rafmacalaba/datause-encoder-data huggingface dataset:
_15_ <!-- Start of picture text --> LS // Dependent Variable is POLDIS<br>Sample: 8/05/94 - 7/12/96<br>Included observations: 102<br>Variable Coefficient Std. Error T-Statistic Prob.<br>POLDIS(-I) 0.990426 0.007569 130.8552 0.0000<br>R-squared 0.941135 Mean dependent var 551.8529<br>Adjusted R-squared 0.941135 S.D. dependent var 184.4645<br>S.E. of regression 44.75499 Akaike info criterion 7.612161<br>Sum squared resid 202303.9 Schwartz criterion 7.637896<br>Log likelihood -531.9520 Durbin-Watson stat 2.008243<br><!-- End of picture text --> 2) The second model is similar to the second one for MEXDIS: _POLDIS = c + ca t + dl + y POLDIS(-1)_ (3) The results are: <!-- Start of picture text --> LS // Dependent Variable is POLDIS<br>Sample: 8/05/94 7/12/96<br>Included observations: 102<br>Variable Coefficient Std. Error T-Statistic Prob.<br>C 504.6681 127.1812 3.968103 0.0001<br>T -1.665897 0.419320 -3.972849 0.0001<br>Dl 75.10052 21.39856 3.509606 0.0007<br>POLDIS(-4) 0.802494 0.050719 15.82226 0.0000<br>R-squared 0.949392 Mean dependent var 551.8529<br>Adjusted R-squared 0.947843 S.D. dependent var 184.4645<br>S.E. of regression 42.12772 Akaike info criterion 7.519838<br>Sum squared resid 173925.0 Schwartz criterion 7.622778<br>Log likelihood -524.2435 F-statistic 612.8235<br>Durbin-Watson stat 1.960488 Prob(F-statistic) 0.000000<br><!-- End of picture text --> where _dl_ is defined as above (dummy with ones after 12/16/94). An ADF on the complete sample based on this model (i.e. with drift and trend) suggests the rejection of the null of unit root at the 5%. '7 '' In order to perfonn a proper ADF test, we split the sample as above: 7/29/94 to 12/16/94 and 12/23/94 to 7/12/96. For the first period we cannot reject the null of unit root even at the 10% level. For the second period, we reject the null of unit root at the 5% critical level.
# **Table 4: Market Share Regressions**

||**Depend**<br>|**ent Variable**<br>**Quantile**<br>|**: Market Sha**<br>**Regressions**<br>|**re(Output)**<br>||**O**<br>|**LS**<br>|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_Quantile_|**10**<br>coef/se|**50**<br>coef/se|**90**<br>coef/se|**95**<br>coef/se|**99**<br>coef/se|**Simple**<br>coef/se|**Weighted**<br>coef/se|
|Ben Ali firm (BA)|0.000**|0.011***|0.163***|0.240***|0.668***|0.056***|0.162***|
||(0.000)|(0.002)|(0.006)|(0.008)|(0.021)|(0.017)|(0.053)|
|BA*Highly Regulated (de Jure)|0.000|-0.002|0.678***|0.651***|0.176**|0.068|0.576***|
||(0.000)|(0.002)|(0.009)|(0.017)|(0.075)|(0.073)|(0.077)|
|Highly Regulated (de Jure)|0.000|0.001**|0.001|0.003|0.037|0.003|-0.005|
||(0.000)|(0.000)|(0.005)|(0.014)|(0.077)|(0.003)|(0.039)|
|Offshore|0.000|0.000|0.001|0.002|0.020|0.002|-0.023|
||(0.000)|(0.000)|(0.004)|(0.008)|(0.022)|(0.003)|(0.031)|
|Foreign|0.000***|0.001|0.008**|0.014*|0.049***|0.004|0.092**|
||(0.000)|(0.000)|(0.003)|(0.007)|(0.015)|(0.002)|(0.041)|
|Firm Age (log)|0.000**|0.000***|0.003***|0.007***|0.028**|0.002***|0.053|
||(0.000)|(0.000)|(0.001)|(0.002)|(0.012)|(0.001)|(0.033)|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|No|No|No|No|No|No|No|
|Weights|None|None|None|None|None|None|Output|
|Number of observations|49,446|49,446|49,446|49,446|49,446|49,446|49,446|
|R2|0.056|0.047|0.051|0.055|0.055|0.059|0.678|

