rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5563

# **Abstract**

This paper examines _de jure and de facto_ measures of regulations, finding the relationship between them is neither one for one, nor linear. “Doing Business” provides indicators of the formal time and costs associated with fully complying with regulations. Enterprise Surveys report the actual experiences of a wide range of firms. First, there are significant variations in reported times to complete the same transaction by firms facing the same formal policy. Second, regulatory compliance appears “under water” as firms report actual times much less than the Doing Business reported days. Third, the data reveal substantial differences between favored and disfavored firms in the same location. Favored firms show minimal variation, so Doing Business has little predictive power for the times they report. For disfavored firms, the variation is greater, although still not significantly correlated with Doing Business. Fourth, where multiple Enterprise Surveys are available, there is little association over time, with _reductions_ in Doing Business days as likely to be accompanied by _increases_ in Enterprise Surveys days. Comparing these two types of measures suggests very different ways of thinking about policy versus policy implementation, what “a climate” for firms in a country might mean, and what the options for “policy reform” really are.

This paper is a product of the Office of the Chief Economist, Financial and Private Sector Development. 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 author may be contacted at mhallward@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
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# **Treaty Data**

As mentioned earlier, the treaty dataset is based on Dinar S. (2007) and includes 226 country dyad observations. Eighty-six of the corresponding rivers are not governed by treaties while 140 are, providing a diverse pool of observations to examine the _scarperation_ contention. Three hundred and eleven treaties were identified and analyzed for their content. Of these, 40 provide only periodical re-affirmation of previous treaties and do not introduce new agreements. These treaties were removed from the analysis, leaving the dataset with 271 treaties.

Treaty cooperation variables are described in our analysis as: (1) _Treaty/no-treaty_ (a dichotomous variable indicating whether or not there is (are) an existing treaty (treaties)—1, or not—0); (2) _Number of treaties_ signed between the river riparians (an integer ranging between 0-N that measures the number of treaties on that river); and (3) _Share of water allocation issues_ in treaties (while each treaty may address several scarcity issues--water allocation, hydropower generation, pollution control, flood protection, and general issues--in this paper we measure the share that the water allocation issue commands among the sum of issues addressed in all the treaties).<sup>xv</sup>

# **Water Scarcity**

As explained earlier, our empirical application utilizes physical water scarcity measures based on national level data.<sup>xvi</sup> We use the index of water per capita as the basis for calculating water scarcity. In this index we capture both the actual scarcity and the perceived scarcity. Decision makers in a particular state consider not only the level of water availability per capita at present, but also future values, calculated based on population growth predictions. We estimate a hyperbolic water scarcity function for the period 1955-2050. Using data on water availability per capita from Population Action International (1993, 1995, 2004), for 1955, 1975, 1990, 2000, and predictions based on medium population growth rates (United Nations 2000) for 2025 and 2050.<sup>xvii</sup> For each state we estimated the following water scarcity function: _W_ = α⋅ _t_ β where _W_ is the available annual water per capita (cubic meters/year), α is an estimated intercept, _t_ is year, and β is an estimated coefficient of decrease in water per capita over time. We argue that β < 0 , means that the function _W_ decreases over time (mainly due to population growth), and that the larger the β the higher the scarcity level the state faces. To demonstrate the possible severity
Voitchovsky, they may be all true, but are best captured by different parts of the income distribution.

Marrero and Rodriguez (2012, 2013) decompose total inequality into inequality due to inequality of opportunity, that is, to the circumstances outside one’s control such as parental education, race, being foreign-born, and the residual, assumed to be due to effort and luck. In two separate applications, one to the European Union member countries and one to the states of the US, they find strong evidence that levels of inequality of opportunity are negatively correlated with growth while the residual (“good inequality”) helps growth.

The evolution of data has played an important role in shaping the empirical inequality-growth literature. A lack of conclusive results may in part be attributed to the limitations of the data that was available at the time. At best, after the much-used Deininger Squire (1996) dataset was released, it consisted of Ginis and in some cases of quintiles of disposable or gross income, available for many countries only at long and uneven intervals.<sup>13</sup> The dramatic improvements in data quality and availability, both through household surveys and fiscal data on top incomes, have made it possible to deconstruct inequality and its relationship to growth.

