rafmacalaba/datause-encoder-data huggingface dataset:
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- [24] Kwan, Y. K. (2007), ‘The Direct Substitution between Government and Private Consumption in East Asia’, in ed. by T. Ito and A. Rose Fiscal Policy and Management in East Asia, NBER (EASE), Volume 16.

- [25] Leeper. E., T. B. Walker and S-C. S. Yang (2009), ‘Government Investment and Fiscal Stimulus in the Short and Long Runs’, NBER Working Paper No. 15153.

- [26] Monacelli, T. and R. Perotti (2006), ‘Fiscal Policy, the Trade Balance, and the Real Exchange Rate: Implications for International Risk Sharing’, mimeo

- [27] Pappa, E. (2005), ‘New-Keynesian or RBC Transmission? The E¤ects of Fiscal Shocks in Labour Markets’, CEPR Discussion Paper No. 5313.

- [28] Perotti, R. (2005a), ‘Estimating the e¤ects of ...scal policy in OECD countries’, mimeo.

- [29] Perotti, R. (2005b), ‘Fiscal Policy in Developing Countries: A Framework and Some Questions’, The World Bank, Development Research Group, Policy Research Working Paper 4365.

- [30] Perotti, R. (2007), ‘In Search of the Transmission Mechanism of Fiscal Policy’, NBER Macroeconomics Annual, 22: 169 - 249.

- [31] Perotti, R. (2011), ‘Expectations and Fiscal Policy: An Empirical Investigation’, mimeo.

- [32] Ramey, V. A and M. Shapiro (1998), ‘Costly capital reallocation and the effects of government spending’, Carnegie-Rochester Conference Series on Public Policy, 48: 145 –149.

- [33] Ramey, V . A. (2009), ‘Identifying Government Spending Shocks: It’s All in the Timing’, NBER Working Paper No. 15464.

- [34] Romer, D. and C. Romer (2009), ‘Do Tax Cuts Starve the Beast? The E¤ect of Tax Changes on Government Spending’, Brookings Papers on Economic Activity, 40(1): 139 - 214.

- [35] Romp, W. and J. de Haan (2007), ‘Public Capital and Economic Growth: A Critical Survey’, Perspektiven der Wirtschaftspolitik, 8(s1): 6 - 52.

- [36] Stock, J. and M. Watson (2002), ‘Has the Business Cycle Changed and Why?’, NBER Macroeconomics Annual, M. Gertler and K. Rogo¤ (eds), MIT Press.

23
|Turkey|-0.01|0.37|0.38|
|---|---|---|---|
|Other countries|-0.14|0.12|0.26|

Source: Analysis using PISA 2015.

Hostile practices from teachers may increase the occurrence of disorder in MENA classrooms _._ There is a clear statistical association between teachers’ hostility and the classroom disciplinary climate. Students in classes that witness higher levels of disruption tend to report more frequent hostile behaviors from their teachers (Figure 4). The link between the hostility and disciplinary climate is strong. On average, increasing hostility by one standard deviation is associated with a decrease of the disciplinary climate by 0.5 standard deviation. The causality could, however, be in either direction, as more difficult students are likely to annoy teachers while hostile teachers may prompt the aggressiveness of their students in return.

**Figure 4. Share of Students Reporting the Following Incident Occur A few Times a Month, Once a Week or More**

<!-- Start of picture text --> 0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Teachers called on Teachers graded Teachers gave me Teachers Teachers ridiculed Teachers said<br>me less often than me harder than the impression disciplined me me in front of something<br>they called on they graded other that they think I more harshly than others. insulting to me in<br>other students. students. am less smart other students. front of others.<br>than I really am.<br>United Arab Emirates Qatar Tunisia Turkey Other countries<br><!-- End of picture text --> Source: Analysis using PISA 2015

# **Deteriorated classroom disciplinary climate hampers student learning in many MENA countries**

Deteriorated classroom disciplinary climate has a negative effect on students’ performance in MENA. On average, improving the classroom disciplinary climate by one standard deviation is associated with an improvement of around 25 to 35 points in mathematics, which corresponds to around three-quarters of a year of schooling (Table 3).<sup>8</sup> Even after accounting for differences in age, gender, and socioeconomic factors between students and between schools, disciplinary climate is one of the variables with the largest explanatory power for student performance. The effect of disciplinary climate on students is more easily demonstrated when considering the extent and frequency of classroom disruptions. There is indeed a robust correlation between the disciplinary climate measure derived from the PISA questionnaire and students’ performance as measured by the same survey. A robust positive relationship between disciplinary climate and students’ performance has been observed since the first wave of the PISA survey in 2000.

