rafmacalaba/data-use-mentions-extended huggingface dataset:
- 4) How likely do you think it is that you will not catch any fish in the month of August if you go fishing 6 days a week?

- 5) How likely it is that you will eat fish at least once during the month of August?

The first question serves to assess numeracy, and whether respondents can express a known probability with the visual aid. The second and third questions can be used together to evaluate whether respondents respect a basic property of probability by asking about nested events: if respondents understand the concept of probability, they should allocate a larger number of beans (or at least as many) to the likelihood of going to town in the next 2 weeks as they do to the next two days. Finally questions 4 and 5 ask about events which are likely to be zero probability (catching no fish in the month) and certain events (eating fish at least once in the month) for the target population, allowing us to see whether they can use the visual aid to accurately represent zero and unity probability events.

# **2.4. Eliciting the Distribution**

After these preliminary questions, respondents were asked to report their subjective distribution of future catches during one day in August by allocating the total number of beans on a sheet of paper divided into intervals to express the likelihood that the catches will fall into various intervals, expressed in Rupees. We compare two different methods for providing the support and number of intervals that should be used for this: a pre-determined support with a large number of intervals, and a self-anchored support with a small number of intervals. These reflect the main two approaches used in the literature.

The pre-determined support is the same for all respondents, and contained 20 intervals. These intervals were chosen based on previous catch data, which gave the range of reasonable possibilities for the value of daily catches.<sup>5</sup> The first interval ranged from 0 to 150 rupees, with the first 14 intervals (up
significantly over the period. The average per country share of subsidies in total expenditures substantially declined, particularly over the last five years of the period. The average share of subsidies steadily fell from 60% in 1985-90 to 58% in 1990-94, and to 44% in 1995–2000. Moreover, in practically all countries the proportion of subsidies in total government expenditures decreased or remained stable, the exceptions being Ecuador and Paraguay. The structure of public expenditures, not surprisingly, varies a lot across countries. Moreover it also exhibits a meaningful and large variability within countries over time. These large and seemingly meaningful variances are extremely important features which allow us to appropriately use panel data econometrics, including fixed effects methods (Nerlove, 2002).

Turning to the remaining variables used, the indicator of trade openness is intended to measure the degree of openness of trade _policy_ rather than trade volumes. Large countries (population and area wise) inherently tend to trade less than small countries, as they are naturally more self-sufficient, regardless of trade policy. If we want to use trade (imports plus exports) over GDP as a proxy for trade policy we must, therefore, normalize for country size and other structural characteristics (access to sea ports, oil wealth, etc.) that may affect the natural tendency of countries to trade. We normalize trade flows following a methodology used by Pritchett (1996), which consists in controlling for countries’ geographic size, population, per capita income and other non-policy variables that are likely to affect the volume of trade. The index can be interpreted as an indicator of a country’s trade policy openness with respect to the norm.<sup>21</sup> The data of agricultural land area per country over time is obtained from FAO sources, and the per capita non-rural income is estimated from national account data. Finally, since no data for agricultural labor exist, we use the total rural labor force as a proxy for L. Summary statistics for all the data used
Narrow tax bases, widespread evasion and poor tax administration translate into low revenue collection _._ Tax revenues in South Asia are lower than in other countries at similar development levels (Figure 6). Low revenue constrains government spending including on infrastructure, education and other essential services. South Asian countries have been spending less than emerging economies or developing countries in other regions at comparable levels of per capita income.<sup>59</sup> Low revenues also contribute to persistent fiscal deficits, high government debts and macroeconomic instability, which hurt the investment climate and economic growth more broadly.

**Figure 6. Tax revenue in South Asia is lower than in other countries at comparable income level**

<!-- Start of picture text --> 40<br>Macao (China)<br>Algeria<br>Denmark<br>30<br>Georgia<br>20 Senegal<br>10<br>India Sri Lanka<br>Bangladesh Pakistan,<br>Oman<br>Nigeria Bahrain UAE Kuwait<br>0<br>6.0 7.0 8.0 9.0 10.0 11.0 12.0<br>Log of GDP per capita, PPP (constant 2011 international $)<br>Tax revenue (% of GDP, 2011)<br><!-- End of picture text --> Source: World Bank. World Development Indicators

# **V. Conclusions and the Way Forward**

Tax policy is one of the most important aspects of a country’s business environment. It creates an incentive framework for private sector development and generates the revenue needed to fund essential public goods. It is also a political battleground as governments strive to balance the objectives of maximizing revenue, encouraging business growth, ensuring equity and winning electoral support. In South Asia, governments have systematically used tax incentives to promote development of certain industries, types of firms or locations. The effectiveness of tax incentives
- Platteau, J.-P. (2000) Institutions, Social Norms, and Economic Development, vol 1. Routledge Press, Abingdon, UK.

