rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table A.32. The DID regressions of households’ income</u>**

|||||Dependent variable|s|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program|-0.1312|-0.8133|-0.6152|0.1825|-0.4320|-0.6369***|-0.4612**|
||(0.1048)|(0.6220)|(0.4070)|(0.2518)|(0.4477)|(0.2458)|(0.2297)|
|(Poverty rate – 50)|0.0004|0.0841|0.0132|-0.0277|-0.0112|0.0549**|0.0181|
||(0.0121)|(0.0600)|(0.0505)|(0.0332)|(0.0502)|(0.0270)|(0.0243)|
|Program * (Poverty rate – 50)|-0.0033|-0.0613|0.0162|0.0239|0.0309|-0.0420|-0.0292|
||(0.0127)|(0.0663)|(0.0533)|(0.0336)|(0.0543)|(0.0302)|(0.0259)|
|Program * Post-program period|<br>0.0269|<br>0.8250|<br>0.8851**|<br>-0.4883***|<br>0.5302|<br>0.8165***|<br>0.6916*|
||(0.0908)|(0.5590)|(0.3517)|(0.1814)|(0.5551)|(0.3137)|(0.3934)|
|(Poverty rate – 50) * Post-|0.0040|-0.1024**|-0.0708*|0.0203|-0.0255|-0.0523|-0.0027|
|program period|(0.0092)|(0.0448)|(0.0386)|(0.0235)|(0.0528)|(0.0338)|(0.0395)|
|Program * (Poverty rate – 50) *|-0.0090|0.0390|0.0481|-0.0115|-0.0156|0.0420|0.0146|
|<br>Post-program period|(0.0098)|(0.0526)|(0.0423)|(0.0240)|(0.0578)|(0.0385)|(0.0432)|
|Age of household heads|0.0300***|0.0604***|0.0649***|0.1311***|-0.0434***|-0.1005***|-0.0721***|
||(0.0024)|(0.0165)|(0.0088)|(0.0127)|(0.0123)|(0.0103)|(0.0104)|
|Age squared of household heads|<br>-0.0003***|<br>-0.0008***|<br>-0.0008***|<br>-0.0013***|<br>0.0006***|<br>0.0013***|<br>0.0008***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|-0.0163|-0.2734**|0.1499*|0.7678***|-0.4285***|-0.1297**|-0.4458***|
|<br>female=0)|(0.0186)|(0.1150)|(0.0802)|(0.0931)|(0.0590)|(0.0657)|(0.0708)|
|Ethnic minorities (yes=1, Kinh=0)|-0.5401***|-0.7838***|-1.2738***|0.8158***|-0.8673***|0.4293***|-0.8206***|
||(0.0454)|(0.2310)|(0.1344)|(0.1023)|(0.1930)|(0.1062)|(0.0961)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.5794***|3.7036***|1.3208***|3.6383***|5.2056***|2.6051***|3.8583***|
||(0.1080)|(0.6313)|(0.3732)|(0.3524)|(0.4262)|(0.3300)|(0.2658)|
|Observations|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|<br>40,468|
|R-squared|0.297|0.053|0.042|0.090|0.059|0.066|0.045|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

82
**Table 6.** OLS Regression of the Effect of Information and Dummy Prices on Adoption of Solar Lantern