> 8 Mathematics performance tends to be less determined by socioeconomic factors and thus more sensitive to changes in pedagogy and features of the education system, which is the reason why mathematics performance rather than reading or average performance is used in this analysis.

8
risk” as in the earlier literature on the subject, or broken down into different components as above) is mainly borne by the owner of the dwelling, and relates to the certainty of present and future cash flows from the investment. On the other hand, renters with high moving costs may care about physical eviction risk.

In this paper we clarify these issues. We show that appropriate data on rents and prices of dwelling units in the formal and informal sectors can be used to isolate two economic variables: the difference in rates of return on housing investment, which reflects the risks borne only by investors, and the premium on rents for the formal sector, which reflects conditions prevailing in rental markets. These two quantities have different economic meanings, and should therefore be analyzed separately. We consider a simple model of investment in the housing market, where investors can choose between two sectors: the formal sector, where physical investment faces no risk of destruction, and the informal sector, where investment, in each period, is subject to an exogenous risk of destruction. Construction costs differ between the two sectors. On the demand side, all households are renters.<sup>9</sup> Renters shop for dwelling attributes and do not care about the sector (formal or informal) _per se_ . There is no moving cost, and renters can choose freely between the formal and informal sector (no segregation in the housing market).

The implications of this simple model are the following. Free mobility of renters implies that rents in the two sectors must be equal. On the supply side, the higher risk faced by investors in the informal sector must be reflected in a higher return on equity invested. Since rents are equal in the two sectors, the values of houses will be lower in the informal sector. Returns on investment, measured by the rent-to-value ratio, will thus be higher in the informal sector. This result relies only on investment side considerations and is independent of assumptions on the households’ side. Thus it will hold even when there are mobility costs or when households have intrinsic preferences for living in the formal sector.

We use a survey conducted by the World Bank in the city of Pune, India. Among

> 9Owner-occupying households are seen both as investors and renters, insofar as they rent their dwellings to themselves.

4
|Table R.N3: OLS|Estimates,|Depende<br>|nt variabl<br>|e: TCC<br>|||
|---|---|---|---|---|---|---|
||||irm|s|BelowVAT|Threshold]|
||TOC|TAC|Tce|TAC|<br>TAC|<br>TAC|
|E-file|0.193|0.3327*|O.195|0.334**|| O.347**|0.347**|
||(O.204)|(O.153)|(0.199)|(0.149)|(0.169)|(O.165)|
|Turnover|O.129*|-O0.0171|O<.117*|-0.0304/|-0.0316|“0.0372|
||(0.0698)|(0.0803)|(0.0688)|<br>(0.0795)]|<br>(0.0865)|(0.0856)|
|Employment|O.218**<br>(0.0879)|O<£.311***<br>(0.104)|6.209%"<br>(0.0869)|O.300***)<br>(0.104)|0.313***<br>|(0.116)|o.304***<br>(0.115)|
|VAT|O.464***<br>|1.256**=<br>|O.454***<br>|<br>1 250*"*)<br>|<br>1.319F**<br>|1.310+**<br>|
||(0.166)|(0.183)|(0.160)|(0.180)|(0.216)|(0.213)|
|TOS|0.320<br>|O<:.612***<br>|(0.284<br>|<br>(.578**=*<br>|<br>)0.635***<br>|o0.602***<br>|
||(0.203)|(0.155)|(0.194)|(0.147)||(0.168)|(0.159)|
|VAT Threshold|0.409**<br>(0.168)|O@<.700**=<br>(0.178)|-0.0577<br>(0.167)|<br>0.230<br>(0.177)|||
|Industry|YES|YES|YES|YES|YES|YES|
|Region|YES|YES|YES|YES|YES|YES|
|Legal Form|YES|YES|YES|YES|YES|YES|
|Acces te Internet|YES|YES|YES|YES|YES|YES|
|Comp. Usage in|||||||
|Tax Ace.|YES|YES|YES|YES|YES|YES|
|Bank Account|YES|YES|YES|YES|YES|YES|
|Constant|P.2rRee=|F209F**|1FRR**|1.733"<br>||7.366***|LB?|
||(1.897)|(1.036)|(0.884)|(1.024)|(1.119)|(1.108)|
|Observations|362|362|362|362|234|234|
|R-squared|0.339|o.490|0.289|0.460|0.432|0.430|

Sampling weigthe are used in all regressions. TCC is total tax carnplianee cost, TAC is total tax accounting costs, All continous varablas (including dependend variable) are in logs, In “All” colurins, firms firms that do all TS in-house are employed, In “below Threshold® columns,anly the firms that are below VAT threshold are included. Induetry.city, legal form, carnpuber wage, acess to internet and bank account dummies are algo controlled but excluded fram the table, **" p<0.01, "* p<0,05, * p01
are compromised by the epidemic. This is the premise underlying the down-stream arguments that HIV/AIDS causes poverty, even though there is (also) weak evidence of this.

