rafmacalaba/datause-encoder-data huggingface dataset:
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" this analysis we have created new income, expenditure, and infrastructure variables that can be compared across countries. There are inevitably conceptual and measurement problems in the creation of such global variables. Our purpose here is to look for broad patterns of infrastructure use by households of different levels of economic well-being. We caution the reader not to make too mnuch of individual results.<sup>9</sup> Cross-country income and expenditure variables were created by converting local currency to 1998 US dollars, using first the official currency exchange rate in the survey year and then the IJ.S. consumer price index.<sup>10</sup> Our cross-country infrastructure variables classify infrastructure options in each sector as "advanced", "intermediate", or "basic" solutions (Table 2). Sections 3, 4, and 5 of this paper examine "advanced" solutions, which are typically provided by a utility (electricity, in-house water taps, sewer connections, and telephones). Section 6 looks at intermediate and basic solutions - more informal or private forms of infrastructure service (e.g. in the energy sector, kerosene would be an intermediate, and wood a basic energy source).

We use monthly household consumption aggregates as income proxies in this analysis because the consumption data are considered more accurate and reliable than the self-reported income data. For the purposes of this analysis, the poorest households are those with the lowest per capita income proxy."' The pooled sample of households from all countries is similarly divided into twenty quantiles of 5 percent each. We divide households in the urban and rural areas of each country into "income" deciles by per capita consumption. We present the results by decile or quantiles of 5 than one survey year was available for a particular country, we used the most recent survey year.

9 This note of caution is particularly important for country-specific results. Some but not all LSMS surveys are selfweighting. In this analysis no weights have been used to adjust for sample design or non-response. Thus, the results are applicable for the sample population only.

**10** Purchasing power parity conversion would have been preferable, but consumption-heading conversion factors (which would have been used to convert information on expenditures) were not available for all sectors, all countries, or all survey years.

" The consumption aggregates used here were prepared by LSMS survey research teams. The aggregates combine information collected from households about their expenditures on and consumption of a host of food and non-food items.

_5_
33 The results in Table 5 suggest that the model is sensitive to the water procong scheme. In the simple case analyzed here, only water was considered as a limited factor of production. The model as well could have included other inputs such as labor, credit, etc... As a result, the various solutions in the table suggest only one crop (Corner solutions), while if more constraints would have been effective, the optimal solution could have more than one crop grown. Farm's profit was calculated directly in the objective function that was aimed at maximizing farm's profit. Water proceeds were calculated according to the pricing scheme used, and cost of water supply was calculated based on the cost equation in the text. Social gains were then calculated as the sum of the farm profit and water proceeds minus the cost of water supply.

The results of the nuerical analysis rely on the crops and the functional forms of the production function selected for the analysis. Therefore, the reaults should be viewed as indicative in nature. However, the results of the analysis rank the various procong schemes in an ordinal order that reflects their reative efficiency with regard to the volumetric marginal cost procong scheme.

As expected, the volumetric pricing is superior, in terms of social gains, to all other schemes. Also, both the input (nitrogen) and output pricing schemes, when optimally selected, outperform the per unit area pricing method. This is as expected because the input and output pricing can achieve second best efficiency (see Section 3) whereas the latter is inefficient. It can be seen also from the example that their are ranges that the output, the input, and the per area schemes are not very responsive (in the social gains) to different tax levels, but do response in terms of water application. Although not significant in this particular example, this effect should be taken into consideration for policy purposes since it may be preferred in situations where water is very scarce, and agricultural activities are very rigid.

The example, however, ignores monitoring and enforcement costs, and per unit area pricing undoubtedly requires the least of such costs. Thus, when transaction (agency) costs are included, the ranking may change.

If the water agency is interested in cost recovery, it can achieve that by appropriate selection of the water rates. A $200/ha land tax provides the agency with the same proceeds collected by using the volumetric scheme, though far from the same efficiency. A nitrogen tax of $42/Lb and a yield tax of 4.9%, both achieve the same level of proceeds as in the case of volumetric pricing.
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that introducing such a “regional passport for financial services” approach requires at least supervisory convergence, if not integration, as the current crisis experience in Europe shows.

