rafmacalaba/datause-encoder-data huggingface dataset:
and Institutional Assessments (CPIA). We employ the linear index in the reported regressions (Table 12); the log index gives the same (insignificant) result. We also introduce dummy variable controls for regions, to account for unobservable geographic factors, and for project types.

# **5.2  Cost Function Estimation: Results**

Table 12 presents estimation results for the unit cost model. We discuss the results in column (2), which excludes governance because the latter is insignificant in column (1). The estimated parameter for rainfall has the expected sign, is highly significant, and indicates an important impact: Estimated unit costs rise about 1.4% for each 1% increase in rainfall intensity. The estimated elasticity for income per capita, our wage proxy, is also large and highly significant. Our results suggest that a 1% increase in income per capita increases road improvement costs by 0.75%. Percent paved is also highly significant, with a decrease of about 0.05 in the log of unit cost for every unit increase in percent paved. Given the strength of these results, we would expect significant intercountry differences in unit improvement costs, because Sub-Saharan countries exhibit wide variation in rainfall intensity, percent of roads paved and income per capita. As Table 11 shows, rainfall intensity varies 3-fold, percent paved over 60-fold, and income per capita about 20-fold in the countries connected by the network.

**Table 11: Variations in Road Cost Determinants: Countries in Sub-Saharan Africa**

|Variable|Min|Q1|Median|Q3|Max|
|---|---|---|---|---|---|
|Rainfall Intensity (mm./day)|6.2|7.9|10.6|13.5|18.9|
|Percent of Roads Paved|0.8|8.3|12.3|20.0|60.0|
|Income Per Capita<br> ($US 2001, PPP)|470|800|1,099|1,932|9,593|

30
**Table 3: Changes in title status by plot between 1990 and 1996 and 1996 and 1999, respectively** **_Between 1990 and 1996_**

||||Document heldi|n1990|||
|---|---|---|---|---|---|---|
|Documentheldin1996|None|Unofficial<br>Document|Agrarian<br>ReformTitle|Fullregistration|Total|Percentage|
|None|31|1|2|3|37|2.1%|
|Unofficial Doc.|22|92|21|11|146|8.3%|
|AgrarianReform Title|170|91|139|19|419|23.9%|
|Fullregistration|13|97|44|996|1150|65.6%|
|Total|236|281|206|1029|1752|100.0%|
|Percentage|13.5%|16.0%|11.8%|58.7%|100.0%||
|||**_Betwe_**|**_en1996and_**_19_<br>Document heldi|_99_<br>n1996|||
|Documentheldin1999|None|Unofficial<br>Document|Agrarian<br>ReformTitle|Fullregistration|Total|Percentage|
|None|36|1|3|4|44|1.9%|
|UnofficialDoc.|36|116|31|15|198|8.4%|
|Agrarian ReformTitle|324|121|220|25|690|29.2%|
|Fullregistration|19|109|82|1222|1432|60.6%|
|Total|415|347|336|1266|2364|100.0%|
|Percentage|17.6%|14.7%|14.2%|53.6%|100%||

Source: Own computation from 2000 World Bank/University of Wisconsin survey.

Note: The number of observations included in each of the transition matrices is lower than the total number of plots (3649) because changes are observed only for plots that had been held already in the initial period (i.e. 1990 or 1996). ** give the number of plots in each of the cases.

25
_5_

# **B. Changes in Earnings Inequality**

At the same time as this remarkable advance in education attainment was taking place, the distribution of income in Mexico worsened notably; see e.g., De la Torre (1997), Panuco-Laguette and Szekely (1996). For example, the Gini coefficient of Mexico's total income distribution increased from 0.43 in 1984 to 0.48 in 1994. This deterioration took place before the 1995 recession and, thus, cannot be attributed to business cycle effects. Instead, the increase in overall income inequality appears to be closely related to an increase in the dispersion of wages and salaries across different schooling levels. Table 1 describes the change in real wages between 1984 and 1994, using two concepts of remuneration (described in Section D.)

