3<br>S5.0<br>56.8|59.0<br>60.9<br>62.8|7d<br>72<br>66|120<br>108<br>97|
|2015|48939|82.2|2014-19|2.46|32.9|8.3|4.11|58.6|64.8|60|86|
|2020|5S3630|75.6|2020-24|2.25|29.8|7.4|3.61|60.5|66.8|54|74|
|2025|619428|69.2|2025-49|1.60|21.9|6.1|2.53|65.4|72.0|37|47|
|2050|924971|47.2|2050-74|0.89|15.8|6.9|2.08|71.6|78.3|6|20|
|2075<br>2100<br>2125<br>2150|1156376<br>1283075<br>1339625<br>1369072|51.5<br>63.9<br>68.7<br>71.2|205-99<br>2100-24<br>2125-49|0.42<br>0.17<br>0.09|13.4<br>12.4<br>12.0|9.2<br>12.7<br>11.2|2.06<br>2.04<br>2.04|76.0<br>79.5<br>80.8|82.6<br>86.1<br>87.6|8<br>3<br>3|10<br>4<br>4|

## 2. Age structure (percent): 199o and 2025

<!-- Start of picture text --> EE5XbTffiSiZZ _<br><!-- End of picture text --> 10 8 6 4 2 0 2 4 6 8 lO <!-- Start of picture text --> 75 +<br>70-74<br>65-89<br>80.84<br>55.59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>~~5-9 S>>>><br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 3. Distribution of population

### (percent): 1990 and 2025

<!-- Start of picture text --> NonIrm c2 C'' BS)<br>P QTW C: E C 60) P ASrC43 *0<br>WEST (36 i<br><!-- End of picture text -->
# **Figure 3 Non-residential depreciation rates in OECD countries**

<!-- Start of picture text --> % per year<br>Switzerland<br>Belgium<br>New Zealand<br>Sweden<br>United States<br>Germany<br>France<br>Denmark<br>Netherlands<br>Finland<br>Hungary<br>Slovenia<br>Iceland<br>Mexico<br>Norway<br>Chile<br>Czech Republic<br>Israel<br>Slovak Republic<br>Estonia<br>Greece<br>Luxembourg<br>Poland<br>China<br>Ireland<br>Italy<br>Austria<br>Australia<br>United Kingdom<br>Canada<br>Japan<br>Spain<br>Portugal<br>Korea<br>0 1 2 3 4 5 6 7<br><!-- End of picture text --> _Source:_ OECD Economic Outlook Database, June 2017

# **_Estimates of non-residential gross fixed asset investment by sector_**

The official Chinese statistics provide estimates for the total nominal value of gross fixed capital formation from 1952 to 2016. There is not any breakdown of the total by sector or by the type of asset. It should be noted, though, that the definition of gross fixed capital formation differs from that found in the standard implementation of the System of National Accounts. In particular, expenditure on a project is counted as capital formation from the start of the project rather than as work-inprogress and being assigned to fixed capital formation when the project is completed and handed over to the final user.

# _Different measures of capital formation_

There is another statistical concept that, superficially, appears close to the concept of fixed capital formation: namely total fixed asset investment (FAI). It has the advantage of being broken down by asset and by sector since 2004 and by asset since 1981. The coverage of this concept is wider than that of capital formation. Most of the items that do not count as part of gross fixed capital formation are included under the heading “other” assets as opposed to being either a structure or equipment. According to Chao (1974), the principal elements included as investment in the category of “other” assets are:

- Cost of acquiring land leases by property developers and factory owners.

- Cost of compensating farmers and village collectives for land acquired for infrastructure.

- Cost of acquiring secondhand assets.

