rafmacalaba/datause-encoder-data huggingface dataset:
# **_I) Policy, Policy Implementation and Policy Actions_**

The conceptual distinction can be illustrated with the example of a seemingly simple policy, like an import tariff. With strong capability for implementation a firm’s import tax payments actually collected (the policy action) are well-predicted by the firm’s import value (a factual state of the world) and the tariff code (the rule or mapping between states of the world (sales) and policy actions (tax to be collected)). In a study of Kenya’s import tariff revenues Pritchett and Sethi (1994) compared the _ad valorem_ collected rate (ratio of revenue collected to declared import value) to the actual _ad valorem_ rate for the 3,392 separate items of the tariff code. There was surprisingly little connection as items with the same official rate had very different collected rates<sup>5</sup> . For instance, of the 435 items in the tariff code with an _ad valorem_ tariff of 40 percent (which recorded positive imports) the 25<sup>th</sup> percentile of the collected rate was zero, the mean was 20 percent and the 75<sup>th</sup> percentile was 24 percent. An item’s tariff rate had very little predictive power for the tariff actually paid due to a large number of officially sanctioned deviations due to exemptions for certain purposes, exemption of certain importers, etc. which meant that even if two firms were importing exactly the same item they might pay completely different rates.<sup>6</sup> A recent study of the acquisition of driver’s licenses in Delhi, India (Bertrand,

> 5 While naively one might imagine a regression of ad valorem revenue on ad valorem rates might yield a coefficient of 1 and an R-squared near one (e.g. items with a 20 percent rate would have 20 percent collection rates and items with 30 percent would have a 30 percent collection rate (this is not about demand elasticity as this is normalized by import value).  Instead, across a variety of functional forms the official rate could only explain about a fifth of the observed variation across items in the collected rate.  Of course this potentially vastly _overstates_ the connection between the official tariff and tariff collected on the actual import of specific items as it does not include smuggling or mis-declaration.

> 6 If exceptions are formally recognized and there are enough of them, then the lack of correlation on the non-exempted tariffs and actual revenue could be an example of the rules actually being applied.  However, the variations within narrow ranges of goods are too great.  Rather, the existence of such exemptions themselves indicates that deals were sought – and granted.

7
credit products will not advertise the total cost of the credit nor the detailed fees and charges. In other words, campaigns will be mostly persuasive.

2. Both naïve and sophisticated customers are aware of all basic requirements, _unavoidable_ fees and other terms to open an account. For example, all customers know the interest rate earned on deposit products or the interest rate charged on credit products.

3. Naïve customers are unaware of _avoidable_ fees and commissions such as the minimum balance fee on deposit accounts or the late payment fee in credit products.

4. Naïve and sophisticated customers are offered the same product, but naïve customers will earn less (if any) interest on savings (or pay more interest for credit) as they also pay fees and commissions.

In Section 6 we test these predictions more formally.

# **4. Experimental Design**

The audit study was conducted in collaboration with Mexico’s National Commission for the Defense and Protection of Financial Service Users (CONDUSEF), the government authority established in 1999 and responsible for financial consumer protection and financial education programs.

Four towns near the greater metropolitan region of Mexico City were selected with populations ranging from 30,000 to 1,600,000: Amecameca, Cuernavaca, Ecatepec, and Los Reyes de la Paz. These towns were selected for their high penetration of financial institutions targeting low-to middle-income consumers, and for their proportion of low-to middle-income population.<sup>13</sup> From the town center, a radius of 1.5 Km was drawn and every financial institution inside the circle was included in the study. We found a total of 26 distinct institutions with 62 branches, of which 13 were banks, 2 were Cajas de Ahorro (SCAPs), 4 were SOFIPOs, and 7 were SOFOMEs. For the purposes of the analysis we group the banks into commercial banks (Scotiabank, HSBC, Banorte, Banamex, etc) and “low-income” banks that target low-income households (Banco Ahorro Famsa, Bancoppel, Compartamos Banco, Banco Walmart and Banco Azteca). Of the low-income banks, all but Banco Compartamos had their branches located inside the department stores of which the banks were a subsidiary, since Banco Compartamos is not affiliated to a department store.

Credit auditors requested a 6-month loan for a household expenditure preferably with monthly installments. The expenditures included house repairs, medical expenses and children’s school supplies, among others. Savings auditors were assigned a quantity of 5,000 Pesos (USD 378) that they wanted to deposit.

Since we wanted to capture all the information that staff would provide to prospective clients up

> 13 Although there are considerable disparities in access by region and between rural and urban populations, the towns selected are similar to other peri-urban towns in Mexico.