**Table 1: Real Wage Rates in** Mexico (Unles **s** tated otherwise, all figures refer to hourly rates expressed in constant 1994 Pesos)

||Wa|ges**& S**|**alaries**|**Mo**|**netary**|**Income**|_Share o_|_f Workers_|
|---|---|---|---|---|---|---|---|---|
|_Schooling Level_|1984|1994|difference|1984|1994|difference|<br>1984|1994|
|0 -less than primary|3.17|3.08|-2.8%|4.83|4.12|-14.7%|48.1%|35.7%|
|1 - primary complete|5.23|4.42|_-15.5%_|7.90|5.39|-31.8%|26.3%|25.4%|
|2 - secondary complete|6.55|5.83|-11.0%|7.55|6.90|-8.6%|13.3%|21.3%|
|3-preparatory complete|9.62|11.68|21.4%|10.64|12.84|<br>20.7%|7.8%|11.1%|
|4-university and above|14.93|21.96|47.1%|16.94|25.55|50.8%|4.5%|6.6%|
|_weighted average_|**5.62**|**6.88**|22.4%|7.00|7.57|8.1%|||

Source: Own calculations based on ENIGH84 and ENIGH94. All figures are weighted averages using expansion factors to reflect national representation.

Even though the average wage increased during the decade spanned by Table 1, most workers experienced a significant decline in their wages. This disparity in the evolution of real wages is most visible in Figure 2, which shows that 83 percent of the working population experienced a decline in real wages during the last decade.

<!-- Start of picture text --> Figure 2<br>MEXICO<br>Percentage Change in Real Wages by Percentile: 1984-1994<br>40.00%<br>30.00%0<br>20.00% ~~based on .6<br>a , 10.00%<br>0.00%<br>e -10.00% 10 15 20 25 30 3 40 45 50 55 606570 85 90 95<br>CL -20.00%<br>.-30.00% ~-ory monetary incomes<br>-40.00%<br>Percentiles<br><!-- End of picture text --> Source: ENIGH84 and ENIGH94.
long-lasting effects on overall (headline) inflation, in particular for countries with less firmly anchored inflation expectations (Walsh, 2011). Food inflation can also be transmitted to core inflation through higher inflationary expectations and workers‟ demands for higher wages. It has been shown that in MENA food inflation exhibits strong short-run effects on non-food inflation (IMF, 2011).

Tight supplies in relatively thin global food commodity markets put countries with high food dependency ratios at risk of disruptions in procurement, shortfalls in food availability, and food inflation (Famine Early Warning Systems Network, 2011). Within these countries, food inflation is a particular threat to the urban poor, allocating a large share of their expenditures to food. Even relatively modest price increases can deprive them of adequate access to food.

This paper aims to assess the magnitude of risks stemming from high international food prices and volatility by country. We provide country estimates of international food price pass-through to domestic food prices in MENA and identify the contribution of other factors – internal and external – to domestic food price fluctuations. As we show, the pass-through estimates vary significantly because of individual country characteristics, including exchange rate and food subsidy policies.

Our empirical results indicate that global food price shocks are a risk for almost all MENA countries. Yet, the exposure to food price shocks varies significantly by country. We find that the full transmission process of international food prices into domestic markets takes about one year. The estimated pass-through coefficients indicate that, on average, a 1 percent increase of world food prices can increase domestic food prices by some 0.2-0.4 percent. The pass-through effects are notably higher for Egypt, Iraq, Djibouti, the UAE, and West Bank and Gaza. By contrast, Algeria and Tunisia appear less affected.

Another important finding is that international price transmission is highly asymmetric. An increase in world food prices is typically transmitted into domestic food markets, but a decline in world food prices rarely transmits at the same degree. Thus, the finding of asymmetric price transmission suggests that not only international food price _levels_ matter, but also food price _volatility_ .<sup>6</sup> For countries with high food dependency ratios, limited fiscal space, and expected volatility in

> 6 In a recent article, Barret and Bellemere (2011) argue that in the popular policy debate there is confusion over the meaning of price volatility. They claim that the core problem for most developing countries is the high food global price level, and not so much volatility.

