rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. Evolution of foreign direct investment and telephone penetration**

<!-- Start of picture text --> 180 8<br>160 7<br>140<br>6<br>120<br>5<br>100<br>4<br>80<br>3<br>60<br>2<br>40<br>20 1<br>0 0<br>FDI % GDP EU2005 new members<br>FDI % GDP Turkey<br>Mobile and fixed phone subscriptions EU2005 new members<br>Mobile and fixed phone subscriptions Turkey<br>Source: authors' elaboration from UNCTAD, ICRG<br>Net FDI inflows to GDP<br>Fixed and mobile suscriptions per 100<br><!-- End of picture text --> A third opportunity for Turkey to increase its appeal to foreign investors would imply strengthening institutions. The cross-country econometric analysis presented in the previous section suggests that, over the past two decades, government stability and law and order have been key determinants of foreign direct investment in Eastern European countries. The International Country Risk Guide _government stability_ index, composed of measures of government unity, legislative strength and popular support, has been in Turkey higher than the regional average. This reflects the political stability the country has enjoyed under the _Adalet ve Kalkınma Partisi_ era, inaugurated in 2001, which may have arguably helped increasing FDI inflows.

On the other hand, however, Turkey has traditionally underperformed in terms of _law and order_ , understood as a product of the strength and impartiality of the legal system and the popular observance of the law. The _law and order_ index for Turkey declined starting in 2011, in a context of a series of controversial judiciary system reforms, which may have undermined the separation of power in the country. It could be argued that these institutional indexes cannot _per se_ reflect the complex and varied nature of institutions across the world (e.g. think of China). Nevertheless, in the same way that sovereign risk ratings by credit agencies influence investment decisions (even if they may present caveats), international investors do take these institutional indexes into consideration when deciding among a few countries in the same region to host their subsidiaries. Thus, any action taken by the Turkish authorities to correct the perceived deterioration in _law and order_ would be likely to help enhance FDI attraction.

25
the price level, converted into dollars at the nominal exchange rate. Comparisons of the price level, adjusted for "structural" differences in nontraded goods prices, can he used to construct indices of outward orientation, with a lower price level indicating a moru outward economy<sup>10</sup> .

Comparisons of price dispersion with price-based openness measures also suggest that for middle income countries, economies with more relativo price dispersion have _louwe,_ price levels. Two pricebased measures of orientation were used: David DoUar's (1991) nieasure based on a sample of 95 developing countries over the period 1976-1985, and a measure drawn from a broader sample including developed economies froni 1965-1985. Both measures, listed in Table 5. confirni the earlier iesult that interventionist middle .ncome countries are also relatively more outward oriented. Again, low and high income countries show the opposite result; high intervention implies relative inward orientation.

Table 6 plots relative price dispersion against the ten year average of price overvaluation"l. No clear pattern emerges, as expected given the correlations described above. The table does help to clear up some anomalies: Both Sri Lanka and Peru have very low price levels, for example, causing them to be considered outward oriented. But they are definitely not liberal; they have the highest measures of price dispersion among the middle income countries.

# 5.3 World Bank Measure of Outward Orientation

If outward orientation is not correlated for middle income countries with trade intervention as measured by relative price dispersion, then what is to be made of taxonomies of trade regime

> '<sup>0</sup> Sticii **nl,ua.IIres ar( .Ii)j('rt** t(, **lII0o%** % 1llv **fit** (1 hal l **lo** lla **io Iif** I itoliliial **exchiaiige rate (alat-(d** lM! **lv iacroecoltoliiC** policies. Sollle attellihp is tliadel to disel **ti he l** or **Oaslrf- nioi,niI f ,challge rate dlianige b%** a%eragillg tlte ilea-sire **over** a period of ev-sral vears ee Dollar. D **1!)!)ihi** "' rne a erage of tl.e p **rv0ou9 teli** yea r' is **highlb** correlatedl wit llthe val **u** for I )9t0 Somie **eonint rie,** liadi **1 ttI** prices significantly higher or lower **lianl** tIheir **tell** Year averages I efe **IalHe lii**