# **3.  The data and regression framework**

We use individual level data from the Integrated Public Use Microdata Survey (IPUMS) which is a large micro-census conducted once per decade (see Ruggles _et al._ , 2010). We use six surveys made over the period of 50 years at regular decennial intervals: in 1960, 1970, 1980, 1990, 2000 and 2010. The IPUMS data provide a 1 percent or 5 percent samples from every state (1 percent for the years 1960, 1970 and 2010; 5 percent for the years 1980 to 2000), which effectively makes it a micro-census. The obvious advantage of working with such large data sets is that it reduces sampling error to a minimum.

> 13 There were many other, technical, problems that were insufficiently appreciated at the time: Ginis calculated across households for household income do differ from Ginis calculated across individuals’ household per capita incomes.  This aspect was either badly documented in Deininger-Squire and World Income Inequality Dataset (WIID), or more commonly, ignored by researchers. The adjustment for consumption or income Gini was done using a dummy variable (most famously by Li, Squire and Zou, 1998).

7
ideas may be as important,<sup>ifnot moreimportant, thanthetransmissionofnew</sup> inputs.

There is indeed some empirical<sup>evidencesuggestingthattradeintegration</sup> has beneficial effects on<sup>theleveland growthrateof output.StudiesbyFrankel</sup> and Romer (1999) and Irwin<sup>and Tervio (2002)have shown that countries that are</sup> more open to trade have<sup>higher incomes.In line withmorerecent theoriesof trade</sup> and growth, Klenow and<sup>Rodriguez-clare(1997) usedacomputablegeneral</sup> equilibrium framework<sup>thataccountsforproductvarietyeffectsthrougha</sup> production function in which<sup>a lower numberof intermediateinputvarieties results</sup> in productivity losses and<sup>lower output, despitethesame capital andlabor inputs.</sup> They found that accounting<sup>forsucheffectscanquadruplethestatic gainsfrom</sup> unilateral trade liberalization.<sup>Coe,Helpman,andHoffmaister(1997)foundthat</sup> trade flows provide a conduit<sup>through which advancedproductiontechniquesand</sup> technological knowledge<sup>are transmittedacrosscountries.3</sup> Wacziarg (1998) found that investment is<sup>themostimportant channel throughwhichopenness</sup> raises growth, accounting<sup>formorethan60percent of the totaleffect. Moreover,</sup> the empirical evidence<sup>alsosuggeststhat theleaming-by-doingandgrowth</sup> effects of these spillovers<sup>are largestin countries withhigher levels of education.</sup> Finally, a recent study by<sup>the WorldBank(2002)suggests that thecountries that</sup> have opened themselves<sup>themosttotradeinthelast twodecades(the"new</sup> globalizers") have, on average,<sup>grownthe fastest.Thesecountriesmanagedto</sup> reduce import tariffs, on average,<sup>by 34 percentagepointssince1980,compared</sup> with only 11 percentage points<sup>for thosedevelopingcountriesthat,onaverage,</sup> saw no growth in per capita<sup>incomesover theperiod.Because tradeisgoodfor</sup> growth, and growth is allegedly<sup>goodfor thepoor (on average,increasedgrowth</sup> raises the incomes of the<sup>poor inproportionto those of the population as claimed</sup> by Dollar and Kraay (2001))<sup>the study concludes thattrade(or,moregenerally,</sup> extend the Grossman-Helpman<sup>framework toaccountforimperfect competitionandscale</sup> economies in the research<sup>anddevelopment sector.</sup> 5
Figure 2: Economy <u>groupings, by average degree of restrictiveness to convert and transfer currency</u>

