rafmacalaba/datause-encoder-data huggingface dataset:
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ A point of departure is simple, univariate, ordinary least squares (OLS) model of growth of output per worker as a function of the growth of capital stock per worker (A-3).

# **<u>Table A-3: Output growth as a Function of Capital Accumulation</u>**

|Dependent Variable:FirstDifferences of Logs of RealOutpu|t perWorker|
|---|---|
|Indépendant Variable<br>Coefficient|T-Statistic|
|DeltaLog, CapitalperWorker<br>0.52967|5.36|
|Constant<br>0.00282|0.43|
|Durbin-Watson d-Statistic: 1.859 R-Squared: 0.42<br>ADFtest on residual:Z(t)= -5.818> 1% CriticalValue|F(1,40) = 28.7|

Define: Coefficient on average capital/worker = b1 Test: <u>b = .35 F(1,40) = 3.30 Prob > F = 0 .0767</u> This univariate OLS regression seems surprisingly robust. The residuals are reasonably well-behaved, and the capital coefficient estimate is of plausible magnitude with the correct sign and a small standard error, hence yielding a “good” T-statistic. This one variable “explains” a considerable part of the dependent variable. We conduct a test of whether the coefficient estimate equals the “standard” of 0.35 and reject the hypothesis, accepting the alternative hypothesis that it is higher than 0.35. Capital stock per worker is a major determinant of productivity in Cote d’Ivoire.

Consider next a bivariate “factor” model of productivity, which adds a term for human capital to the univariate regression above, in this case the first differences of our estimate of average schooling per worker. The schooling coefficient point estimate comes in at **b2** = 0.0553, not far from the “ballpark” expected value of 0.079 (which we would revise downward anyway if we accept the proposition that Cote d’Ivoire has a high MPK and hence **a >** 0.35). However, the coefficient estimate has a wide confidence interval and low T. It seems that our estimate of human capital is imprecise, but of some utility.

Total Factor Productivity in a deterministic framework is a residual, that share of output that is not attributed to quality-adjusted labor (human capital) or physical capital. The concept of “Solow-residual” TFP is not directly observable; there is no times series of direct observations to rely upon. For the regression analysis, analysts have traditionally used proxy variables or instrumental variables, ranging from the urbanization rate or population, to trend variables, and so forth. But, in Cote d’Ivoire specifically and (and in some other developing countries<sup>28</sup> TFP growth over time is not a simple quasi-linear trend; it grew in the 1960s and 1970s and plunged thereafter. Proxies or instruments must be carefully selected.

> 28 See for example Bosworth and Collins (2003).

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# THE SIGNIFICANCE OF THE 'EUROPE AGREEMENTS" FOR CENTRAL EUROPEAN INDUSTRIAL EXPORTS'

Bartlomiej Kaminski

# I. INTRODUCTION

The European Association Agreements concluded between the EC and CentraltSouthern European economies (hereafter, the CEE-51) set a new framework for their mutual economic relationships. To emphasize the difference from the Agreements on Association signed earlier by the EC with other countries, they are referred to as the 'Europe Agreements." Recognizing the time-consuming process of ratifying the Association Agreements by parliaments of both EC members and Central European countries, it was agreed that the trade component-the so-called Interim Agreement on Trade and Trade Related Matters (hereafter, ITAs)-will be implemented before the treaty is ratified. Former Czechoslovakia (FCSK), Hungary and Poland signed the Association Agreements in December 1991, and the bilateral Interim Agreements on Trade went into force on March 1, 1992. Bulgaria and Romania concluded negotiations with the EC in 1992, and the trade provisions became operational in 1993 (Romania) and 1994 (Bulgaria).

While some details in the Europe Agreements differ, they broadly follow the same pattern Their major provisions include: (i) the introduction of free trade in industrial goods within a period of 10 years; (ii) improved access for agricultural products, similar to that stpulatd in the Lom6 Convention and Medierranea association/cooperation agreements; (iii) a comnmitment to harmonize economic legislation with that in the EC; (iv) the EC's financial and technical assistance (albeit no specific amounts have been indicated); and (v) the possible introduction of free trade in services. In addition, the EC committed itself to gradually eliminating tariffs and/or increasing quotas on "sensitive" products-mainly textiles, iron, and steel.

