rafmacalaba/datause-encoder-data huggingface dataset:
We use the data set to construct the outcome variables _Non-performing loans_ ( _number of accounts_ ) and _Non-performing loans_ ( _share of credit_ ). The data, summarized in Table II, reveal significant cross-sectional and time series variation in loan performance. The median bank branch in the sample records a non-performing loan share of 6% (s.d.20). In the preprogram period, the median non-performing loan share stands at 13.6% (s.d.=12.3). This figure gets reduced to 6.25% (s.d.=7.3) in the first year after the Adwdrs bailout.

# **3.3 Productivity, Wages, and Employment**

Our primary source for data on agricultural productivity is the Indian Agricultural Statistics database,<sup>9</sup> which contains district-level information on crop yields and area planted for the 25 most common crops grown in India. We combine this data with commodity prices to calculate the variable _Productivity_ , which measures the value of agricultural production per hectare in each district and year in the dataset. Because we wish to focus on the component of productivity that can be affected by households, we use constant commodity prices for the base year 2001, so that our measure of productivity may be interpreted as a measure of quantity TFP (TFPQ).

The Indian Agricultural Statistics database also provides district-wise wages for unskilled rural labor. Wages are reported seasonally, for every district in India and for a range of agricultural occupations. We focus on unskilled daily wages for field and non-field agricultural labor and calculate the variable _Real wage_ , which measures a district’s rural wage as the average of these two wage groups over all crop seasons for which data are available, to account for seasonal variation in labor demand and rural wages.

Data on household consumption are calculated from the Indian National Sample Survey

> 9The dataset is published by the Indian Ministry of Agriculture and consists of the database of Agricultural Wages in India (AWI), as well as the database on Agricultural Prices and Crop Yields (APY). Appendix E provides additional information on the construction of variables based on the dataset.

12
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# **II. AGRICULTURE IN TANZANIA**

Tanzania is traditionally heavily dependent on agriculture. Although its contribution to the economy has been declining due to other emerging industries, such as mining, the agriculture sector still employs about 75 percent of the workforce and produces about one-fourth of GDP (Ministry of Agriculture Food Security and Cooperatives, 2008). In Tanzania, about 95 percent of total food consumption is met by domestic supply. Particularly in rural areas, the vast majority of people depend on subsistence agriculture for their livelihood, with rural poverty endemic. The poverty ratio of Tanzania was estimated at 33 percent in 2007. Therefore, agricultural development continues to be one of the most important development issues in the country.

Agriculture productivity in Tanzania has been improving but continues to compare unfavorably with other African countries. According to FAO statistics, Tanzania’s yields are below African averages for many major crops (Figure 4). Food crop production is dominated by small-scale subsistence farming with little modern technology adopted. Even for traditional export crops, such as cotton, coffee, cashew nuts and tea (Figure 5), there appears to be considerable potential for productivity improvement.

The Government of Tanzania has been making significant efforts toward modernizing the sector and increasing agriculture production by supporting irrigation and mechanization and providing agricultural research and training services. Agricultural exports are among the important foreign exchange earners and identified as one of the most important priority sectors to strengthen.<sup>2</sup> Agricultural exports account for about 15 percent of the total exports. Agriculture also has the potential for import substitution and food self-sufficiency. The country imports US$750 million of agricultural products, such as wheat, palm oil and rice,

> 2 “Tanzania: Five Year Development Framework, 2011/12-2015/16.” Ministry of Agriculture Food Security and Cooperatives, Tanzania.
As control variables we use a group of economic and political variables previously analyzed in the literature of successful fiscal consolidations.

In the economic variables we include lagged real GDP growth, lagged inflation rate, and international interest rate (long-term U.S. government bond yield: 10 years). These variables are taken from the IMF World Economic Outlook (October 2018).