|Average restriction<br>type|Number of<br>economies||Econ<br>|omies<br>||
|---|---|---|---|---|---|
|Unrestricted<br>(or with minor<br>administrative<br>requirements)|53|Afghanistan<br>Armenia<br>Australia<br>Austria<br>Bolivia<br>Bulgaria<br>Cambodia<br>Canada<br>Chile<br>Congo, Dem.<br>Rep.<br>Costa Rica<br>Croatia<br>Cyprus|Czech Republic<br>Dominican<br>Republic<br>Ecuador<br>Egypt, Arab Rep.<br>France<br>Georgia<br>Germany<br>Greece<br>Guatemala<br>Hong Kong SAR,<br>China<br>Indonesia<br>Iraq|Ireland<br>Italy<br>Japan<br>Jordan<br>Kenya<br>Kosovo<br>Kyrgyz Republic<br>Malaysia<br>Mauritius<br>Mexico<br>Montenegro<br>Netherlands<br>New Zealand<br>Nicaragua|Peru<br>Poland<br>Romania<br>Saudi Arabia<br>Singapore<br>Slovak Republic<br>Spain<br>Taiwan, China<br>Turkey<br>Uganda<br>United Kingdom<br>United States<br>Yemen, Rep.<br>Zambia<sup>2</sup>|
|Administrative<br>requirements<br>(or with occasional<br>moderate<br>restrictions)|21|Albania<br>Azerbaijan<br>Bosnia and<br>Herzegovina<br>Brazil<br>Burundi|Colombia<br>Ghana<br>Honduras<br>Kazakhstan<br>Korea, Rep.<br>Macedonia,FYR|Madagascar<br>Moldova<br>Nigeria<br>Papua New<br>Guinea<br>Philippines|Russian<br>Federation<br>Rwanda<br>Serbia<br>Tanzania<br>Thailand|
|Moderate<br>restrictions|19|Algeria<br>Argentina<br>Bangladesh<br>Belarus<br>Burkina Faso|Cameroon<br>Chad<br>Côte d'Ivoire<br>India<br>Mali|Morocco<br>Mozambique<br>Pakistan<br>Senegal<br>South Africa|Sri Lanka<br>Tunisia<br>Ukraine<br>Vietnam|
|Heavy restrictions|5|Angola<br>Ethiopia|Nepal|Solomon Islands|Venezuela, RB|

Note: see Annex 1 for the quantitative methodology used to establish the classifications.

# Restrictiveness by economy size and income level

Economies that are larger and that have higher incomes per capita tend to have fewer foreign exchange restrictions as measured by CTC. The relation between the average indicator score per economy and GDP per capita (Figure 3) is much stronger than the relation between the CTC indicators and economy size (Figure 4). This suggests that a relaxation of foreign exchange restrictions related to FDI tends to happen as countries progress to higher overall levels of development.

Despite this trend of higher-income economies being more open (Figure 5), there is significant variation in restrictiveness between economies at similar income levels. All high-income economies do have unrestricted regimes as measured by CTC. But many upper-middle-income economies impose substantive restrictions on many FDI transactions (Figure 6). The foreign exchange regimes are unrestricted in 45% of the 29 upper-middle-income economies covered, but 24% impose moderate or heavy restrictions across most transactions related to FDI (per the economy classifications in Figure 2).

2 Reforms to Zambia’s foreign exchange regime in 2013 introduced new regulations that would be scored as restrictions per the CTC methodology. As the CTC data were gathered and reviewed through mid-2012, Zambia’s classification here as ‘Unrestricted’ is based on the previous regime.

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Table B.3: The Impact of Initial Conditions on Employment of Refugees: Baseline Estimates

||(7)|(7’)|(8)|
|---|---|---|---|
|Unemployment rate|-0.0205<sup>_∗∗∗_</sup>|-0.0232<sup>_∗∗_</sup>|-0.0224<sup>_∗∗_</sup>|
||(0.00685)|(0.0106)|(0.0104)|
|Network|0.00164|0.000620|0.000782|
||(0.00265)|(0.00512)|(0.00509)|
|Attitudes|0.0278<sup>_∗∗∗_</sup>|0.0216<sup>_∗∗∗_</sup>|0.0213<sup>_∗∗∗_</sup>|
||(0.00623)|(0.00814)|(0.00811)|
|Current year FE|Yes|Yes|Yes|
|Country of birth FE|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|
|Canton of arrival FE|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|
|Education FE|No|No|Yes|
|Sample|Main|Restricted|Restricted|
|Observations|696135|127737|127737|

Dependent variable is Employment dummy. All regressions also include age, age squared, gender, marital status. Standard errors are clustered at the level of Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 59
# **1.  Introduction**

This paper presents the latest update of our aggregate governance indicators.<sup>1</sup> The indicators measure six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. They cover 213 countries and territories for 1996, 1998, 2000, and annually for 2002-2005. The indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 31 separate data sources constructed by 25 different organizations. We assign these individual measures of governance to categories capturing key dimensions of governance, and use an unobserved components model to construct six aggregate governance indicators in each period. We present the point estimates of the dimensions of governance as well as the margins of errors for each country and period.