# **_Early Marriage_**

An alarming demographic trend in developing countries has been the steadily increasing percentages of adolescents and women who are HIV–positive. These patterns have been identified as reflecting marriage patterns and risk. Clark, Bruce, and Dude (2006) argue that early marriage by females presents an important risk factor for HIV infection that is generally not being addressed and that could be contributing to the increase in HIV among this relatively large segment of the population (almost a third of girls 10–19 in developing countries marry before their 18th birthday).

Using data from 22 DHS conducted in Africa and Latin America and the Caribbean, Clark, Bruce, and Dude (2006) conclude that four factors increase the vulnerability of young brides to HIV infection. First, marriage dramatically increases the frequency of unprotected sex for most young brides. In almost all of the countries studied, the proportion of females 10–19 reporting having had unprotected sex in the past week was higher among those who were married than those who were not. Second, many young brides marry older men, who are more likely to be HIV–positive because of their increased window of sexual activity. Young brides are also more likely to be second or third wives in polygamous marriages. They have little power to ensure their husbands have only one partner, inside or outside marriage. Third, young brides often have less education than older brides, as well as less exposure to media, both important sources of information about HIV. Fourth, because of the age and education gaps between young brides and their husbands, young married girls and women have little possibility of using the most commonly promoted HIV prevention techniques of abstaining from sex or using condoms.

Based on these hypothesized pathways, the authors conclude that young married females are at significant risk for HIV infection. Few prevention efforts are targeted at these girls and women. Though the context for each country demands tailored policy approaches, Clark, Bruce, and Dude (2006) suggest that efforts to delay early marriage 11
information reduces search costs and facilitates co-ordination among agents, thereby increasing productive and market efficiency. Mobile phones also create employment and increase incomes by spurring demand for mobile-related services. In addition, mobile phones enable social networks to respond more immediately to idiosyncratic shocks, thereby reducing household exposure to risk.

On the basis of a study of 409 small business entrepreneurs in Kenya, Mbogo (2010) finds that for micro-businesses, factors responsible for adoption and continued usage of mobile money transfer include convenience, support, cost, satisfaction and security.

Camner and Sjöblom (2009) compare the adoption of M-PESA in Tanzania to adoption in Kenya. They find much lower adoption rates in Tanzania, which are explained by the lower mobile telephony market share of Vodacom, which is the M-PESA provider in Tanzania, its inability to build a strong agent network, and its lower marketing budget. Other factors are Tanzania's generally lower GDP growth, smaller proportion of unbanked population, higher financial illiteracy, more dispersed population and the absence of a national ID.

# **4 The Basics of Mobile Money**

Mobile money systems consist of electronic money accounts that can be accessed via mobile telephones. They are often likened to simple bank accounts, although a basic mobile money system does not pay interest or provide loans. Each of the mobile service providers in Kenya currently has a mobile money service. Safaricom‘s M-PESA was introduced in March 2007, Zain‘s Zap (now known as Airtel Money) was initiated in February 2009, yuCash started in December 2009, and Orange‘s Orange Money was launched in November 2010. The following description is based on M-PESA, which is by far the largest system, although the other systems are similar.

To have a mobile money account and make a deposit, a customer must own a cell phone SIM card with the mobile operator and register for a mobile money account. The customer then can make cash deposits at the physical offices of one of the operator‘s mobile money agents. These cash deposits create electronic money credit in the customer‘s account.

6
Policy ReseaRch WoRking PaPeR 4897

# **Abstract**

Aggregate fluctuations in emerging countries are quantitatively larger and qualitatively different in key respects from those in developed countries. Using data from Mexico and Canada, this paper decomposes these differences in terms of shocks to aggregate efficiency and shocks that distort the decisions of households about how much to invest, consume, and work in a standard model of a small open economy. The decomposition exercise suggests that most of these differences are explained by fluctuations in aggregate efficiency, distortions in labor decisions over the business cycle, and, most importantly, fluctuations in country risk. Other distortions are quantitatively less important.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to understand macroeconomic volatility in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at chevia@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 Research Support Team
shock in our parlance) captures the disease environment effect, which, according to the discussion above, is likely to reduce the child’s weight. At this point in the agricultural calendar, however, the household cannot yet avail itself of the increased purchasing power induced by the higher than normal rainfall; this windfall is realized only after the harvest, which has yet to take place. Hence, the income effect of a contemporaneous rainfall shock on the child’s weight is essentially zero.<sup>5</sup> In contrast, excess rainfall during the _last_ completed monsoon season is likely to operate through both the income _and_ disease environment channels. High past monsoon rainfall has a negative impact on previous period nutritional status through _Et-1_ , which is then carried through to the current period by _Ht-1_ in equation (1). Moreover, plentiful rainfall during the last monsoon results in a better harvest, increasing _It._<sup>6</sup> In sum, the reduced form impact of excess rainfall in the last monsoon on child weight consists of two opposing effects: a (negative) lagged disease environment effect and a (positive) income effect; the reduced form impact is thus a _lower bound_ on the income effect of rainfall shocks on child weight. Indeed, in principle, _the reduced form effect of excess rainfall in the last monsoon can be negative._