The information from political variables (majority government and election year) come from the Database of Political Institutions 2017, which is hosted at the Inter-American Development Bank and has coverage of 180 countries from 1975-2017. Rule of law comes from the PRS Group in their International Country Risk Guide. The scores are based on evaluations from experts surveyed and the data are available since 1984 for a group of 140 countries. The Gini coefficient comes from the data set “All the Ginis” created by Branko Milanovic. Table 2 shows some summary statistics of the control variables for the sample with available data to identify need of fiscal adjustment.

**Table 2: Descriptive statistics of the control variables**

||<br>|a) Sample|with pr<br>|imary ga<br>|p|b) Sample wit<br>|h debt/<br>|revenue<br>|ratio|
|---|---|---|---|---|---|---|---|---|---|
||N|mean|SD|min|max|N<br>mean|SD|min|max|
|GDP growth||0.03|0.04|-0.16|0.30|0.04|0.05|-0.46|0.92|
|Inflation||0.06|0.10|-0.09|2.02|0.06|0.10|-1.30|2.02|
|Interest rate||3.91|1.55|1.80|8.55|4.11|1.64|1.80|8.55|
|Gini||39.09|9.84|17.50|74.30|40.31|9.78|17.50|77.40|
|Rule of law||4.10|1.40|1.00|6.00|3.89|1.37|0.00|6.00|
|Margin of Majority||0.59|0.17|0.05|1.00|0.64|0.20|0.03|1.00|
|Election'syear||0.29|0.45|0.00|1.00|0.25|0.43|0.00|1.00|
|Observations|2002|||||4046||||

# V. Results

In general, the results provide evidence that budget rigidities do affect the probability that countries get into financial distress; in addition, we find evidence on rigidities being a constraint for successful fiscal consolidations. Indeed, the factors found to increase the probability of achieving a successful adjustment effort are not necessarily the same as those favoring the maintenance of sound fiscal policies.

## On the determinants of need of fiscal adjustment

In general, the results presented in Table 3 and

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parameters and forms the product of the four indices, each weighted by its exponential CD parameter.

The most critical question for our modeling exercise relates to the potential variation in outcomes for different user settings of model parameters. To address this question, Table 2 provides rank correlations for the 8 variables incorporated in the model. The results are almost evenly divided between positive and negative correlations; some are strong but most are relatively weak. Overall, these results indicate that the habitat priorities and implicit budget shares calculated by the model are highly dependent on user-specified weights.

|**Table 2: Rank Correlations**<br>Obs = 74|**for Model Var**<br>Subspecies<br>Numbers|**iables**<br>Subspecies<br>Distribution|Subspecies<br>Habitat<br>Threat|Habitat<br>Size|Habitat<br>Percent<br>Remaining|Habitat<br>Land<br>Opp.<br>Cost|Habitat<br>Labor<br>Cost|
|---|---|---|---|---|---|---|---|
|Subspecies Numbers|1.00|||||||
|Subspecies Distribution|0.06|1.00||||||
|Subspecies Habitat Threat|-0.75|-0.43|1.00|||||
|Habitat Size|0.22|-0.18|-0.21|1.00||||
|Habitat Percent Remaining|0.41|0.20|-0.46|0.16|1.00|||
|Habitat Land Opp. Cost|-0.57|-0.13|0.62|-0.43|-0.30|1.00||
|Habitat Labor Cost|-0.28|-0.35|0.51|0.13|-0.03|0.16|1.00|
|Country Protection<br>Effectiveness|-0.78|-0.44|0.87|-0.23|-0.43|0.66|0.41|

# **5.  Three Illustrations**

To illustrate the possible range of variation, we implement the model for three sets of parameter weights. The first two give extra weight to subspecies threats and project cost elements, while the third assigns equal weight to all variables.

The subspecies threat scenario assigns unit weights to first-stage variables except for subspecies numbers (T) and distributions across countries (N), which are assigned weights of 5.

