rafmacalaba/datause-encoder-data huggingface dataset:
potential inefficiencies, which could<sup>take theform of both price-(e.g., high interest margins)and</sup> non-price (e.g., excessive branching) behavior,<sup>common for high inflationaryenvironments.2 3</sup> Because of difficulties with obtaining<sup>a consistentserieson a broad range of prudential</sup> requireInents, the choice of indicators<sup>describing the regulatory environmentwas the most</sup> difficult one to make. We, nevertheless,<sup>were able to construct a data series on (1)capital</sup> adequacy, (2) maximum exposure<sup>to a single borrower, and (3)a limit on the foreign exchange</sup> open position for four consecutive years,<sup>1995-1998.</sup> Differences relating to the institutional and<sup>generalbusiness environment are captured in</sup> the regressions by including the EBRD<sup>indexes of legal/institutionalquality and enterprise</sup> restructuring. Developments in capital<sup>marketsand non-bank financial institutionsand their</sup> effect on commercial bank performance<sup>are captured by (1)stock market capitalization,and (2)</sup> EBRD index of securities markets and<sup>non-bank financialinstitutionsdevelopment.</sup> Finally, sub-regional dummy variables<sup>are used (distinguishing betweenCentral</sup> European, South-East European/Baltic<sup>andCIS countries) to capture any differences in</sup> efficiency indicators not accounted for<sup>by the above measures.</sup>

# _Regression Outcome_

Due to the limited nature of the dependent<sup>variable(note that the DEA indexranges between 0</sup> and 1), a censored Tobit regression model<sup>wasused to estimate Equation 6.Unlike a</sup> conventional Ordinary Least Squares<sup>estimation,in cases with limited dependent variables, Tobit</sup> models are known to generate consistent<sup>estimatesof regressioncoefficients.The results of</sup> estimation are presented in Table 9.<sup>Overall, the regressions providea reasonably good fit and</sup> are robust with respect to the inclusion of<sup>new variables.The outcome is definitely worth paying</sup> some attention to!

able to control), it also had adverse effects<sup>on economiesof the entire region (theFSU countries</sup> in particular). It is a well documented fact<sup>that a number of countries in the region,(Armenia,</sup> Hungary, Kyrgyz Republic, Ukraine,<sup>to name a few)were perhaps hit almost as hard as Russia</sup> itself in terms of the effect on quality of<sup>bankassets,the stock markets,purchasing power of</sup> economic agents, etc. Now, the question<sup>is, do these countries qualify to get a value of 1 as far</sup> as the dummny variable for crises is concerned?<sup>Where does one draw the line?Yet, this said,</sup> we assume that the regression will still<sup>capture the events of the crisis since relevant information</sup> is likely to be contained in the rate of inflation,<sup>stock market capitalization,and per capita</sup> income.

17
the rural Gini coefficients because of the high ratio of urban to rural household income per capita. There appeared to be a lull in this increase because the national Gini was 0.45 in both 1995 and 2002. However, in 2007, the urban Gini was 0.34, the rural Gini was 0.36, and the national Gini was no less than 0.50.<sup>1</sup> Adjusted for regional price differences, the Gini was 0.43 in 2007, having risen from 0.40 in 2002 (Li et al. 2013).

Ravallion and Chen (2007), who had partial access to the NBS microdata, found growing income inequality: all three Gini coefficients increased by 3 percentage points over the six years between 1995 and 2001. These authors’ estimate of the national Gini in 2001 was 0.45. NBS statistics for urban China show that income inequality continued to rise after 2001. The share of the lowest three quintiles fell monotonically over the period of 2000–2008, whereas that of the highest quintile increased sharply. Moreover, the national Gini coefficient based on grouped NBS data was estimated to rise by 5 percentage points between 2000 and 2008 (Lin et al. 2010).

