rafmacalaba/datause-encoder-data huggingface dataset:
Many SIDS are highly exposed to tropical cyclones, because of their geographical position, and are highly vulnerable because of the small size of their landmass and lack of economic diversification. The EM-DAT records 185 incidences of tropical cyclone disasters in the 16 Caribbean SIDS during the 28-year period between 1995 and 2022. It also recorded 74 incidences in the 12 SIDS in Oceania during this period.<sup>5</sup> The EM-DAT records total estimated damages, comprising all damages and economic losses related to the disaster, but these data pertain to less than half of the recorded tropical cyclone disasters. Table 1 shows the total damages caused by tropical cyclones and recorded in EM-DAT in the Caribbean and Oceanian SIDS from 1995 to 2022, by country and region, estimated in 2022 US dollar prices. These data must be underestimates of the actual damage caused by tropical cyclones, given the absence of data on total damages for more than half of the tropical cyclone disasters recorded by EM-DAT. It is likely that some SIDS provide more comprehensive data on damages than others; for example, data from Cuba appears to be more comprehensive than that from Haiti.<sup>6</sup> The data in disaster databases is unavoidably imperfect, and are subject to problems such as over or under reporting of certain hazard types and other forms of bias (Gall, 2015).

**Table 1 Total Recorded Damages caused by Tropical Cyclones in SIDS in the Caribbean and Oceania; 1995-2022 (US dollar millions, 2022 prices)**

|**Caribbean**|**Damages USD M.**|**Oceania**|**Damages USD M.**|
|---|---|---|---|
|Cuba|18,374|Fiji|1,080|
|The Bahamas|8,478|Vanuatu|557|
|Dominican Republic|4,586|Tonga|253|
|Haiti|3,241|Samoa|170|
|Dominica|2,709|||
|Jamaica|2,273|||
|Grenada|1,387|||
|St Kitts and Nevis|1,193|||
|Antigua and Barbuda|1,167|||
|Belize|985|||
|All Caribbean SIDS|44,606|All Oceania SIDS|2,076|

_<u>Source: EM-DAT</u>_ Total recorded damages from tropical cyclones suffered by SIDS in the Caribbean during 19952022 was USD 44 billion, while SIDS in Oceania suffered damages of USD 2 billion. in 2022

> 5 Some tropical cyclones hit multiple SIDS.

> 6 For most of the tropical cyclone disasters in Cuba for which deaths were reported or where there were large numbers of people affected, total damages are also recorded. But Haiti was struck by several tropical cyclone disasters which killed many people but for which no figure for damages were recorded; e.g. Hurricane Noel in 2007 which killed 90 and affected more than 100,000 people, Hurricane Gustav in 2008 which killed 85 and affected 73,000 people, Hurricane Hanna in 2008 which killed 529 and affected 48,000 people and Hurricane Laura in 2020 which killed 39 and affected 44,000 people. Jones et al (2023) discuss the problem of missing data in disaster databases and how it is handled in empirical research.

5
||**Table**|**2: Sum**|**mary st**|**atistics on**|**covariates of**|**poverty**|||
|---|---|---|---|---|---|---|---|---|
|**Category**|**Nigeria**<br>2018|**Mali**<br>2009|**India**<br>2011|**Pakistan**<br>2018|**Tajikistan**<br>2015|**Indonesia**<br>2017|**Yemen**<br>2014|**Colombia**<br>2017|
|Years of schooling|7.0||5.5|5.4||8.21|6.12|8.22|
|Asset index|2.51||2.12||1.77|2.21|2.68||
|Asset ownership:<br>computeror landline||0.04||0.14||||0.43|
|Literacy|0.72|0.35|0.68|0.58||0.96|0.71|0.93|
|Not employed in the<br>agricultural sector|0.92|||0.70||0.65||0.80|
|Access to electricity|0.64|0.22|0.80|0.91|0.98|0.98|0.65|0.98|
|Piped drinkingwater|0.03|0.64||0.93|0.46|0.11|0.48|0.98|
|Improved sanitation|0.58|0.22||0.70|0.96|0.76|0.59|0.90|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: These statistics are computed on a subsample of household heads only. The first two covariates of poverty,</u> mean years of schooling and average asset index, are continuous variables. The remaining covariates are binary indicators. For example, 4% of household heads in Mali (2009) own at least a computer or landline. The asset index is not comparable across countries but is added to the table for completeness. Mali, Pakistan, and Colombia have data on only three assets. In these cases, _asset ownership_ variable is instead created as a binary variable of owning at least a computer or landline phone. Empty cells indicate missing data, or cases that are not applicable (i.e., asset index and asset ownership which are mutually exclusive). The survey period spans two consecutive calendar years in Nigeria (2018.75), Mali (2009.89), India (2011.5), and Pakistan (2018.5). By convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019. The floor of the survey period is indicated in the table.

