rafmacalaba/datause-encoder-data huggingface dataset:
Mastruzzi (2005) measures six dimensions of governance from 1996 to 2005: voice and accountability, political stability and absence of violence, government e¤ectiveness, regulatory quality, rule of law, and control of corruption. It covers 213 countries and territories for 1996, 1998, 2000, and annually for 2002-2005.

In unreported regressions, we consider OLS regressions using these data sets. We ...nd a signi...cant and positive correlation between the emigration rate and institutional quality indexes, but these regressions su¤er from a lot of shortcomings. First, it is di¢ cult to ...nd an appropriate baseline speci...cation, because di¤erent economic, political and cultural factors can be important in explaining the quality of institutions. As Alesina et al. (2003) noted, various explanatory variables have been used in the literature on the determinants of institutions, such as log of gdp per capita, legal origin dummies, religious variables, latitude, fractionalisation indices, etc. The main problem with these variables relates to the fact that the pattern of cross-correlations between explanatory variables cannot be ignored and that in many cases the results of cross-country regressions are sensitive to the econometric speci...cation. For example, they point out that their index of ethnic fractionalization is highly correlated with latitude and with the log of gdp per capita (which, in addition, is very likely endogenous). Moreover, legal origin dummies are highly correlated with religious variables etc. In a panel dimension instead, it is possible to control for unobservable heterogeneity and for all time-invariant variables a¤ecting institutional quality.

Another problem of cross-sectional analysis refers to endogeneity and reverse causality problems (i.e., bad institutions can cause migration). Attempting to confront the endogeneity issue directly requires ...nding a suitable instrument. This is not easy in our context. To properly instrument for migration we need a variable that is correlated with the emigration rate but not directly correlated with our endogenous variable, institutional quality. In the migration literature, country’s geographical features are often used to instrument for emigration. However, in the institutions and growth literature, the very same geographical characteristics, such as latitude or country size, are also used as determinants of institutions, which would seem to question their theoretical validity as candidate instruments. Finally, an additional problem in cross-section analyses has to do with the fact that institutional quality is a quite persistent variable, therefore a dynamic model would seem to be more suited to study the relationship between emigration and institutions. Moreover, several papers discuss the in‡uence of education on institutional quality, therefore it is worth to include 7
**The impact of property rights on households' investment, risk coping, and policy preferences:**

# **Evidence from China**

<!-- Start of picture text --> Klaus Deininger<br>and<br>Songqing Jin<br><!-- End of picture text --> World Bank, Washington DC University of California, Davis CA _We thank China 's State Statistical Bureau, in particular Zude Xie, Pingping Wang, Xinhua Yu, and CCER, especially Yang Yao, Justin Lin and Jing Lifor making available the data and assisting in the survey underlying this analysis We are also gratefulfor the helpful comments from Keiyjro Otsuka, Scott Rozelle and Michael Carter Support from the Norwegian ESSD Trust Fund (Environment Window) is gratefully acknowledged_
increase the average annual growth rate of physical capital per worker by 0.2 percentage point and enhance TFP growth by 0.6 percentage point per year.

# **4 Potential Growth Benefits of Greater International Trade Integration: A Comparative Statics Analysis**

This section uses the econometric analysis from section 3 to conduct a series of exercises through which we calculate the potential growth benefits of improving international trade integration in SubSaharan Africa. In this section, improving international trade integration involves illustrating the growth effects of: (a) expanding manufacturing trade volumes, (b) increasing export product diversification, and (c) increasing intra-regional trade. The (potential) improvement of the aforementioned dimensions of international trade integration implies comparing the trade performance of Sub-Saharan African countries (including country groups classified by their extent of natural resource abundance within the region) to a series of regional/international benchmarks.

Specifically, the comparative statics analysis conducted in this section consists of increasing the degree of manufacturing trade, reducing the Herfindahl index of export product concentration, and raising the extent of intra-regional trade to a determined benchmark. The comparative analysis is conducted across two different dimensions: first, we compute the growth benefits of narrowing the gap (using the latest time series observation for each country in the panel database) in manufacturing trade, export product diversification, and intra-regional trade for the whole region (and natural-resource-dependent country groups in Sub-Saharan Africa) vis-à-vis select regional benchmarks. Second, we compute the growth benefits from improvements in manufacturing trade, product diversification and intra-regional trade over time (say, over either a 10- or a 20-year horizon). Note that, in both cases, we compute the growth effects as well as the impact on physical capital accumulation, and TFP growth. Furthermore, the comparative statistical analysis is illustrative rather than conclusive because —among other simplifying assumptions— it is based on the implicit hypothesis that changes in trade do not lead to changes in any of the other growth determinants.

