rafmacalaba/datause-encoder-data huggingface dataset:
|Distance from homestead in minutes|0.000|0.000|0.001*|-0.004***|-0.004***|
|---|---|---|---|---|---|
||(0.944)|(1.095)|(1.799)|(5.243)|(5.224)|
|Number of years plot possessed|0.001|0.001|0.003**|0.001|0.001|
||(1.457)|(1.319)|(2.565)|(0.623)|(0.771)|
|Plot located in wet land|0.004|0.008|0.024|-0.036|-0.032|
||(0.053)|(0.094)|(0.289)|(0.651)|(0.582)|
|Irrigated land|0.008|-0.011|-0.039|-0.080|-0.080|
||(0.097)|(0.147)|(0.494)|(1.070)|(1.071)|
|Crop shock|-0.213***|-0.209***|-0.203***|-0.217***|-0.218***|
||(6.131)|(6.031)|(5.829)|(6.202)|(6.215)|
|Share of area cultivated with grains|-0.409|-0.488*|-0.542**|-0.307**|-0.287**|
||(1.609)|(1.960)|(2.166)|(2.285)|(2.154)|
|Share of area cultivated with tubers|-0.180|-0.259|-0.265|-0.398***|-0.389***|
||(0.699)|(1.028)|(1.042)|(2.954)|(2.912)|
|Share of area with tree crops|-0.001|-0.100|-0.194|0.143|0.158|
||(0.004)|(0.396)|(0.765)|(1.041)|(1.156)|
|Plot topography dummies/shares|Yes|Yes|Yes|Yes|Yes|
|Soil type dummies/shares|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.076|0.065|3.475***|0.127|0.131|
||(0.264)|(0.248)|(12.299)|(0.619)|(0.667)|
|Number of observations|3,062|3,062|3,062|7,307|7,307|
|R-squared|0.579|0.575|0.572|0.429|0.425|
|**Output elasticities**||||||
|Land|0.305***|0.308***|0.339***|0.246***|0.245***|
||(18.525)|(18.922)|(21.566)|(16.971)|(17.040)|
|Labor|0.405***|0.410***|0.399***|0.480***|0.481***|
||(19.305)|(19.620)|(13.850)|(22.097)|(22.277)|
|Chemical fertilizer|0.139**|0.094***|0.095***|0.132***|0.134***|
||(2.334)|(3.505)|(3.121)|(3.766)|(3.877)|
|Pesticides|0.053|0.101***|0.107***|0.162*|0.142***|
||(1.122)|(2.696)|(2.823)|(1.771)|(2.893)|
|Manure|0.115***|0.118***|0.113***|0.103***|0.102***|
||(7.555)|(7.943)|(7.581)|(5.060)|(5.140)|
|**Marginal products**||||||
|Male family labor in US$|||0.666<br>(1.285)|||
|Female family labor in US$|||0.539<br>(0.959)|||
|**Village level market wages**||||||
|Male family labor in US$|||0.911|||
|Female family labor in US$|||0.871|||
|**Returns to scale**|1.017|1.030|1.054|1.122|1.104|
|Test of const. ret. to scale:_F_-value|1.43|0.45|0.28|2.25**|3.00*|

_Note:_ All holding-level regressions include village fixed effects while plot-level regressions are estimated using household fixed effects. Most of the Allen cross elasticities are not statistically different from zero. Jointly, they are not or only marginally different from zero. Marginal products of male and female family labor (shadow wages) are calculated using the formula MPL= β̂(Y<sup>�</sup> ⁄L) where β̂ is the coefficient on log of male (female) family labor, Y<sup>�</sup> is predicted value of output, and _L_ is male (female) family labor use in days (Jacoby 1993). Absolute value of t-statistics in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 1%.