Note: *,**,*** denote significance at the 10%, 5% and 1% significance level respectively. Standard errors are clustered by sector. The sample is confined to 2006-2010 and to firms which report using hired labor, producing more than 1600 USD worth of output and having labor productivity in excess of 400 USD, operating in sectors governed by the investment code in which at least one Ben Ali firm is contemporaneously active. Highly Regulated (de Jure) is a dummy variable indicating whether the particular 5 digit sub-sector the firm operates in is subject to both authorization requirements and FDI restrictions at time t. Standard errors are clustered at the 5-digit sector level.

31
|**Table 7. Consumption and the choice of migration destination --**|**using migra**|**nts from**|**the NLSS 2**|**002/3**|||||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Consumption**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|
|Average consumption expenditures (log)|-0.163|-0.238|-0.161|-0.236|||||||
|Log consumption differential due to education and high caste|||0.069|0.236|||||||
|Combined average and differential|||||-0.007|-0.026|||-0.108|-0.160|
|Relative log consumption controlling for education and ethnicity|||||||0.484|0.155|1.566|0.188|
|**Prices and amenities**|||||||||||
|Log of rice price|1.991|1.445|1.999|1.438|1.852*|1.685|1.854*|1.656|1.949|1.411|
|Housing price premium (log)|0.328***|3.000|0.327***|2.990|0.329***|3.081|0.328***|3.064|0.327***|3.000|
|Time travel to nearest paved road|-1.469**|-2.402|-1.469**|-2.400|-1.394**|-2.560|-1.391**|-2.573|-1.444**|-2.379|
|Time travel to nearest bank|1.049|1.514|1.040|1.513|1.008|1.523|0.999|1.511|1.029|1.504|
|Elevation in meters|-0.048|-0.116|-0.047|-0.115|-0.065|-0.158|-0.065|-0.158|-0.053|-0.126|
|**Population**|||||||||||
|Population density|-0.504|-1.095|-0.496|-1.100|-0.532|-1.105|-0.527|-1.129|-0.509|-1.126|
|Log(population)|2.733***|4.492|2.726***|4.539|2.748***|4.288|2.743***|4.344|2.734***|4.508|
|Ethnicity similarity index|0.897*|1.699|0.907*|1.722|0.912*|1.728|0.921*|1.733|0.910*|1.728|
|Language similarity index|1.917***|2.981|1.926***|3.044|1.860***|2.727|1.865***|2.811|1.906***|3.021|
|Religion similarity index|-1.036|-1.418|-1.036|-1.421|-1.065|-1.530|-1.066|-1.555|-1.045|-1.432|
|**Distance**|||||||||||
|Distance above 100 Km|-11.458***|-6.827 -|11.454***|-6.830 -|11.473***|-6.783|-11.470***|-6.781|-11.460***|-6.818|
|Log-Likelihood|-623.0|8|-623.|06|-623.|14|-623.|13|-623.|10|
|Number of observations|16,21|4|16,21|4|16,21|4|16,2|14|16,21|4|
|PseudoR2|0.388||0.38|8|0.38|8|0.38|8|0.38|8|

The estimator is Fixed Effect Conditional Logit. Standard errors are corrected for clustering across district of origin. *** p<0.01, ** p<0.05, * p<0.1
Policy Research Working Paper 8595