We will first look at the impact of narrowing the gaps in international trade integration on growth per worker, the accumulation of physical capital per worker and TFP growth for specific period in time (in our case, it is the most recent available). The benchmark countries/regions selected for this comparative exercise are: (i) the country leader in Sub-Saharan Africa (SSA), (ii) the 90<sup>th</sup> percentile of the world sample excluding SSA countries, (iii) the median value of the industrialized countries, and (iv) the median for the 24
forward. Finally, high malnutrition rates in the developing parts of the region suggest that a large number of households are highly vulnerable to food price shocks.

**Figure 1. World Food Prices and Inflation, 2006:12-2011:10**

(Index 1999:12=100) (%, year-on-year change) <!-- Start of picture text --> 300 80<br>280<br>60<br>260<br>40<br>240<br>220 20<br>200<br>0<br>180<br>-20<br>160<br>140 -40<br>IV I II III IV I II III IV I II III IV I II III IV I II III IV IV I II III IV I II III IV I II III IV I II III IV I II III IV<br>2007 2008 2009 2010 2011 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: World Bank, Development Prospects Group (DECPG).

The economics literature provides little empirical insight into how much international food price shocks impact on domestic prices in individual MENA countries. The exception is a working paper by Albers and Peeters (2011) presenting food price transmission effects for a few countries in the Mediterranean region. Also, Crowley (2010) finds that commodity prices exhibit a strong and mostly significant impact on domestic inflation for a cross-section panel of countries in MENA and Central Asia. Limited access to data has been a key reason for the absence of empirical work on MENA countries. This paper, therefore, fills a void by presenting new estimates of food pass-through coefficients from international to domestic prices for all MENA countries, using a data base collected from various sources throughout the region.

As international food prices increased significantly in recent years,<sup>3</sup> nearly all countries experienced an increase in their domestic food prices. Still, the question concerning the extent to which international food prices affect domestic food prices requires an empirical investigation. Domestic price levels can be affected not only by world food prices movements, but also by a number of country-specific factors, including food price policies, such as price controls and subsidies, trade and production policies, domestic supply chain issues, food stock management,

> 3 International food prices, measured by the World Bank food price index, increased by 76 percent from December 2006 to April 2011.

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the research facility itself was established in Kuujjuac, Arctic Quebec. Its objectives ares To develop an indigenous scientific and research capacity. To identify, initiate and conduct wildlife research/management projects which respond to Inuit needs and concerns and incorporate traditional and local environmental knowledge.

To collect, analyse and disseminate scientific and technical information to Inuit that result from the ongoing research in Northern Quebec.

To provide a base of operations for training and educating Quebec Inuit in wildlife research and management.

To act as an irformation/documentation centre on environmental and wildlife research/management issues.

Kuujjuac Research Centre (1987) Current research projects include the monitoring of subsistence and commercial fisheries, community harvest studies that record the take of individual hunters, evaluation of salmon stocks, beluga whale census, caribou physiology and studies of eider duck populations. A bilingual (Inukitut and English) management manual "Mitiq" has been produced for the c--mon eider, a traditional source of down throughout Northern Quebec (Kuujjuac Research Centre, no date).

The Centre has also pioneered methodologies for community- based harvest studies, by which data on all animals taken are recorded on a long term basis. This has yielded valuable information on seasonal changes in patterns of resource utilisation and enabled Inuit in northern Quebec to map and codify their relationship with the land. They are thus in a position to respond to development proposals with immediate hard data on existing practice. These methodologies have 'een adapted by Inuit in the Baffin region for their harvest studies Permanent staff include two non-Inuit scientific/training instructors and three Inuit manageritechnicians. The laboratory has trained young Inuit to assume responsibilties for field work, laboratory analysis and cartography. The centre has plans for expansion under a new name, Kuujjuac Research Centre (1987)

# 4.2.5. Arctic Research Center (ARC), Pond Inlet

This private research unit was not started by Inuit but by an enterprising scientist who decided to develop a locally based capability that would train and employ Inuit to gather and process environmental data. The language of instruction and operation is Inuktitut. The work concentrates upon marine stuCies in Lancaster Sound.