9
Let _B_ be the tax base and all variables are in log levels. _θY_ consists of a sum, across all tax types, of the elasticity of each tax with respect to its tax base times the elasticity of its tax base with respect to output, weighted by the share of the tax in total tax revenue: The response of tax to output in this setting encompasses stabilization policy, so long as the policy is pre-ordained in the tax code: for instance, an extension of the duration of unemployment insurance when the economy is in a downturn.

The system in 20 - 22 can be written in matrix form as: or more compactly, _Aut_ = _Dεt_ . Therefore: Since the sample analogue of _E_ [ _utu_<sup>_′_</sup> _t_<sup>] is a three-by-three covariance matrix, it provides</sup> six independent identification restrictions. There are nine parameters in the original system, so the structural VAR will be just-identified if one imposes these three additional restrictions above.

Mertens and Ravn(2014) provides identification restrictions by utilizing information from available narrative series of exogenous fiscal shocks. The original purpose of this approach was to reconcile the marked differences between estimates of the fiscal multiplier from the SVAR approach and the narrative approach. Let _mt_ be the 18
households in urban areas by some 30,000 people. Most of the newly food poor are farmers or livestock owners (88%) while 6% are self-employed and 4% without employment. The elderly are somewhat overrepresented: 40% of the newly food poor live in a household headed by someone older than 60 years, whereas this group represents only 32% of the population.

Asset ownership among those who have fallen below the food poverty line due to these shocks is relatively low. The median worth of these household assets is on average about 10,100 FCFA per capita compared to the median of 18,400 FCFA for the rest of the population (table 9). In contrast, livestock ownership tends to be higher among the group that has fallen below poverty line (a median of 27,000 FCFA per capita compared to 18,000 FCFA among the rest of the population).. The median worth of household assets and livestock combined is 48,000 FCFA per capita among this group, or about half the food poverty line. So if they fall 10% below the food poverty line, selling all their assets would enable them to get back to the food poverty line for just six months. This clearly would be nothing more than a very temporary solution.

**<u>Table 9. Value of assets per capita in FCFA for the new food poor (following shocks)</u>**

||**The new food poor**<br>**following production and**<br>**price shocks**|**Rest of the population**|
|---|---|---|
|Median value of household assets|10,100|18,400|
|Median value of livestock assets|27,104|17,934|
|Median value of both household and livestock|47,800|57,200|
|assets|||
|Mean family remittances|2,400|10,400|
|Median transfers from NGOs orgovernment|8,000|12,500|

_Source: Authors’ calculations using ELIM –MICS 2010 household survey data. Note: The "new food poor" are those that will fall below the food poverty line following the production and price shocks._ Remittances and support by NGOs and the government can play an important role in cushioning shocks. Households can be identified using the ELIM 2010 survey. Using this data we estimate that of the 610,000 people who have become food poor following the two shocks about 33% receives family remittances. But only about 0.1% have received any transfers from NGOs or the government The amounts received per person per year are small. Average levels per capita of these remittances are FCFA 2400 per year for those that have fallen under the food poverty line following the shocks as compared to FCFA 10.400 for the rest of the population.

Under the scenario of the country experiencing a 25% drop in cereal production and a 25% cereal price increase, and assuming perfect targeting of aid, an estimated 2.7 billion FCFA (US$5.4 million) per year of additional assistance will be needed to raise the level of consumption of the households who have fallen below the food poverty line back up to the 12
# **E.21 Controlling for Unbalanced Baseline Variables**

Covariates included (all measured as proportions of households): (1) beyond primary education, (2) Mende, (3) ill in last month, and (4) own mobile phone.

**Table E.26:** Effects on Reported Cases Controlling for Unbalanced Baseline Variables

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.281|0.191|0.153|0.23|-0.077|5,440|
||(0.727)|(0.086)**|(0.103)|(0.101)**|(0.112)||
|Confrmed|0.011<br>(0.129)|0.068<br>(0.027)**|0.07<br>(0.031)**|0.067<br>(0.029)**|0.002<br>(0.027)|5,440|
|Negative|0.238<br>(0.648)|0.105<br>(0.063)*|0.056<br>(0.076)|0.153<br>(0.078)**|-0.098<br>(0.089)|5,440|
|IHS(Ebola Cases)|||||||
|Total|0.206|0.096|0.082|0.11|-0.029|5,440|
||(0.47)|(0.043)**|(0.053)|(0.051)**|(0.059)||
|Confrmed|0.009|0.033|0.029|0.037|-0.007|5,440|
||(0.1)|(0.011)***|(0.013)**|(0.013)***|(0.013)||
|Negative|0.179|0.066|0.047|0.086|-0.038|5,440|
||(0.433)|(0.036)*|(0.045)|(0.043)**|(0.051)||