24
<!-- Start of picture text --> — 3month-lockdown — 5 month-lockdown<br>120<br>2 ~~<br>2 7S<br>2 ~~).<br>o 9 ~~<br>rs ~<br>= ~~.<br>Sc<br>><br>6<br>o<br>2 ee<br>><br>oO 9<br>c=<br>o<br>Sg2<br>fa)<br>0 30 60 90<br>Wage subsidy (% of payroll covered)<br><!-- End of picture text --> <!-- Start of picture text --> Revenue Loss: * 3 month-lockdown<br>*<br>2<br>3°<br>s<br>Ss<br>3<br>05<br>oD<br>= 2<br>3<br>><br>© *<br>=S 4 * °<br>o2 *<br>Sy<br>a<br><Da<br>0<br>T 8 9<br>log(GDP per capita)<br><!-- End of picture text -->
Table 3 also compares the level of labor productivity between informal firms of different types such as firms whose owners have higher education vs. those that do not, male- vs. femaleowned firms, et cetera (columns 1-6). The goal is to explore the degree of heterogeneity in the productivity of informal firms. We do find significant productivity differences within the informal sector associated with the education level of the firm owner, firm size, access to finance, to cite a few. We provide additional evidence on how the productivity gap between informal and formal firms may vary with basic firm characteristics such as age, size, et cetera (Figure 1).

# 3.2 _Empirical specification and controls_

The empirical estimation of the productivity gap relies on the following equation: where the subscript i denotes the firm, Y is the dependent variable (labor productivity), X is the main explanatory variable (informal firm dummy), � Z������ is a set of firm-level controls, XZ� are interaction terms that aim to capture how the productivity gap may vary depending on basic firm characteristics such as firm’s age, firm-size, et cetera (details below), CFE is country fixed effects, and ε is the error term. The equation is estimated using ordinary least squares (OLS) method with Huber-White robust standard errors.

We use a parsimonious structure of controls partly due to data limitations and to limited research on the drivers of productivity in the informal sector.<sup>10</sup> Our choice of controls is guided by existing studies on the determinants of labor productivity, largely focused on formal firms.

> 10 The formal and informal surveys that we exploit are based on different questionnaires and, therefore, the overlap in the questions is limited to basic firm characteristics such as age, size, et cetera.

11
<!-- Start of picture text --> Figure 1: Margins of Error for the WGI, 2007<br>Political Stability and Absence of Violence<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>-2.5<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Percentile Rank<br>Control of Corruption<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>-2.5<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Percentile Rank<br>BEL COSTBOTS DENM SLOVENCANADA<br>CUBAUKRA BELA BULGAR IA ITALYGREECELIBYA CHILE HUNGAR Y GIUM ICAA RWANA ARK IA<br>INE RUS<br>Governance Rating<br>PARA<br>GEOR<br>GUAY<br>INDO THAIL ECUA GIA<br>B ZIMB NE AN DOR<br>ANG A SIA D<br>LA BWE<br>DESH<br>NIGER<br>IA<br>DEN<br>CA<br>N MARK<br>ADA<br>BE<br>CH<br>ILE<br>LGIUM<br>GR COHU N ITAL Y SLOVENIABOTSW ANA<br>EECE STA R GARY<br>BUCU ICA<br>Governance Rating TH GE BA<br>U K IN D ORGIA LGARIA<br>BEEC LIB AILAND<br>I ZIM B BA NIGERI A PAR LARUSUADOR YA RAINEONESIA<br>RAQ AGUAY<br>ABWE NGLADESH<br><!-- End of picture text --> 27
# **<mark>Table 1 - Competitive Advantage in Foreign Banking</mark>**

The dependent variable is the ratio of the sum of assets of foreign banks in host country _i_ that are owned by banks from source country _j_ , divided by the total amount of bank assets in host country _i_ . _Insthost_ is the simple average of six indicators of quality of institutions in the host country in 2000 as measured by Kaufmann, Kraay and Mastruzzi (2005). _DiffInst_ is the absolute difference between quality of institutions of source and host countries. _CompAdv_ is defined as Insthost*(Instsource-Instcomp), see main text for explanation. _Collinks_ is dummy which is 1 if host and source country have had colonial links either between colonizer and colony or between those countries having been colonized by the same colonizer. _Border_ is dummy which is 1 if the countries share a border. _Comlang_ is dummy which is 1 if the countries share the same language. _Distance_ refers to the log of the distance between the host and source countries. Trade is the log of export plus import in 2000 between the two countries. _Legaldif_ is a dummy which is 1 if the origin of the legal system of host and source country differ. _GDPhost_ is the log of the host country's GDP in 2000. _FinDepthhost_ is the log of M2 as percentage of GDP in the host country in 2000. _Entryres_ is dummy which is 1 if foreign bank entry is restricted. _GDPsource_ and _GDPcapsource_ are the log of respectively GDP and GDP per capita in the source country in 2000. Dregion are dummies for each region, South-Asia is the ommited region. All regressions include a constant. Coefficients are marginal effects. The robust p-values appear in brackets and ***, ** and * correspond to one, five and ten percent level of significance respectively.