In the second stage, we assign a weight of 3 to subspecies threat and unit weights to the other 10
# **Figure 2. Incidence of rural poverty by province**

<!-- Start of picture text --> Ruryal poverty headcoxunt<br>0-20%<br>20 40%<br>-40 - 50%<br>- 50 - 60%A<br>60 - 70%<br>m 70 -100%<br>0 100 200 300 400<br>Kilometers<br><!-- End of picture text --> Source: Estimated from urban-rura! regression models _of 1998_ VLSS and household characte±istics in the 1999 Population and Housing Cmsus 26
2007). Following leading studies in the literature (Blau and Kahn 2007; Heim 2007), we estimate two different specifications to identify the own-wage effect. One exploits wage variation across districts, the other exploits wage variation across state-age-education groups. The two sources of variation give very different estimates, which do not allow us to draw firm conclusions about the impact of wages on women’s labor force participation. However, the estimated effects of other explanatory variables, including education, are robust to the different specifications and to excluding the own wage from the model.<sup>16</sup> Given the difficulties in identifying the own wage effect and the inability to include the self-employed in models that include own wages, we focus here on the results without including own-wage effects. Results for own-wage estimates and more details of the estimation method are discussed in appendix 2.

Table 1 contains sample means for all variables. The largest changes over time are increasing education (women’s own education levels and those of their male partners and household heads, as well as the district population share with graduate level education) , fertility decline, and increasing household income. Further note that employment has shifted over time from agriculture, manufacturing, and white-collar services to construction and other services. To explore in more detail to what extent the determinants of labor force participation differ between low- and highly educated women, equation (1) is also estimated separately for women with less than secondary education and women with secondary or higher education.

# **_Estimation Results_**

> 16 The level of the own education effects does change but in all specifications we find a strong U-shape and a large decline in the effect of secondary and graduate education over time.

18
# Table 2: Correlates of Equity Returns (1990-2023)

Panels (a) and (b) present results from quantile regressions. Regressions in columns (1)–(5) are estimated at the median (50th percentile) of the return distribution, while columns (6)–(9) extend the analysis to other quantiles based on the specification used in column (5). Panel a uses annualized TVPI as a dependent variable, while panel b uses returns adjusted by PME-MSCI. All macroeconomic variables are annual changes over the duration of the investment cycle. Size is the initial contribution, and duration is the investment holding period measured in years. Bootstrapped errors in parenthesis. * 10%, ** 5% and *** 1%.

||(1)|(2)|(3)|(4)<br>Quan|(5)<br>tile Regre|(6)<br>ssion|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||0.5|0.5|0.5|0.5|0.5|0.1|0.25|0.75|0.9|
||||Panel a|. Depend|ent Variab|le: Annu|al TVPI|||
|GDP growth|0.465***|||0.259**|0.349***|0.194|0.284|0.465***|0.521|
||(0.108)|||(0.123)|(0.113)|(0.611)|(0.320)|(0.151)|(0.374)|
|E||-0.713***||-0.705***||||||
|||(0.062)||(0.066)||||||
|Infation||0.685***||0.676***||||||
|||(0.064)||(0.067)||||||
|RER|||-0.671***||-0.678***|-0.242|-1.092***|-0.880***|-0.923***|
||||(0.061)||(0.063)|(0.341)|(0.179)|(0.084)|(0.209)|
|Ln(Size)|0.003*|0.007***|0.006***|0.006***|0.005***|0.026***|0.007|0.005**|-0.012**|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.010)|(0.005)|(0.002)|(0.006)|
|Ln(Duration)|0.014***|0.017***|0.003|0.020***|0.008*|0.300***|0.085***|-0.028***|-0.123***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.027)|(0.014)|(0.007)|(0.016)|
|_R_<sup>2</sup>|0.005|0.019|0.018|0.020|0.020|0.117|0.036|0.033|0.093|
|Observations|1668|1668|1668|1668|1668|1668|1668|1668|1668|
||||Panel|b. Depen|dent Varia|ble: PME|-MSCI|||
|GDP growth|0.562|||0.321|0.524|0.228|1.362*|1.871|3.667|
||(0.729)|||(0.752)|(0.737)|(0.359)|(0.781)|(1.589)|(4.055)|
|E||-0.222||-0.254||||||
|||(0.384)||(0.404)||||||
|Infation||-0.025||0.048||||||
|||(0.395)||(0.408)||||||
|RER|||-0.097||-0.167|0.043|-0.720*|-0.079|1.196|
||||(0.392)||(0.411)|(0.200)|(0.436)|(0.887)|(2.263)|
|Ln(Size)|0.029**|0.031***|0.031***|0.029**|0.029**|0.008|0.026**|-0.018|-0.150**|
||(0.012)|(0.012)|(0.011)|(0.012)|(0.012)|(0.006)|(0.012)|(0.025)|(0.064)|
|Ln(Duration)|-0.013|-0.025|-0.019|-0.013|-0.012|0.025|-0.053|0.263***|0.654***|
||(0.032)|(0.031)|(0.030)|(0.032)|(0.032)|(0.016)|(0.034)|(0.069)|(0.177)|
|_R_<sup>2</sup>|0.003|0.004|0.003|0.004|0.003|0.003|0.005|0.014|0.050|
|Observations|1668|1668|1668|1668|1668|1668|1668|1668|1668|

