rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6. Distribution of Tariffs Paid on Imports from LDCs**

<!-- Start of picture text --> 100%<br>90% Australia Brazil Canada China<br>80%<br>Iceland India Japan Korea<br>70%<br>60% Mexico New zealand Norway Switzerland<br>50%<br>Turkey USA<br>40%<br>30%<br>20%<br>10%<br>0%<br>80% 82% 84% 86% 88% 90% 92% 94% 96% 98% 100%<br><!-- End of picture text --> _Source_ : Bouët, Laborde and Mevel (2008) The binding commitment to provide duty-free-quota-free access to LDCs reduces the uncertainty about market access that has historically discouraged investment to take advantage of preferences. Developed country members will be required to provide a list of products consistent with allowing 97 percent of products originating in LDCs to enter duty free. If products cannot be deleted from this list, the longer-term benefits might be greater than suggested by Figure 6, as LDCs develop the ability to competitively produce products on the DFQF list. If, on the other hand, products can be deleted as LDCs develop the ability to compete, this advantage could be reduced, greatly eroding the value of the DFQF proposal since most developing countries—including much higher income countries than the LDCs—tend to specialize in a relatively small set of products.

Another potentially important remedy for the market access problems facing the least developed countries might be to reduce the restrictiveness and costs associated with rules-of-origin. The favorable experience of the African Growth and Opportunity Act (AGOA) LDCs with their more liberal rules of origin for textiles and clothing provides strong evidence of the important liberalizing potential of such changes for textiles and clothing. The proposals for improvements in the rules of origin for LDCs appear to be “best-endeavors” in nature, rather than providing binding commitments for improvement, although industrial country members will have to notify the WTO of their progress in implementing both duty free quota free access and rules of origin.

13
**Table 5. Stock of Territorial Governments' Debt to Banks, 1991-97**

|(billions of current pesos)||||||||
|---|---|---|---|---|---|---|---|
|_Level of government_|_1991_|_1992_|_1993_|_1994_|_1995_|_1996_|_1997_|
|Departments||||||||
|Direct administration|56|73|154|330|422|665|966|
|Electricity sector|15|25|193|177|480|513|864|
|Other enterprises|124|135|160|201|170|134|106|
|Municipalities||||||||
|Direct administration|137|159|305|594|802|1,050|1574|
|Electricity sector|224|323|385|499|753|840|653|
|Other enterprises|142|183|187|257|336|556|757|
|Total|698|897|1,383|2,057|2,962|3,757|4,920|
|As a_percentage of GDP_|**_2.6_**|_2.7_|**_3.2_**|**_3.6_**|**_4.0_**|**_4.2_**|**_4.6_**|
|As_a_percentage of GDP,<br>excluding the electricity<br>sector|1.8|1.6|1.8|2.4|2.4|2.7|3.2|

_Source:_ Superintendencia Bancaria.

The imbalances were not distributed equally, and some municipalities and departments were in much worse shape than average. The subnational debt in Colombia has been problematic mainly because the departments have little discretion over their receipts or spending, most of which has to go for salaries. Neither the departments, the creditors, nor the Ministry of Finance took sufficient account of this inflexibility in their ex ante evaluations of ability to pay.

Most territorial borrowing in Colombia is from private banks. See table 6. They typically lend for short to medium terms, at variable rates of interest (pegged to the domestic cost of funds). Banks normally require territorial govemments to pledge specific revenue sources as collateral. Some territorial governments have pledged _situado_ or municipal transfers for this purpose, although of questionable legality, because these resources are earnarked. The increasing discretion (theoretically but not actually) in the use of _situado_ resources and the increase in municipal transfers are said to be partly responsible for the increase in territorial borrowing during the mid-i 990s. The surges of borrowing in 1993-94 and in 1996-98 correspond to periods of more lax banking supervision with respect to subnational debt.