||(1)|(2)|(3)|
|---|---|---|---|
|_Insthost_|-0.012||-0.004|
||[0.429]||[0.788]|
|_DiffInst_||0.019||
|||[0.133]||
|_CompAdv_|||0.042 ***<br>|
||||[0.000]|
|_Collinks_|0.652 ***|0.640 ***|0.534 ***|
||[0.000]|[0.000]|[0.000]|
|_Border_|0.023|0.025|0.048|
||[0.523]|[0.492]|[0.197]|
|_Comlang_|0.175 ***|0.171 ***|0.167 ***|
||[0.000]|[0.000]|[0.000]|
|_Distance_|-0.136 ***|-0.136 ***|-0.121 ***|
||[0.000]|[0.000]|[0.000]|
|_Trade_|0.010 ***|0.010 ***|0.008 ***|
||[0.000]|[0.000]|[0.001]|
|_LegalDif_|-0.002|-0.004|-0.018|
||[0.901]|[0.811]|[0.273]|
|_GDPhost_|0.027 ***|0.027 ***|0.025 ***|
||[0.000]|[0.000]|[0.000]|
|_FinDepthhost_|-0.021|-0.020|-0.019|
||[0.183]|[0.183]|[0.181]|
|_Entryres_|-0.040 **|-0.040 ***|-0.037 ***|
||[0.013]|[0.008]|[0.009]|
|_GDPsource_|0.054 ***|0.054 ***|0.052 ***|
||[0.000]|[0.000]|[0.000]|
|_GDPcapsource_|0.052 ***|0.042 ***|-0.018|
||[0.000]|[0.005]|[0.303]|
|_Dregion=East Asia and Pacific_|0.410 ***|0.415 ***|0.461 ***|
||[0.005]|[0.004]|[0.002]|
|_Dregion=Europe and Central Asia_|0.209 **|0.214 **|0.247 **|
||[0.024]|[0.020]|[0.010]|
|_Dregion=Latin Americ and Caribbean_|0.441 ***|0.445 ***|0.431 ***|
||[0.000]|[0.000]|[0.000]|
|_Dregion=Middle East and Northern Africa_|-0.016|-0.015|0.001|
||[0.773]|[0.787]|[0.987]|
|_Dregion=Sub-Saharan Africa_|0.519 ***|0.519 ***|0.501 ***|
||[0.000]|[0.000]|[0.000]|
|No. Obs.|5,532|5,532|5,532|

17
# **Figure 2 Procyclicality of government expenditures**

# **A. Fiscal procyclicality: EMDEs versus advanced economies**

# **B** . **Fiscal procyclicality: EMDE commodity exporters versus commodity importers**

<!-- Start of picture text --> Correlation<br>1.0<br>0.8 ***<br>0.6<br>0.4 ***<br>0.2<br>0.0<br>-0.2<br>-0.4<br>AEs EMDEs<br><!-- End of picture text --> <!-- Start of picture text --> Correlation Commodity importers<br>0.6 *** *** ***<br>0.4<br>0.2<br>0.0<br>Metal<br>Energy<br>Commodity exporters Agriculture exporters exporters exporters<br><!-- End of picture text -->

# **C. Fiscal procyclicality in commodity exporters, by regions**

# **D. Fiscal procyclicality in commodity-exporters, by income groups**

<!-- Start of picture text --> Correlation<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>SAR SSA LAC EAP ECA MNA AEs<br><!-- End of picture text --> <!-- Start of picture text --> Correlation<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>LICs FCSs LMCs UMCs HICs<br>F. Share of countries with procyclical fiscal<br>policy<br>Percent Commodity importers<br>100 Commodity exporters<br>80<br>60<br>40<br>20<br>0<br>1994 1997 2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

# **E. Shifts in fiscal procyclicality, 1980-2020**

**F. Share of countries with procyclical fiscal policy**

<!-- Start of picture text --> Correlation 1980-2006 2007-2020<br>0.8 *** ***<br>0.6 ***<br>0.4 ***<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>LICs FCS<br>exporters importers<br>Commodity Commodity<br><!-- End of picture text --> _Sources_ : Arroyo Marioli and Végh (2023); International Monetary Fund; World Bank.