Columns (5) - (9) show that real exchange rate depreciation is negatively associated with equity 17
|**Mauritius**|650.1|24.7|23.5|22.2|3.6|3.6|
|---|---|---|---|---|---|---|
|**Mozambique**|14273.4|10.6|23.5|33.8|2.7|5.7|
|**Niger**|-8732.6|4.8|13.6|13.6|2.4|2.4|
|**Nigeria**|240781.0|32.6|22.4|27.9|1.9|6.1|
|**Rwanda**|5889.5|1.1|18.4|166.2|3.1|7.1|
|**Sao Tome &**|||||||
|**Principe**|1059.1||||3.4|24.3|
|**Senegal**|-13077.3|8.7|19.7|19.7|3.9|3.9|
|<br>**Seychelles**|2986.3|21.3|23.5|38.5|6.4|11.7|
|**Sierra Leone**|7005.4||||2.0|10.1|
|**South Africa**|17492.3|20.3|16.4|14.9|5.7|5.7|
|**Sudan**|16325.0|14.7|18.4|16.4|2.4|3.3|
|**Swaziland**|1342.6|11.7|20.1|21.1|4.8|5.5|
|**Tanzania**|9963.4|9.5|18.3|19.1|2.3|3.4|
|**Togo**|-4064.6||||4.2|4.2|
|**Uganda**|6853.7|7.3|20.3|22.0|2.7|4.0|
|**Zambia**|19814.3|16.0|22.8|37.8|3.2|7.8|
|**Zimbabwe**|24556.0|8.1|10.6|30.3|5.6|12.1|
|**Sample**|**606733.7**|**10.4**|**18.5**|**29.6**|**3.5**|**5.0**|

Sources: capital flight figures from Ndikumana and Boyce (2008); saving, investment and education ratios from WDI 2007.

**26**
approach to bank regulation that stresses private-sector incentives is associated with greater bankingsystem success than an overall approach to bank regulation that emphasizes official government oversight and regulation of bank activities. Consistent with the work in LLSV (1998) and the findings in Beck, Demirgiiu-Kunt, and Levine (2001 b), the legal origin variables jointly enter all of the Table 5 regressions significantly.19 We also considered a wide assortment of interaction terms to assess whether the adverse effects of government control of banks is mitigated in different political and institutional settings. Panel B in Table 5 indicates that Official Supervisory Power has less of an adverse impact on bank development in politically open economies. Using the same example as above, the Panel B regression implies that in a country like Korea with an intermediate level of political openness (Political Openness is approximately 0), a one standard deviation increase in Official Supervisory Power would induce a decrease in bank development of 0.09 (=1*0.092). This is a large enough change to move from Korea's level of bank development down to that of Chile's. In contrast, the same increase in official supervisory power in France (where the Political Openness variable equals 2.7) would actually be associated with an _increase_ in bank development, +0.07 (= -0.09* 1 + 0.06*2.7*1). Thus, official supervisory power is particularly harmrful to bank development in countries with closed political systems. Besides political openness, we examined whether the same array of additional political, supervisory, and media factors that we used in the corruption analyses mitigate the damaging influence of official supervisory power and restrictions on bank activities. As above, we find that the answer is no.

