rafmacalaba/data-use-mentions-extended huggingface dataset:
higher grade. Those who were unable to attain the required learning levels would be provided with opportunities to achieve them in the higher grade (The World Bank, 1999).

# III. **Data**

This study utilizes data collected in the context of a randomized controlled trial of the impact of financial incentives on student performance. The experimental study was implemented in public schools in Nepal to understand if financial incentives are effective in boosting student achievement. The intervention was applied on grade eight students in semi-urban areas of Kathmandu during the 2009 – 2010 school year lasting from April 2009 to March 2010. From a pool of 33 schools, a randomly selected subset of 11 schools comprised the treatment group while the remaining schools constituted the control group.

Grade eight curriculum consists of five mandatory subjects – Nepali, English, Math, Science, and Social Studies – and four peripheral courses. The exam for each core subject is worth 100 points and the pass mark is 32. The exams for the non-core subjects are worth 50 points each with pass mark of 16. Eighth graders take three exams every year: two trimester exams internal to the school and the year-end exam common at the district level. Students in the treatment group were eligible for cash rewards in direct proportion to their scores in each of the three exams. Incentives based on average aggregate score were offered for all three exams taken in the year. In this paper, I focus on the scores in the five core courses for the sake of comparability across all schools.<sup>5</sup> Two student surveys, one at the beginning of the academic year before the announcement of the incentives and one towards the end of the year, collected information on household characteristics and student time use at home. Because the survey was administered in school, the survey information is missing for students who were not present in school on the day of the survey. Since the official information on student attendance is not always reliable, I use the number of students who filled
# Tamil Nadu

Figure 9a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 9b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Tamil Nadu<br>40<br>30<br>20<br>10<br>0<br>-10<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Tamil Nadu<br>120000 13.5%<br>100000 13.0%<br>12.5%<br>80000<br>12.0%<br>60000<br>11.5%<br>40000<br>11.0%<br>20000 10.5%<br>0 10.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text -->

# Uttar Pradesh

Figure 10a: Annual Percentage Change in Stamp Duty Revenues and Rates

## **Uttar Pradesh**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>-100<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br><!-- End of picture text --> Figure 10b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Uttar Pradesh<br>120000 16.0%<br>100000 14.0%<br>12.0%<br>80000<
Policy Research Working Paper 10738

# **Abstract**

Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à-vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.

This paper is a product of the Development Data 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/prwp. The authors may be contacted at hdang@worldbank.org and tkilic@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
forward. Finally, high malnutrition rates in the developing parts of the region suggest that a large number of households are highly vulnerable to food price shocks.

**Figure 1. World Food Prices and Inflation, 2006:12-2011:10**

(Index 1999:12=100) (%, year-on-year change) <!-- Start of picture text --> 300 80<br>280<br>60<br>260<br>40<br>240<br>220 20<br>200<br>0<br>180<br>-20<br>160<br>140 -40<br>IV I II III IV I II III IV I II III IV I II III IV I II III IV IV I II III IV I II III IV I II III IV I II III IV I II III IV<br>2007 2008 2009 2010 2011 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: World Bank, Development Prospects Group (DECPG).

The economics literature provides little empirical insight into how much international food price shocks impact on domestic prices in individual MENA countries. The exception is a working paper by Albers and Peeters (2011) presenting food price transmission effects for a few countries in the Mediterranean region. Also, Crowley (2010) finds that commodity prices exhibit a strong and mostly significant impact on domestic inflation for a cross-section panel of countries in MENA and Central Asia. Limited access to data has been a key reason for the absence of empirical work on MENA countries. This paper, therefore, fills a void by presenting new estimates of food pass-through coefficients from international to domestic prices for all MENA countries, using a data base collected from various sources throughout the region.

As international food prices increased significantly in recent years,<sup>3</sup> nearly all countries experienced an increase in their domestic food prices. Still, the question concerning
to study the development of human opportunities by using data from 12 countries of one continent in the last 50 years. The standard approach in the literature focuses on case studies that evaluate the development path of specific countries, but such studies mainly focus on extremely successful or unsuccessful examples. This paper depicts a broader image of the LAC region, but also finds relevant stylized facts by country. Finally, this study complements long-term growth studies of aggregate data by taking into consideration information at the individual level that accounts for the disparities in coverage of public services and education among households.

This paper finds that the rise in human opportunities is universal, but increases have been heterogeneous across countries and dimensions. However, we find some common patterns in the evolution of access to opportunities between groups of countries. There are specific demographic characteristics that explain the process of increasing access to basic services. Development strategies during the last four decades are reflected in such evolution. Finally, geographic characteristics explain a significant proportion of the inequality of human opportunities.