We begin by describing the data used to construct this round of the governance indicators in Section 2. As discussed in more detail below, we have added one new data source in this round. In the interests of greater comparability over time, we have included it in our past estimates as well, and also dropped a few minor data sources we have used in the past but are no longer regularly available. We have also made minor changes to the scaling of our indicators in earlier years in order to make over-time comparisons of the aggregate indicators more robust to year-to-year changes in the composition of the sample of countries included in the indicators. These revisions have resulted in minor changes to our historical biannual data for 1996-2004, and so the new dataset described here supersedes previous releases. Going forward we plan to continue to update our governance indicators on an annual basis, in order to enable a more timely monitoring of governance worldwide. While we recognize that in many countries year-over-year changes in governance are small, by moving to annual data we aim to assist users to spot and monitor those cases where there are substantial changes in the short term.

> 1 This paper is the fifth in a series of estimates of governance across countries.  Each of these papers has reported on data updates as well as new empirical results using the governance indicators Documentation of the previous rounds of the indicators and analytical findings can be found in Kaufmann, Kraay, and Zoido-Lobatón (1999a,b, 2002), and Kaufmann, Kraay, and Mastruzzi (2004, 2005, 2006).  Kaufmann, Kraay, and Mastruzzi (2004) contains a complete description of the statistical methodology underlying the indicators.

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|__________________<br>Table 8: Export sta|_____________<br>rters and non-|__________<br>starters thr|_______________<br>ee years after th|__________________<br>e start|______|
|---|---|---|---|---|---|
|Country<br>__________________|Year of<br>start<br>_____________|Labor p<br>premia<br>(percen<br>__________|roductivity growth<br>of export-starters<br>t) [p-value]<br>_______________|No. of<br>No.<br>observ.<br>star<br>__________________|of<br>ters<br>_______|
|Austria|2002|0.06|[0.11]|551|16|
|Belgium|1999|38.27|[0.02]|116|5|
||2000|-18.61|[0.04]|130|7|
||2001|18.22|[0.08]|167|15|
||2002|20.29|[0.15]|157|17|
|Chile|1993|2.10|[0.526]|1,366|50|
||1994|1.92|[0.657]|1,355|50|
||1995|1.85|[0.577]|1,250|24|
||1996|1.59|[0.759]|1,158|34|
|China|2001|-4.65|[0.00]|24,923|475|
||2002|-4.37|[0.00]|<br>24,918|602|
|Colombia|1984|-9.96|[0.03]|2,053|27|
||1985|-0.06|[0.99]|2,164|36|
||1986|-510|[037]|2254|28|
||<br>1987|.<br>3.42|.<br>[0.46]|,<br>2,263|<br>40|
||1988|4.60|[0.36]|2,188|41|
|Denmark|1998|NA||2|1|
||1999|NA||2|1|
|France|1993|0.08|[0.94]|1,385|204|
||1994|0.02|[0.99]|1,427|211|
||1995|038|[067]|1423|194|
||<br>1996|.<br>-0.64|.<br>[0.51]|,<br>1,350|<br>149|
||1997|-1.12|[0.33]|1,409|143|
||1998|-2.30|[0.05]|1,464|173|
||1999|-0.22|[0.86]|1,532|167|
||2000|-0.05|[0.96]|1,526|157|
||2001|-1.60|<br>[0.11]|<br>1,550|183|
|Germany (West)|1998|-0.46|[0.80]|6,517|194|
||1999|-0.32|[0.87]|6,180|171|
||2000<br>|0.24<br>|[0.90]<br>|6,719<br>|195<br>|
||2001|1.15|[0.53]|6,524|180|
|Germany (East)|1998|-076|[075]|1764|60|
||<br>1999|.<br>7.94|.<br>[0.02]|,<br>1,866|<br>69|
||2000|-7.13|<br>[0.02]|<br>1,968|61|
||2001|-3.71|[0.19]|1,981|55|
|Italy|1992|4.15|[0.03]|1,279|147|
||1993<br>1994|4.32<br>6.06|[0.05]<br>[0.06]|1,128<br>889|144<br>85|

