rafmacalaba/datause-encoder-data huggingface dataset:
In column 5 we include the institutional variables on whether the sector is regulated or not and on whether this has been privatized, conditional on the sector being regulated.1<sup>9</sup> Interestingly, the results suggest that while regulation boosts internet diffusion, as soon as the sector is privatized, intemet growth returns to the pre-regulation level.<sup>20</sup>

# 3.4 **The determinants of growth in internet use**

Rather than internet hosts, we now consider internet users (taken from infoDev) as our LHS variable. As before, first we look at the whole world (Table 6), then Latin America (Table 7). The results in Table 6 suggest again that there is no sign of diffusion with country fixed effects taken into account and specification 1 is the preferred one. Coefficients have the right sign, although they are less significant than before. The main engines of growth in internet users are access to PCs and to phone lines, while GDP and costs seem to matter less once fixed country effects are taken into account. A 10% rise in lines per capita tends to increase the number of internet users by about 2 and a half times as much. Also, an important additional difference with the focus on hosts is that the F-test could not reject the hypothesis that LA countries are jointly different in terms of the determinants of internet usage.

**_Table 6. Dependent variable: Ln internet users pc in the world_**

|(firstcolumnis|coefficient,sec|ondisp-val|ues)||
|---|---|---|---|---|
||1 .|_<br>_|2|l|
|Laggeddependentvariable|||-0.452|0.172|
|LnGdppc|0.236|0.606|0.363|0.565|
|LnCosts|-0.173|0.503|-0.29|0.383|
|LnLinespc|0.801|0.299|2.061|0.118|
|LnPC'spc|2.182|0.00(|2.580|0.001|
|Obs.|383||383||
|R2|0.986||0.976||
|F-testforLatinAmerica|2.53|0.041|2.86|0.024|

All specifications control for additive year and continent effects, interactions of the two and country effects.

> 19 Note that the privatization was preceded by the establishment of a regulator in all the countries covered by the sample but this is a somewhat misleading impression of the diversity in strength and independence of regulatory institutions. Argentina's regulator has grown to be significantly more competent during the 1990s to become one of the most capable towards the end of the period. Many others however have much more limited capacity to tackle the problems they are expected to address. In addition to regulatory endowment problems, capture has been and continues to be an issue in the region and hence the Levy-Spiller (1994, 1995 and 1996) concerns continue to be quite relevant even if they are not addressed explicitly here.

> 20 Again, some care must be exerted in interpreting these results, since it might well be possible that the full effects of regulation and privatization haven yet not taken place in LA.

15
Policy Research Working Paper 9314

# **Abstract**

This paper uses high-resolution satellite data on the proportion of buildings in a 250x250 meter cell to study the evolution of human settlement in Ghana over a 40-year period. The analysis finds a strong increase in built-up area over time, mostly concentrated in the vicinity of roads, and also directly on the coast. There is strong evidence of agglomeration effects in the static sense—buildup in one cell predicts buildup in a nearby cell—and in a dynamic sense—buildup in a cell predicts buildup in that cell later on, and an increase in buildup in nearby cells. These effects are strongest over a radius of 3 to 15 kilometers. No evidence is found that human settlements are spaced more or less equally over the landscape or along roads. By fitting a transition matrix to the data, this paper predicts a sharp increase in the proportion of the country that is densely built-up by the middle and end of the century, but there is no increase in the proportion of partially built-up locations.

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 fshilpi@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
1 9 '''-'r-lit. ':n9>i:n''r inr F:r--:i,ti.:n. it c-arn **t: >,** shown that the optimll -> ta*. **¶t>'K** ..:'. **i:.** n a furw't .-r of zutrut.-F the r,aa cost o-f capit-l **rini** the Aa.la liti--f d4t:t. FsrniaJiv: KC K Y . -4 i (1 **4I** p arameters r **and** p den.:te the :--rprt> ta rate and the percentage reduction in q indiuct. the s **-** ,r fiscal and financial in-entives available t, investors in Mi.-r~-r-. Fu'l details For the calculations of r arnd p are provilr-l in W-Drlid **dlk** (1990?.