The evidence in this paper suggests that children’s universal access to basic services and education may enhance their ability to generate income during adulthood by improving human capital in society. Population groups with low access to services and education in their earlier years of life are more likely to have lower household incomes 30 years later than those that enjoyed higher access to the same opportunities.

The remainder of the paper is organized as follows. The next section presents an overview of the main sources of information and the creation of the relevant data sample. Section 3 describes the stylized facts found in the data. Finally, Section 4 concludes and proposes a research agenda.

4
Real wage changes, in turn, can be positive or negative, and in our Mexican data substantial declines in the real wage are quite common. More specifically, Mexico experienced very high inflation rates during our sample period with annual declines in the _aggregate_ real wage as high as 25 percent during this time (see, for instance, Hanson (2003)), implying that the wage rates of some individual workers declined by an even larger amount. Thus, despite the often cited downward rigidity of wages, our sample includes large numbers of workers whose _real_ wages declined dramatically.

# _II.5. Results_

As described before, we have individual income data for the time period 1987-1998 covering 21 different manufacturing sectors in Mexico. Using the methodology outlined above, we estimate the risk parameters _σε_<sup>2and</sup><sup>_σ_</sup> _η_<sup>2foreachquarterandeachmanufacturingsector.In</sup> Tables II and III we provide the average estimate of _σε_<sup>2and</sup><sup>_σ_</sup> _η_<sup>2for each year (averaged across</sup> industries) and for each industry (averaged over time) respectively.<sup>20</sup> The mean value (across industries and over time) of the quarterly variance of the persistent shock, _σε_<sup>2,isestimated</sup> to be 0.008, or 0.032 annualized (i.e., _σε_ , is estimated to have a mean quarterly value of 0.09 and a mean annualized value of 0.18).<sup>21</sup> As expected, given the extent of measurement error in the income data (see our discussion in Section II), the estimated variances of transitory shocks are much larger in magnitude. More precisely, the mean value of the annualized variance of transitory shocks is 0 _._ 2 (an annual standard deviation of 45 percent), which is

>
br>workers<br>(4)|<br>number of<br>female non-<br>administrative<br>workers<br>(5)|number of<br>female non-<br>administrative<br>workers<br>(6)|
|FDI customers dummy|253.7*|154.7|2.584*|2.054*|276.3*|161.7|
||(127.3)|(107.3)|(1.345)|(1.071)|(145.1)|(116.3)|
|Firm size (log of total sales)||194.6**||1.466***||205.0**|
|||(67.07)||(0.249)||(81.25)|
|Exporter dummy||-250.5||-1.311**||-244.5|
|||(141.1)||(0.566)||(176.7)|
|Other apparel dummy||-102.4||5.100***||-328.5|
|||(309.8)||(1.194)||(335.3)|
|Apparel woven dummy||58.21||1.126*||64.41|
|||(56.28)||(0.568)||(73.68)|
|Textiles dummy|||||||
|EPZ dummy||484.7||-0.532||509.1|
|||(292.3)||(0.696)||(332.2)|
|Observations|293|290|
# **5.1 Average program impacts on refugees pooling survey rounds**

Table 4 presents the estimated treatment effects on refugees for the pre-specified primary outcome measures, as well as statistically significant effects on other outcomes.

In terms of primary outcomes, the estimated treatment effect on the housing quality index is positive and moderate in size, at 0.31 standard deviation units, though not statistically significant at traditional confidence levels. Regarding rental payments, there is a significant reduction in households’ out-of-pocket housing expenditures of 82.05 USD (SE 32.07) and the estimate survives the False Discovery Rate (FDR) multiple testing adjustment (q-value=0.07). This measure was reliably collected at the endline survey when the subsidy itself was ending for some households but the program’s contractual rental level controls should still have been in place. Taken together, the two findings indicate that the program at least partly achieved some of its main proximate objectives of improving housing quality and reducing the rental burden for refugee households, although the housing quality estimate is suggestive. As we are unable to directly measure the reduction in out-of-pocket housing expenditures during the midline survey (as noted above), the endline estimate likely understates the true program benefit to treatment households in terms of reduced rental payments.