The objective of this paper is to dissect the trade provisions of the Agreements and identify the extent of change in market access in terms of tariff and non-tariff barriers as well as in terms of their current significance (using actual trade data through 1992) for CEE-5 exporters of industrial products. An imporant I would like to thmk Paul Armington, Ronald Duncan and Vikram Nderu for iheir suggestions and comments. I am also indebted to Maciej Lesny for his assistance in coilecting and processing data for this project. These include Bulgaria, the former Czechoslovalda, Hungary, Poland and Romama.
all bigger establishments with over 50 employees and then sampling at the lower end.<sup>18</sup> Although the UBI covers the entire country, due to confidentiality issues and limits on the capacity to share this data outside of the Uganda Bureau of Statistics (UBOS), our data have coverage only of Kampala and furthermore we are restricted by the abridged version of the data and statistics that have been provided to us. Our data cover the Greater Kampala metropolitan area for a sample of 2,342 firms, each with information on their GPS location, total number of employees, value added per worker, and industry classification. Value added is calculated as the net output of a sector after adding up all outputs and subtracting out all intermediate inputs but labor.

# **6.2 Matching the data to density measures**

We measure the effects of density on firm productivity by matching the firm coordinates and data from the UBI survey with our population, employment, and Landscan ambient population data, used elsewhere in this paper. As detailed in section 1, our data on population are from the 2002 census and are at the level of 174 parishes within the Greater Kampala administrative area. We assign that data to the 1km grid square level, through a weighted sum to the survey area numbers, so as to be consistent with the spatial resolution of Landscan. Our employment data are from the 2002 Census of Business Establishments [COBE] and provides the GPS location of supposedly the universe of firms and employment in Greater Kampala.<sup>19</sup> The data allow us to study density effects at two spatial scales. First is the coarser one with 1km grid squares upon which population and Landscan data are defined. For this each firm point in the UBI dataset is overlaid by the 1km grid square level data on population, employment, and Landscan. Each firm is then assigned own-square measure

> 18Business establishments were first stratified by industry sector and within each industry sector, business establishments were further stratified by employment size as per the following categories of employment size 1, 2-4, 5-9, 10-19, 20-49, 50-99, 100-499 and 500 or more employees; and further by turnover; thus less than 5 million shillings, between 5 and 10 million shillings and more than 10 million shillings. Given the significant contribution of the larger establishments, from previous surveys, to the value of production; all the establishments with 50 or more employees were supposed to be sampled with certainty, (a probability of 1), while those employing fewer than 50 employees were subjected to probabilistic sampling.

> 19An issue is that if we look in the 2002 census data, matching it alongside the firm locations from our 2002 survey, we see that for over half of our firms we cannot find other firms from the census that are in the same industry and within 150 meters of the survey firms, indicating either that there is a lot of firm turnover or issues in recording locations.

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|**Table 4: Attrition rates and Busin**<br>**Panel A: Control Vs Communicati**|**ess Closure**<br>**on Blocks**<br>Control<br>Mean|**Impacts by T**<br>Free Cost<br>Difference|**reatment Group**<br>Communication<br>Difference|
|---|---|---|---|
|Interviewed at Follow-up|0.636|-0.0428|-0.0274|
|||(0.0396)|(0.0353)|
|Closed at Follow-up|0.142|0.0139|0.0297|
|||(0.0268)|(0.0277)|
|Refused or Absent at Follow-up|0.238|0.0321|0.00105|
|||(0.0332)|(0.0274)|
|Sample Size|685|329|328|
|**Panel B: Control Vs Inspector Blo**|**cks**|Inspector|Indirectly|
||Control|Assigned|Inspected|
||Mean|Difference|Difference|
|Interviewed at Follow-up|0.537|0.0354|-0.0332|
|||(0.0247)|(0.0278)|
|Closed at Follow-up|0.166|-0.0184|-0.00850|
|||(0.0188)|(0.0209)|
|Refused or Absent at Follow-up|0.316|-0.0241|0.0326|
|||(0.0253)|(0.0257)|
|Sample Size<br>Notes: Standard errors in parenth<br>*, **, and *** indicate statisticall<br>10, 5 and 1 percent levels respect<br>randomization strata. Sampling w<br>for uneven sampling probabilitie|1383<br>eses, clust<br>y different<br>ively, after<br>eights are<br>s.|577<br>ered at the b<br>from control<br>controlling f<br>used in Pane|593<br>lock level.<br>mean at the<br>or<br>l B to account|

36
**22**

# **5.2 Omitted Variable Bias**

In this section, we examine whether there might be unobservable determinants of incomeearning that are also correlated with our measure of noncognitive skills, self-esteem.