Economically and operationally, the ARC has been a success, collecting and delivering environmental data to government agencies and to hydrocarbon exploration companies. The majority of the field work is done
<!-- Start of picture text --> 901, = max eu Wor + C1(9o1) = >n-1i=1 O' (Mr esi _ Wotsi) 17 of<br>9124 = max ie > OW, + C2(O42) — €,(42) = >n-1i=2 O' (W214 _ Wr e+i)<br>2-91 - 0<br>n-1<br>Gort = 902? Wit + OW e441 + C2(Oo2) = > 6! (Woes: _ W114i)<br>i=2<br>1-91 2-1-2<br>Bort S 92,4 SF F12,<br>Vor = F(6o12), U2t =1- F(@122)<br>Oo1t+1 P1241 9o1t-2n+5) +» Fort 91 2t-2n45) +1 912,<br><!-- End of picture text -->
present the state-space representation that is used in this empirical method following the literature on time series as in Harvey (1994), Hamilton (1995), Engle and McFadden (1999) and Durbin and Koopman (2005). The state-space representation of the system consists of the transition equation which can be written using the standard matrix notation in this literature e for t = 1985; ::; 2013, where �t = ALt, Wt = [RUIIt�1], �t = u<sup>AL</sup> t , T = �2 and � = �1 under the restriction that the intercept is zero according to the theoretical model, with E (�t) = 0 and variance �� = �<sup>2</sup> AL<sup>.Themeasureequationcanberepresentedasthefollowingstate-spacerepresentationfor</sup> t = 1985; ::; 2013 in its standard notation in the literature, following Harvey (1994) where the indicator variable is Yt = KSt, the explanatory variables are denoted as according to equation (45) and the parameters are de...ned as Z = [�5] and where the stochastic variable "t = �u<sup>K</sup> t � is such that E ("t) is a constant and the variance is given by �" = ��<sup>2</sup> K�. Note that in the estimation method of the Kalman ...lter we impose the following restrictions according to de...nitions in equation (46): �0 = �0 = 0 and �3 = �1. With these three restrictions and 29 observations for the period 1985 to 2013 we have only 8 parameters to estimate which gives us at least 21 degrees of freedom. We use the same initial value A0 for AL1984 as in equation (47). The initial values for the parameters T , �, Z and � were obtained by peforming 1200 replications of uniform distributions on intervals that corresponded to the theoretical values expected. In Appendix B we describe the recursive optimizing algorithm that is used in combination with the Kalman ...lter to estimate the system of equations (50) and (51).

# 9 Empirical Results

In this section we report the main trajectories for the key unobservable ALt for the two di¤erent methods used to estimate this stock for the Colombian economy from 1985 to 2013.

41
<!-- Start of picture text --> 50 Flood depth-damage curve functions<br>——— based on Hallegatte et al. (2013)<br>—— based on Englhardt et al. (2019)<br>_ 40<br>BS<br>2<br>=)<br>% 30<br>2<br>i)<br>L<br>® 20<br>Do<br>14]<br>=<br>©<br>Ze)<br>10<br>0<br>0 50 100 150 200 250 300<br>flood depth (cm)<br><!-- End of picture text -->
without a household link to formal employment) compared to the counterfactual scenario where all operators are working out of free choice, all else equal based on observables. To the extent that Harare’s informal sector is homogenous (given what can be observed), this effect is thus applicable— it will be less generalizable in the absence of observing data on other characteristics, such as entrepreneurship or, say, a preference to work informally.

# VIII. Conclusions

Businesses in the informal sector may face differing risks of exposure to bribe requests than formal firms; it is not unreasonable to argue that such exposure operates differently outside of the formal economy. This paper puts forth a basic model where the informal sector’s risk of encountering a bribe occurs in an interaction characterized as unstructured harassment, as opposed to a rent extraction in the context of a formal firm soliciting an official service, such as a license or permit. The expectations of this basic model are that an inelastic ability to formalize—in short, a lack of opportunities outside informality—allows for more frequent extraction of bribe payments. Those with a greater ability to pay (in terms of sales) are also expected to encounter demands from corrupt officials. While evidence from a survey in Harare shows robust evidence for the former expectation, there is limited evidence for the latter.

Further work will surely require more data. Such relationships may vary under different circumstances, in different cities, and at different times. These data will hopefully be increasingly available. While this study investigates bribe exposure among heterogenous informal businesses, it does not have information on the size of those bribes; the model’s expectations are that businesses more able to pay will have larger bribes demanded of them. Under constraints of the exposure of corrupt officials, the expectation is that larger-sized bribes are initially preferable to more frequent, pettier rents. Without data on the size of such bribes, these expectations are not testable. While not the aim here, a similar model can be extended over repeated periods, where expectations of future interactions can affect current behavior. For instance, extensions could model the incentives of officials wishing to keep informal businesses informal (in order to extract bribes continuously) or a model of competing and corrupt officials. Such extensions will require additional, micro-level information under varying circumstances.