# **Abstract**

A burgeoning area of social science research examines how state capabilities and bureaucratic effectiveness shape economic development. This paper studies how the management practices of civil service bureaucrats correlate to the delivery of public projects, using novel data from the Ghanaian Civil Service. This paper combines hand-coded progress reports on 3,600 projects with a management survey in the government ministries and departments responsible for these projects. The analysis finds that management matters: practices related to autonomy are positively associated with project completion, yet practices related to incentives/monitoring of bureaucrats are negatively associated with project completion. The negative impact of incentives/monitoring practices is partly explained by bureaucrats having to multitask, interactions with their intrinsic motivation, their engagement in influence activities, and project characteristics such as the clarity of targets and deliverable outputs. The paper discusses the interplay between management practices and corruption, alternative methods by which to measure management practices in organizations, and the external validity of the results. The findings suggest that the focus of many civil service reform programs on introducing stronger incentives and monitoring may backfire in some organizations, and that even countries with low levels of state capability may benefit by providing public servants with greater autonomy in some spheres.

This paper is a product of the Development Research Group, Development Economics. 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 drogger@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
account.

The coefficients all have the expected sign. Higher money stock has a positive effect on the level of the parallel premium. A smaller budget surplus (or a higher budget deficit) will result in a higher parallel premium. A unifiedexchange rate is expected to drive the parallel premium down (to zero) and hence the coefficient for DUMMYU is negative. When the dual exchange rate system is official, the element of risk involved in trading on the parallel market no longer exists and the premium should be smaller. We find that the coefficient for DUMHYO is negative but statistically insignificant. We also estimated the same equation with dummies for restrictions on payments on the current account and the capital account t RESCUR and RESCAP respectively5 ). The dummy for restrictions on payments on the capital account is statistically significant, while the dummy for restrictions on payments on the current account is not. The coefficient obtained for RESCAP is somewhat large, but this could be due to the nature of the (0,1) dummy being used. The sign of the coefficient for DUMMYO changes but is still statistically insignificant.

We can conclude from these results that it makes little difference to the level of the parallel premium whether the dual exchange rate system is official or not. The evolution of monetary aggregates and the fiscal surplus are found to be important in determining the parallel exchange rate. This corroborates the findings of the previous section. In addition, the results broadly support the view that restrictions on the capital account have a significant effect in increasing the premium while restrictions on the current account do not appear to matter. While this is consistent with more casual empirical evidence (i.e. in countries with no capital controls there is no premium), the results should be taken with some caution because the variable that we constructed does not measure the intensity of cortrols.

# IV. Insulation Properties on the Official Exchange Rate and Domestic Prices

5 The dummies were constructed using information from "Exchange Arrangements and Exchange Restrictions (IMF, various issues)
# Appendix 2: The role of networks in explaining migrants’ occupational choices

To better understand the role of networks of co-national migrants in the occupational choices of migrants, we estimate the following regression using two different waves of the EU-LFS: Log(pct_migrant_2018) cok = α + β log(pct_migrant_2005)cok + ηc + Ωo + φk + ε cok (2) where log(pct_migrant_2018)cok is the logarithm of the percentage of migrants in 2018 in each ISCO 3-digit occupation _c_ that are from the region of origin _o_ in NUTS2 division of destination _k_ . Log(pct_migrant_2005) cok is the logarithm of the share of migrant population from region of origin _o_ in each occupation _c_ residing in NUTS2 division of residence _k_ in 2005. Ωo is a vector of regions of origin _o_ (native, EU15, NMS13, EFTA, Other European countries, MENA, SubSaharan Africa, Asia, Latin America and non-Europe OECD countries); ηc is a vector of dummy variables for each ISCO 3-digit occupation; and φk is a vector of dummy variables for each NUTS2 division of residence K. In order to avoid overlapping of migrants in the two periods, we restrict the sample of migrants in 2018 to those that arrived within the previous ten years.