Although there is no indication that income inequality in urban areas stopped increasing in the NBS data, the CHIP data suggest that it was no higher in 2007 than it had been in 1995. The explanation for this discrepancy is likely to be found in the definition of income. In contrast to the NBS’s definition, CHIP’s definition includes various regressive subsidies received by urban _hukou_ residents, particularly 5
Policy Research Working Paper 8698

# **Abstract**

Understanding the magnitude and importance of income shocks, such as drought or conflict, in causing and perpetuating poverty is critical to designing policies aimed at building resilience and contributing toward the goal of ending poverty. This paper uses micro-data from two waves of the Somali High Frequency Survey to assess the impact of the severe drought that Somalia experienced in 2016/17 on poverty, hunger, and consumption. The analysis uses a regression framework to quantify the effects of the drought, relying on spatial variation in drought exposure and the timing of data collection, which took place before and during the drought, for identification. The drought is found to have a sizable effect on poverty, consumption, and hunger in rural areas, where agricultural households and those lacking access to infrastructure and basic services are most severely affected. A renewed drought shock could lead to an increase in poverty of 9 percentage points. The findings underscore the importance of investing in rural resilience, especially among agricultural households.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at upape@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
Some studies have also analyzed the Taxpayer Opinion Survey (TOS), data collected in the United States in 1987 and providing a broad set of taxpayers‘ opinions and evaluations of aspects as the tax system, the Internal Revenue Service, tax evasion, cheating on taxes etc. The TOS offers the possibility to separately analyze two determinants of tax evasion, overstating of deduction or expenses and underreporting income, as dependent variables. Furthermore, it allows for the possibility of developing different tax morale variables to check the robustness of the results. Unfortunately, the TOS has not been used by many researchers (see, e.g., Smith, 1992; Sheffrin and Triest, 1992). Even if the data set is quite old, the huge amount of questions and the fact that not many papers have used the data set, makes it attractive for newer research projects (see, e.g., Forest and Sheffrin, 2002; Torgler, Demir, Macintyre and Schaffner, 2008; Torgler, Schaffner, and Macintyre, 2010). Furthermore, the TOS has not been conducted post 1990.

In general, extracting only a single question from the WVS also reduces complexity problems inherent with the construction of an index, especially regarding the measurement procedure or a low correlation between the items. Even so, one should recognize that there are some good reasons to use a multi-item index instead of a single question to measure tax morale.<sup>2</sup> Tax morale is likely to be a multi-dimensional concept, which may require a multi-item measurement tool, as in psychometric studies. In this context, a single-item measure like ours has some disadvantages compared to a multiitem index (Lewis, 1982; Jackson and Milliron, 1986). For example, a single-item measure may have difficulty in capturing adequately the interrelated facets of tax morale, and may also be adversely affected by random errors in measurement. Further, a multiitem index has the advantage that errors should tend to average out, therefore producing a more reliable measure. Compared to a single-item measure, a multi-item index likely provides better score reliability by pooling together information that the items have in common; a multi-item tool also increases validity by providing a more representative sample of information about the underlying concept, and it increases precision by

> 2 For example, Kirchler (1997, 1999) uses several items to measure tax morale.  He confronted subjects with various scenarios, in which a fictitious individual overspends/underreports income on a tax return. After reading the scenarios, subjects could express their disagreement with or acceptance of tax evasion.

9
Table 4: Estimation of Rural and Urban Log-Normal Parameters

||Ru|20<br>ral|00<br>Urb|an|Ru|20<br>ral|07<br>Ur|ban|
|---|---|---|---|---|---|---|---|---|
||Dir.|Emp.|Dir.|Emp.|Dir.|Emp.|Dir.|Emp.|
||7.265|7.265|7.880|7.827|7.518|7.521|8.001|7.907|
|meanlog|(0.007)|(0.007)|(0.008)|(0.010)|(0.114)|(0.011)|(0.010)|(0.015)|
||0.595|0.604|0.696|0.753|0.615|0.608|0.716|0.928|
|sdlog|(0.006)|(0.006)|(0.007)|(0.006)|(0.009)|(0.008)|(0.009)|(0.013)|

_Source_ : Authors’ calculations based on Moroccan HBS.