In Table 11, we add a number of other personality traits as covariates: time- and risk-preferences, and agricultural self-efficacy. We find that the correlation between self-esteem and incomeearning in export-oriented agriculture is not driven by these other traits. While columns (1) and (3) use the same specification as in Table 4 on the subsample of observations for which all personality traits variables are non-missing, columns (2) and (4) additionally control for these other traits. All previous results remain robust.

**Table 11:** Income-earning in export-oriented agriculture (w/other noncognitive skills)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Income-e|arning in e|xport-orie|nted agriculture|
||Fem|ale||Male|
|Self-esteem (fem)|0.02<sup>_∗∗∗_</sup>|0.02<sup>_∗∗∗_</sup>|-0.07<sup>_∗∗∗_</sup>|-0.06<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|(0.02)|(0.02)|
|Self-esteem (male)|-0.03<sup>_∗∗∗_</sup>|-0.03<sup>_∗∗∗_</sup>|0.04<sup>_∗_</sup>|0.04<sup>_∗_</sup>|
||(0.01)|(0.01)|(0.02)|(0.02)|
|Present bias (male)||0.01||-0.00|
|||(0.02)||(0.04)|
|Present bias (female)||0.01||-0.03|
|||(0.02)||(0.04)|
|Risk aversion (male)||-0.00||-0.04|
|||(0.02)||(0.03)|
|Risk aversion (female)||-0.00||-0.01|
|||(0.02)||(0.04)|
|Self-effcacy (male)||0.01||-0.01<sup>_∗_</sup>|
|||(0.01)||(0.01)|
|Self-effcacy (female)||-0.00||0.00|
|||(0.00)||(0.01)|
|Outcome mean|0.05|0.05|0.56|0.56|
|R-squared|0.043|0.046|0.124|0.128|
|Observations|1254|1254|1254|1254|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.10, ** p<0.05, *** p<0.01.

In addition to these traits, there could also be other characteristics that matter but that we do not have data on. To address this issue, we employ the Oster (2019) test for omitted variable bias. The method enables the estimation of identified sets for the parameter of interest in the presence of omitted variable bias, under the assumption that selection on observable controls is proportional to selection of unobservable controls. Table 12 reports the estimated sets. First, consistent with the Oster test specification, we observe that the sets never include the value zero. Second, we observe that the sets are tightly bound around the estimated coefficient of interest. These findings enable us to reject the hypothesis that the association between selfesteem and spouse’s income-earning is driven by unobservable heterogeneity correlated with both variables.
# **4.2. Mean Group (MG) and Pooled Mean Group (PMG) estimation results**

We now discuss the results of the Mean Group (MG) and Pooled Mean Group (PMG) estimation as well as statistical tests of hypotheses ( _i_ ) and ( _ii_ ) raised at the end of Section 2. Since we are primarily interested in the long-run equilibrium relationship between credit and its macroeconomic determinants, we only report the MG and the PMG estimates of the long-run equilibrium parameters **_β_** , as well as the intercept and the speed of adjustment terms _c_ and _α_ , and do not report results related to the short-run dynamics.<sup>25</sup> We use standard asterisk (<sup>_∗_</sup> ) symbols in Table 1 and Table 2 to denote significant values at the 10% (<sup>_∗_</sup> ), 5% (<sup>_∗∗_</sup> ) or 1% (<sup>_∗∗∗_</sup> ) level.

## **4.2.1. Mean Group estimates**

Consider first the results reported in Table 1. The upper part of Table 1 provides the Mean Group estimates of the long-run equilibrium parameters that are computed from the crosssectional average of each individual country’s ARDL regression as **_β_**<sup>**ˆ**</sup> _MG_ = _N_<sup>_−_1 �</sup> _i_<sup>_N_</sup> =1<sup>**_β_ˆ**</sup> _i_<sup>.</sup> Note that the long-run coefficient on GDP is highly significant, centered at a value of 2.96. Testing the null hypothesis _H_ 0 : _β_<sup>_gdp_</sup> _MG_<sup>=1againsttheone-sidedalternative</sup><sup>_H_1:</sup> _β_<sup>_gdp_</sup> _MG_<sup>_>_1 yields a</sup><sup>_t−_statistic of (2.96</sup><sup>_−_1)/0.33</sup><sup>_≈_6.This result, therefore, provides strong</sup> statistical evidence against a unity restriction at the aggregate cross-country level that the commonly used credit-to-GDP ratio imposes when employed as the dependent variable in the estimation of equilibrium credit.