**fJne** -r'2,blem .w;ith the Formulatio-n of eq. 10 is that it containr one oh' i-uF'] Ssndigenous variable, the le,el of output Y . In what follows we rel; lr th- Following procDedure. It is assumed that, because oF delivery lags, flrms must determine their desired level of output, and therefore their investment, one period in advance. They will need therefore to predict, based on available inFormation, the optimal level of capaoity outrut For the Following period. This will in turn determine their demand fo r investmenrt gcods. We alsco assume that expectations about thp determinants of the output decision can be simply modelled by a Fir-st-o-rder autoregressive process. The expected, optimal level of cutput (t- Iy t ' ,,iJll thereFoDre be equal to:

w.here the inFormatilon set has been augmented1 **to** include both Ipub (the level cF public investnment on the ground that this may affect the pro-ducti,on relationship > and MS (the stock of money, **as** a further predictor of prices). In estimating eq. I 1 we must allow For the fact that t Yt is not observatle. Howeve-, under rational expectations, it will YVfetr From actual output Y only for a random term uncorrelated w\.ith an'. available informaticn at t-I. We can therefore use the actual product :-n level Y Es the dependent variable and take the Fitted '.alue oF eq. I I as the estimate of expected output.

2'. estimating eq. 11, it is now possible to take into account the endogeneity oF Y in eq. 10. We follow again Pagan's (198t suggestion and take the estimated value of Y (Y.F) in eq. 1I as an instrument For the actual level oF output in eq. 10. We present First the estimates of
This version: 15 Feb 2024 17 <mark>Among other emerging trends in quantitative indicators, at the national level, relative income inequality fell between 1970 and 2019 (Gini coefficient of 0.513 and 0.407 respectively). Decomposition analysis reveals that reduced income inequality</mark> _<mark>within</mark>_ <mark>ethnic groups was a major contributor to falling inequality at the national level (Table 1). The reduction in within-group inequality was most pronounced for the Bumiputera, but also substantial for Chinese and Indian Malaysians. This was bolstered by a reduction in inequality</mark> _<mark>between</mark>_ <mark>ethnic groups, as differences in mean incomes across ethnic groups narrowed.</mark>

**<mark>Table 1:</mark>** <mark>Decomposition of income inequality by ethnic group, 2004–2016</mark>

||2004|2007|2009|2012|2014|2016|
|---|---|---|---|---|---|---|
|_Within-group in_|_equality (Th_|_eil L index)_|||||
|Bumiputeras|0.378|0.348|0.373|0.327|0.282|0.273|
|Chinese|0.338|0.317|0.318|0.303|0.281|0.276|
|Indian|0.333|0.334|0.349|0.376|0.298|0.274|
|National|0.406|0.375|0.384|0.354|0.307|0.300|
|_Between-group i_|_nequality_||||||
||0.041|0.036|0.025|0.029|0.024|0.026|

<mark>Source: Authors, based on DOSM data. Notes: Inequality indices are Theil L index, also known as the mean log deviation (MLD), based on household income per capita.</mark> <mark>However, while relative inequality was decreasing, absolute inequality was increasing rapidly. That is, poorer Malaysians were gaining a larger share of total income, but the currency-denominated income gap between poor and rich was</mark> 17
20

||fiscal decentralization|log p.c. real GRP|**Tab**<br>log p.c. real GRP, PPP|**le 3. Pai**<br>log population|**rwise cor**<br>log area|**relations**<br>election votes spread|**between**<br>fractionalization|**selected**<br>fuel industry share|**variables**<br>excess budget revenue|<br>privatization rate|share of social transfers|child mortality|corruption|urbanization|number of students|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|fiscal decentralization|1.00|||||||||||||||
|log p.c. real GRP|-0.24|1.00||||||||||||||
|log p.c. real GRP,PPP|-0.22|0.86|1.00|||||||||||||
|log population|0.08|0.48|0.18|1.00||||||||||||
|logarea|-0.07|0.40|0.65|0.08|1.00|||||||||||
|electionvotes spread|-0.23|-0.04|-0.10|0.01|-0.14|1.00||||||||||
|fractionalization|-0.43|-0.19|-0.09|-0.29|0.03|0.21|1.00|||||||||
|fuel industryshare|-0.48|0.35|0.32|0.13|0.29|0.15|0.39|1.00||||||||
|excess budget revenue|-0.06|-0.27|-0.02|-0.41|0.08|0.09|0.30|-0.18|1.00|||||||
|privatization rate|0.36|0.10|0.08|0.13|0.03|-0.21|-0.62|-0.21|-0.31|1.00||||||
|share of social||||||||||||||||
|transfers|0.15|-0.51|-0.50|-0.35|0.09|0.08|0.52|0.14|0.21|-0.32|1.00|||||
|child mortality|0.02|-0.13|0.05|-0.37|0.28|0.01|0.16|0.14|0.37|0.01|0.17|1.00||||
|corruption|0.27|-0.35|-0.34|0.00|-0.25|0.12|-0.07|-0.40|-0.06|0.41|-0.3|0.25|1.00|||
|urbanization|-0.11|0.64|0.55|0.36|-0.01|-0.12|-0.39|-0.03|-0.15|0.23|-0.63|-0.26|-0.17|1.00||
|number of students<br>Notes: Based on 1999 un|-0.52<br>less other|0.13<br>wise indi|0.19<br>cated in t|0.34<br>he text|-0.36|0.16|0.01|-0.09|-0.26|0.09|-0.14|-0.24|0.05|0.22|1.00|