11
bXvi SP **sIoL ToPic 1:** PRWECTING AIDS MORTALrY <!-- Start of picture text --> Figure S2. Total Population Cbillions)<br>8i4. Saharan Africa, 1985.2020<br>1.3<br>12.2<br>'1.1<br>0.9<br>0.7<br>0.6<br>0.5<br>0.4 E ' .<br>199S 1990 1995 2000 2005 2010 2015 2020<br>a ND AIDS + AIDS<br><!-- End of picture text --> <!-- Start of picture text --> Figure S3. Population Growth Pate C%)<br>Sih-Sahar.n Africa, 1985-2020<br>3.1<br>3<br>2.9<br>2.8<br>2.7<br>2.6<br>2.5<br>2.4<br>2.3<br>2.2<br>2.1<br>1985-90 1990-95 1995-00 2000-05 2005-10 2010-15 2015-20<br>O No AIDS + AIDS<br><!-- End of picture text -->
|(4)<br>(5)<br>(6)<br>plied vs. recommended<br>Total<br>Urea (kg/ha)<br>DAP (kg/ha)<br>KCl (kg/ha)<br>-4.96<br>1.89<br>-0.82<br>(10.80)<br>(5.69)<br>(3.79)<br>12.86<br>3.96<br>8.07***<br>(8.25)<br>(3.84)<br>(1.92)<br>-76.70***<br>-31.81***<br>-27.54***<br>(9.33)<br>(4.92)<br>(2.65)<br>2.55<br>-1.74<br>5.07**<br>(7.57)<br>(3.37)<br>(2.25)<br>665<br>665<br>665<br>0.22<br>0.20<br>0.27<br>114.90<br>37.83<br>32.66<br>82.62<br>47.92<br>33.98<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>es. Using the sample for the full sample of 678 farmers, except for 13 farmers<br> +_βFFlexiblei_ +_αc_+_εit_, where_i_corresponds to a farmer,_Y_ is the outcome of<br>we report the mean and the standard deviation of the outcome for the control<br>inal study design. We use data from the Commercialization survey conducted<br>commended dosages of Urea, DAP and KCl, respectively. In columns 4-6, we<br>p_<_0.1.|
|---|
|(1)<br>(2)<br>(3)<br>Absolute difference, ap<br>At sowing<br>Urea (kg/ha)<br>DAP (kg/ha)<br>KCl (kg/ha)<br>Extension (1=Yes)<br>-1.71<br>0.35<br>-1.43<br>(1.98)<br>(2.43)<br>(1.32)<br>Individual recommendation (1=Yes)<br>3.04<br>3.23<br>1.82**<br>(2.08)<br>(2.02)<br>(0.90)<br>Grant (1=Yes)<br>-30.34***<br>-16.26***<br>-13.41***<br>(2.00)<br>(2.25)<br>(0.85)<br>Flexible (1=Yes)<br>3.54*<br>2.37<br>3.41***<br>(1.89)<br>(1.57)<br>(1.02)<br>Observations<br>665<br>665<br>665<br>R-squared<br>0.47<br>0.32<br>0.41<br>Mean dep. var. control<br>38.69<br>19.40<br>16.44<br>SD dep. var. control<br>14.64<br>21.36<br>14.42<br>T1: _βE_+_βI_+_βG_ =0<br>0.00<br>0.00<br>0.00<br>T2: _βE_+_βG_ =0<br>0.00<br>0.00<br>0.00<br>T3: _βE_+_βG_+_βF_ =0<br>0.00<br>0.00<br>0.00<br>Note: This table reports results on the usage of fertilizers by the farmers in our study in the 2015 season, compared to the recommended dosag<br>for which we do not have data on usage of fertilizers, we run the following regression: _Yit_ = _β_0+_βEExtensioni_ +_βI IndRecommi_ +_βGGranti_ <br>interest and_t_is the time period. We include randomization strata fxed effects and compute robust standard errors. At the bottom of the table,<br>group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coeffcients that map into the orig<br>in June 2016. In columns 1-3, the dependent variables are the absolute differences between the amount applied by farmers at sowing and the re<br>report analogous outcomes for the total amount of fertilizers applied in the full season. Standard errors in parentheses. *** p_<_0.01, ** p_<_0.05, *|

37
government interaction and a foreign _×_ subnational government interaction, following the same intuition.

This regression, together with values of _σ_ and _θ_ and the estimates from regression (21), will allow us to identify the relative entry costs imposed by the two government types (see Appendix J for details). In particular, we can show that exp 1 _<u>γ−θ</u>_ <u>1</u> _σ_ = _E_<sup>_Ens/_</sup> _/α_<sup>_αsn_andexp</sup> 1 _<u>γ−θ</u>_ <u>2</u> _σ_ = _EE_ ̃ ̃ _n_<sup>_s_</sup> _//αα_ ̃ ̃ _n_<sup>_s_.The</sup> � � � � relative entry cost faced by domestic non-local firms is identified by comparing the “subnational participation gap”, ( Participation<sup>Participation</sup> rate<sup>rate</sup> in<sup>in</sup> national<sup>subnat</sup><sup><u>g</u></sup> gov<sup>ov),fornon-localfirmsrelativetolocalfirms.Inthe</sup> same way, the relative entry cost to non-domestic firms is identified by comparing the difference in the “subnational participation gap” between local and foreign firms.

# Table II

Estimating Governments’ Home bias

||**Intensive **|**margin**|**Extensi**|**ve margin**|
|---|---|---|---|---|
|Dependent variable:|log(Value|<sup>_g_</sup>(_f_)_o,r_)|Share of|plants<sup>_g_</sup><br>_o,r,g_|
||(1)|(2)|(3)|(4)|
|domestic non-local=1 _×_ sub.gov=1|-0.262|-0.239|-0.597|-0.468|
||(0.056)|(0.042)|(0.069)|(0.082)|
|foreign=1 _×_ sub.gov=1|-0.021|1.263|-0.804|-1.157|
||(0.558)|(0.624)|(0.304)|(0.247)|
|Plant FE|Yes|Yes|N/A|N/A|
|Origin x Dest. FE|Yes|Yes|Yes|Yes|
|Gov type x Dest. FE|Yes|Yes|Yes|Yes|
|Gov type x Sector FE|Yes|Yes|Yes|Yes|
|Including Capital Regions|No|Yes|No|Yes|
|Observations|97859|116017|142235|173828|
|R-squared|0.564|0.561|0.425|0.440|
|Estimator|OLS|OLS|PPML|PPML|