||Without|control|Wit|hcontrol|
|---|---|---|---|---|
|Variables|Coef.|Se.|Coef.|Se.|
|Less than 25% subsidy|-0.772***|0.045|-0.786***|0.049|
|25% to 50% subsidy|-0.656***|0.045|-0.649***|0.047|
|50% to 75% subsidy|-0.312***|0.067|-0.313***|0.071|
|Information|-0.041|0.025|-0.038|0.028|
|Information X Less than 25% subsidy|0.059|0.066|0.067|0.067|
|Information X 25% to 50% subsidy|0.082|0.067|0.063|0.069|
|Information X 50% to 75% subsidy|0.196**|0.088|0.176**|0.087|
|Gender of household head (1=Male,<br>0=Female)|||0.116|0.092|
|Marital status (1 if married, 0 otherwise)|||-0.105|0.086|
|Education of household head in years|||0.002|0.005|
|Age of household head in years|||-0.001|0.001|
|Household Size|||0.000|0.007|
|Value of total assets in Birr|||0.000|0.000|
|Number of rooms in the house|||0.013|0.018|
|Household connected to grid (1 if yes, 0<br>otherwise)|||-0.221***|0.070|
|Household uses solar light (1 if yes, 0|||-0.034|0.042|
|otherwise)|||||
|Household uses kerosene for lighting (1 if yes,|||0.078**|0.032|
|0 otherwise)|||||
|Household uses fuel wood for lighting (1 if yes,|||0.070|0.064|
|0 otherwise)|||||
|Access to credit (1 if access to 400 Birr or|||0.020|0.032|
|more, 0 otherwise)|||||
|Distance to the market|||0.003|0.002|
|Distance to the nearest road|||0.000|0.000|
|Distance to the nearest town|||0.000|0.001|
|Village has access to grid (1 if yes, 0 otherwise)|||0.067|0.041|
|Constant|0.972***|0.014|0.839***|0.096|
|Observations|808||806||
|R-squared|0.416||0.436||

Note: *** p<0.01, ** p<0.05, * p<0.1; Adoption =1 if household purchased at the randomly drawn price and =0 otherwise Consistent with the descriptive results above, provision of information about the private and public benefits of the solar lantern did not make a significant difference in the rate of adoption (Tables 5 and 6). Further, a combination of price incentives and information (i.e., their 26
Most studies report an increase in the supply of bioenergy over time. A review of 17 earlier studies on this subject by Berndes, Hoogwijk, and van den Broek (2003) reveals that estimates for potential contribution of biomass in the year 2050 range from below 100 EJ/yr to over 400 EJ/yr (Berndes, Hoogwijk, and van den Broek 2003). In comparison to the current level of bioenergy of 45 EJ/yr, this represents a doubling to a tenfold increase. A study by the International Institute of Applied Systems Analysis and the World Energy Council predicts that bioenergy would supply 15% of global primary energy by 2050 (Fischer and Schrattenholzer 2001). A study by the Natural Resources Defense Council predicts that an aggressive plan to develop cellulosic biofuels between now and 2015, could help the United States produce the equivalent of nearly 7.9 million barrels of oil per day by 2050. This is equal to more than 50% of the current total oil use in the transportation sector (Greene 2004). A majority of the increase is accounted by cellulosic biomass like switchgrass.

However, it is also possible to envision scenarios that involve reduction in cropland while meeting the future food needs for a larger and wealthier population. One of the drawbacks of the above assessment is that it is static and does not take into account future changes in technologies and the demand for food. An analysis of the demand for cropland based on fundamental forces responsible for expansion of cropland by Waggoner and Ausubel (2001) suggests that sustained technological progress in crop production could meet the recommended nutritional requirements for a population of 9 billion and simultaneously reduce cropland by 200 million hectares by the year 2050. It is even claimed that under the best-case scenario the land withdrawn from agriculture could be as high as 400 million hectares. At the same time, they warn that such improvements would come about only through sustained investments in productivity, experimentation, and deployment of better technologies (Waggoner 1996, Waggoner and Ausubel 2001). Extending on their analysis, we depict in table 7 a hypothetical scenario in which the 200 million hectares of freed cropland is allocated equally to switchgrass and Miscanthus for producing lingo-cellulosic biomass. Assuming a conversion efficiency of 330 liters per ton, about 1,100 billion liters of gasoline-equivalent ethanol could be produced, which at today’s consumption levels can offset about 64% of the global demand for gasoline.

22
understanding of its impact on performance (the problem of sustainability is not addressed here; see Taliercio, 2003b).

Thus, the goal of this section is modest: to show whether or not autonomy impacted agency management, and, if it did, to tell a plausible story about how autonomy-enhanced management is linked to changes in performance. This microanalytic methodology also addresses some of the difficulties raised above by moving beyond crude macro indicators like revenue collections and clumsy indices of autonomy. The paper contends that case studies that focus both on administrative reforms as well as performance outcomes are the best way to shed light on the question of whether autonomy matters.