-5-
||_Number of_<br>_observations_|_Mean discrepancy_|_Number of_<br>_observations_|_Mean discrepancy_|
|---|---|---|---|---|
|Austria|5|−0.008|6|−0.088|
|Cyprus|5|0.138|-|-|
|Egypt|-|-|8|0.269<sup>*</sup>|
|Germany|5|0.095|9|−0.019|
|Greece|9|−0.061|9|−0.035|
|Israel|5|−0.092|8|−0.233|
|Italy|8|0.131<sup>*</sup>|6|−0.032|
|Morocco|8|−0.061|9|−0.035|
|Netherlands|6|0.101|-|-|
|Saudi Arabia|-|-|5|−0.756<sup>***</sup>|
|Spain|7|−0.037|-|-|
|Sweden|7|−0.324<sup>***</sup>|-|-|
|Switzerland|-|-|6|−0.059|
|Tunisia|5|0.096|-|-|
|Turkey|-|-|9|−0.096|
|United Kingdom|8|−0.123<sup>**</sup>|-|-|

Notes: a. Romanian trade flows in the EFI Database are mainly for the 1990s.

b. Sample includes only trade flows with reported imports and exports both ≥ 100 m<sup>3</sup> .

c.<sup>***</sup> ,<sup>**</sup> ,<sup>*</sup> : mean is significantly different from zero at 1%, 5%, 10% level.

# **3.2 Romania as an importer of sawnwood**

Romania imported sawnwood from a much shorter list of countries during 1982-97: just 17 in the case of NC sawnwood and 13 in the case of C sawnwood. The exporters were overwhelmingly European countries: 13 and 12, respectively. Appendices 5 and 6 list the countries that exported the two products to Romania.

Table 3 presents summary information for imports by Romania, parallel to the information in Table 1 for exports. The first thing to note is that the number of observations is many fewer: Romania is on the receiving end of the sawnwood trade less often than it is on the shipping end. Only the mean for NC sawnwood is significantly different from zero, and it is positive and large: on average, Romania’s reported imports of NC sawnwood exceed the amounts reported by its trading partners by about half.

The minimum of five years of data occurred for just one exporting country for both products, Germany. Table 4 shows the mean discrepancies. The mean discrepancy is positive for both products—Romania’s reported imports exceed Germany’s reported exports—but it is significantly different from zero only for NC sawnwood. The value, 0.656, is very large and implies that imports reported by Romania were on average nearly 4 times larger than the exports reported by Germany.

6
Table 4: Probability that minimum desirable effect size was achieved under various priors

|Outcome|Academics|Firms|Policymakers|Literature|Default|
|---|---|---|---|---|---|
|2019||||||
|Export At All|0_._007|0_._011|0_._010|0_._023|0_._009|
|Number of Products|0_._005|0_._005|0_._006|0_._007|0_._004|
|Number of Countries|0_._109|0_._119|0_._136|0_._248|0_._106|
|Number of Product-Countries|0_._181|0_._186|0_._304|0_._154|0_._117|
|Export Innovation|0_._065|0_._070|0_._060|0_._101|0_._057|
|Export Value|0_._676|0_._693|0_._870|0_._804|0_._197|
|Exports Productivity|0_._170|0_._215|0_._567|0_._767|0_._106|
|2020||||||
|Export At All|0|0|0|0_._0003|0_._0001|
|Number of Products|0_._0001|0_._0001|0_._0001|0|0|
|Number of Countries|0_._008|0_._009|0_._012|0_._041|0_._008|
|Number of Product-Countries|0_._002|0_._007|0_._053|0_._002|0_._001|
|Export Innovation|0_._002|0_._002|0_._002|0_._017|0_._002|
|Export Value|0_._608|0_._653|0_._847|0_._549|0_._034|
|Exports Productivity|0_._062|0_._196|0_._506|0_._604|0_._019|

Note: Export Innovation refers to "exporting to a new product-country combination". All inference is generated by MCMC draws. Export Value and Export Productivity results reflect very little updating from priors.

36
**In an increasing number of MENA countries a financial sector regulatory authority, such as a central bank or non-bank regulator, has supervisory responsibility for microfinance providers** . However, in a number of countries there is no clear supervisory responsibility or framework, or the supervisor is a non-financial agency such as a Ministry of Social Affairs or Planning (see Annex A2.6 for detail by country). This makes the development of a sustainable, transparent and efficient microfinance sector that is able to intermediate deposits or borrowing less likely, and dependence on declining or unpredictable donor funding may continue. When compared to developing countries, MENA has a higher proportion of non-regulated institutions and a higher proportion of MFIs that fall under a non-financial regulator (table 8).