# **3. Data**

# _Anthropometrics_

Child and household-level data are drawn from three rounds of the nationally representative Demographic and Health Surveys conducted in Nepal in 2001, 2006 and 2011. The DHS samples were selected using a stratified, two-stage cluster design. The 2001 round consists of 8602 households in 257 clusters of which 84% are rural; 2006 and 2011 rounds are larger with, respectively, 9036 households in 260 clusters (68% rural) and 10,826 households in 289 clusters (71% rural).

> 5 Excess precipitation could have a _contemporaneous_ effect on household income via changes in the local agricultural labor market.  In particular, if the rainfall shock is realized prior to planting _and_ there is significant scope to increase area planted or the use of inputs that require a lot of labor to apply, then wages paid to hired farm workers may rise.  We suspect, however, that such effects are of second order importance at best in the context of rural Nepal.

> 6 To reiterate, it is not necessary to be a food producer in order to reap the benefits of higher yields if food markets are not perfectly integrated.   In addition, rural households engaged in non-farm activities may experience an increase in demand, and hence income, when local agriculture is doing well.

6
Policy ReseaRch WoRking PaPeR 4390

# **Abstract**

The author uses a new database of EU product standards in the textiles, clothing, and footwear sectors to present the first empirical evidence that international standards harmonization is associated with increased partner country export variety. A 10 percentage point increase in the proportion of internationally harmonized standards is associated with a 0.2 percent increase in partner country export variety, whereas a 10 percent increase in the total number of standards is associated with a nearly 6 percent decrease in product variety. Although small, the harmonization elasticity is statistically significant, and proves highly robust to sample changes and instrumental variables estimation using instruments motivated by political economy considerations. Moreover, it is found to be around 50 percent higher for low income countries, which suggests that they may be particularly constrained in adapting products to meet multiple standards. Numerical simulations show that these findings are consistent with a heterogeneous firms model of trade in which harmonization is beneficial at the extensive margin provided that any increases in compliance costs are not too large.

This paper—a product of the Trade Team, Development Research Group—is part of a larger project on trade costs, trade facilitation, and economic development, supported through a Trust Fund of the U.K. Department for International Development (http://econ.worldbank.org/projects/trade_costs). Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bshepherd@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
The incremental benefit of moving from a strategy of No DOTS to the Global Plan is much greater than that of moving from Sustained DOTS to the Global Plan because much of the benefits of TB control are captured in moving from no DOTS to sustained DOTS. Due to the greater uncertainty surrounding implementation of the Global Plan relative to Sustained DOTS, in some countries, notably Indonesia, Vietnam, and the Philippines, the range of benefits may be quite large and not statistically different from zero. Nevertheless, for half the countries the estimated benefits could exceed the costs by more than a factor of 10, suggesting that there are significant economic benefits in reaching beyond 2005 DOTS-coverage levels to achieve Global Plan targets.

Annual GDP growth rates are contrasted with annual full-income growth rates in Table 9. Since full-income growth rates incorporate benefits from increased longevity, they are greater than GDP growth rates in countries where life expectancy is increasing. Only in South Africa, where life expectancy is declining, are full-income growth rates lower than GDP growth rates. Implementation of either type of TB-control strategy (Sustained DOTS or Global Plan) does not make a significant difference to annual fullincome growth rates ─ differences are on the order of 0.002-0.015 percentage points.

# **Uncertainty analysis**

Partial rank correlation coefficients (PRCCs) were calculated in the same manner as Blower and Dowlatabadi (1994) for input parameters sampled using the Latin hypercube scheme and the two outcome variables (deaths and economic burden). PRCCs help to determine the independent effects of each parameter on outcome variables, even when the parameters are correlated, and the relative importance of input variables in determining the imprecision of the result can be assessed by comparing PRCCs. Correlation coefficients are presented in Tables 10–12 (for the three scenarios for the Africa region).<sup>12</sup> We find that the fraction of infections leading to progressive primary disease was the most significant input parameter in evaluating the range of the result. Other parameters, including death rates, fraction of infected individuals susceptible to re-

> 12 PRCCs also were calculated for high-HIV+ countries and low-HIV+ countries in the Africa region, as well as for other regions and countries, with similar results.

18