Applying eq. [10], we obtain the figures shown in table [5], which reports the entire time series of inequality indicators between 2000 and 2007; 2000 and 2007 are calculated based on the sample data while the rest is imputed.

During the period considered, inequality slightly increases in urban areas while remaining relatively stable in rural ones. Growth in the early 2000s is less volatile than in the previous decade and posts substantially higher annual rates (Clementi et al., 2022). This growth, however, has not been accompanied by rapid economic modernization or significant labor market outcomes. Indeed, the Moroccan economy created an average of 115,000 jobs annually between 2000 and 2010. However, this could not absorb the annual increase in the working-age population (Lopez-Acevedo et al., 2021). Over the same period, the slight increase in inequality resulted from two counter-balancing trends: convergence of development across regions and increased intra-region inequality in some regions. Indeed, inequality increased mainly in the coastal, urbanized regions while decreasing in the — mainly rural — inner and eastern parts of the country (HCP, 2018).

As additional verification of the validity of this trend, we compared inequality measures in rural areas with average annual precipitation data.<sup>9</sup> As the Moroccan economy highly depends on agriculture, we expect a significant correlation between rainfall and inequality. In periods of drought, we expect inequality to increase since most of the agriculture is rainfed and only a few very developed areas (for example the Settat area) are equipped with modern irrigation systems that enable farmers to withstand shocks, which exacerbate rural inequality. In periods of abundant rainfall, we anticipate better performance overall of the agricultural economy and thus a decline in inequality. As expected, the correlation coefficients between the three indices and this data are, respectively, (G=-0.75, T=-0.67, HCR=-0.15). From these, we can deduce that the trend of the indices is consistent with rainfall patterns, confirming the validity of our hypothesis and the economic significance of our estimated results.

> 9https://climateknowledgeportal.worldbank.org/country/morocco/climate-data-historical

14
14 Unsurprisingly, too, health shocks are more likely than drought to trigger health insurance reimbursement. Dissaving shows few systematic patterns, other than that it is more commonly used in response to pest infestation than in response to drought. Borrowing, by contrast, is less likely to be used in response to pest infestation and crime than in response to a drought, while borrowing is _more_ likely to be used in response to an illness. The sale of assets is more common in response to a death than in response to drought and less common in response to floods. Wealthier households are less likely to resort to an early harvest; otherwise there is no pattern in the data. Health shocks are more likely than drought to result in households pawning their possessions; the same is true of households experiencing floods. Again, these patterns likely reflect in apart the differences across shocks in their idiosyncraticity: selling assets is more attractive financially for idiosyncratic risks like health shocks than for covariate risks like drought since multiple households selling assets will drive down the price of the asset and reduce the returns from asset sales.

# VII. **IMPACTS OF SHOCKS ON WELL‐BEING**

Traditionally, the welfare cost of shocks—and hence the potential value of formal insurance programs—has been analyzed through consumption‐smoothing regressions. The (log of the) change in consumption is regressed on a vector of shocks, and a significant negative coefficient on the shock variables is considered evidence of an inability to smooth consumption in the face of shocks and hence a positive value of formal insurance arrangements (cf. e.g. Townsend 1994; Gertler and Gruber 2002; Asfaw and von Braun 2004). The Laos survey does not allow us to estimate a standard consumption‐ smoothing regression because it does not contain pre‐shock consumption. Instead, we estimate an equation linking consumption to shocks. This can be thought of as a misspecified version of the correct consumption‐smoothing model linking current consumption to shocks and lagged consumption, where the coefficient on the latter is constrained to be one. We cannot include lagged consumption but we can
<!-- Start of picture text --> Rwanda<br>=<br>o<br>ml<br>i) 10 20 30 40 Quantile50. 60 70 80 90 4100<br>—+— Education ——+— Enperience<br>—+*— Numberof Children ——*—\ Married<br>—*— Industry —+— Monthly Hours<br><!-- End of picture text --> <!-- Start of picture text --> Tanzania<br>=<br>TMN<br>o<br>ml<br>i) 10 20 30 40 Quantile50. 60 70 80 90 4100<br>—+— Education ——+— Enperience<br>—+*— Numberof Children ——*—\ Married<br>—*— Industry —+— Monthly Hours<br><!-- End of picture text --> <!-- Start of picture text --> Uganda<br>=<br>TMN<br>o<br>SSS<br>ml<br>i) 10 20 30 40 Quantile50. 60 70 80 90 4100<br>—+— Education —+— Experience<br>—+— Numberof Children ——*—\ Married<br>—+— Industry —+— Savings<br><!-- End of picture text -->
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