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Covariates included (all measured as proportions of households): (1) beyond primary education, (2) Mende, (3) ill in last month, and (4) own mobile phone. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

A62
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Weig|ht-for-age z-s|cores|||
|completed [5-7] years of sch.|0.103|0.100|0.094|0.086|0.087|0.075|0.074|
||(0.070)|(0.070)|(0.070)|(0.070)|(0.070)|(0.070)|(0.070)|
|completed [8-10] years of sch.|0.026|0.024|0.016|0.001|0.008|-0.021|-0.005|
||(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|
|completed 11 + years of sch.|0.031|0.027|0.024|0.008|0.016|-0.011|-0.003|
||(0.089)|(0.089)|(0.089)|(0.089)|(0.089)|(0.088)|(0.088)|
|Self-employed in agriculture|0.026|0.029|0.033|0.034|0.034|0.037|0.036|
||(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|(0.047)|
|Ethnicity||||||||
|Brahman/Chhetri|-0.612***|-0.608***|-0.606***|-0.597***|-0.596***|-0.590***|-0.595***|
||(0.116)|(0.116)|(0.116)|(0.116)|(0.116)|(0.115)|(0.116)|
|Middle class|-0.869***|-0.857***|-0.847***|-0.868***|-0.872***|-0.836***|-0.870***|
||(0.133)|(0.133)|(0.133)|(0.133)|(0.133)|(0.132)|(0.132)|
|Janajati|-0.495***|-0.490***|-0.493***|-0.479***|-0.487***|-0.479***|-0.486***|
||(0.118)|(0.118)|(0.118)|(0.118)|(0.118)|(0.117)|(0.118)|
|Dalit|-0.731***|-0.725***|-0.718***|-0.714***|-0.728***|-0.714***|-0.725***|
||(0.127)|(0.127)|(0.127)|(0.128)|(0.127)|(0.126)|(0.127)|
|Others|-0.818***|-0.806***|-0.807***|-0.821***|-0.822***|-0.790***|-0.801***|
||(0.143)|(0.143)|(0.143)|(0.143)|(0.143)|(0.143)|(0.143)|
|Female-headed HH|0.103*|0.099*|0.102*|0.105*|0.102*|0.092*|0.084|
||(0.056)|(0.056)|(0.056)|(0.056)|(0.056)|(0.055)|(0.056)|
|_Mother's Education_||||||||
|Literate, or < 5 years of sch.|0.167**|0.164**|0.152**|0.163**|0.167**|0.168**|0.170***|
||(0.066)|(0.066)|(0.066)|(0.066)|(0.066)|(0.066)|(0.066)|
|completed [5-7] years of sch.|0.230***<br>(0.069)|0.227***<br>(0.069)|0.212***<br>(0.070)|0.244***<br>(0.071)|0.251***<br>(0.071)|0.245***<br>(0.070)|0.246***<br>(0.071)|
|completed [8-10] years of sch.|0.251***|0.249***|0.236***|0.238***|0.249***|0.252***|0.244***|
||(0.084)|(0.084)|(0.084)|(0.084)|(0.084)|(0.083)|(0.083)|
|completed 11 + years fo sch.|0.462***|0.458***|0.445***|0.439***|0.455***|0.399***|0.432***|
||(0.092)|(0.092)|(0.092)|(0.091)|(0.092)|(0.093)|(0.092)|

66
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|ADDITIONAL FINANCING FOOD SECURITY AND SEED IMPORTS|Bobojon Yatimov|
|---|---|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Irrigation Development Project|Regassa Ensermu Namara|
|Second Programmatic Private Financial Development PolicyLoan|Irina Astrakhan|
|Second Programmatic Private Financial Development PolicyLoan|Irina Astrakhan|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|Support to Protected Areas Management|Salimata D. Follea|
|ImprovingtheQualityand Efficiencyof Public Expenditure Manage|mDiego R. Dorado Hernandez|
|ImprovingtheQualityand Efficiencyof Public Expenditure Manage|mDiego R. Dorado Hernandez|
|ImprovingtheQualityand Efficiencyof Public Expenditure Manage|mDiego R. Dorado Hernandez|
|Nepal REDD Readiness Preparation Program|Dora Nsuwa Cudjoe|
|Nepal REDD Readiness Preparation Program|Dora Nsuwa Cudjoe|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
|Egypt EnhancingAccess to Finance for Micro and Small Enterprises|Laila Ashraf AbdelKader Ahmed|
12 _uncompetitive prices_ , and _unreliable delivery_ constitute important reasons to import inputs instead of using local suppliers.