19
Table 9. Militarization and Cederman and Girardin’s measure of ethno-nationalist exclusion, 1988–2002

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Ethexclusion (Cederman &|Military<br>Expend.<br>2.310|Military<br>Expend.<br>1.180|Military<br>Pers.<br>1.275|Military<br>Pers.<br>1.121|Arms<br>Imports<br>-4.870|Arms Imports<br>-4.606|
|Girardin)|||||||
||(1.10)|(0.52)|(0.91)|(0.81)|(1.08)|(1.01)|
|GNI p.c. (ln)|0.139|0.275|0.208|0.221|-0.618|-0.719|
||(0.53)|(0.74)|(1.28)|(1.29)|(0.84)|(0.97)|
|Economic growth|1.983**|1.887***|0.126|0.041|-1.738|-1.754|
||(2.57)|(2.59)|(0.12)|(0.04)|(0.49)|(0.50)|
|Polity|-0.001|0.012|-0.033**|-0.032**|0.035|0.053|
||(0.03)|(0.53)|(2.14)|(2.13)|(0.34)|(0.53)|
|Government expenditures|0.177***|0.175***|0.008|0.007|0.037|0.039|
||(5.01)|(5.27)|(0.50)|(0.45)|(0.66)|(0.70)|
|Population (ln)|-0.303|-0.952**|-0.245**|-0.305***|0.123|0.086|
||(1.33)|(2.17)|(2.57)|(2.71)|(0.50)|(0.37)|
|Contiguous militarization|0.100*|0.078|0.547*|0.531*|0.397**|0.387**|
||(1.75)|(1.44)|(1.94)|(1.88)|(2.17)|(2.07)|
|Peace years (civil war)|-0.023*|-0.026*|0.000|-0.002|0.005|0.007|
||(1.81)|(1.77)|(0.06)|(0.42)|(0.23)|(0.32)|
|Peace years (intern. conflict)|0.014|0.014|-0.008|-0.009|-0.026|-0.026|
||(1.56)|(1.52)|(1.06)|(1.07)|(1.48)|(1.49)|
|Civil war|0.149|0.128|-0.079|-0.096|0.542|0.572|
||(0.66)|(0.59)|(0.82)|(1.06)|(0.69)|(0.74)|
|International conflict|-0.027|0.013|0.336|0.346|1.684*|1.694*|
||(0.09)|(0.05)|(1.43)|(1.46)|(1.69)|(1.74)|
|Observations|876|876|671|671|678|678|
|Countries|69|69|70|70|70|70|

Notes: Absolute t- and z-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

29
uncertainty that is later extended to account for complexity. Finally, Appendix B provides further details on the variables used in the empirical analysis.

# **<u>2. Experimental Design and Survey Data</u>**

The experiment was carried out as part of the Malawi Technology Adoption and Risk Initiative (MTARI), a cooperative effort among several partners: the National Smallholder Farmers Association of Malawi (NASFAM), Opportunity International Bank of Malawi (OIBM), the Malawi Rural Finance Corporation (MRFC), the Insurance Association of Malawi (IAM), and the Commodity Risk Management Group (CRMG) of the World Bank. NASFAM is an NGO that provides technical assistance and marketing services to nearly 100,000 farmers in Malawi. It is by far the largest farmer association in the country. The farmers in the study were current NASFAM members. NASFAM field officers disseminated the information on the insured and uninsured loans to farmers, and handled the logistics of supplying farmers with the hybrid and improved seeds purchased on credit. OIBM and MRFC are microfinance lenders and provided the credit for purchase of the hybrid and improved seeds. OIBM is a member of the global Opportunity International network of microfinance institutions, while MRFC is a government-owned corporation. IAM designed and underwrote the actual insurance policies with technical assistance from the World Bank.

The microfinance institutions offered the loans for the hybrid and improved seeds as group liability contracts for clubs of 10-20 farmers. Take-up of the loan was an individual decision, however, and only the subset of farmers who took up the loan were jointly liable for each others’ loans. NASFAM contacted clubs in June and July 2006 and offered them the opportunity to be included in the study. Our study sample consists of 159 clubs from four different regions of central Malawi: Lilongwe North, Mchinji, Kasungu, and Nkhotakota. Figure 1 shows the study locations. In these clubs there were 787 farmers who agreed to be part of the study and were available to be surveyed in the following September.