# **Appendix 1 - The Hypotheses tested in econometric studies**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
|**Zhang et al.**<br>**(2002)**|_H0: Privatisation leads to_<br>_higher operating efficiency_<br>_and asset utilisation._|-operating efficiency: net<br>electricity generation<br>(MWh) per employee (#)<br>(PI)<br>-asset utilisation: electricity<br>generation (MWh) / average<br>capacity (MW)<br>(PI)|-privatisation: existence of<br>private generation (dummy)<br>(MS/RM)<br>_Result: insignificant_|-GDP per capita (US$ 95)<br>(CL)<br>_Result: significant at the_<br>_1% level_<br>-urban population as % of<br>total<br>(CL)<br>_Result: significant at the_<br>_1% level_<br>-industrial output as % of<br>GDP<br>(CL)<br>_Result: significance varies_<br>_across models_<br>-degree of economic<br>freedom (based on 10-<br>point indices in ‘Economic<br>Freedom of the World:<br>2002 Annual Report’)<br>(CL)<br>_Result: significant at the_<br>_1% level_|51 LDCs from 1985-2000.<br>Generation and capacity data:<br>APERC database and World<br>Dev. Indicators.<br>Labor: Industrial Statistics<br>Yearbook, International<br>Labour Organisation.<br>Privatisation, regulation, and<br>competition: The Yearbook<br>of Privatisation, EIA, WEC,<br>and APERC.|
||_H1: Privatisation with_<br>_supportive regulation leads_<br>_to higher output and_<br>_capacity._|-output: net generation<br>(MWh) per capita<br>(PI)<br>-capacity: generation<br>capacity (MW) per capita<br>(PI)|-privatisation: existence of<br>private generation (dummy)<br>(MS/RM)<br>-regulation: existence of<br>independent regulatory agency<br>(dummy)<br>(I)<br>_Result: taken together, the_<br>_variables are significant (on_|||

48
# **Table of Contents**

|**1.** **Trends and Deviations From Trend........................................................................ 2**|
|---|
|Time Series Of Key Indicators and Trends............................................................. 2|
|Output Measures and Trends .................................................................................. 3|
|Trade Indicators and Trends ................................................................................... 3<br>|
|Commodity Time Series and Trends ...................................................................... 4|
|Cyclical Patterns In Deviations From Trend .......................................................... 6|
|**2.** **Transitions And Break Points................................................................................ 11**|
|Transition Points in the Economy of Cote d’Ivoire.............................................. 11|
|Hypothesis: Cote d’Ivoire has Market Power in Cocoa........................................ 12|
|Fully Modified Least Squares Model of Cocoa Prices and Output ...................... 13|
|**3.** **Dynamic Analysis of The Impact of Shocks ......................................................... 15**|
|Impulse Response Function Results ..................................................................... 15|
|**4.** **VAR Forecast: Continuing Decline....................................................................... 18**|
|**5.** **Conclusions.............................................................................................................. 20**|
|**Annex I: Quantitative Methodology.............................................................................. 21**|
|HP Filtering Technique......................................................................................... 21|
|VAR Estimation Procedure................................................................................... 22|
|**Annex II: Additional Data and Information ................................................................ 24**|
|Time Series and Trends ........................................................................................ 24|
|Cycles in Coffee Prices, Output and Yields.......................................................... 25|
|Cote d’Ivoire and World Cocoa Markets.............................................................. 30|
|**Bibliography.................................................................................................................... 32**|
# **I. Introduction**