28
67 tATIN AMERICA AND THE CARIBBEAN Poputation projection (thousands) Susiry for 25 Year Periods

|AGE GROIP|1985|2000|2025|2050|2075|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|TOTAL N+F|398867|529974|720543|834917|874500|893678|905606|910743|
|**MALES**|||||||||
|0-4|27946|29724|29200|29174|29219|29286|29280|29261|
|5-9|25309|29272|28760|28977|29100|29181|29216|29233|
|10-14|23434|28039|28345|28926|29071|29147|29186|29212|
|15-19|21416|27129|28482|28809|29034|29089|29139|29176|
|20-24|19388|24669|28779|28810|28923|28984|29065|29118|
|25-29|16305|22624|28844|28658|28731|28853|28971|29053|
|30-34|13677|20454|28400|28187|28511|28732|28888|28985|
|35-39|11087|18394|27005|27645|28351|28621|28791|28901|
|40-44|9000|15365|25873|27589|28084|_28468_|28641|28771|
|45-49|7675|12729|23203|27574|27837|28165|28369|28537|
|50-54|6480|10078|20788|27074|27216|27588|27890|28103|
|55-59|5468|7855|18007|25653|25893|26617|27068|27324|
|60-64|4317|6264|15059|22834|23950|25150|25716|25990|
|65-69|3178|4752|11173|19644|21663|22821|23555|23828|
|70-74|2310|3387|7658|14775|18402|19495|20222|20501|
|75+|2329|3384|7392|17528|26634|29997|32311|33213|
|TOTAL|199320|264118|356968|411858|430620|440196|446309|449207|
|FEMALES|||||||||
|0-4|27084|28833|28270|28082|28069|28087|28045|28005|
|5-9|24718|28536|27943|27956|28002|28019|28012|28001|
|10-14|22940|27410|27600|27950|28010|28011|28005|28000|
|15-19|20987|26523|27815|27988|28025|27994|27990|27993|
|20-24|19179|24296|28227|28105|27994|27951|27969|27982|
|25-29|16271|22409|28433|28072|27891|27893|27927|27968|
|30-34|13728|20345|28147|27717|27760|27844|27900|27950|
|35-39|11139|18505|26921|27301|27695|27815|<br>27870|27925|
|40-44|9129|15634|25891|27394|27642|27774|27816|27879|
|45-49|7831|13096|23509|27601|27609|27648|27704|27791|
|50-54|6732|10482|21386|27464|27318|27372|27511|27621|
|55-59|5782|8387|1897|26633|26535|26927|27207|27349|
|60-64|4690|6908|16599|24605|25411|26310|26714|26870|
|65-69|3579|5530|13088|22264|24212|25227|25766|25927|
|70-74|2722|4188|9683|18072|22140|23261|23869|24062|
|75.|3036|4776|11093|25855|39567|45349|48993|50212|
|TOTAL|199547|265856|363576|423059|443880|453482|459297|461536|
|BIRTH RATE||26.1|18.6|14.8|13.6|13.2|12.9|12.7|
|DEATH RATE||6.7|6.4|9.0|11.7|12.3|12.4|12.5|
|NET MIGRAT|ION RATE|-.5|-1|.0|.0|.0|.0|.0|
|**GROWTH**RATE||1.89|1.23|.59|.19|.09|.05|.02|
|TOTAL FERTI|LITY|3.180|2.270|2.092|2.090|2.088|2.080|2.066|
|**e(O)**- BOT|H SEXES|68.05|72.01|74.19|75.75|77.65|78.91|79.27|
|**INR**- **BOTH**|SEXES|40.3|24.4|17.2|15.0|13.5|11.1|7.3|
whether one takes a normative or non-normative view; and holding normativity at bay even just for a moment is important to seeing the lived world of a bureaucracy.

In addition to this point about the normativity of ideal types, a second theme can be drawn out of the literature from anthropology: that of the fragmentary nature of institutions. A common theme in recent anthropology of bureaucracy is that government, development, and other institutions are heterogeneous rather than monolithic, riven with conflict and instability, and staffed by people who vary by more than simply their hierarchical position (Bierschenk and Olivier de Sardan 2014; Blundo 2014; Herzfeld 2005; Hull 2012b; Mathews 2011; Mosse 2005; Li 2007; Robbins 2000; Willott 2014; see also Warner and Low 1976; cf. Scott 1998). Indeed, while the public might envision a totality, bureaucrats themselves might experience instead a set of fragmented units, social groups, or even floors of a building (Vike 2012). This insight emerges in anthropology partly from the influential work on the state, which challenged the notion that the state is “a thing” and instead proposed that it is merely an idea—an effect of discourses and practices—and therefore has no singular purpose (Mitchell 1999; Abrams 1988). Ethnographic treatments of bureaucratic practice have partially affirmed this perspective, demonstrating the contingency, partiality, and materiality of bureaucratic knowledge (e.g., Dery 1998; Hoag 2010; Hull 2012b; Mathews 2011; Mosse 2005).