The instrumental variable results in Table 6 confirm that (a) the denial of entry applications, regulatory restrictions on bank activities, and government ownership of banks hurt bank development, while (b) regulations that boost private monitoring of banks and tight capital requirements promote bank development. 20 In the instrumental variable results, the coefficient on Official Supervisory Power becomes insignificant. Nevertheless, the results remain inconsistent with

> **19** Using alternative control variables does not change these conclusions. For instance, we obtain the same results when we include the religious composition variables. Furthermore, since Boyd, Levine, and Smith (2001) demonstrate the rate of inflation hinders financial development, we also controlled for the average rate of inflation over the 1985-1998 period and found that it does not change the Table 5 results.

> 20 Similar to the corruption regressions, we use the legal origin dummies, religious composition variables, and latitudinal distance from the equator as exogenous variables in the Table 6 regressions.

29
OPTIMAL SIMPLE FISCAL RULES 38 <!-- Start of picture text --> 0.3 commodity price 0.5 public assets<br>BBRCY SSRCY OSR<br>0.2<br>0<br>0.1<br>0 -0.5<br>2 4 6 8 10 12 14 2 4 6 8 10 12 14<br>years years<br>0.5 private assets 2 × 10 -3 interest rate<br>0 0<br>-0.5 -2<br>-1 -4<br>2 4 6 8 10 12 14 2 4 6 8 10 12 14<br>years years<br>1 × 10 -3 rental rate of capital 0.1 capital stock<br>0 0.05<br>-1 0<br>-2 -0.05<br>2 4 6 8 10 12 14 2 4 6 8 10 12 14<br>years years<br>hours worked non-resource GDP<br>0.02 0.04<br>0.01 0.02<br>0 0<br>-0.01 -0.02<br>2 4 6 8 10 12 14 2 4 6 8 10 12 14<br>years years<br>% s.s. dev<br>% nonres.gdp<br>% s.s. dev<br>% nonres.gdp<br>% s.s. dev<br>% nonres.gdp<br>% s.s. dev % s.s. dev<br><!-- End of picture text --> Figure 11. **Endogenous Feedback from Commodity Prices to NonResource GDP:** Impulse response function of key variables to an one standard-deviation (24%) shock of commodity prices in the baseline calibration of the RBC model.

<!-- Start of picture text --> Welfare Loss Consumption of the Ricardian HH<br>0.018 BBRSSR 0.07<br>0.016<br>0.014 0.06<br>0.012<br>0.01 0.05<br>0.008<br>0.006 0.04<br>0.004<br>0.03<br>0.18 0.74 0.88 0.98 0.18 0.38 0.88 0.98<br>persistence of oil-price shock (annual) persistence of oil-price shock (annual)<br>Consumption of the HtM HH Public Assets<br>1.2<br>0.12<br>1<br>0.1 0.8<br>0.08 0.6<br>0.4<br>0.06<br>0.2<br>0.04<br>0.18 0.38 0.78 0.88 0.98 0.18 0.38 0.88 0.98<br>persistence of oil-price shock (annual) persistence of oil-price shock (annual)<br>ss<br>% of C<br>annual std deviation<br>annual std deviation annual std deviation<br><!-- End of picture text --> Figure 12. **Welfare loss and shock persistence with BBR and SSR** : Benchmark calibration of the RBC model and keeping the total variance of the commodity price shock constant ( _V_ (ˆ _pt_ ) = _σp_<sup>2</sup><sup>_/_(1</sup><sup>_−ρ_2</sup> _p_<sup>)).</sup>
overheads amount to a percentage upon transportation fees and reflect the proliferation of procedures and rent seeking activities, administrative costs reflect the diseconomies of scale and impose a high penalty on smaller shipments in small and distant markets.