# **5. LOOKING FORWARD**

Africa’s financial systems have seen deepening and broadening over the past years, the result not only of improvements in the macroeconomic and institutional framework, but also of the worldwide liquidity glut, which directed more capital flows into Africa. The current global crisis threatens to reverse this trend and undermine this recent progress. In these adverse circumstances, it is even more important to upgrade the necessary frameworks for sound, efficient, and inclusive financial systems. As we have discussed in this paper, this work calls for further institution building—such as judicial reform and the establishment and reform of collateral and credit registries—as well as cautious and context-specific government intervention to help financial market participants expand financial services to the frontier of commercially sustainable possibilities. Efforts to deepen financial sovereign and corporate bond markets need to be intensified, to improve the capacity for local debt financing, to provide instruments of suitable maturity and security for longer-term savings, and to facilitate the financing of African infrastructure.

These market-enabling policies require strong authorities that take an active role in redefining regulatory frameworks to include competition, inclusion, and efficiency as goals, while crowding in rather than crowding out private initiative. In those situations where governments are called upon to intervene in financial markets, they have the opportunity to provide market-conforming interventions, such as partially guaranteeing credit to groups of borrowers, such as SMEs, that are vulnerable to crowding out, while encouraging private banks 28
Policy Research Working Paper 8126

# **Abstract**

Services play a role in global value chains in many ways, similar to goods. But services deserve special attention because of how they are transacted, how they affect downstream sectors, how they are regulated, and how international cooperation can contribute to integrating national markets. Databases on trade in value added, which cover only cross-border transactions in services, reveal a high and growing share of services in trade in value added across countries and industries. Although international transactions in services that take place through foreign investment are difficult to measure, their economic impact can be estimated. The resulting improved access to financial, communications, and transport services facilitates the emergence of global value chains, enhances downstream manufacturing firms’ productivity, and shifts the pattern of comparative advantage toward sectors intensive in these services. Despite significant unilateral liberalization, service markets in many countries remain protected by restrictions on the entry of foreign services and service providers, as well as discretionary and discriminatory regulatory requirements. International cooperation in services has attempted to follow the example of reciprocal market opening for goods, but this approach has delivered little incremental liberalization. More could be achieved through greater emphasis on international regulatory cooperation.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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 cheuser@worldbank.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 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
- 2.38 -

# Vill. ALTERNATIVES TO THE **COCOA** EXPORT TAX

The first questlon to ask is whether a cocoa<sup>export tax Is desirable, or whether</sup> cocoa farmors should be taxed either through<sup>an agricultural Income tax, or a land tax</sup> (ie a tax on presumptive Income). The arguments<sup>In favor of a land tax are sknple -- It</sup> Is a tax on an Inelastically supplied factor, which therefore causes no distortions provided It is levied on the unkiproved site vaiue<sup>(le on the value of the land before It</sup> Is cleared and prepared for cultivation). To the extent that land ownership Is correlated with wealth, It Is also an equitable tax, and so<sup>desirable on both grounds. The difficulties</sup> with the land tax are equally sinple. It will be unpopular amongst landowners, who frequently have the polltical power to block the<sup>tax, and It requires a cadastre (le a land</sup> register giving the unimproved value or potential<sup>rental of the land, Its locatlon and</sup> ownership). Such cadastres are costly to construct, and none yet exists for Ghana. Moreover, It Is not clear how high the unimproved site<sup>value of uncleared agricultural land</sup> Is, sinre the major cost of bringing land<sup>Into production Is the clearing and land</sup> preparation. If the land has a relatively low value then<sup>the tax base will also be small,</sup> whilst If the value of the land Is taken as Its<sup>uIproved value, then the tax will essentially</sup> be elther a labor tax (le a tax on the labor needed<sup>to prepare the land) or a tax on</sup> agricultural output, and In particular cocoa output<sup>(or potential output, le on trees).</sup> It Is therefore difficult to see a land tax replacing other<sup>forms of agricultural tax In Ghana,</sup> at least In the foreseeable future.