Migrant networks are particularly strong among co-nationals in the same area of residence. We run the OLS regression in equation (2) in which the cell of analysis is the ratio of migrants from world region of origin _o_ to native in NUTS2 division of residence _k_ working in occupation _c_ . The results of this regression analysis show a strong path dependence in occupational choices of migrants even after controlling for occupation, region of origin and NUTS2 division of residence fixed effects (Table A1). Our preferred regression includes all occupations – even those without a single migrant – and origin region, host NUTS2 division and occupation fixed effects. The results (Column 2) show that a 1 percent increase in the share of co-national migrants in an occupation in 2005 is associated with a 0.13 percent increase in the share of new migrants in that occupation in 2018.

Table A1: Regression analysis on the role of networks in occupational choices of migrants in the EU

|Dependent variable: log (% of migrants in<br>2018) per 3-digit occupation, nuts2 division<br>of residence andregionoforigin|Model 1<br>(OLS)|Model 2<br>(OLS)|Model 3<br>(OLS)|Model 4<br>(OLS)|
|---|---|---|---|---|
|Log(% migrants in 2005)|0.188***|0.127***|0.0828***|0.0344***|
||(0.00539)|(0.00518)|(0.00628)|(0.00611)|
|Constant|-6.983***|-6.529***|-3.830***|-2.944***|
||(0.0472)|(0.129)|(0.0421)|(0.256)|
|Observations|92,294|92,294|8,439|8,439|
|R-squared|0.060|0.262|0.041|0.500|
|NUTS2 division of residence FE|NO|YES|NO|YES|
|Region of origin FE|NO|YES|NO|YES|
|ISCO 3-digit Occupation FE|NO|YES|NO|YES|
|Including occupations with nomigrants|YES|YES|NO|NO|

Note: Robust standard errors are reported in parentheses; ***: p<0.01, **: p<0.05, *: p<0.1.

56
22

# Table 7: Real Interest Rate (in X)

|1970-80|1981-88|
|---|---|
|Bangladesh<br>-5.7|<br>0.1|
|India<br>-0.9|<br>1.1|
|Pakistan<br>-3.3|<br>1.7<br>|
|Sri Lanka<br>0.6|7.8|
|Thailand<br>-0.8|<br>7.4|
|Malaysia<br>1.3|<br>5.0<br>|
|Indonesia<br>-0.1|<br>6.4|
|Philippines<br>-3.0|<br>-1.5|
|China<br>3.3|<br>0.3|
|Korea<br>0.2|5.3|
|Average of the above<br>-0.8|<br>3.4<br>|
|Average of the HICs<br>-2.9|<br>-89.9|

Note: HICs are 17 highly indebted countries<sup>as defined in the World Debt</sup> Tables 1988-89.

Source: World Bank data.
# II. Impact of recent cereal price and production shocks on the food-poor

Mali has experienced shocks to cereal prices due to the international rise in cereal prices in recent years (2008/2009 food price rises as well as 2011 price rises). Also, the country experienced partial drought in 2011, affecting production of cereals, especially rice. In February 2012 the government of Mali assessed 111 communes at risk of food crisis while another 85 were identified as communes in economic difficulty due to the drought. Globally, there was need to assist some 3.575 million Malians. In this section we use the ELIM-MICS 2010 data to simulate the impact on the food poor of such cereal price and production shocks.

The food-poor are highly dependent on purchases and self-production of cereals in their total consumption. Nationwide, cereals form on average about a quarter of household consumption, half of which are purchased grains with the other half being self-produced grains. The share of purchased grain in overall consumption is highest in Timbuktu and Gao (22%) and lowest in Sikasso (6%). In Timbuktu and Gao (Northern Mali) cereals form about a third of total household consumption indicating the overall vulnerability of this region to changes in cereal prices and production.