The section focuses explicitly on how autonomy affected agency management, including personnel management and administrative costs. A clear understanding of how autonomy shaped human and financial resource management initiatives undertaken by the ARA allows for stronger inferences about how autonomy affected organizational output and outcome indicators. The section then examines the performance of ARAs in terms of revenue collection, compliance management, and taxpayer services, qualifying to the extent possible the impact of exogenous factors and counterfactual considerations.

# <u>Managing with Autonomy: Evidence from the Cases</u>

This section examines how managers used their autonomy to improve overall agency management, focusing on human and financial resource management. The contention is that those ARAs that made the most progress in these areas should also be the ones making the greatest improvement in organizational performance.

# _Human Resource Management_

The major issue addressed by greater autonomy has been human resource management, given the severe problems that pre-reform agencies faced in attracting and retaining high caliber professional staff. This section argues that behind any gains in performance are internal reforms to management and personnel systems, most of which would not have been possible without autonomy. Both the KRA and SUNAT used their autonomy to make significant advances in the area of personnel management. SARS has 13
# **Active Labour Market Policies in the OECD and in Selected Transition Economies**

## **by Hartmut Lehmann**

> 1 Ifo Institute for Economic Research, Munich and Centre for Economic Performance, London School of Economics. The first version of this paper has been written as a background paper to the World Development Report 1995. Research assistance by Donata Hoesch and Ingrid Leiprecht is gratefully acknowledged. The author is especially thankful to the Polish Central Statistical Office and the _Social Policy Reform Programme_ for permitting access to the results of the August 1994 Labour Market Policy Supplement of the Polish LFS which were partially analyzed in G6ra, Socha and Sztanderska (1995). The author would like to thank Tito Boeri, Rolf Dumke, Marek G6ra and Paul Walsh for comments and discussions on a previous draft of the paper.
15 The level of hospital expenditures on drugs outside the norms exceeds the expected cost of drugs under the norms for some diagnoses. For cataract surgery, hernias and hypertension the hospital spends more on drugs than would be spent if the norms were followed. However, it is not clear whether it is substitutions among drugs, or additional drugs due to complications or secondary diagnoses that inflate the cost of drugs. Moreover the reasons for variation from good clinical practice are not evident. Conceivably, shortages of some drugs may force substitution of more or different drugs any of which may be more costly than those indicated in the norms. Overall, expended drugs represent about 45 percent of the cost of the required drugs under the norms.

The "savings" from not ordering drugs is substantial. DR$5,601.76 (US$892.00) is saved from not ordering the indicated drugs. This means that on average only 9.4 percent of the required drugs are provided to patients. Only DR$3,376.69 (US$537.69) is saved (or 60 percent of norm cost) if the comparison is between the value of what should have been ordered and what the hospital spent on drug treatment beyond the norms for these patients.

A striking result from this analysis of drug ordering is the high cost of certain drug therapies. In particular, the cost of treating tonsillitis is extremely high because it entails ten days of antibiotics that are very costly. There are alternative therapies for sorne conditions that can be treated by equally effective and less costly drugs, but these are not the products specified in the norms. Every drug has a price attached to it, based on where the hospital has obtained the drugs. The relative costs of drugs available in the hospital are generally unknown to physicians, however. Without knowledge about how much drugs cost or encouragement to use less expensive drugs over other, more costly, alternatives, there is neither the information nor the incentive to adapt prescription practices to financial realities. These circumstances will inflate hospital drug costs, which are a significant proportion of total costs, especially for inpatients. Moreover it is not clear whether or to what extent alternative or additional expenditures enhance quality.

# VI. CONCLUSIONS AND POLICY RECOMMENDATIONS

# Conclusions

The study has demonstrated the fallacies implicit in earlier cost studies that a basic level of quality and efficiency can be assumed in public hospitals. The low quality and efficiency in Aybar Hospital raises questions regarding the appropriateness of expenditure studies in guiding health policy. Moreover, Donabedian's (1988) model is ideal for measuring quality in a developing country's public hospital setting.