If land taxes are not feasible, would an<sup>agricultural Income tax be a better</sup> a'ternative? In practice this would amount to a tax on gross<sup>output at crop-specific</sup> rates (the rates being adjusted to reflect notional<sup>Input costs).</sup> (Usually farmers are allowed to Itemize their costs If they can prove<sup>them to be higher than the notional</sup> allowance, but In practice this Is rare In countries<sup>which have a large farming population</sup> which they attemPt to tax by this method). It Is<sup>hard to hmagine food crops being taxed</sup> on gross output as opposed to marketed sales,<sup>and even here the taxation would</sup>
# **3. Results**

## **3.1 Main Results**

Table 1 provides the main results. A negative precipitation shock leads to a reduction in sales, labor productivity, and the likelihood of investing in fixed assets for firms across the MENA countries in the sample. In terms of magnitude, a negative precipitation shock leads to a 40 to 52 percent reduction in sales, depending on whether ADM1 fixed effects or country fixed effects are included, respectively. The coefficient is statistically significant at the 10% level with the inclusion of country fixed effects and at the 5% level using ADM1 fixed effects. Similar magnitudes are uncovered for labor productivity. A negative precipitation shock reduces labor productivity, defined as sales per worker, by 42 (with ADM1 fixed effects) to 53 percent (with country fixed effects). The coefficients are statistically significant at the 10% and 5% level respectively. Next, we estimate the impact of negative precipitation shocks on investment. On one hand, if weather shocks limit access to finance, firms are less likely to invest. On the other hand, firms affected by weather shocks might be more likely to invest in fixed assets as they replenish damaged capital (Benincasa et al., 2024). The results show that the former mechanism is more likely at play in the MENA region. Negative precipitation shocks reduce the likelihood of investing by 9 percentage points (including ADM1 fixed effects) to 12 percentage points (including country fixed effects). The former is statistically significant at the 10% level, while the latter is statistically significant at the 5% level. This corroborates with our results in section 3.2 that shows that access to finance deteriorates with negative precipitation shocks. Although these findings are in contrast with Benincasa et al., (2024) that find a positive effect on investment, their analyses use self-reported measures of exposures to extreme weather events. However, their sample encompasses only one wave of surveys and includes both MENA and Europe, while our sample is based on multiple waves of surveys for only MENA economies.

With regards to other weather-related covariates in the estimation, the coefficient of a positive precipitation shock is not robust and largely statistically insignificant. The same is the case with temperature. For firm9
an estimation stage and a prediction stage. For the machine-learning methods, we perform this procedure at two different levels of analysis, namely the PSU and municipality levels. Our focus is nevertheless on obtaining estimates of poverty rates at the municipality level, meaning that when we conduct estimation and prediction at the PSU level, we further aggregate the predictions to the municipality level using population-weighted averaging. Further, recall that we have two alternative sets of covariates available, including geo-referenced and census-based variables. Given that the geo-referenced data is only available at the municipality level, we conduct estimation and prediction at the municipality level for any implementation that uses these covariates. When using census-based covariates, however, we consider implementations at both the PSU and municipality levels.<sup>13</sup>

# **3.2 Traditional small-area methods**

We consider three alternative traditional methods for obtaining small area estimates of poverty as a way to benchmark the performance of the non-parametric machine-learning approach.<sup>14</sup> Specifically, we consider area-level models, unit-level models, and a method that attempts to combine unit- and area-level models. First proposed by Fay and Herriot (1979), area-level models conduct estimation and prediction using data aggregated to the geographic area of interest (e.g., the PSU or municipality level).

The area-level model consists of two basic parts. The first part of the model links the actual or true poverty rates _yi_<sup>_a_forthe</sup><sup>_ith_entitytoavectorofarea-levelcovariates</sup><sup>_xi_asfollows:</sup> where _β_ is a vector of parameters to be estimated and _εi_ represents random area-level effects that are assumed to be mean zero with a constant variance _σε_<sup>2.Sincewelackinformationonthetrue</sup> poverty rates, this model cannot be immediately fit.