**Table 8: Microfinance Regulation and Supervision by Region**

||**MENA**<br>|**Developing**<br>|**All**<br>|
|---|---|---|---|
||**(%)**|**Countries(%)**|**(%)**|
|Not regulated|50.0|37.9|47.7|
|Supervised onlybya bank regulator|30.0|42.7|35.4|
|Supervised onlybyother regulator|20.0|7.8|7.7|
|Supervised byboth|0.0|11.7|9.2|
|Number of countries|10|103|130|

Source: M Khaled, CGAP, 2010

**Interest Rate flexibility is constrained.** Interest caps are in place in at least 6 MENA countries, whether or not they are actually enforced.<sup>13</sup> Most MENA countries use disclosure requirements (70 percent) rather than usury ceilings in practice, to try and ensure that interest rates are transparent and understood. Currently, most MFIs charge interest rates that are much higher than the interest rate caps, in order for micro-level products to be viable, and in so doing they run a risk of being challenged in court.  A more transparent approach would be to remove interest rate caps and instead strengthen consumer protection against abusive practices such as excessive fees on overdrafts, miss-selling of financial products and lack of consumer redress.

# _Consumer Protection and Financial Inclusion_

**Consumer protection and financial literacy are increasing priorities worldwide** , as financial inclusion increases, with almost two thirds of middle income countries assigning responsibility for these areas to the financial regulator.  MENA lags other regions in terms of assigning responsibility for consumer protection or financial literacy to a financial regulator. However a majority of MENA countries introduced consumer protection reforms in 2009. Of 11 MENA countries that responded to the CGAP and World Bank Financial Access survey in 2010 survey, 7 had introduced consumer protection reforms, indicating that MENA is broadly in line with global trends in terms of increasing focus on consumer protection.

> 13 To date, however, there have been no known legal challenges to interest rates in this region.

21
tighter credit constraints experience both higher volatility of growth and lower growth rates. Acemoglu et al (2003) find that macroeconomic and financial variables do not have a significant role in explaining volatility once they account for institutions. Institutions are the key determinant of volatility; the only macro variable that may have some additional impact on volatility is the real exchange rate. These papers do not examine whether the effect of finance varies by size.

The paper is organized as follows: the first section examines the relationship between finance and economic volatility. In particular, it analyzes the relationship between total private credit in an economy and GDP or consumption per capita growth volatility and then considers the relationship of these two with household credit.<sup>3</sup> The second part of the paper is devoted to borrower characteristics, whether households or firms and growth in GDP per capita. I also examine what effect borrower characteristic may have on the composition of output by differentially affecting growth of certain sectors over others.

# **Data and Methodology**

The data for total private credit are taken from the World Bank’s Global Financial Database and covers 150 countries during 1964-2013. All regressions use 5-year averages. The data for household and firm credit are taken from the Bank for International Settlements. Most regressions cover around 40 countries during 1960-2013. The first section of the paper revisits the relationship between financial development (total private credit over GDP) and volatility in output and consumption per capita. The following section looks separately at household credit. All other data sources and time periods are described in Annex 1. The relationships are estimated using OLS, IV and GMM methods, as appropriate.

# **_Section 1. Volatility_**

This section examines the relationship between financial system development and volatility that was explored earlier in Easterly et al (2000) and has been explored since in various papers (Beck et al, 2001, Dabla-Norris and Srivisal, 2013). The base regression of interest

> 3 The analysis is also conducted for enterprise credit, but the results are not shown here for all the cases analyzed.

7
**Table 2a: Estimation results based on Equation (2) with double interactions**