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induced by a given price change, as well as for changes in the price of nontradables. To summarize, the price elasticity of money-metric utility varies across households according to: (1) consumption shares, _νj_ , _including_ that of services, _νS_ ; (2) income shares due to land ( _λ_ Π) and manual labor ( _λW_ ); and (3) sectoral labor shares, the _βj_ (through _ψ_ and _δ_ ), for the district, or for the labor market more generally.

At a theoretical level, the welfare implications of a food price change look quite different when labor markets are effectively segmented.<sup>30</sup> In particular, the counterpart to equation (13) has where _λA_ is the share of income derived from agriculture (both from land and manual labor) and _λNA_ is the share of income derived from nonagricultural manual labor. Here, obviously, the sector in which the household supplies its manual labor matters. Inasmuch as _φ < ψ <_ 1 _/_ ( _αL_ + _αK_ ), landless households working in agriculture stand to gain more from higher food prices than those working outside of agriculture.

In what follows, I consider the distributional consequences of a _uniform_ percentage increase in all agricultural commodity prices _relative_ to the price of manufactures, the numeraire. According to equation (12), the elasticity of money-metric utility or household welfare with respect to this price is simply _ε_ = Ω _− νA_ , where _νA_ is the expenditure share of agricultural goods (essentially the food share); in the counterfactual segmented labor markets scenario, _ε_<sup>_′_</sup> = Ω<sup>_′_</sup> _− νA._

# **4.2 Baseline results**

Drawing on a sample of around 61,000 rural households in 472 districts of the 18 major states from the nationally representative consumer expenditure survey (CES) collected as part of

> 30Artuc et al. (2010) make a similar point in the context of trade shocks using a dynamic model of costly intersectoral labor mobility. Perfectly segmented labor markets can be viewed as an extreme case, retaining the analytical convenience of a static model.

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# **1 Introduction**

The global digital economy accounted for 15.5 percent of the world’s GDP in 2016 ($11.5 trillion). Yet, not everybody has benefited equally from the arrival of digital technologies. There are still huge disparities across and within countries when it comes to the adoption and usage of digital technologies (Comin and Mestieri 2018). While more than half of the world’s population now has access to the internet, the penetration rate in the least developed countries is only 15 percent, or 1 in 7 individuals (World Development Report 2019).<sup>1</sup> The benefits of adopting digital business solutions like email, launching a business website, or connecting to two-sided digital platforms can be substantial especially for firms (Goldfarb and Tucker 2019). The transfer of information and data over the internet helps reduce production costs and expand demand for a firm’s goods and services and thus for its factors of production. Reductions in search costs enable buyers and sellers of products or services to get better access to the other side of the market, by increasing the speed or efficacy with which firms find workers or input suppliers (De Loecker 2019). Digital business solutions also help expand market opportunities. Reductions in search, transaction, or tracking costs allow firms to overcome geographical barriers, penetrate new markets, and enlarge the volume of trade (World Development Report 2020).

The existing evidence on the impact of digital-technology adoption on productivity and factor demand, however, is surprisingly thin, especially for developing countries. It is even thinner when it comes to quantifying these effects using firm-level data. This paper aims to fill these gaps in the literature. Specifically, we estimate the effects of adopting digital business solutions, namely email to communicate with clients and suppliers and launching a business website, on firm-level revenue-based total factor productivity (TFPR) and demand for labor and capital. We rely on publicly available data from the World Bank’s Enterprise Survey database (WBES), which collects information on sales, factor and input usage, exporting status, managerial experience, and digital-technology adoption at the manufacturing

> 1. https://www.worldbank.org/en/topic/digitaldevelopment/overview.

2