Instead, direct estimates of poverty rates from survey data _y_ ˆ _i_<sup>_d_areusedastheoutcomevariable.</sup> These estimates are nevertheless subject to sampling error because they are based on the areaspecific sample data. The second part of the model, thus, assumes that the direct estimates are centered around the true poverty rates as follows:

> 13In all cases, we pass every eligible variable to the machine-learning model and let the algorithm conduct variable selection.

> 14For a thorough review of small area estimation methods refer to Rao and Molina (2015), for a review of methods in the context of poverty mapping refer to Molina et al. (2022) as well as reviews by Pfeffermann (2013) on other developments in the area.

11
# **1. Introduction**

One of the most obvious facts about corruption is that poor countries tend to be the most corrupt. Available data at the country level support this view. For instance, there is a 0.81 correlation between GDP per capita and Transparency International’s Corruption Perception Index, one of the most commonly used measures of corruption.

Beyond this simple observation, understanding corruption is not an easy task. For instance, it is debatable whether corruption is a cause of low incomes per capita, one of its consequences or, as it seems more likely, whether the relationship between corruption and income is an intricate one, made of a web of dynamic interactions, whereby some countries appear trapped in a condition of low incomes and high corruption.

An even more daunting task is to find a cure for corruption, since historical experience does not provide many obvious examples of countries that have been successful in eradicating it. Corruption may be endemic and linked to deep-rooted cultural or “institutional” features of a society, which are not easily overturned by specific policy measures. For example, increasing the wage of public officials may prove ineffective in the absence of credible mechanisms to sanction deviant behaviour. Furthermore, it is difficult to identify a reservation wage for public officials, beyond which their incentive to accept or demand bribes is reduced. From the point of view of researchers and policymakers, this is so because corruption is not easily measured or quantified. The first challenge, then, is to delimit the field of investigation by providing a working definition of corruption and to find adequate sources of information that allow quantifying its extent.

In common parlance and in academic research corruption can take many forms. Most often it is understood as _bribery_ , whereby an official demands informal payments to perform an official task - e.g. issuing a license - or to circumvent laws and regulations. _State capture_ may also qualify for the definition of corruption, when bureaucrats subject themselves to more or less legal forms of lobbying, involving monetary bribes or other forms of exchange of favours, to afford preferential treatment to certain private interests. _Political patronage_ , _nepotism_ and 2
spatially explicit estimates of forest loss to the country-level to create a panel dataset of estimated annual forest loss for 193 countries from 2001 until 2014. While high-resolution satellite imagederived estimates represent a dramatic improvement over previously used data for country-level forest loss estimates (Steininger _et al._ , 2001; DeFries _et al._ , 2002), there are notable limitations that warrant some discussion.

First, the data only provide estimates of forest _loss_ at the annual level, not forest gain. Because tree growth is a slower process than tree clearing, the data only reports estimates of forest gain over a 12 year period (from 2001 - 2012). Consequently, our empirical work only considers forest loss, not net forest change, from RTAs. While RTAs may lead to shifts in the distribution of forested land over the long run (on the order of decades, at a minimum), we argue that differences in the short run dynamics of deforestation and afforestation will likely still create acute environmental externalities. Hence, given the nature of our setting and question of study, we believe that annual forest loss is the important metric to study. Second, the data do not distinguish between different types of forest loss. The classification of tree cover is any vegetation greater than 5 meters in height. Thus, this classification does not allow us to separately examine deforestation in primary forests from harvesting trees from timber plantations (for example). Finally, the detection algorithm used in our version of the data (version 1.2) changes slightly in 2011 - 2014 relative to the earlier years. At the time of writing this paper, Version 2 of the dataset, which intends to provide the entire sample period with a consistent detection algorithm, is not yet available. We believe this is only a minor issue and the use of year fixed effects should account for year-to-year changes in detection common to the entire sample.

# **3 Methodology**

We develop a matched _agreement-level_ approach that aims to control for the potential endogeneity of environmental provisions. We create an agreement-level panel by aggregating outcome variables of interest across RTA signatories by year. For example, we create an annual time series of deforestation associated with the US-Peru trade agreement by adding forest loss in Peru and forest loss 9