||(1)|(2)|(3)|
|---|---|---|---|
||Salesper worker(log)|Sales(log)|Employment(log)|
|Energy intense|-0.410|-0.543|-0.579**|
||(0.787)|(0.794)|(0.257)|
|Price oil, log|11.34***|10.90***|-1.997***|
||(0.455)|(0.457)|(0.142)|
|Price electricity, log|-1.923***|-2.064***|-0.594***|
||(0.301)|(0.308)|(0.101)|
|Explicit petroleum subsidy (% GDP)|1.309|1.316|-0.516*|
||(1.006)|(1.027)|(0.302)|
|Explicit electricity subsidy (% GDP)|8.107***|7.950***|-0.469*|
||(1.121)|(1.121)|(0.269)|
|log Price oil*Energy intense|0.483***|0.490***|-0.0398|
||(0.179)|(0.179)|(0.0542)|
|log Price elect*Energy intense|0.689***|0.633***|-0.473***|
||(0.172)|(0.174)|(0.0573)|
|Constant|-52.99***|-49.09***|7.733***|
||(2.382)|(2.399)|(0.744)|
|Observatons|21,629|21,629|21,629|
|R-squared|0.226|0.332|0.474|
|Country FE|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Weighted by country sample size Controls for size clusters<sup>[1]</sup> , age, wave, and subsector Clusters constructed using employment in columns (1) and (2), and sales in column (3) 19
2 income poverty in our sample countries to changes in economic environment-the macroeconomic and sectoral policy frameworks, and the institutional setting. We then exploit the survey data to greater depth by taking a _micro-perspective._ This assesses how households (and poor households in particular) have been affected by the events of the 1990s, distinguishing between the effects of policies and of shocks. When available, household panel data have been used (Ethiopia and Uganda), though important insights were also obtained from repeated cross sections (Zimbabwe, Ghana, Madagascar). The paper highlights the main insights emerging from this selected sample of micro-econometric country studies on Africa.

Considering that well-being is multifaceted, the paper begins with a review (in section ID of the changes that have occurred in income, education, health and nutrition. We first examine how these four different dimensions of well-being have evolved during the 1990s at the aggregate level. We then move beyond the aggregates and look at their evolution across income quintiles, focusing particularly on how welfare of the poorest groups fared. The section concludes by describing the evolution of overall income poverty and inequality, and its relation with economic growth. In the two sections that follow we seek to explain the systematic changes in income distribution and poverty in Africa, taking both macro (Section III) and micro (Section IV) perspectives. Concluding observations are made in the final section.

# II. **Living standards during the 1990s**

To set the scene, Table 1 reports four basic measures of well-being: private consumption, primary school enrollment, child malnutrition, and child mortality. The first and obvious point to note is that living standards are very low in these countries. By the close of the decade, no country enjoyed an annual per capita consumption in excess of $500, and in Ethiopia it was just $87. All countries fall far short of universal primary enrollment, and in some (for example, Ethiopia) primary enrollments are unacceptably low. Malnutrition is also a very serious problem, especially in Ethiopia and Madagascar. In Ethiopia, about two thirds of children exhibit signs of stunting or long-term malnutrition (defined as the percentage of children with low height for age compared with a reference population). Even in Ghana, Mauritania and Zimbabwe, there is evidence of stunting in about a quarter of the population under _5_ years of age. Perhaps the most poignant indicator of the very low welfare levels of these countries is the incidence of child on an analysis of time series data from the Demographic and Health Surveys. References to these _Poverty_
and informative study on local governance in rural Indonesia in the immediate pre-crisis period<sup>7</sup> .

First, we need to replace the potentially misleading word "village" with the Indonesian term _"desa"_ A _desa_ is fundamentally _apolitical and administrative_ designation, rather than a geographic or social one. Although the term _desa_ is often translated as "village" it needs to be understood as a structure imposed on local communities by the central government. A 1979 law designated the existing boundaries of the _desas_ to create a complete, homogenous structure for local governance. The resulting geographical units of the _desa_ therefore do not necessarily correspond to the definition of a "village" as a cluster of living units or to individuals' own perceptions of their basic social reality. Rather, especially in less densely populated areas, a _desa_ may contain several widely dispersed clusters of household residences and primary social affiliations may be to these clusters rather than the _desa._ Second, the structures of _desa_ government created in the 1979 law did not consolidate existing practice but rather _supplanted_ the existing structures of local leadership. Indonesia, a large and diverse country, has a wide range of ethnic and social groups and a corresponding variety of indigenous forms of governance organizations. Traditional _(adat)_ leaders or structures were not formally recognized in the new laws. The new law on local administration created hierarchical structures ranging from the _desa_ ' We draw heavily on Evers (2000) because it is the best study, not only because it is part of the Local Level Institutions study, but also because it is among the few studies of the mechanics of local politics. The New Order Indonesian government banned not just the development of political organizations in rural areas but also research on local politics (which could be easily enforced since all fieldwork required official permission).

7