# _Trade Imbalance_

Transport costs are asymmetric worldwide, especially for LLDCs. Along the Northern Corridor (Rwanda to Mombasa) transporting one 20’ container by road costs 1,850 USD and 2,300 USD from Mombasa to Kigali (TTCA 2005) the export transport rates can be close to 50% of import on some rail and road links with limited return traffic.<sup>47</sup> Transport companies experience difficulties to find backload and charge the empty return in their import tariffs. In Central Asia, the cost of transporting a 40’ container by road between Central Asia and Europe varies from USD 6,000 for the West-East direction, to the EastWest charge for transport is only USD 4,000<sup>48</sup> . With few exceptions, shippers are unable to find freight for the return journey.

# _Expansion of the Concept of Landlocked Countries: The Emergence of “de facto” LLDCs?_

The quest for scale economies is vital for maritime transport. Hummels and Skiba (2002) demonstrate that although Japan and Ivory Coast are equidistant from the US (West coast in the first case, East coast in the second case), shipping costs for imports from the Ivory Coast is twice as that of Japan, even adjusting for differences in the commodity composition. Large trade flows are conducive to scale economies in transport, which lower transport costs and thereby increase trade. The lack of scale economies creates _de facto_ landlocked countries, and can apply to coastal LDCs. Because of the low volumes in smaller ports, shipping lines set higher tariffs to call in these ports.<sup>49</sup> To illustrate this tendency that will probably increase in the coming years, we give the example of exporters of fruits and vegetables from South Mauritania. Because of the maritime

> 47  Goma (DRC)-Mombasa transport costs are 2,000 USD for exports and 4,000 USD for import.

> 48 For Data between Paris and Tashkent, _see_ Raballand et al. 2005.

> 49 Like Cullinane and Khanna (2000) demonstrate, the container market expanded with ever growing container vessels. Higher tariffs also depend on voyage length and port efficiency. The scale economies in ship size decline as route lengths shorten and port efficiency worsen. The last factor is weak in several African ports, which is conducive to higher tariffs.

43
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international finance are investigated, such as the question of "liquidity vs. solvency," the degree to which discrepancies between official and black market exchange rates can predict future financing problems, and the importance of world factors exogenous to a country in causing debt crises. Ve also investigate whether large surpluses run by the oil-exporting nations significantly affect the international credit markets. These models are then tested for structural breaks following major external events, like the 1973 oil shock and institutional changes in meeting the debt crisis in the early 1980's. In the same section, we construct indices of exchange rate over-valuation, based on discrepancies between black market and official exchange rates, and examine whether overvaluation acts as one of the precursors of debt crises. Econometric methodological issues are discussed in Appendix 1.

Section 3 discusses the estimation results and some of their implications for policy. Ye show that, as expected a _p7iori,_ reliable econometr **i** nferences c on these issues requires explicit accounting for unobservable persistent heterogeneity among nations as well as for the impact on bankers' perceptions ofa history of bad debt performance. Section 4 attempts to evaluate the significance of world economic factors, exogenous to developing countries, in precipitating crises. Examples of such factors include measures of imports by industrialized countries, inflation in the OECD nations, and world interest rates. The findings are important for policy-related questions that address the "adjustment efforts" by LDC's and the distinction between economic mismanagement and adverse world developmen **t** s hat are beyond the control of country administrators. Section 5 examines the econometric stability of the models after the major oil shocks of the 1970's and after 1981, the last year of observation of most past studies of creditworthiness in the literature. Section 6 discusses the iss zs related to debt overhang and describes some of the characteristics of the recently developed secondary market for outstanding