A suitable measure showing how keen regions were on pursuing reforms is the share of privatized enterprises in retail trade and services as of July 1, 1996, i.e., approximately 4 years after the launch of mass privatization. It is a suitable measure because regions did not have a choice of whether or not to participate in the privatization program, but they retained significant discretion over the pace of the privatization process. As a result, the 1996 privatization outcome varied greatly across regions with the share of privatized companies covering the range from 20 percent to 100 percent (see Table 1). As expected, the index of ethnolinguistic fractionalization is highly negatively correlated with the privatization measure of reforms (the coefficient is -0.62, see Table 3). If this “attitude to reforms” explanation is valid, the privatization variable will be
Figure 1: Number of TAs with and without enforceable provisions in government procurement <!-- Start of picture text --> With gov. procurement Without gov. procurement<br>195819601962196419661968197019721974197619781980198219841986198819901992199419961998200020022004200620082010201220142016<br>20 200<br>15 150<br>10 100<br>5 50 Cumulative number of TAs<br>N. of TAs that enter into force<br>0 0<br><!-- End of picture text --> Note: Authors’ calculations on the basis of the Deep Trade Agreements data from Mattoo et al. (2020). We exclude “Partial Scope Agreements”.

Against this seemingly contradicting policy landscape (with unilateral discriminatory measures being adopted together with liberalizing ones), this paper aims to assess the importance of trade barriers in government procurement and identify the impact of trade agreements on cross-border flows. We employ data from the Trade in Value-Added (TiVA) database of the OECD on 62 countries between 1995 and 2015. Our preferred definition of purchases by the public sector sums the “General Government expenditures” and the “Public Administration”, “Health” and “Education” output columns of the inter-country input-output (ICIO) tables. Bilateral trade values are distinguished by goods and services. Descriptive trends in the data show that the public sector spends considerably more on services than on goods compared to the private sector. What is more, the import share of expenditure in government procurement relative to the one in the rest of the economy is particularly low in services, although important heterogeneity emerges across countries.

To move beyond descriptive evidence and estimate trade barriers in government procurement, we apply a canonical gravity framework relating bilateral cross-border procurement flows to different variables proxying for trade costs (or their inverse) and multilateral resistance terms. We also apply the same gravity framework to bilateral trade – – in the rest of the economy what we refer to as the private sector or market which we use as a benchmark as well as a way to observe possible spillovers of procurement-specific 3
land size and -0.83 in the specification using the GPS measure of agricultural plots. Both estimates therefore support the IR hypothesis. When the GPS measure of farm size is used, however, the absolute value of the coefficient increases, indicating an even stronger IR.

The inclusion of hired labor and inputs on the right hand side poses problems as they may capture part of the effect of market imperfections, not allowing for their full effect to be reflected in the farm size coefficient<sup>5</sup> . To check for the extent to which this problem might affect our results we run the same regression without these variables and the key results (not reported) did not change. In both specifications, the coefficient of land size decreases of 0.1 but remains negative and statistically significant.

The hypothesis according to which the IR would be a statistical artifact due to small farmers under-reporting their farm size is therefore strongly rejected by the data. In our sample, small farmers in fact over-report land size, and it is the very large famers who are actually more likely to underestimate their holdings, thus resulting in artificially higher yields. When more accurate land measures are used thanks to the introduction of GPS devices, the estimated slope of the function becomes steeper indicating an even stronger IR than what one would conclude based on similar estimates performed using farmers‟ self-reporting.

As expected, good soil quality shows a positive association with farms profits, which remains stable across specifications. The irrigation and steepness variables, on the other hand, have the expected signs (positive for irrigation, negative for steep land) but the estimated coefficients are not statistically significant.

As discussed in Section 3 above, one issue with our data is the sample selection bias introduced by dropping from the productivity analysis the households for which we do not have both measures for all plots. To investigate whether this may be a factor invalidating our main findings we re-estimated model (2) using the entire sample, and tested (a) whether the group means of predictors estimated using the two samples are equal, and (b) whether the main coefficients of interest for the IR (i.e. the coefficients on the land variable) are equal. Results are reported in Annex Table 2.