To minimize concerns about fairness if farmers discovered that other farmers in the study were being treated differently, the treatments were randomized at the level of
# **Deposit Insurance Database**

**<mark>Asli Demirgüç-Kunt, Edward Kane, and Luc Laeven</mark>**<sup>**1**</sup>

JEL Classification Numbers: G20 Keywords: Deposit insurance; Financial institutions; Financial crises

> 1 Demirguc-Kunt, World Bank, ademirguckunt@worldbank.org; Kane, Boston College, edward.kane@bc.edu; Laeven, IMF, llaeven@imf.org.  We would like to thank Belen Bazano and Lindsay Mollineaux for excellent research assistance, and IMF and World Bank colleagues for their useful comments. The views expressed here are our own and do not represent those of the IMF, World Bank, their Executive Directors, or the countries they represent.
28 and a large pool of the unemployed with strong labour market attachment, a misguided policy.

Thirdly, a "problem group" easily identifiable are men and women with vocational training. For all data points this educational category has the highest unemployment rate with an above average participation rate for men and a slightly below average participation rate for women if we only consider the first five most qualified groups. The relative low job accession rates and high inflow rates into unemployment of persons in the vocational category might be an indication of the fact that human capital acquired by this group before the regime switch has depreciated relatively faster than the human capital of other educational categories. Vocational training in socialist economies has produced very narrow and inflexible work profiles many of which are virtually useless under the new conditions. Alternatively, educational category which is _not_ a good proxy for qualifications might be a good indicator of unobservable individual characteristics; persons with more education are more flexible and dynamic and have a better understanding of how to adapt to the new economic environment. Whatever the reasons behind the bad labour market experience of persons with vocational education in Poland, they are a large group among the population with strong labour market attachment and should be consequently identified as one of the primary group at which ALMPs should be targeted. Whether training measures increase this group's re-employment probabilities and should, therefore, be the chosen policy tool is, however, an open question as there exists an abundant supply of people who are unemployed, much better prepared for further training and retraining courses and have more desirable characteristics as far as prospective employers are concerned.

Finally, LTU _shares_ are not a function of education. Yearly oufflow rates from unemployment are uniformly low in international comparison and result in large shares of LTU no matter what the educational category. This is further evidence of very weak labour demand generating a stagnant pool of unemployment in Poland.

3.2.2. Analysis of raw data of supplement to August 1994 PLFS on labour market policies The supplement on labour market policies added to the PLFS in August 1994 produced the first data set in Poland as far as active labour market policies participants are concerned. In future, questions of how or whether participation determines higher earnings or re-employment probabilities can be investigated with
others appeared counterproductive. Specifically, successful companies favored managing and controlling employees, and placed a strong emphasis on cost control, rather than on creating a supportive and enabling environment.

Column (3) provides estimation results when the interaction terms of both sources of innovation are added. The coefficient of external innovation becomes larger than in column (2), showing that external innovation’s effect on output productivity is larger for those firms that invest in only external sources of innovation than for firms that invest in both internal and external sources of innovation. The coefficient of internal innovation remains statistically insignificant.

Meanwhile, the coefficient of the interaction term is close to zero and statistically insignificant, implying that there is no synergy in using both sources of innovation simultaneously. This result further supports the argument that internal innovation does not play a role in absorptive capacity in Tunisia, in contrast to earlier studies that argue it does by adapting external innovation. Therefore, there is a complementary effect between internal and external sources of innovation.

Results from table 5 might suggest that, in a developing country like Tunisia where firms face resource constraints and have limited internal skills and capability to innovate, it is better to increase investment exclusively in external innovation rather than dividing scarce resources between the two sources of innovation.

These results contradict some previous studies that find complementary effects for using both sources of innovation. For instance, Lokshin, et al. (2008) found complementarity in Dutch manufacturing firms, and Cassiman and Veugelers (2006) found it in Belgian manufacturing firms. My results contrast those studies, probably because of sample selection bias. For instance, Lokshin, et al. (2008) conducted their studies only on 304 Dutch manufacturing firms that answered that they were innovating.