Budget data in public hospitals do not accurately reflect costs. First, because they underestimate the resources required to provide a basic level of service to patients (i.e., they do not measure efficiency). Second, because budget figures mask qualitative differences that effectively save resources but reduce quality of care, the implicit, underlyin assumption of quality in public hospitals is unlikely to hold.
20

# Wages and Value Added

Although the evidence does not support the idea that workers are determined to raise wages at all costs, this could change as profitability declines. Given the continuous decline of the norm real wage so long as inflation persists, workers may feel justified in raising real wages above the norm and paying the PPWW (or incurring arrears) even if it means severely diminishing retained earnings. Out-of-sample behavior partly supports this view, with real wages rising strongly from July 1991 onwards despite the sharp fall in profitability and perhaps facilitated by the devaluation of May 1991, when the exchange rate anchor was relaxed for the first time since the start of the ETP.

Figs. 3-7 show that the share of wages in gross value-added increased alarmingly after falling in first quarter 1990.7' This upward trend is disturbing, capturing the fact that worker controlled SOEs have no particular incentive to maximize gross value added through cost minimization, product mix change or restructuring; but have full control on how it is disposed of.

# HARD CURRENCY EXPORT PERFORMANCE"

Without doubt, the 40% rise in the volume of hard currency exports in 1990 was one of the stellar early achievements of the ETP. At the economy-wide level, two manufacturing sectors in particular, metallurgy and chemicals, displayed considerable export dynamism.

Why did hard currency exports increase so rapidly? Three basic hypotheses have been advanced: (a) the 46% devaluation of the zloty at the start of the ETP increased export profitability and hence, exports rose; (b) the export boom occurred mainly because firms attempted to offset declining domestic sales ("demand barrier") with higher sales abroad; (c) the increase in exports was a one-time structural shift in geographical composition following trade liberalization.

Drawing on the 10 metallurgy and 12 chemical firms in the sample (for which a complete data set was available and which accounted for a significant 22% and 33% of the respective economy-wide sector exports in 1990), this section traces changes in export profitability from the third quarter of 1989 to the first quarter of 1991. The increase in exports coincided with diminishing profits relative to domestic sales, suggesting that both the domestic demand barrier and a possible structural shift (hypotheses (b) and (c) above) played a role in the export boom. The main findings of this section are: (i) Exports rose in 1990 despite a falling profit differential with domestic sales. Surprisingly, this differential was the highest in the fourth quarter of 1989, rather than in the first months of the ETP, as one may have suspected.

71n Figs.3-7, **gross** value-added is everything excluding the darkly s'uaded portion of the bar chart (roughly, net **sales** minus mnaterials and energy costs).

**'This** section focuses exclusively on convertible (hard) **curncy** exports and **iiaores** tmnsferable rmble exports.
# **VIII Tables**

Table 1. Validity of the Local Continuity Assumption – All Sample (RAMV and Non-RAMV Venezuelans)

|Variables in STD.|Knew job<br>opportunity<br>before<br>migrating [=1]|Ever<br>worked [=1]|Employed at<br>private<br>frm [=1]|Employed<br>with<br>gov. [=1]|Self-employed or<br>employer [=1]|Written<br>contract [=1]|Gap between<br>last job and<br>migration<br>(months)|**Years**<br>of educ.<br>before<br>migration|HH. Ven.<br>parents or<br>siblings [=1]|HH. Ven.<br>partner or<br>spouse [=1]|HH. Ven.<br>others [=1]|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Robust|-0.322|0.098|-0.051|0.285|-0.189|-0.025|-0.122|0.456|-0.555***|0.135|0.140|
||(0.163)|(0.153)|(0.182)|(0.168)|(0.198)|(0.165)|(0.135)|(0.180)|(0.148)|(0.152)|(0.156)|
|q-values|[0.486]|[1.00]|[1.00]|[0.686]|[1.00]|[1.00]|[1.00]|[0.145]|[0.001]|[1.00]|[1.00]|
|Obs. left|483|597|465|480|427|604|489|436|536|620|561|
|Obs. right|912|1254|939|1017|835|1289|1090|852|1134|1308|1245|
|Observations|4,177|4,177|3,895|3,895|3,895|3,895|3,872|4,177|4,177|4,177|4,177|