31
# **6. Conclusion**

This is the background paper for the TFP extension of the World Bank’s Long-Term Growth Model (LTGM). It proposes a way to project the future path of TFP growth for most developing countries around the world if they were to follow a program of reforms that would approach them to regional and global leaders. The paper is accompanied by an Excel-based toolkit, which can be used for scenario analysis on TFP and corresponding income growth (available at the LTGM’s website: https://www.worldbank.org/LTGM).

Based on a comprehensive literature review, we select innovation, education, market efficiency, infrastructure, and institutions as the five main categories of TFP determinants. For each of these categories, we construct an index as a linear combination of representative indicators (or proxies) by factor analysis, that is, by accounting for as much of the common variance in the indicators as possible. We then combine the five subcomponent indexes into an overall index by the principal component analysis, which accounts for as much of the total variance in the subcomponent indexes as possible.

Using dominance analysis, the variance decomposition of the TFP growth rate into the main subcomponent indexes shows that for OECD countries, market efficiency contributes the most to the variance of TFP growth and infrastructure, the least for the recent decade; and for developing countries, the contribution of education increases continuously and is the largest among the determinants in the recent decade. Although the variance decomposition of TFP into its determinants is not necessarily a guide for policy reform, it illustrates how the observed variation in TFP growth can be explained differentially over time and across development levels. This suggests patterns that countries can use to assess their own progress in the various determinants of productivity.

On its part, regression analysis shows that an increase in the overall determinant index is significantly associated with an increase in the TFP growth rate, controlling for the initial TFP level and country- and time-effects. Countries that have a larger room for improvement in the determinants of TFP and make a stronger effort of reform would experience a larger increase in TFP growth, which is expected to rise over time and then taper down. The slowdown of TFP growth in the long run is explained by the increasing difficulty of expanding TFP 35
Policy Research Working Paper 9267

# **Abstract**

Can consumption taxes reduce inequality in developing countries? This paper combines household expenditure data from 31 countries with theory to shed new light on the redistributive potential and optimal design of consumption taxes. It uses the place of purchase of each expenditure to proxy for informal (untaxed) consumption which enables characterizing the informality Engel curve. The analysis finds that the budget share spent in the informal sector steeply declines with income, in all countries. The informal sector thus makes consumption taxes progressive: households in the richest quintile face an effective tax rate that is twice that of the poorest quintile. The paper extends the standard optimal commodity tax model to allow for informal consumption and calibrates it to the data to study the effects of different tax policies on inequality. Contrary to consensus, the findings show that consumption taxes are redistributive, lowering inequality by as much as personal income taxes. These effects are primarily driven by the shape of the informality Engel curve. Taking informality into account, commonly used redistributive policies, such as reduced tax rates on necessities, have a limited impact on inequality. In particular, subsidizing food cannot be justified on equity or efficiency grounds in several poor countries.

This paper is a product of the Development Research 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 pbachas@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
- 12 -

teaching and school administration. Support services should be aimed at helping school personnel carry out their routine jobs better: bringing them simple approaches to classroom organization, student motivation, techniques of teaching; and developing supplementary teaching materials from within the immediate rural environment. In other words, the emphasis should be on teaching techniques for school staff. Support services for school administrators should be oriented towards helping the administrator provide instructional leadership.