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- Verwimp, P., Bundervoet, T
baseline mobile phone survey, one-third completed the entire survey. Considering that in our study non-response is higher for mobile phone surveys than face-to-face surveys (statistically meaningful non-response rates are observed only for short-survey households), this yielded a disproportional drop in baseline information of about 45 percent (from 7,085 observations to 3,148).<sup>26</sup> However, despite the different response rates for both data collection methods, analysis of the follow-up data suggests that attrition rates are evenly distributed across experimental groups. Table A2 displays results from a regression of a dummy variable that indicates sample in- and outflows on the treatment indicators for the main outcome variable for the entire sample and separately for households participating in the face-to-face as well as mobile phone surveys at baseline. Attrition rates are orthogonal to treatment status, which we take as indication

> 26 Mobile phone response rates for our main outcome variables were slightly higher as we placed them at the beginning of the questionnaire anticipating increasing non-response rates with survey duration.

13
Policy Research Working Paper 9264

# **Abstract**

This paper describes the Services Trade Policy Database, a joint initiative by the World Bank and the World Trade Organization Secretariat, which builds on a database developed by the World Bank nearly 10 years ago and draws on a recent Organisation for Economic Co-operation and Development database. The Services Trade Policy Database offers comparable information on services trade policies for 68 economies in 23 subsectors across five broad areas—financial services, telecommunications, distribution, transportation, and professional services. The database features several improvements. First, the data are collected according to a newly developed policy classification, consistent with the earlier World Bank database and the current Organisation for Economic Co-operation and Development database, enabling a comparison of services policies over a significant period and across a large cross-section of industrial and developing economies. Second, in addition to trade policies, the database contains information on licensing conditions and data restrictions. Third, policy restrictiveness is quantified following a more systematic approach that aggregates the information within a single consistent and transparent framework. Building on these innovations will make it possible to identify global patterns of services trade policies and secular trends in policy making over the past decade.

This paper is a product of the Development Research Group, Development Economics and the Office of the Chief Economist, East Asia and Pacific Region. 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 i.borchert@sussex.ac.uk, Joscelyn.Magdeleine@wto.org, juan.marchetti@wto.org, and amattoo@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
census model, the estimation method for the geospatial model, and the use of two proprietary geospatial indicators.

# A. The Size of the Partial Registry

Given the impressive predictive performance of the partial registry predictions, one might wonder whether increasing its size would further improve performance. Figure 7 compares the performance of the partial registry predictions for partial registries of 444 villages (10 percent), 666 villages (15 percent) and 888 villages (20 percent). Overall, expanding the size of the hypothetical partial registry offers only limited improvements in predictive accuracy, and is not worth the added expense it would entail.

**Figure 7.** Expanded sample for the partial registry method

<!-- Start of picture text -->
1.00<br>0.89 0.88 0.89<br>0.90<br>0.78 0.80<br>0.80 0.75<br>0.70 0.63 0.64<br>0.57<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Rank correlation AUC R-squared<br>Sample 1: 10%-90% Sample 2: 15%-85% Sample 2: 20%-80%<br><!-- End of picture text -->

# B. Village welfare measure

Second, we consider how the results are affected by the choice of welfare measure. This is important because the results until now have used a non-standard welfare measure, namely the average predicted per capita consumption of the bottom half of households in the village. This was based on a conscious decision to match the UBR administrative data, which only contains PMT scores for the bottom 50 percent of households in each village. This section considers how the results change when we consider mean village
households that have migrant children from those households which do not. While this exercise can only demonstrate the current risks associated with reliance on transfers, ongoing changes in the rural demographic structure make it likely that any risks will become more important in the future.