These insights may be somewhat biased, however: the bulk of deep ethnography of institutions has focused on street-level, service-oriented bureaucracies to the neglect of mid- and upper-level officials. One reason for this is that street-level workers are the functionaries of the state with whom conventional anthropological subjects (i.e., the subaltern) most commonly engage. Understanding the social worlds of such bureaucrats therefore helps anthropologists to make sense of existing anthropological studies on the ways that the state impacts “everyday 9
**Figure 4: World and Wholesale Price Margins: Palm and Wheat (2 week lagged crude price)**

<!-- Start of picture text --> Wholesale-World Price Margin: Lagged World Price<br>01jan2008 01jan2009 01jan2010 01jan2011<br>mdyr<br>Palm Wheat<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Figure 5: Palm Oil Import ('000 mt)**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>2009-10 2011-12<br><!-- End of picture text -->
3 future. For these households one may want to make improvements in the non-piped water distribution system to improve access to safe water.

Few studies have estimated residential water demand in developing countries. Using household survey data from 17 cities in Central America and Venezuela, Strand and Walker (2005) derive price elasticities for piped (non-piped) households equal to -0.3 and -0.1 (similar to that of many developing countries). Nauges and Strand (2005), using the same dataset, estimated water demand of non-piped households in four cities in El Salvador and Honduras, where the vast majority of the surveyed households relied on one water source only (private tap, public tap, public well, or truck). They found non-tap water demand elasticities with respect to total water cost (defined as the sum of water price and collection time costs) of between –0.4 and –0.7. Basani et al. (2004), using cross-sectional household-level data from seven provincial Cambodian towns, estimated the price elasticity of water demand of connected households to lie in a range between -0.4 and -0.5. Rietveld et al. (2000), using data from Indonesia, found a price elasticity for connected households of -1.2.

The present paper contributes to this literature by providing an empirical analysis of the water demand function of piped and non-piped households from Southwest Sri Lanka. Data come from a survey of 1,800 households conducted in August-October 2003. Section 2 describes the background and data. In section 3, we discuss the specification of the water demand models and estimation strategy. Estimation results are described in section 4, while policy implications and conclusions are found in section 5.

# **2. Background and data**

The population of surveyed households covered three districts in Southwest Sri Lanka: Gampaha, Kalutara and Galle. The survey was undertaken to support the design of two private sector transactions in this part of Sri Lanka which the then Government of Sri Lanka (GoSL) was proposing: one for the town of Negombo, north of Colombo, and one stretching along a coastal strip south of Colombo, from the town of Kalutara to the town of Galle. The population in these two service areas in 2001 was slightly more than 1.6 million<sup>3</sup> .

> 3 The total population considered in the Greater Negombo service area was about 367,000, while the service area covered by the coastal strip from Kalutara to Galle had a total population of 1,254,000 in 2001.
**Effects are robust to different specifications.** We include controls for pre-shock municipal characteristics interacted with year fixed effects to flexibly control for differential municipal trends. The variables in this vector include baseline night-light density measured in 1992; urban coverage, water bodies, and tree-cover loss observed in 2001; and proxies for political repression collected in 2004. As we illustrate in the main tables, our main results remain largely unchanged with and without the inclusion of these controls. A related concern is that the return migration of Colombians, whose historical presence in Venezuela motivates the instrument, may have independently affected local labor markets and economic conditions. To the extent that this channel operates through differential pre-existing economic trends across municipalities, it is absorbed by municipality fixed effects and baseline controls interacted with time trends, including baseline night-light and oil production controls that capture the primary economic shock driving both return migration and local economic conditions.

**Effects are not driven by differential government targeting.** A further concern is that the Venezuelan government may have disproportionately targeted municipalities with higher or lower foreign settlement shares through economic policies, potentially confounding our estimates. Three salient policy episodes are relevant during our analysis period: the Carnet de la Patria social welfare program, mass energy blackouts, and expropriations of private sector firms. We collect municipal-level temporal variation for each of these from multiple sources and include them as controls in Tables E.3 and E.4.<sup>21</sup> The main results remain unchanged, indicating that differential government targeting does not account for the estimated effects of forced migration.

> more than 4.8 million features as of 2018 (Sabbatino, 2018). This control also addresses concerns about the effect of gas flaring on night-light density.

> 21See Appendix B for a description of all data sources.

26