To what extent do firms rely on each other’s trustworthiness and mutual interests or invoke the assistance of various third parties when ensuring the fulfillment of their agreements to buy and sell goods and services? Which combinations of these approaches do firms use? How do answers to these questions vary across countries? Williamson (1979) refers to such combinations as transactional governance structures. In the 40 years since this concept was introduced, no study has produced economy-wide, cross-country data on a comprehensive set of approaches to supporting transactions, showing which governance structures are most commonly found effective by firms. This is surprising given the consensus that firm performance and levels of development are associated with the effectiveness of arrangements for enforcing agreements (Williamson 1985; North 1990; Moran and Ghoshal 1999, Greif 2001, World Bank 2002, 2017).

The aim of this paper is to fill this gap in the literature. We rely on data that are generated from the responses of individual firms to a survey question that is designed to be interpretable by a respondent from any firm in any sector in any country. The data reflect the experiences of 3,430 firms in six South American economies. This is a small number of countries, but the methodology is sufficiently general that it could be applied in any country, sector, or region in the world. By using representative samples and survey weights, we are able to characterize the aggregate importance of different governance structures in a very large part of economic activity.<sup>1</sup> To our knowledge our paper is the first to present and analyze data that provide such a comprehensive picture of the effectiveness of the different approaches that firms use to support the fulfillment of agreements. While there are many data sets on particular elements of governance, such as the strength of contractual institutions, our data uniquely capture four elements: reflecting

> 1 The samples are designed to be nationally representative of non-agricultural, non-extractive, formally registered, private firms with at least five employees. See Section II.3 for more details.

1
Table 5: DIDM estimates of infrastructure provision (Industry GVA share)

||_Depe_|_ndent varia_|_ble: Industry GVA share_|
|---|---|---|---|
|||N2E|N2H<br>H2B|
|1970-2000 (E)|coef.|0.049<sup>_∗∗∗_</sup>|0.020<br>0.027|
||s.e.|(0.016)|(0.020)<br>(0.029)|
|1970-1980|coef.|0.088<sup>_∗∗∗_</sup>|0.030<br>0.042|
||s.e.|(0.025)|(0.042)<br>(0.069)|
|1980-1996|coef.|0.027|0.026<br>-0.000|
||s.e.|(0.023)|(0.028)<br>(0.040)|
|1996-2000|coef.|0.034|-0.009<br>0.090|
||s.e.|(0.033)|(0.020)<br>(0.059)|
|1970-2000 (D)|coef.|0.051<sup>_∗∗_</sup>|0.058<sup>_∗∗_</sup><br>0.025|
||s.e.|(0.020)|(0.027)<br>(0.053)|
|Placebo|coef.|0.023|0.022<br>-0.009|
||s.e.|(0.022)|(0.015)<br>(0.023)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision._ _(D): estimated effect one period after the infrastructure provision._

# **4.3 Causal Mechanisms**

We now investigate in greater detail the possible mechanisms that can explain how infrastructure provision affects output and productivity. Assuming a Cobb-Douglas aggregate production technology at the municipal level, we focus specifically on the capital/labor ratio (capital intensity) and the total factor productivity (TFP), which captures technological improvements (see derivation and estimation procedure of TFP in Appendix 1). Capital and labor data are available only for the period 1970-1980, which, fortunately, is a period when significant investment was made. However, as we explain in section 2.2, the data are only available for some municipalities, leaving us with a smaller sample of municipalities during that period. Therefore, we first apply the DIDM estimator to this restricted subsample to make sure that the main results in the second row of Table 2 are unchanged. Appendix Table A2 shows that this is indeed the case. Because the subsample does not include municipalities with highway access that were subsequently electrified during the period, we cannot estimate 17