Most evidence as to how characteristics of local suppliers restrict the level of local sourcing is based on the perception of foreign firms. An exception is a study (Jordaan 2011b) that utilizes survey information obtained from a random sample of local suppliers of two types of firms: MNC affiliates and large domestic producer firms in northeastern Mexico. For 10 of 11 different problems that local suppliers experience with their client firms, suppliers of MNC affiliates indicate that they experience these problems more frequently. Most suppliers of MNC affiliates indicate that their client firms have very strict price demands. Other problems that these suppliers often encounter include an inflexibility to change product specifications, the need for client-specific investments, and rigid demands regarding product quality.

_Improvements in performance triggered by problems with inputs (pecuniary externalities)_ . Although these problems may limit the use of local suppliers, they may also _trigger changes in conduct and foster technology dissemination_ , culminating in productivity spillovers. Positive pecuniary externalities occur when domestic firms respond to stringent input demand conditions by improving their performance. Good examples of this effect are provided by Atkin, Khandelwal, and Osman (2017) and Godart and Görg (2013). The latter use WBES data for more than 1,000 domestic firms in a number of emerging economies to estimate drivers of productivity, including variables that capture whether domestic firms supply to MNC affiliates, whether their client firms have put pressure on them to lower production costs and/or supply better products, and an interaction term between having foreignowned client firms and experiencing pressure to improve. Their findings show that of these three control variables, only the interaction term carries a significant and positive coefficient, suggesting that the strict requirements that foreign firms apply to their demand for inputs result in positive productivity effects among domestic suppliers.

_Support to local suppliers from MNC affiliates_ . As for technology dissemination, MNC affiliates often provide support to (potential) local suppliers when they perceive that doing so provides them with higher-quality and more cost-competitive inputs. A variety of types of support can be distinguished related to product technologies, process technologies, and organizational and managerial know-how (UNCTAD 2001). Although the provision of specific types of support is context specific and conditional on characteristics of MNC affiliates and domestic firms, evidence for a variety of host economies indicates that support that is focused directly on improving the quality and cost-
questionnaires were given only to children who were enrolled in school and who attended school the day of the exam. Indigenous students were identified with a question on language spoken.

The second source, for Guatemala, is a year 2002 exam of math and reading in Spanish that we refer to as “Laboratorio Guatemala”. Representatives from Guatemala’s Ministry of Education and Universidad del Valle designed math and Spanish reading exams that combined questions from the regional Laboratorio exam with nationally-developed questions that would include more culturally familiar content for Guatemalan students. In the math exam, 7 questions were nationally developed and 33 were from the regional Laboratorio exam. In the reading exam, 21 questions were nationally developed and 19 questions were from the regional Laboratorio exam. Third and fourth grade students completed the reading and math exams; the same content was tested in each grade. The tests were accompanied by background questionnaires for principals, teachers and students of the schools selected for the exam. Data including test scores and background questions were collected from 179 randomly selected schools. In total, 5,825 students and 249 teachers were surveyed (further description of the Guatemala Laboratorio data appears in Wu, Goldschmidt and Hara 2003).

The Guatemala Laboratorio study uses different questions to identify indigenous students, teachers and principals. Students were asked, “What language do you speak at home most of the time,” with answer choices of Spanish or Other. Teachers were asked, “Do you speak a language besides Spanish,” with answer choices of K’iche, Kaqchikel, Mam and Q’eqchi’. Principals were asked, “Do you belong to one of these ethnic groups,” with answer choices of Ladino, K’iche, Kaqchikel, Mam and Q’eqchi’.

11
Policy Research Working Paper 10845

# **Abstract**

This paper presents evidence of the impact of a five-year package of interconnected interventions intended to improve learning environments in eight disadvantaged districts in Malawi. The intervention, which was implemented over five years, provided additional finance to schools to support the hiring of additional teachers and construction of learning shelters to improve class sizes in lower primary, along with constructing classrooms and providing results-based finance to reward improvements in staffing. The interventions were targeted to eight districts with longstanding disadvantages in staffing, learning environments, and learning outcomes, particularly for girls. Employing administrative data and data from a nationally representative independent sample of public primary schools, the analysis finds that these investments closed the gap in learning outcomes between the targeted districts and the rest of Malawi. There is also suggestive evidence that the program reduced learning gaps between girls and boys. The findings suggest that even in a low-income environment with significant constraints, targeted efforts to reduce class sizes can close district-level gaps in learning.

This paper is a product of the Education Global Practice. 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/prwp. The authors may be contacted at sasim@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