# How to Deliver Rural School Services

41. How to organize the delivery of support services to rural schools is a critical management task. The most difficult aspect of providing a support service to a rural school is assuring consistency and frequency. The lack of consistency and frequency in most rural support services-and education is no exception-is commonly cited as a resource problem. Resources, however, are only part of the problem. In particular, school support services for rural areas have never been fully institutionalized in most LDCs. Unlike systems of agricultural extension, most systems of school supervision/inspection arose primarily out of the need to link the growing number of rural schools organizationally and bureaucratically to central administrative structures, usually--though not exclusively--the Ministry of Education or its delegate at the state, departmental or provincial level. At best, these systems were established as administrative surrogates to maintain standards, i.e. mechanisms of control (see para. 14 above).

42. Improving support services for rural schools will depend upon measures taken to deal with the institutional, organizational and cost issues discussed in section (iii) above. The principal weaknesses of traditional supervision/inspection systems and the upgraded approaches implemented through Bank projects are the organizational and managerial aspects. Bank projects, as well as other government and internationally sponsored programs, have attempted to address some of the institutional and resource issues. They have been quite clear as to a reordering of priorities away from control, data collection, and personnel towards improving classroom teaching practices among -ural teachers. What has been less well specified is how to achieve this reorientation beyond making broad institutional level changes, e.g. creating a parallel cadre of pedagogic advisors or moving to decentralize the entire administrative structure, and providing incremental resources.

43. Most frequently, these programs try to modify classroom teaching behaviors by using either pre-service/in-service teaching programs or, in some cases, through media and/or correspondence distance learning courses. While these approaches have proven to be effective in upgrading teachers' knowledge of subject matter, neither has been especially effective in getting teachers to change their classroom teaching practices. An evaluation of the USAID-sponscred radio teacher training in Nepal (1983) stated the problem in exactly those terms. Similar criticisms have been
**Table 6: The impact of parents’ Non‐farm participation on Children’s Non‐farm participation (Probit)**

|**Adult Children**|**Parents' Occ.**|**Parents' Occ. +**<br>**Coastal Dummy**|**Parents' Occ. +**<br>**Province Fixed**<br>**Effect**|**Parents' Occ. +**<br>**County Fixed**<br>**Effect**|**Parents' Occ.**<br>**+County Fixed**<br>**Effect + HH**<br>**Wealth**|**Parents' Occ. +**<br>**HH Wealth**|**Parents' Occ. +**<br>**County Fixed**<br>**Effect + HH**<br>**Wealth + Other**<br>**Controls**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_1988‐‐daughters_||||||||
|Marginal Effect|**0.24**|**0.22**|**0.18**|**0.18**|**0.15**|**0.17**|**0.12**|
|T statistics|15.88|14.78|12.26|7.54|5.01|9.73|3.73|
|_1988‐‐sons_||||||||
|Marginal Effect|**0.25**|**0.24**|**0.21**|**0.16**|**0.12**|**0.18**|**0.12**|
|T statistics|13.71|12.92|11.57|6.66|3.47|7.72|3.44|
|_2002‐‐daughters_||||||||
|Marginal Effect|**0.09**|**0.08**|0.04|‐0.04|**‐**|**‐**|**‐**|
|T statistics|4.06|3.35|1.59|‐1.61|‐|‐|‐|
|_2002‐‐sons_||||||||
|Marginal Effect|**0.14**|**0.11**|**0.07**|0.05|0.04|0.04|0.04|
|T statistics<br>NOTES|7.45|6.19|3.59|1.59|1.52|1.44|1.42|

1. Dependent variable is adult children's occupation dummy, 1=Non‐farm; parents' occupation  dummy, 1=at least one parent is in non‐farm;

2. "HH wealth" stands for indicators of household wealth: includes parents' communist party member dummy, ethnic minority dummy, female household head dummy, mother's age, father's age, irrigation dummy, land, house value and electricity dummy; 4. "Other controls " include topography and coastal location dummy; labor constraint; mother's and father's years of schooling, old revolutionary base dummy, ethnic minority region, impoverished region dummy, and suburb of big city and adult child's age and age 56
magnitude of the estimated elasticities further support the contention that data transparency could be a more powerful driver of growth than either trade or schooling, even without taking into account the costs that would be entailed in raising trade or schooling over time relative to improving data systems.