The Wald test rejects the null hypothesis that all coefficients are equal for both samples, which one would expect given the systematic differences between the two samples we reported in Annex Table 1, and discussed earlier in the paper. What matters for our key results, however, is that the test fails to reject the null for the land coefficient. In other words, the IR estimated using the self-reported measure is as strong on the full sample as it is on the restricted sample for which we have complete GPS information. For productivity analysis at large, however, the rejection of the test of equality between the two runs of the model does point towards the need for future surveys to solve the

> 5 The input variables also pose possible endogeneity problems being choice variables for the farmers.

11
the household. Clearly, any migration decision is not irrevocable, thus a permanent migrant may decide to return at some point in the future; while a current household member with past migration experience may decide to migrate permanently at some time in the future. Hence the distinction, common in migration studies, between temporary and permanent migration, is inherently problematic. The issue is particularly thorny for the most recent migrants who may still be in the process of making a final decision on where to settle indefinitely. For this reason, we only include migration episodes that have occurred up to December 2004; thus, we only consider migrants who have been abroad for at least six months (for current migrant), or must have returned in Albania at least six months before the survey (for past migrants).<sup>2</sup>

# _Current migrants_

As shown in Figure 1, the flows of (current) migrants have fluctuated considerably, more than doubling in the aftermath of the collapse of the notorious pyramid scheme in 1997, peaking in 2000 at about 50,000 new migrants per year, and steadily decreasing after that.<sup>3</sup> The latter downward trend is particularly relevant to the current policy debate. Is migration from Albania now tapering off? What is driving the downward trend of recent years? Several pertinent hypotheses will be explored later in the paper when we analyze changes in the determinants of migration over time.

**Figure 1** _Flow of current migrants, by year of migration_

<!-- Start of picture text --> year<br>total<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>50000<br>25000<br>first-time migrants<br>0<br><!-- End of picture text --> In terms of place of origin within Albania of these external flows, the largest proportion comes from rural areas and the gap across location has increased over time: in 4
# **Table 8: Connected Firms and Competition – Sector Level Evidence**

|**Connected Firms and Competition (20**<br>Sector Regressions<br>|**00-2010) – O**<br>|**LS**<br>||
|---|---|---|---|
|**Dependent Variable**|**Entry**<br>(1)<br>coef/se|**Exit**<br>(2)<br>coef/se|**Herfindahl**<br>(3)<br>coef/se|
|_A. Baseline_||||
|Ben Ali Market Share|-0.014<br>(0.017)|0.039**<br>(0.017)|0.305<br>(0.216)|
|Year Dummies|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|
|Number of observations|1,567|1,423|1,567|
|R2|0.354|0.455|0.874|
|_B. De Jure Regulatio_|_n_|||
|Ben Ali Market Share (βBA)|-0.021<br>(0.022)|0.027<br>(0.022)|0.076<br>(0.193)|
|Ben Ali Market Share*Highly Regulated (de Jure) (βRBA)|0.023<br>(0.030)|0.038<br>(0.026)|0.806***<br>(0.224)|
|βBA+βRBA(Ben Ali Market Share in Highly Regulated Sectors)|0.002|0.064***|0.883***|
|Year Dummies|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|
|Highly Regulated Dummy|Yes|Yes|Yes|
|Number of observations|1,567|1,423|1,567|
|R2|0.354|0.456|0.883|
|_C. De Facto Regulati_|_on_|||
|Ben Ali Market Share (βBA)|-0.019|0.032|0.068|
||(0.025)|(0.022)|(0.217)|
|Ben Ali Market Share*Highly Regulated (de Facto) (βRBA)|0.014<br>(0.025)|0.023<br>(0.022)|0.666*<br>(0.346)|
|βBA+βRBA(Ben Ali Market Share in Highly Regulated Sectors)|-0.006|0.054***|0.734***|
|Year Dummies|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|
|Number of observations|1,567|1,423|1,567|
|R2|0.354|0.456|0.881|

Note: *,**,*** denote significance at the 10%, 5% and 1% significance level respectively. The sample is confined to 2000-2010. Standard errors are clustered at the 5-digit sector level. Ben Ali Market Share is a variable that measures the aggregate market share of connected firms at time t. _Highly Restricted (de Jure)_ is a dummy variable indicating whether the 5 digit sub-sector the firm operates in is subject to both authorization requirements and FDI restrictions at time t and 0 otherwise. _Highly Regulated (de Facto)_ is a dummy variable taking the value 1 if the 5 digit sector in which the firm is operating is subject to an authorization which is de facto difficult to obtain according to one of Tunisia’s leading lawfirms, and 0 otherwise. The sample is confined to sectors with an average annual output larger than 20 million Tunisian Dinars, that have never been monopolized, and for which entry and exit rates are strictly lower than 1 (i.e. that do not appear of disappear between 2000 and 2010).

35
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