|Variables in STD.|Migrated for<br>health<br>reasons [=1]|Friends/<br>family in<br>Col. [=1]|Had<br>smartphone [=1]|Owner of<br>dwelling in<br>Venezuela [=1]|Electricity in<br>Venezuela [=1]|Running<br>water in<br>Venezuela [=1]|Sewage in<br>Venezuela [=1]|Female [=1]|Age<br>(years)|Number of<br>children|Time in<br>Colombia|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|
|Robust|-0.107|0.044|0.156|-0.191|-0.072|0.080|0.007|-0.034|-0.105|-0.182|-0.079|
||(0.256)|(0.164)|(0.165)|(0.215)|(0.045)|(0.177)|(0.243)|(0.190)|(0.180)|(0.139)|(0.093)|
|q-values|[1.00]|[1.00]|[1.00]|[1.00]|[0.686]|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|[1.00]|
|Obs. left|352|423|533|436|277|413|409|494|422|620|408|
|Obs. right|606|803|1123|852|506|781|763|940|805|1308|783|
|Observations|4,177|4,177|4,177|4,177|4,177|4,177|4,177|4,177|4,173|4,177|4,050|

_Notes:_ The table depicts the sharp RDD model results of pre-migration and pre-RAMV controls used as outcome variables. These variables correspond to all the controls described in equation 1 and specified in our preanalysis plan.
Targeting for Social Safety Nets: Evidence from Nine Programs in the Sahel Pascale Schnitzer<sup>*</sup> & Quentin Stoeffler<sup>†</sup><sup>**_‡_**</sup>

**Keywords** : targeting; proxy means testing; community-based targeting; social protection; poverty; Africa

**JEL Codes** : D63, I32, I38, O12, O15, R23, R28

> * World Bank.

> † University Mohammed VI Polytechnic. Corresponding author: quentin.stoeffler@um6p.ma.

> ‡ This study was supported by the World Bank Sahel Adaptive Social Protection Program, funded by the United Kingdom, France, Germany and the Wellspring Foundation. We are very grateful to Diana Cheung, Mame Fatou Diagne, Yves Kameli, Papa Sosthène Konate, Patrick Premand, Solene Rougeaux and Kalilou Sylla for providing us with the data analyzed in this paper. We are also grateful to Arthur Alik-Lagrange, Christian Bodewig, Caitlin Brown, Margaret Grosh, and Patrick Premand for their comments.
measures, various specifications, and variations in the relevant panel sub-sample. The effects of wealth, gender and household size are also consistent throughout. These results give us more in the robustness of our results.

# **9 Conclusions**

Interest in the study of subjective well-being (SWB) has increased in recent years, as has research on index insurance in rural areas of the developing world. To date, much of the SWB research in low-income countries has focused on the relationship between SWB and income or assets. There is limited understanding of how institutional factors, access to services, or policy-related issues influence SWB, if at all (Fafchamps & Shilpi, 2008). Furthermore, few studies link policy-related variables, such as uptake of index based livestock insurance (IBLI), with changes in SWB (Kaminski, 2014). This study addresses that important gap in the literature while simultaneously making an important contribution to disentangling the _ex ante_ and _ex post_ welfare effects of insurance by isolating the buyer’s remorse effect that arises from lapsed insurance policies. Empirically, we demonstrate that index insurance such as IBLI, which reduces the drought-related risk faced by pastoralists in southern Ethiopia – has the potential to impact not only material well-being e.g., by replacing lost assets – and reducing adverse coping behaviors but also to improve non-material well-being, by providing valuable peace of mind for risk averse buyers even if they can reasonably anticipate experiencing buyer’s remorse if a policy lapses without payout.

We use three rounds of annual household panel data collected between 2012 and 2014, bracketing the introduction of IBLI in southern Ethiopia, and randomized encouragements to buy the product to identify the causal effect of IBLI on SWB. We separate out the _ex ante_ SWB effects of current coverage from the _ex post_ buyer’s remorse effect, exploiting the fact that some households had purchased IBLI in the second survey round and those policies had lapsed by the third survey round. We also show that if buyer’s remorse effects exist and there is any persistence in insurance purchases, such that current and lapsed coverage are positively correlated, then ignoring lapsed policies results in downwardly biased estimates of the well-being effects of insurance.

We find that current IBLI coverage has a strongly positive and statistically significant effect on SWB. We also find statistically significant evidence of a buyer’s remorse effect. The 23