# INSERT TABLE 1 HERE

_−_ The MG estimate of the GDP Deflator coefficient is 0.27 with a _p_ value of 0.19, indicating that it is not statistically different from zero. Testing the null hypothesis _H_ 0 : _β_<sup>_de f_</sup> _MG_<sup>= 1</sup> against a one-sided alternative results in a _t−_ statistic of (0.27 _−_ 1)/0.32 _≈−_ 2.28, which _−_ has a corresponding one-sided _p_ value of 0.01. The statistical evidence against the unit restriction on the GDP Deflator parameter is thus somewhat weaker than for the GDP parameter itself. From a visual inspection of the _β_<sup>ˆ</sup><sup>_de f_</sup> distribution plotted in Panel (b) of Figure 1 it may seem surprising that the null of unity is rejected at, for instance, a significance level of 5%, given the relatively large dispersion of _β_<sup>ˆ</sup><sup>_de f_</sup> over the _−_ 6 and 5 interval. It should be stressed here again that we are testing the Mean Group estimator, which is

> 25We used a modified version of the specialised GAUSS code of Pesaran _et al._ (1999) for MG and PMG estimation available from http://www.econ.cam.ac.uk/faculty/pesaran/jasa.exe. The complete regression output from the individual country regressions is large and of no particular interest to us, apart from model checking purposes. We thus do not report the full results here, but these are available from the authors upon request.

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Table 4. Real equipment investment before and after the training program (GHS)

|||Fabricators|||Machinists||
|---|---|---|---|---|---|---|
||Treatment<br>(1)|Control<br>(2)|_p_-value<br>(3)|Treatment<br> (4)|Control<br>(5)|_p_-value<br>(6)|
|2006|154.8|40.5|0.276|197.3|487.2|0.386|
|2007|108.1|39.5|0.263|258.1|299.6|0.201|
|2008|135.5|217.6|0.621|905.0|174.4|0.047|

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Table 2: Regression of US Tropical Cyclone Impacts on Intensity and Vulnerability

|Model|Constant|Log<br>(Minimum<br>Pressure)|Log<br>(Income)|Log<br>(Population<br>Density|Adj Rsq/<br>F Stat|
|---|---|---|---|---|---|
|Damage|607.5|-86.3|0.370|0.488|0.501|
||(10.39)|(9.96)|(0.45)|(1.53)|35.76|
|Fatality|247.5|-33.3|-2.36|1.28|0.208|
||(4.10)|(3.69)|(1.74)|(2.78)|4.42|

Note: There were 111 observations in the damage regression and 40 observations in the fatality regression. The t statistics are in parenthesis. The functional form of the regression is log log. Source: NOAA(2009).

Table 2 also shows the impact of tropical cyclones on US fatalities. Only 40 of the 111 storms to strike the US caused any deaths. The results are consequently less accurate than the damage results. Nonetheless, there are some interesting findings. More powerful storms cause more deaths, although the elasticity of intensity is not as large as in the damage regression. The 95% confidence interval for the minimum pressure fatality coefficient lies between -15 and -51. Higher incomes lead to lower deaths but the coefficient is not significant at the 5% level (the t-statistic is less than 2). More dense US populations had higher fatalities. Cities in the US were more vulnerable to fatalities than more rural locations.

In order to understand the role of vulnerability, we turn to international data. The international regressions measure the relationship between damage and fatalities and population and income (using international data EMDAT 2009). Storm intensity is not recorded in the international data so it is an omitted variable. Table 3 reveals that damages increase with income but fall with population density. These coefficients are significantly different from zero. Perhaps more important, they are significantly different from unitary (1). The elasticities of income and population are less than 1, contrary to assumptions in the literature Pielke and Landsea 1998, (Pielke et al. 2008, and Nordhaus 2010). It is likely that people privately adapt to reduce damages as their incomes rise and as they urbanize. The damages consequently do not rise proportionally with income or population in a specific location. Note that the coefficients on income and population density are far more significant in Table 3 than in Table 2. This is probably because there 12
in Mexico. In fact, this was the focus of an evaluation by Catteno et al. (2009). With these caveats, Linn (2010) selected the following research issues.