# **3. How Do Transfers Respond to Household Income of the Elderly?**

## **A Framework for Analysis**

Much early research on intra-family transfers used data from developed countries and focused on efforts to distinguish motives for transfers.<sup>9</sup> Such distinctions may be important as they have implications for how transfers respond to exogenous income received by elderly households. Becker (1974) and Cox (1987) emphasize that transfers may reflect altruistic or exchange motives, or some mixture of the two. We follow Cox et al (2004) and frame our analysis in the context of a utility function that captures the potential for both altruistic and exchange motives in transfers between parents and children. We assume that the utility of individual �, ��, may incorporate the well-being of her relative, ���: �� and ��� are consumption levels for individual � and her relative, ��, respectively. Services provided by relative �� to � as part of an exchange relationship are denoted by �. If individuals care about each other, then we would expect ����⁄ �0 and ����⁄ �0, and this would raise the likelihood of observing altruistic transfers. Exchange motives may be present as well if �

> 9Barro (1974), Becker (1974) and Cox (1987) made important theoretical distinctions highlighting different motives for transfers.  Much of the empirical research in the US has suggested that inter-generational inter-vivos transfers are driven by an exchange motives (e.g., Cox and Rank, 1992; McGarry, 1999) rather than based on altruistic motives. It is important to remember, however, that in the US, the social security safety
by the number of households in each village and the standard errors are clustered at district level.

# 3. Methodology

We propose four different targeting methods using census and survey data combined with geospatial data to understand the most effective way to target the poor population: (1) the PMT scores calculated in the UBR data of 2017; (2) the Relative Wealth Index from Meta (3) combining survey data and geospatial indicators to predict average welfare; and (4) a census sample to simulate a _partial registry_ data set used to train models using satellite data.

We rely on rank correlations, Area Under the Curve coefficients, and R-squared coefficients to compare the accuracy of each targeting method. In each case, we compare the predictions with a measure of benchmark welfare constructed from the census model.

## 3.1 Construction of Benchmark Welfare using the Census model

We define a benchmark welfare measure of “ground truth” in order to evaluate different targeting methods. As noted above, the primary welfare measure is the average predicted log per capita consumption of the poorest 50 percent of households in each village. This measure echoes the UBR structure limited to the bottom half of households in each village according to the average Malawi poverty rate. This measure takes advantage of the rich data in the census sample, and it is easier to predict than measured consumption due to reduced measurement error and its inability to capture temporary shocks.<sup>15</sup> To construct the benchmark welfare in the census, we use the IHS 2016 to estimate a model and then impute welfare in the census.

The variables included in the model are selected so that both data sets contain the same information. All the variables are measured at the household level. We include education variables such as the literacy of the household head, the maximum level of education achieved by men and women in the household, dependency ratios, household size and overcrowding,

> 15 Due to data availability, we use the per capita consumption as a welfare measure
**<u>Table 7. Structural Model Estimates</u>**

# **Dependent variable: Shock to Credit**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Fixed E**|**ffects**|**Instrumental**|**Variables(b )**|
|Currency ratio shock (a)|-0.414**||-1.529***||
||0.168)||(0.453)||
|Excess reserves ratio shock (a)||0.636<br>(0.469)||0.439<br>(0.300)|
|Anderson LR statistic Weak Instrument Test|||43.37|161.05|
|Chi-sq(4) p-value|||0.00|0.00|
|Hansen J statistic Overidentification Test|||0.38|1.48|
|Chi-sq(3) p-value|||0.54|0.22|
|Country Fixed effects|x|x|x|x|
|Year effects|x|x|x|x|
|Seasonal Dummies|x|x|x|x|
|Nbr. obser.|374|374|364|360|

Note: Robust clustered standard errors in parentheses Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.

Shocks are residuals obtained after estimation of the system of equations (9) and (10) using seemingly unrelated least squares (a) The currency ratio is the ratio of currency in circulation (oustide the banking system) over demand deposits and the reserves ratio is the ratio of excess reserves
in essence, providing teachers and principals with more information on _how_ to help students stay in school. The second treatment arm adds to the first arm the list of sixth-grade students at high risk of dropping out (component 2), providing school actors more accurate information on _who_ needs the most help. The third treatment arm adds to the first two arms small behavioral nudges (component 3) to try and keep dropout prevention top-of-mind for principals and teachers. A fourth, control arm included schools that received no intervention. This multi-arm design, summarized in Diagram 1, allows us to disentangle the impact of each component on dropout.