(30)
a basic intercept model, solely with random effects for interviewers and enumeration areas - i.e., the hierarchy defined for the multi-level model: where yihjk is the module duration for individual i (or household h if it is a household interview) who is interviewed by interviewer _j_ in enumeration area _k_ ; β is the intercept; _uj_ is the interviewer random effect; _ujk_ is the EA random effect; and _eihjk_ is the error term, with ujk~N(0, σu) and eihjk~N(0, σe).<sup>11,12</sup>

**Equation 2** builds on Equation 1; is estimated for the individual questionnaire modules; brings in vectors of individual ( _I_ ) and household ( _H_ ) attributes; and otherwise leaves the hierarchy unaltered:

**Equation 3** is the equivalent of Equation 2 that is estimated for household questionnaire modules and that only controls for household attributes:

Furthermore, each survey attempted to administer an individual questionnaire in private to each man and woman in sampled households. This setup enables us to estimate additional specifications as sensitivity checks to see whether an alternative hierarchical structure may change the results. Instead of including EA random effects, we include household random effects in addition to interviewer effects. **Equation 4** is this new specification for the intercept-only model and **Equation 5** builds on Equation 4 and controls for the vectors of individual and household attributes:

> 11 For further descriptions of multi-level models, see Gelman and Hill (2007).

> 12 The main objective of the surveys that inform our research was not in analyzing interviewer effects — as such, the interviewers were not randomly assigned to sampled households and individual interview targets. Following common practices in large-scale household surveys, NSOs considered regional language requirements in composing field teams that were in turn assigned to regions with matching language profiles. While there may be unobserved variables that may confound our analyses, given the lack of randomized assignments of interviewers to sampled households and individual interview targets, we prioritize the discussion of the results from the estimations that control for observable individual- and household-level attributes.

19
# **TABLE OF CONTENTS**

|**I.**<br>**Introduction ______________________________________________________________ 4**|
|---|
|**II.**<br>**Increasing inequality in transition: what do we actually know?____________________ 6**|
|**III.**<br>**Towards Comparable Data on Inequality in Transition________________________ 8**|
|**IV.**<br>**Main Drivers of Inequality in Transition ___________________________________ 11**|
|Driver 1. Wage decompression and growth of the private sector _______________________________12|
|Driver 2: Restructuring and unemployment _______________________________________________14|
|Driver 3. Changes in government expenditure and taxation___________________________________14|
|Driver 4. Price liberalization, inflation and arrears__________________________________________14|
|Driver 5. Asset transfer and growth of property income______________________________________15|
|Driver 6. Technological change and globalization __________________________________________15|
|Models of restructuring _______________________________________________________________16|
|**V.**<br>**Decomposing inequality change in transition __________________________________ 18**|
|V.1. Decomposition of inequality by income sources________________________________________18|
|V.2. Decomposition of inequality by groups ______________________________________________23|
|Urban-Rural (Location) (Table 7 A) ___________________________________________________23|
|Education (Table 7 B) ______________________________________________________________28|
|Labor market (Table 7 C) ___________________________________________________________29|
|Summing Up _____________________________________________________________________32|
|**VI.**<br>**Changing Inequality in China ____________________________________________ 33**|
|**VII.**<br>**Conclusions and policy implications._______________________________________ 36**|
|**Bibliography _________________________________________________________________ 39**|

3
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_

# **1. Argentina’s currency board experience**

Argentina’s experience with a currency board is an attractive subject of study because of the dramatic macroeconomic events that occurred during and after the decade during which the board was in place, and because of the lessons that Argentina’s experience holds for other open economies that may be considering a fixed exchange rate regime.

Argentina’s central bank failed with its currency board because of an explosive combination of factors—chief among them the impossibility of conducting monetary policy (by definition endogenous), foreign currency developments in the leading country to which the currency was pegged (the United States), difficulties in designing and implementing fiscal policy to compensate for imbalances in other sectors (monetary, financial, labor), institutional rigidities, and issues of political economy issues (Carrera 2002).<sup>7</sup> For some time, the results of the currency board were considered impressive for their stabilizing effect. Hyperinflation was reduced to almost zero, and growth rates increased until the verge of the crisis (figure 1.1).

Figure 1.1 Argentina’s GDP at constant prices, 1991–2001 Millions of pesos <!-- Start of picture text --> 288000<br>272000<br>256000<br>240000<br>224000<br>208000<br>192000<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> Source: IFS-FMI.

> 7 We do not analyze the political economy or institutional failures in this paper.

4