**Notes:** Columns 1 and 2 show the results of estimating equation 21. Column 3 and 4 show the results of estimating equation 24. The variable “domestic non-local x sub.gov” refers to the interaction variable 1 ( _o_ = _r . c_ = _c_<sup>_′_</sup> ) 1 ( _g_ = _s_ ). The variable “foreign x sub.gov” refers to the interaction variable 1 ( _c_ = _c_<sup>_′_</sup> ) 1 ( _g_ = _s_ ) _._

**Results.** Columns 3 and 4 of Table II report the results from estimating equation (24) in the subsample without capital regions as well as in the complete sample. First, we find a negative and significant coefficient on the domestic non-local bias, i.e., _γ_ 1 = _−_ 0 _._ 597, which implies that the “subnational participation gap” of non-local domestic firms is around 44% lower than for local firms. The result is slightly smaller but very similar in magnitude when we use the sample

26
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Given that access to piped water and electricity is granted to the top quintile of the income distribution, the subsidy policy is very regressive (figure 30). Estimates show that the distributional incidence of water subsidies is 0.02 and of electricity subsidies is 0.06 (below 1 is regressive, and closer to 0 is highly regressive).

**Figure 30. Distributional incidence of water and power subsidies**

# a. Water

b. Power <!-- Start of picture text --> Senegal<br>CAR<br>Gabon<br>Congo<br>Nigeria (Kaduna)<br>Togo<br>DRC<br>Nigeria (FCT)<br>Cameroon<br>Côte d'ivoire<br>Niger<br>Chad<br>Cape Verde<br>Burundi<br>Malawi (Blantyre)<br>Ghana<br>Guinea<br>Uganda<br>Malawi (Lilongwe)<br>Burkina Faso<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Measure of distributional incidence<br>Nigeria<br>Gabon<br>Congo<br>Côte d'ivoire<br>Cape Verde<br>Togo<br>Sao Tome<br>Senegal<br>Cameroon<br>Mozambique<br>Ghana<br>CAR<br>Guinea<br>Burundi<br>Burkina<br>Chad<br>Malawi<br>Uganda<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Measuring of distributional Incidence<br>Regresive Progresive<br>Regresive Progresive<br><!-- End of picture text --> _Source:_ Banerjee and others 2009. _Note:_ CAR = Central African Republic; DRC = Democratic Republic of Congo.

41
-9-

instability in the world rnarket. But because changes in Brazil's sugarcane sector policies have the potential to release great amounts of sugar on to the world market, Brazil also has the potential to influence the world price **and** needs to consider how it uses this influence to best effect.

Estimates of export demand elasticities provide a measure of the impact an increase in exports of a product could have on world price'. Borrel (1991) estimates Brazil's price elasticity of export demand at between -3.0 and -14.0. Under the most likely set of assumptions and assuming efficient policies, the estimate is around -9.0. One interpretation of this is for a one percent increase in Brazil's exports the world price would decline by 0.11 percent (Houck 1965). Although this degree.of influbncb seems insufficien **t** o allow Brazil to affect the world price to its advantage, the vast amounts of sugar Brazil could potentially divert to the world market (perhaps an increase in excess of 500%) would have a big impact on the world price. Arother finding of Borrell is that because of Brazil's potential impact on the world market, it has the potential to reduce world price variability. This has significant policy implications that will be discussed later.

# _The price of gasoline determines the value of ethanol -- or does i?_

Since ethanol is being used as a substitute for gasoline, its value should be assessed relative to the cost of the gasoline it displaces. Presently the imported price of oil plus refining cost is around $25 to $29 per barrel. Ethanol, meanwhile, costs around $40 per barrel to produce in the CenterSouth and around 18 percent more in the North/Northeast, in energy equivalen **t** erms. The net contrbution of ethanol to the Brazilian economy appears to be. highly negative.

But ethanol has other product attributes which may make its potential value to the economy greater than its value as a fuel substitute. Present policies force ethanol to be marketed as a fuel substitute. In the absence of these policies it could be marketed to exploit more fully its other attributes.

# Its attibutes include:

- a torque advantage which may compensate for its lesser efficiency than gasoline and which could mean a smaller ethanol engine could be used in place of a gasoline engine

- an exhaust emission advantage as alcohol cars produce 57 percent less crbon monoxide, 64 percent less hydrocarbons, 13 percent less nitrogen oxides and no lead emissions compared to gasoline engines (World Bank, 1989)

- 1 **There are many problems with making estimates of export elasticities of demand (sue Orvutt 1950) and there are good reasons for believing most estimates ovastas the** impacL