Conceptually, there are several channels through which data transparency can affect development. First, credible and timely data serve as the basis for policy and reforms. Data are about records. Take the example of a business. A manager has the primary goal of raising profits. To achieve this, performance is benchmarked historically and compared with that of competitors. Collateral must be evaluated and leveraged to obtain financing to pursue new ventures. Risk and reward must be balanced. Investors need to be enticed. Without record-keeping, many of these goals could not be achieved. Indeed, experimental evidence indicates that simple interventions that help manufacturing plants improve inventory record keeping in a developing economy are associated with improved performance (Bloom et al., 2020). A similar set of challenges face governments. Countries need to grow, and, to expand options, data must be reliably transparent to provide guidance. Countries with high quality and broadly accessible information can make better decisions. Through data and evaluation, existing policies may be reformed and refined, while new policies may be experimentally evaluated (Rodrik, 2010). Gatti et al., (2024) find that better data transparency reduces growth forecast errors.

Second, data that are accessible to the broader civil society can generate better policies and reforms. Substantial expansions in the frontier of knowledge occur when data are available to a large base of analysts. Researchers test hypotheses, debate and dispute findings, establish robust facts and relationships to facilitate the emergence of the best ideas for addressing challenges. If the initial stock of information in a country is large, then there would be a better knowledge base to inform research and policy. Improved data access has been found to increase scientific research (Nagaraj et al., 2020). Stacy et al., (2024) finds that by analyzing more than 1 million academic articles, the number of articles on a country is strongly correlated with a country’s national statistical system.

6
The evidence seems thus to suggest that discretionary FX intervention is associated with larger volumes of intervention, and thus higher variability of FX reserves, compared to rules-based intervention. This stylized fact would be consistent with the signaling channel for the transmission of the effects of FX intervention analyzed in the literature: To the extent rules improve the visibility of central bank intentions they may affect the perceptions of market participants and thus change exchange rates with relatively lower volumes of intervention.<sup>8</sup> This tentative evidence would also be consistent with a microstructure of the FX market that includes some big strategic players. If the central bank could direct these players to a desired equilibrium exchange rate through a preannounced schedule of intervention contingent on market outcomes, it could affect the exchange rate with low volumes of intervention. This theoretical model of strategic central banking is analyzed in the next section.

<!-- Start of picture text --> Figure 3.5: Volatility of changes in FX reserves (2000–2012) 1<br>1.6<br>1 Standard deviation of monthly changes in FX reserves; in percent of GDP over 2000-12<br>1.4<br>1.2<br>1.0<br>0.8<br>Average = 0.603<br>0.6<br>0.4<br>0.2<br>0.0<br>Sources: IMF World Economic Outlook and World Bank Global Economic Monitor.<br><!-- End of picture text -->

> 8 An equivalent stylized fact could be that, for a given level of FX intervention, the impact on the exchange rate would be stronger when the central bank uses an exchange rate-based FX intervention rule than when it intervenes with discretion. A study by Adler and Tovar (2011) examined the impact of FX intervention through an empirical exchange rate model on a panel of 15 countries over six common episodes when flows into emerging markets were high or rising strongly. The regressions found evidence that FX intervention affects the pace (acceleration) of exchange rate appreciation but not the rate (speed) of appreciation. When the regression controls for discretionary vs. rules-based central bank intervention, the estimated impact of intervention on the pace of exchange rate appreciation is higher when intervention is rules-based than when it is discretionary, although the difference is not significant.