In a central finding of the study, we next show that the average estimated program effect on consumption across all survey rounds is small and not statistically significant (Table 4). Specifically, across all rounds for which data are available (which varies across the measures, see notes in Table 4), there are no significant impacts on household food consumption nor log total household consumption expenditures. Likewise there is no significant effect on respondent depression as assessed using the widely used CES-D scale. It appears that a substantial year-long housing subsidy plus shelter upgrade was not sufficient to meaningfully improve these key living standards measures, although it is worth noting that, as with the housing quality effects, the relatively weak first stage increases standard errors on
(rather than FDI announcements). It also explicitly considers how an IPA’s targeting of a priority sector in one country may affect the relative effectiveness of IPAs in _other countries_ targeting the same sector, by including sectoral IPA targeting within a structural gravity model framework. The paper also contributes to the literature around characteristics of effective IPAs. This paper avoids some of these limitations of the literature by taking a data-driven approach to identifying ‘high-performing’ IPAs. In addition, it compares ‘high-performing’ IPAs to other IPAs across a highly detailed set of characteristics, so as to provide a detailed assessment of potential dynamics that may shape the effectiveness of IPAs in attracting FDI.

New research should focus on replicating the findings with a larger database of sectoral FDI. This would allow the analysis to study more IPAs. Having a broader timeframe would also be important (both on FDI stock, and on FDI sectoral targeting), as many of these dynamics are slow moving. Such analysis will likely be possible in the near-future as major FDI source countries and regions are increasingly publishing sectoral FDI statistics.

24
|(0.039)|
|3 years|1,562|0.593|0.315***|1,279|0.056|-0.021*|-0.063*|
|post application|||(0.019)|||(0.012)|(0.037)|
|5 years|1,685|0.582|0.196***|1,106|0.083|-0.036**|-0.149**|
|post application|||(0.022)|||(0.015)|(0.061)|

_Notes:_ Columns (1) to (6) report impacts of several programs on self-employment and migration. The last column reports estimates from a two-stage least squares regression of the migration indicator on an indicator for self-employment instrumented by treatment status. Panel A uses the sample of participants to the Uganda YOP (Blattman et al., 2014, 2019). Each coefficient comes from a weighted least squares regression that controls for district fixed effects and a vector of baseline covariates. Regressions are weighted by inverse probabilities of attrition and selection into the endline tracking sample. Standard errors are clustered at the group level. Panel B uses data from the Uganda WINGS program (Blattman et al., 2016). Treatment effects are computed as the linear combination of the impact of an indicator for assignment to the general treatment group, and an indicator for assignment to the group dynamics training. Each regression controls for a vector of baseline covariates and strata fixed effects. Standard errors are clustered at the village level. Panel C uses data on the sample of participants to the Nairobi microfranchising interventions discussed in Brudevold-Newman et al. (2017). We further distinguish between franchise and grant treatment in sub-panel (a) and (b), respectively. Each regression controls for baseline covariates and fixed effects for randomization stratum
believe it will be too difficult to judge the skills of outsiders (i.e., they can’t assess service quality). Two other frictions impeding firms from going to the market include: concerns over trusting the new worker or professional (19% for marketing services up to 23% for HR services); and worries about time or hassle costs (20% to 24% depending on the service). Surprisingly, however, very few firms actually believe the supply of human capital is of low quality – only 9% of firms think accounting services are poor and just 11% of firms view marketing services to be low in quality.

The GEM intervention helps overcome both these informational and cost-benefit concerns. It provides firms with a list of screened providers, reducing search costs and acting as some signal of quality. Appendix 8 reports on a survey we did with 333 business service providers across the Nigerian market. We find that the GEM providers are larger and have more skilled staff on average than the typical non-GEM provider, but they serve similar customer markets and have similar quality guarantee policies. At least for accounting and marketing services, the GEM intervention may have helped screen providers who have some additional capacity to expand and serve new customers. Likewise, for consulting, the program’s implementation partner (Growbridge) screened, trained and tested providers to ensure high quality consultancy support. In addition, across interventions, the GEM project also required all providers to start their services by first doing a diagnostic, to help the firm assess what type of help they could use. It then subsidizes the initial costs of using these services (overcoming financial frictions) and gives firms a chance to learn whether these services deliver value, reducing uncertainty about the returns.