**Table 7.** **<u>Share of purchased and self-produced cereals in total consumption by region</u>**

|**Region **|**Share of**<br>**purchased**<br>**cereals in total**<br>**consumption**<br>**(%)**|**Share of self-produced**<br>**grain in total**<br>**consumption (%)**|**Share of both purchased**<br>**and self-produced grain**<br>**in total consumption**<br>**(%)**|
|---|---|---|---|
|Kayes-Koulikourou|11|11|22|
|Sikasso|6|17|23|
|Mopti-Ségou|11|16|27|
|Tomboucto-Gao|22|10|32|
|Bamako|15|1|16|
|Total|12|12|24|

_Source: authors’ calculations using ELIM –MICS 2010_ We assess the impact of recent cereal price and production shocks on the number of food poor, that is, the people that have a total consumption level that is below the food poverty line of 118 173 FCFA per person per year. We do this by looking first at the amount of cereals households purchased in 2010 using the ELIM survey of that year and the share of purchased and self-produced cereals in total consumption. We then calculate the extra amount those households would have to pay to purchase that same amount of cereals, given the post price increase. We assume that quantities of cereals bought do not change 10
Table 7: Test for Difference in Means

|EC9's IMPORT|FROM GR|EECE (Case|1)|||||
|---|---|---|---|---|---|---|---|
||Value<br>1971-80<br>Average|($MIS)<br>1981-90<br>Average|Share<br>1971-80<br>Average<br>before|(%)<br>1981-90<br>Average<br>after|Difference|t-statistics||
|Agriculture|439.51|2,240.1|0.62728|1.00018|0.3729|5.13836|A|
|Manufacturing|874.25|3,458.94|0.39025|0.40596|0.01571|0.43097||
|GREECE'S IMP|ORT FRO<br>Value|M EC9 (Case<br>($MIS)|2)<br>Share|(%)||||
||1971-80|1981-90|1971-80|1981-90||||
||Average|Average|Average<br>before|Average<br>after|Difference|t-statistics||
|Agriculture|251.98|1,486.58|28.0235|63.5014|35.4778|26.0782|A|
|Manufacturing|2,151.31|4,901.78|59.9696|66.2924|6.32284|3.835|B|
|<br>ECO'S IMPOR|<br>T FROM S<br>Value|<br>PAIN AND P<br>($MIS)|ORTUGAL<br>Share|(Case 3)<br>(%)||||
||1976-85|1986-95|1976-85|1986-95||||
||Average|Average|Average|Average|Difference|t-statistics||
||||before|after||||
|Agriculture|2,691.47|7,863.8|2.72996|3.51329|0.78333|4.33583|A|
|Manufacturing|8,005.94|32,637.9|1.89638|3.01757|1.12119|4.63569|A|
|IMPORT OF SP|AIN AND|PORTUGAL|FROM EC I|O (Case 4)||||
||Value|($MIS)|Share|(%)||||
||1976-85|1986-95|1976-85|1986-95||||
||Average|Average|Average<br>before|Average<br>after|Difference|t-statistics||
|Agriculture|1,122.49|6,887.01|16.8723|27.5678|10.6955|4.50586|A|
|Manufacturing|9,108.58|46,442.5|64.2141|64.7192|0.50513|0.7957||

Note: A: Significant at 0.1 percent level B: Significant at 1 percent level 32
# _Motivation_

Consumption data are not always collected in a household survey for various reasons. The main reason is that since a typical consumption module (e.g., like that of a Living Standards Measurement Survey) usually consists of up to hundreds of items of food and non-food consumption, it takes time and a certain level of technical expertise to design such a module well. Furthermore, these items need to be updated from time to time to reflect changes in household consumption patterns (e.g., buying a smart phone is becoming an increasingly common purchase these days, but might not be so just 10 years ago). Other factors such as seasonal variations in household consumption (e.g., consumption during holidays can be larger than that during regular times) could only be appropriately accounted for by fielding the consumption module in different months throughout the year. Consequently, collecting household consumption data requires considerable financial resources, time, and logistical capacity, which result in most household consumption surveys being implemented every few years rather than on an annual basis.<sup>5</sup> Given this data situation, for the years when consumption data are not collected, but other nonconsumption surveys such as labor force surveys (LFS) or small-scale surveys are available, these surveys may be “repurposed” to generate some substitute variable for consumption data. Indeed, one well-known example is the DHS that (usually) do not collect consumption data but offer a wealth index instead.