# 1. <u>Slum improvement</u>

The prevalence and growth of slums remains a major challenge, as reflected in the fact that slum improvement is the only urban target included in the MDGs. While much has been learned over the years, there remain major unresolved issues, including whether or not to focus on the urban poor where ever they are, or specifically on slum areas; whether slums are predominantly a problem or an opportunity; whether targeted interventions to improve or reduce slums are worth pursuing; whether one should focus on improving existing slums or on preventing the growth of new slums; whether to focus on large or rather on small and medium cities, etc. Linn (2010) explains how the slum improvement research agenda could be shaped to consider

- different challenges (e.g., gender issues, crime and violence, employment creation and access to transport services),

- different instruments (comprehensive versus narrowly focused slum improvements, relying on community participation and multi‐stakeholder alliances, or employing conditional cash transfer programs),

- different analytical tools (happiness analysis, impact evaluation, longitudinal analysis, GIS and poverty mapping, and different planning tools),

- different institutional constraints and opportunities (fragmentation of governmental responsibility, the role of international partners, the effectiveness of aid), and

- different geographic dimensions (a special focus on Africa, or on South Asia; or learning from the decade‐long experience of Latin America)

Among the highest priorities from these choices for research would in our view be an assessment of African slum growth and improvement, bringing to bear the international experience with comprehensive slum improvement programs, combining various tools of impact analysis, longitudinal studies, and life‐satisfaction approaches, with a special focus on the role of participatory planning. Of particular interest is the impact of policies on housing affordability and slums and could include an examination of the huge South African low‐income housing program that was able to meet ambitious targets, albeit with big subsidies.

# <u>Employment</u>

In contrast to slums, employment is rarely a focus of attention in today’s debates about urbanization, even though lack of employment and uncertain, dangerous, low productivity employment are key determinants of urban poverty and surveys show that employment – or lack thereof – is a key determinant of peoples’ life satisfaction in cities.. The relative lack of focus of research and policy on urban employment issues could be due to the perception among economists that labor markets generally work relatively well in developing countries and that employment issues need to be treated as “spatially neutral”. (World Bank, 2009a) However, Linn (2010) 9
DECENTRALIZATIONi,t = ai + bt +cMANAGEMENTi,t + ei,t (3) where DECENTRALIZATION is our index of plant decentralization, and ai and bt are plant fixed effects and time dummies. In column (1) we run the OLS estimation and find a significant and positive coefficient, indicating that firms which improved their management practices during the experiment have also delegated more decisions to their plant managers. Given that the decentralization index has a standard deviation of 1 the magnitude of this coefficient is large – increasing the adoption of management practices by 37.8% (the mean change for the treatment group) is associated with a 0.55 standard-deviation change increase in decentralization. So typically this would mean the owner reduces his factory visits from daily to three times a week, while also letting the plant manager make hiring decisions for weavers, award small weaver bonuses, and plan the weekly maintenance schedule. In column (2) we run the IV estimation, using the log(1+weeks since the implementation phase began) as the instrument, and again find a positive and significant impact. Finally, in column (3) we report a positive ITT.

The consultants provided no advice on delegation and decentralization. It occurred in large part because the better monitoring of the factory operations allowed owners to delegate more decisions without fear of being exploited (the monitoring channel in the principal-agent group of organizational theories). For example, with daily inventory, quality and output data it is harder for the factory manager to steal inventory or output without detection by the owner.

# **_VI.B The impact of management practices on computerization_**

A major topic over the last decade has been the relationship between IT and productivity. A growing literature finds that the productivity impact of IT is substantially larger than its cost share (e.g. Bresnahan, Brynjolfsson and Hitt, 2002). The literature argues this is because IT is complementary with modern management and organizational practices, so that as firms invest in IT they also improve their management practices. This leads to a positive bias on IT in productivity estimates because management and organizational practices are typically an unmeasured residual.<sup>24</sup> But none of this literature has any experimental evidence.

So to investigate the potential complementarity between IT and management practices we collected computerization data on nine aspects of the plants, covering the use of Enterprise Resource Planning (ERP) systems, the number of computers, the age of the computers, the number of computer users, the total hours of computer use, the connection of the plant to the

> 24 See, for example, Bartel, Ichniowski and Shaw (2007) and Bloom, Sadun and van Reenen (2009a).

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