# **6.2 Potential returns from using a business services marketplace?**

Are these services worth it for firms? We have noted how challenging measuring the returns are for us, even with hundreds of firms to look at, so for participant
# Figure 6: Average Outcomes by Group

<!-- Start of picture text --> Precarious housing Ownership land & house<br>Years of education [24+) Use computer [14+)<br>.6 .8<br>.6<br>.4<br>.4<br>.2<br>.2<br>0 0<br>Tonocoté Chorote Pilagá Mbyá Guaraní Wichí Vilela Chulupí Lule Ava Guaraní Toba-Qom Kolla Tapiete Tupí Guaraní Chané Omaguaca Mocoví Ona Atacama Quechua Diaguita-Calchaquí Guaraní Otros Aymara Huarpe Sanavirón Mapuche Maimará Tehuelche Querandí Pampa Charrúa Comechingón Rankulche Lule Vilela Chané Rankulche Diaguita-Calchaquí Tehuelche Maimará Mapuche Kolla Wichí Sanavirón Toba-Qom Atacama Querandí Comechingón Omaguaca Charrúa Pilagá Guaraní Mocoví Huarpe Pampa Tupí Guaraní Ava Guaraní Tonocoté Otros Tapiete Quechua Mbyá Guaraní Ona Chorote Chulupí Aymara<br>.8<br>10<br>.6<br>.4<br>5<br>.2<br>0 0<br>Otros Charrúa Querandí Comechingón Sanavirón Ona Pampa Rankulche Aymara Diaguita-Calchaquí Huarpe Tehuelche Maimará Tupí Guaraní Quechua Guaraní Atacama Mapuche Lule Kolla Omaguaca Toba-Qom Mocoví Chané Ava Guaraní Tapiete Chorote Tonocoté Chulupí Wichí Pilagá Mbyá Guaraní Vilela Ona Querandí Charrúa Comechingón Otros Rankulche Pampa Tehuelche Sanavirón Mapuche Huarpe Tupí Guaraní Aymara Quechua Diaguita-Calchaquí Guaraní Atacama Maimará Toba-Qom Kolla Mocoví Omaguaca Lule Chané Tapiete Ava Guaraní Mbyá Guaraní Chorote Wichí Chulupí Tonocoté Pilagá Vilela<br><!-- End of picture text --> _Sources:_ REDATAM INDEC Census Argentina 2010 (Instituto Nacional de Estad ́ıstica y Censos, 2010). _Notes:_ Solid horizontal lines show average for Natives, dashed horizontal lines show the average for non-Natives, and the dotted horizontal lines show the first and ninth decile by department.
feared were the risk of losing one’s property and the risk of going bankrupt; hence the bankruptcy law might play an important role in preventing firm creation.

Although the cost of failure is difficult to measure, the World Bank has gathered information on the time and cost of going through an insolvency process in each of the countries under analysis. For more details, please refer to Appendix 2.

# **I.2. Which institutions and policies affect the survival and growth of new firms?**

Entrepreneurs face many obstacles. Complying with administrative regulations and their related costs is one of the most significant burdens. But also the red tape, the degree of flexibility of the labour market or the tax burden constraint business performance.

Institutions affecting the day-to-day operation of businesses are important for two reasons: first, they affect the expected benefits and costs from creating a firm, which are taken into account by would-be entrepreneurs. Moreover, even if they do not prevent entrepreneurship, they might force entrepreneurs into the informal economy, where their prospects of prospering and growing are low. Second, once the entry decision has been taken, they have an impact on survival and expansion –and therefore on job creation-- of existing businesses.

# **_Government interference_**

The government imposes price controls and cumbersome regulations, carries out over-inquisitive tax inspections and still holds or controls fundamental sectors of the economy. Another important set of factors may consist in the actions governments are not taking. Some examples are poor enforcement of the law, poor protection of property rights and laissez-faire with respect to bribes and corruption.

For operational purposes, data on government interference has been organized around four big issues: the **quality of the legal system** , **market regulation** (which includes credit and labor market regulations), **size of government** and **corruption** . Please refer to Appendix 2 for detailed definitions of each of those components.

**Box 2: Efficient but
Policy Research Working Paper 5944

# **Abstract**

This paper brings new evidence on the impact of services liberalization on the performance of manufacturing firms. Using a unique database of Ukrainian firms in 2001–2007, the authors utilize an external push for liberalization in the services sector as a source of exogenous variation to identify the impact of services liberalization on total factor productivity (TFP) of manufacturing firms.

The results indicate that a standard deviation increase in services liberalization is associated with a 9 percent increase in TFP. Allowing services liberalization to dynamically influence TFP through the investment channel leads to an even larger effect. The effect is robust to different estimation methods and to different subsamples of the data. In particular, it is more pronounced for domestic and small firms.

This paper is a product of the Growth and Competitiveness Unit, World Bank Institute. 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://econ.worldbank.org. The authors may be contacted at oshepotylo@eerc.kiev.ua or Vakhitov@kse.org.ua.

_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