18
Table 5-2. City Size Growth Equation (continued)<sup>a,b,c</sup> (robust standard errors in parentheses)

||(1)|(2)|
|---|---|---|
||GMM-IV|OLS|
|Δ Ln(ruralpop. supplymarket|5.727**|3.227***|
|potential)|(2.488)|(0.684)|
|Δ Ln(rural income opportunities:|-0.534|0.229|
|marketpotential)|(0.917)|(0.359)|
|Δ Ln(marketpotential)|1.546|2.127***|
||(1.257)|(0.355)|
|Average schooling (t-1)|0.064***|0.035***|
||(0.016)|(0.011)|
|Δ Average schooling|0.323**|0.093**|
||(0.138)|(0.034)|
|Δ Ln(inter-citytransport costs)|-0.082*|-0.059|
||(0.043)|(0.036)|
|state capital dummy|0.139***|0.113***|
||(0.036)|(0.030)|
|Ln(population) (t-1)|-0.044***|-0.023**|
||(0.008)|(0.008)|
|Manu / service(t-1)|0.067**|0.066**|
||(0.032)|(0.027)|
|Ln(homicide /pop) (t-1)|-0.115***|-0.092***|
||(0.033)|(0.025)|
|Public industrycapital /|-0.764**|-0.780|
|total industrycapital in 1980|(0.298)|(0.502)|
|time dummies|Yes|Yes|
|Observations|245|245|
|R<sup>2</sup>||0.469|
|Hansen J statistic<br>(overidentificationtest)|5.549||
|(p-value)|(.698)||
|Average of Partial R<sup>2</sup>|.498||
|Average of Partial F's|3014.5||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. Public industry capital / total industry capital (1980) is assumed to be exogenous by adding it to the IV list of (3).

- b. GMM estimates are from the two-step efficient GMM in the presence of arbitrary heteroskedasticity and arbitrary intra-group (within-state) correlation.

- c. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

35
Empirically, our strategy consists of estimating a profit equation for each activity (“profit” meaning the net value of production approximated using the data available in the household survey) using a switching-regression technique. This gives us two expected profits for each farmer _i,_ conditional on _i_ ’s individual characteristics: one under the “subsistence” regime and one under the “market” regime. For a subsistence farmer, the difference in predicted profits between the market regime (where he is not) and the subsistence regime (where he is) is the opportunity cost of not being in the market, which of course depends on his individual characteristics. The present discounted value of this estimated stream of profit differentials for the marginal farmer is then our estimated entry cost.

As the decision to be in the market or not is endogenous to the profit differential, endogenous switching-regression methods must be used (Maddala and Nelson 1975). An additional difficulty comes from the fact that there is no natural definition of “subsistence farming”, as the proportion of farm output sold on the market is, in the data, a continuous variable.<sup>2</sup> Rather than imposing an arbitrary cutoff in terms of proportion of output self-consumed, we let the data determine the best way of splitting the sample using a two-step procedure developed for switching-regression models with unknown switch point. Namely, in the first step we obtain consistent estimates of all parameters for a given value of the unkown cutoff by maximum likelihood, taking endogeneity into account; in the second step, we search for the cutoff value that yields the _maximum maximorum_ of the log-likelihood function.

The threshold obtained by this method turns out to be at zero. That is, the sample is split between farmers who are completely cut from markets (about 10% of them) and farmers who sell something. This accords with the intuitive idea that indivisibilities are involved (purchase of capital equipment, availability of intermediaries, transport cost, etc.). Once those hurdles are overcome, adjustment in terms of quantities sold is smoother. Estimated entry costs are large relative to income —between 124 and 153 percent of a subsistence farm’s annual output evaluated at market prices. Interestingly, however, the economic opportunity cost associated

> 2As defined by the Encyclopedia Britannica, “subsistence farming is a form of farming in which nearly all of the crops or livestock raised are used to maintain the farmer and his family, leaving little, if any, surplus for sale or trade.” However, most farmers sell some and keep some for home consumption.

4