More importantly, there might be differences in the way innovation works in developing and in developed countries. Specifically, firms in developing countries, such as Tunisia, have a lower level of innovation investment to start with. At the early stages of technology 42
**Table 3 Measurement error over time for the 3 countries**

|**Indonesia**|**Malaysia**|**Vietnam**|
|---|---|---|
|**2000-2007**<br>-0.16|-0.13||
|**2008-2015**<br>-0.18|-0.28|-0.3|
|Source: Authors’ calculations|||

All these insights are combined in the calculation of the potential gains from reallocation, which provides (i) a comprehensive measure of distortions that affect an economy and (ii) the significance of measurement error. The potential gains from reallocation are large, even after accounting for measurement error (Table 4).

**Table 4 Potential TFP gains from removing misallocation**

||**Indonesia**<br>**Malaysia**<br>**Vietnam**|
|---|---|
||_Panel A: without correction (HK)_|
|**2000-2007**|169.7<br>59.3<br>-|
|**2008-2015**|176.2<br>74.2<br>91.3|
||_Panel B: with correction for measurement error (BKR)_|
|**2000-2007**|89.6<br>31.2<br>-|
|**2008-2015**|86.1<br>18.7<br>22.3|

Note: Entries are the percent TFP gains from equalizing TFPR across plants in each industry. <u>Source: Authors’ calculations</u> We present different measures of the potential productivity gains that can be achieved from reallocation. First, Panel A of Table 4 provides the estimates of potential gains if distortions to both revenue and inputs are removed, and factors of production (labor, capital and intermediate inputs) are reallocated towards their most productive uses, using the HK approach. Second, Panel B of Table 4 accounts for the presence of measurement error, based on BKR. Accounting for measurement error significantly reduces the magnitude of the potential gains from reallocation. These changes are as low as 28 percentage points in Malaysia and up to as high as 90 percentage points in Indonesia. Yet, even after accounting for measurement error, the estimates indicate that productivity can substantially increase when distortions are removed. Interestingly, accounting for measurement error affects the trajectory of the potential productivity gains from removing misallocation. Gains are declining (growing) over time based on the BKR (HK) approach.

Interestingly, the potential gains in Malaysia or Vietnam are lower than the potential gains estimated by BKR for India. Without and with the measurement error correction, these gains are 102% and 65.2% in India during the period 1985-2011. While BKR is mute on this, HK assess the gains also for China. They appear declining over time, from 115.5% in 1998 to 95.8% in 2001 to 86.6% in 2005.

A complementary view comes from looking at the variance. If the variance of the true distortions is smaller than the variance of TFPR, then measurement error exaggerates misallocation. To shed light on this, in Table 5, we turn to estimates of the parameter , which by construction is the ratio λλ 14
||**Table A.8:**|**Summary o**|**f treatment by **|**case**|||
|---|---|---|---|---|---|---|
||**(1)**<br>**Repr **|**(2)**<br>**esentative s**|**(3)**<br>Diff<br>**ample**|**(4)**<br>**Dua**|**(5)**<br>**lpractice s**|**(6)**<br>Diff<br>**ample**|
||Public|Private|erence<br>(2)-(1)|Public|Private|erence<br>(5)-(4)|
|**Panel A: Unstable Angina**|||||||
|Correct treatment|0.03|0.06|0.03|0.03|0.29|0.26***|
|Correct treatment (alternate)|0.46|0.37|-0.09|0.42|0.61|0.20*|
|Palliative treatment|0.78|0.71|-0.07|0.92|0.90|-0.01|
|Unnecessary treatment|0.73|0.80|0.07|0.67|0.74|0.08|
|Aspirin|0.03|0.04|0.02|0.03|0.23|0.20***|
|Anti-platelet agents|0.03|0.01|-0.02|0.00|0.03|0.03|
|Referred|0.30|0.24|-0.05|0.22|0.32|0.10|
|ECG|0.24|0.23|-0.02|0.28|0.35|0.08|
|ECG & Referred|0.11|0.12|0.01|0.08|0.16|0.08|
|Antibiotic|0.14|0.17|0.03|0.28|0.23|-0.05|
|Number of observations|37|180||36|31||
|**Panel B: Asthma**|||||||
|Correct treatment|0.38|0.50|0.12*|0.58|0.67|0.09|
|Palliative treatment|0.28|0.29|0.01|0.47|0.29|-0.18**|
|Unnecessary treatment|0.74|0.83|0.09*|0.92|0.89|-0.04|
|Bronchodilators|0.33|0.36|0.03|0.52|0.59|0.07|
|Theophylline|0.13|0.22|0.09*|0.31|0.31|0.00|
|Oral Corticosteroids|0.15|0.31|0.16**|0.16|0.24|0.09|
|Antibiotic|0.38|0.40|0.02|0.59|0.46|-0.14*|
|Number of observations|39|184||64|70||
|**Panel C: Dysentery**|||||||
|Correct treatment|0.08|0.13|0.05|0.33|0.22|-0.11*|
|Palliative treatment|0.44|0.61|0.18**|0.75|0.61|-0.13*|
|Unnecessary treatment|0.28|0.56|0.28***|0.35|0.40|0.05|
|ORS|0.05|0.12|0.07|0.33|0.21|-0.12*|
|Asked to see child|0.33|0.14|-0.20***|0.27|0.42|0.15**|
|Antibiotic|0.44|0.61|0.18**|0.75|0.61|-0.13*|
|Number of observations|39|183||63|67||