# _Method Description_

A well-established method to produce poverty estimates for surveys that have no consumption data but have data on household assets and the physical characteristics of the house is to construct a wealth index from these assets. This method typically consists of two steps. The first step is to identify the list of assets to be used in the construction of the index, and the second step is to apply

> 5 We return to more discussion on this point in Section VII.

8
<!-- Start of picture text --> Figure 111.15<br>Total External Debt to Export Ratios, 1996<br>300<br>250<br>200<br>150<br>100~<br>Sosur.<cr Debt Rp.,flwg S,Yzrat.. I,ashhr nnO? ]?f,,seso<br><!-- End of picture text --> <!-- Start of picture text --> 0~~~~~~~~~~~~~~~~~099 Figure 11I.16<br>Short-temi dDbt flows. 1989-92 and 1993-98<br>(percentage of GDP}<br>china<br>PhilippineS .St'.'3'3'9SS93s<br>Thailand ij'1i ^9. ,M 'di.tEi$"|ij? SEb' kS. i>X igiOi>s3" -<br>Argentina<br>Brazil 2§=P3 2i'<br>Chile<br>Mexico = =3<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5<br><!-- End of picture text -->

<!-- Start of picture text --> Source: IMF, World Economic Outlook, Database.<br><!-- End of picture text --> The most telling indicator of vulnerability was the ratio of short-term external debt to external reserves prior to the crisis (Figure FII.17). It shows that short-term debt exceeded external reserves by a large margin for Korea, Thailand and Indonesia in June 1997, exceeding that for many other developing countries. This high ratio of short-term obligations to liquid foreign exchange assets made these countries much more vulnerable to a potential run on their currencies in the face of a loss of investor confidence. See also Sachs, Tornell and Velasco, 1996 and Radelet and Sachs, 1986.

42
Datastream. As an alternative index of bank riskiness, we use the Z-score, which is a bank’s distance from default, computed as the sum of the bank’s contemporaneous return on assets and capital assets ratio, divided by the standard deviation of the return on assets. A higher Z-score indicates higher bank stability.

Finally, the table provides summary statistics on the indicators of a banks’ systemic size. Liabilities are a bank’s liabilities-to-GDP ratio, with a mean of 3.7 percent. Sum of liabilities is the ratio of banking-system liabilities to GDP, with a mean of 1.11. Other Liabilities is the difference between Sum of Liabilities and an individual bank’s own Liabilities variable, while Liabilities sq is the square of Liabilities. The variables Big 0.1, Big 0.25, Big 0.5 and Big 1.0 are dummy indicators of systemic size. For instance, Big 0.1 is a dummy variable that equals 1 if a bank’s total liabilities exceed 10 percent of GDP, while it is zero otherwise. Big 0.25, Big 0.5 and Big 1.0 are defined analogously. In the tables, we see, for instance, that 5.5 percent of banks have a liabilities-to-GDP ratio that exceeds 0.1.

To conclude this section, it is interesting to see how systemically important banks tend to differ from smaller banks. To this effect, Table 6 provides the means of our set of variables in 2008 separately for banks with a liabilities-to-GDP ratio exceeding 0.5 and for smaller banks. The table shows that systemically important banks have significantly lower CDS spreads. The larger banks further have a higher average earning assets ratio and higher leverage. The larger banks in addition tend to be located in countries that experienced fewer banking crises with correspondingly lower past fiscal costs of banking crises, while on average they are located in countries with lower fiscal balances for the year 2008.

# **3. The empirical evidence**

In this section, we will examine how bank size, in absolute terms and relative to the national economy, potentially affects bank valuation and CDS pricing on account of differential access to the financial safety net subsidies. We first discuss our tests of whether banks are too big too fail and too big to save. Then we present our main empirical results, followed by some robustness checks.

12