**Diagram 1** : four-experimental groups

<!-- Start of picture text --> TREATMENT 1<br>Guide/training<br>1000 schools<br><!-- End of picture text --> <!-- Start of picture text --> TREATMENT 2<br>Guide/training<br>+ List<br>1000 schools<br><!-- End of picture text --> <!-- Start of picture text --> TREATMENT 3<br>Guide/training<br>+ List<br>+ Reminders<br>1000 schools<br><!-- End of picture text --> <!-- Start of picture text --> CONTROL<br>No intervention<br>1000 schools<br><!-- End of picture text -->

# Data

Our main data source is the administrative school data set compiled by SIRE. This data set includes unique student identifiers that has enabled longitudinal tracking of student academic records since 2011. The academic records include basic demographic information and detailed grades earned by subject and year, as well as information on enrollment status, including whether the student dropped out and whether they passed the grade. This data was the main input for estimating the dropout prediction model, as well as the data source
**Recorded RC imports from the DRC are only slightly more important** , amounting to some 1.12 percent of total RC imports in value terms. Table 5 presents data obtained from the RC authorities and shows that imports from the DRC account for more than 10 percent of formal RC imports in only three goods: gasoline, glass bottles and motorcycles (see column 6 of Table 3). These data most likely tell us more about which types of goods are imported formally than about which types of goods are imported from DRC, as most cross-border trade simply seems to escape the regular control of the customs authorities. The overall figures are undoubtedly a large understatement of the true trade share and points toward considerable potential for an expansion of formal trade.

**Officially recorded trade flows between the DRC and the RC are largely limited to transit trade flows, which seem to be increasing.** According to official data supplied by RC authorities the weight of transit imports from DRC was about three times higher in 2007 than in the two preceding years, and transit trade seems to have increased further since. Briceño-Garmendia and Foster (2009) estimate the share of RC imports that transit through the DRC at some 8.6 percent. The data in Table 4, based on DRC statistics, also show some signs of a revival in transit trade since 2009. Official data certainly underreport the importance transit trade. The large informal flows of clothing and textiles from Brazzaville to Kinshasa, for instance, mostly originate in West Africa and arrive through Pointe Noire or Douala in Brazzaville. These large informal flows do not appear in any statistics.

_Table 4: DRC-RC Transit Trade, 2007-2010_

|||_2007_|_2008_|_2009_|_2010*_|
|---|---|---|---|---|---|
|Vl USD 1000|<sup>DRC-RC</sup>|22,693|67,312|89,401|106,
payoffs may take much longer to materialize than what can be captured by our six-year panel for Chile, thus further research using longer panel datasets will be better placed to capture the impacts of past weak performance on subsequent plant innovation choices.

# **6. Conclusion**

Innovation is not only potentially very costly but can also expose plants to significant survival risks as the launch of new products may result in lower than expected sales. At the same time, innovation is a potentially powerful source to allow longer plant survival in the marketplace. Focusing on Chilean manufacturing plants, this paper shows that product innovation reduces the probability of plant death under certain circumstances. Risk plays an important role for the innovation-survival link in that only innovators that retain diversified sources of revenues and innovators facing lower market risk benefit in terms of longer survival. By contrast, we do not find the technical risk of innovation leads to differential impacts in terms of survival. In particular singleproduct plants that innovate are at greater risk of exiting than non-innovating plants. These are not irrational decisions since plants reap positive payoffs from such innovations, but those payoffs are not always higher than those from more cautious types of innovations suggesting that constraining factors impede these plants from engaging in more cautious types of innovations.

Our findings have several policy implications. First, our results show that while there are positive micro-level effects of innovation on the survival of some manufacturing plants, these do not hold for plants engaging in more risky types of innovation. This suggests an additional reason for underinvestment in innovations from the point of view of the plant, beyond the usual fears of not appropriating all the benefits from innovation. Second, while cautious innovation is the most desirable innovation strategy to obtain a survival benefit, it may not be feasible for all types of plants nor in all types of industries. Policy actions may be required to improve for example the capacity of small plants to engage in cautious innovation given the desirability of small plant survival in terms of securing employment. Where risky innovation is the only