Notes: In Unstable Angina, alternate definition for correct treatment codes referrals and referrals for ECG as correct. Note the large and significnat differences in "asked to see the child" across public and private providers in the representative and dual samples. If we were to assume the same rate of correct treatment in these cases, then the differences in correct treatment are no longer significant in either sample. If we carry out a bounding exercise, the differences are still not significant, and the standard errors are too wide for meaningful inference. This is why exclude the dysentery case in our pooled analysis of treatment across cases.
and 2008 and the p-value of the mean comparison Student tests are shown in Table 1. It confirms that there has been a statistically significant worsening of welfare between 2007 and 2008. The weight-forage and BMI-for-age z-score too have worsened between 2007 and 2008, while the height-for-age, a longer-term measure of nutritional status, does not change.

We use a difference-in-differences specification to assess whether there was a specific impact of the crisis on HIV households. The equation estimated is: where<sup>_y_</sup> _it_<sup>is a measure of welfare,</sup> _X it_ is a set of controls that include characteristics of the head of household (age, age squared, education, sex), the number of children aged 0 to 14 in the household and province dummies (Maputo city province is the reference), _HIVi_ is a dummy that equals 1 if household _i_ is HIV, 0 otherwise, _<mark>d</mark>_ <mark>2008</mark> is a dummy that equals 1 if year _t_ is 2008, 0 otherwise. This model is first estimated at the household level, where the dependent variable is income, consumption and food consumption. The second specification has anthropometric measures as the dependant variable, and is estimated at the child level. It controls for the age and sex of the child.

> In this equation, coefficient  2 indicates whether HIV households are worse off or better off in general, whatever the year and  3 , capturing the trend, signals whether the economic situation of

> all households improved or worsened between 2007 and 2008. Coefficient  is the difference-indifferences estimator: it shows whether, once initial characteristics and the trend are controlled for, HIV households did better or worse than comparison households between the two waves.

Endogeneity of the explanatory variable _HIVi_ is likely in this setting as there could be some unobservable heterogeneity between households that would explain both the level of income (or other welfare measures) and the HIV status. For example, HIV-positive individuals are often better off than average in developing countries. To control for this source of bias, we use a second specification with household fixed effects: 9
_h_ {( _t_ ),( _Z_ 1, _Z_ 2,.. _Zm_ )} = _h_ 0( _t_ ).exp( φ 1 _Zi_ +φ 2 _Z_ 2 + ... φ _mZm_ ) (5)

> where _Z_ is a vector of _m_ covariates, and _h_ 0 (<sup>_t_</sup> ) is the baseline hazard when the values of all the covariates are set to 0.  This model can be made linear by dividing both sides of equation (5) by _h_ 0 (<sup>_t_</sup> ) and taking natural logarithms:

This leaves an equation (6) that is readily estimable, and from which the predicted value of φ . _Z_ can be calculated. We use the relative hazard of program entry for firms with attributes _Z_ as the propensity score for defining the region of common support for matching successive cohorts of treated firms and their control group.

**Table 19. Estimates from Cox Proportional Hazards Model. Results from Any Program Participation Model**

|Independent variables|Hazard Ratio|Standard<br>Error|Z-statistic|
|---|---|---|---|
|Region||||
|Center|1.196|0.1133|1.89|
|Mexico City|0.824|0.0804|-1.98|
|Southern|1.251|0.1833|1.53|
|Sector||||
|Textile industry, clothing and<br>leather industry|0.921|0.0998|-0.76|
|Wood products|1.150|0.1537|1.05|
|Paper products|0.471|0.1048|-3.39|
|Chemical|0.851|0.1044|-1.32|
|Mineral products|0.708|0.1487|-1.64|
|Basic metalic industry|0.819|0.2027|-0.81|
|Metallic products, machinery<br>and equipment|1.064|0.1250|0.53|
|Other manufacturing industries|1.535|0.5984|1.1|
|Age of firm|1.006|0.0020|3.24|
|The firm is a branch|0.768|0.0670|-3.03|

24
- 27 -

# Table 3.1. Values of Private Infrastructure Provision as Percent of Total Value of Machinery and Equipment (Percent)

|Private<br>Provision||NIGERIA<br>_|||INDONESI<br>_|A||THAILAN|D|
|---|---|---|---|---|---|---|---|---|---|
|||SiL<br>Firms|**Large** <br>|Firm||Al|_SmLL_<br>Firm|Large<br>Firms|| ALL<br>||SMAll<br>Fires|Lsrge<br>Firm|ALL|
|Generators&<br>Accessories|22.10|<br>9.65|9.96|8.14|13.62|13.41|72.73|5.05|5.12|
|*oreholes&<br>Accessories|2.81|1.91<br>I_I|1.91|0.91|0.41|O."|3.62|1.60|1.61|
|Radio<br>,<br>Equipment|1.48|0.59|0.59|1.30|0.09|0.10<br>I|1.37|0.01|0.01|
|Vehicles for<br>Workers|5.58|2.84<br>____|2.86|1.73|2.37|2.34||5.29|5.29|
|Vehicles<br>for<br>Shipment of<br>Goods__|10.95<br>_<br>_<br>_|4.47<br>_<br>_<br>_<br>_|4.62|9.63|1.60|1.82|28.65|5.00|5.68|
|Vehicles for<br>Garbage<br>Disposa|0.15|0.48|0.48|25.0|0.03|0.03||0.86|0.86|
|sotorcycLes<br>for<br>- essengers|<br>0.49<br>_____|0.04|0.04|1.93|0.07|0.09|2.95|0 14|0.15|
|AlL Private<br>Infrastructur|30.52|13.27|13.67|12.47|15.90|15.73|23.08|732|7.79|

NOTE: (1) The vaLues are for the firm in each cell ss **a** haole (i.e., ratlos of the **sm).** (2). The values of generators, boreholes, and radio quipment ore included in the total value of macHnery and equipment, but those of vehicles **and** motorcycles **are** not included.

(3) From Appendix TabLes El - ES, Fl - _FS,_ nd _G1_ - C8.

it/ SmalL firms are estabLishments with less than 50 espLoyees.

12 As percent of the total vaLue of capita. The numerator includes all private infrastructure provisions (generators, borehoLes, radio quipment, vehicles nd motorcycles); the denominator includes machinery and quipment. .U vehicles and motorcycles.

SMace: World lank Nigeris Infrastructure Project Establishment Survey 1988. World Bank Research Project tRPO 676-71) Establi shmnt Survey for Irlre sia and ThoIl nd 1992.

C:\1"U50CCC\TABLE3S. 1