There are no departmental or municipal banks in Colombia, unlike in Brazil and Argentina. Moreover, direct national government lending to subnational governrments is small (except for the 35
is poor and who is receiving government benefits. However, as argued by Coady et al. (2013), relying solely on household self-reporting of beneficiary status does not allow for a full understanding of what happens at the multiple stages of the targeting process, before benefits are delivered to households. Our findings relate to those of Coady and Parker (2009) and Coady et al. (2013), who consider a three-step, program-specific targeting process comprising information, self-selection to apply, and ranking stages. For the Indonesian targeting registry’s two-step process—registration based on enumeration pre-listings complemented by community suggestions and then ranking—we find large gains in performance from improving the initial registration stage relative to the ranking stage. As a result, we are able to prioritize policy options to minimize the potential exclusion of the poorest households from increasingly used targeting registries of the sort we study in Indonesia.

Our findings have important implications for ongoing policy debates in developing countries concerning the design of efficient and equitable targeting registries. Overall, our results provide further evidence on the difficulty of accurate targeting in countries like Indonesia where there is considerable clustering of households around the poverty line, substantial churning in and out of poverty, and relatively limited geographic concentration of poverty. Nevertheless, we clarify how improvements in the enumeration process can lead to large gains in overall targeting effectiveness. If poor households are not enumerated in the first place, even a perfect PMT algorithm cannot prevent their exclusion.

The remainder of the paper is organized as follows. The next section provides background information on targeted social assistance in Indonesia, including the single registry. Section 2 presents the SUSETI survey and its features. Section 3 assesses the predicted targeting accuracy of the UDB. Section 4 explores the contributions of the registration and ranking stages to UDB accuracy. Section 5 concludes with policy recommendations.

# **I. Targeted Social Assistance Programs in Indonesia**

This section first presents the two social assistance programs at the core of our analysis, focusing on their beneficiary selection mechanisms before the introduction of the Unified Database for Social 6
<!-- Start of picture text --> MER cumulative growth<br>30 @ Advanced economies 300<br>«200<br>a0 e EMDE e 250<br>150 oe ® 150<br>100 oo age °° 100<br>: ree ><br>tee °<br>90<br>-150-100 -50 0 50 100 150 200 250 300<br>Penn-PPP cumulative growth<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>300 Unchanged club<br>«200 Highest club only in PPP<br>250<br>150<br>100<br>SSS SER SIRS TISGEE<br>p2Ss8 2252225555<br>ges 284 N25<br>= ae<br><!-- End of picture text -->
The demand for advice: Theory and empirical evidence from farmers in Sub-Saharan Africa<sup>*</sup> Dominik Naeher<sup></sup> Matthias Sch ̈undeln<sup></sup> _Keywords:_ economic development, advice, agricultural extension, rational inattention, Sub-Saharan Africa _JEL Codes:_ D91, O13, Q16

> * The authors thank Eric Edmonds, Mirko Wiederholt, and anonymous referees for extensive comments and suggestions, and Sandro Ambuehl, Luc Christiaensen, Rema Hanna, and Sara Savastano for helpful discussions.

>  Corresponding author. University College Dublin, Ireland (e-mail: dominik.naeher@ucd.ie).

>  Goethe University Frankfurt, Germany (e-mail: schuendeln@wiwi.uni-frankfurt.de).
Important questions are then: What is the deadweight loss associated with taxation in low-income countries; and what is the scope for reduction in existing taxes? And, what would the income distribution effects of such tax reductions be? Note here first that when the tax substitution option is chosen, the government essentially foregoes the resulting potential income gain, and hands this over to the private sector. Apart from its effect via reduced distortions, this may or may not be desirable; in particular it may matter which population and business segments then stand to benefit from tax reductions. Note that such a tax swap policy automatically removes the “spending” issue discussed in Section 3.b above: the revenue is already “spent” in the form of tax reductions elsewhere.

Ideally, the most burdensome or distortive taxes ought to be reduced. But this may be difficult in practice, as there are likely to be many types of motivations behind the various taxes, some of which are not governed by pure efficiency considerations (which, of course, may help to explain why different taxes, leading to highly different levels of deadweight loss, can exist in the first place).

Estimating the marginal cost of public funds (MCPF) for different tax types in different countries is no trivial matter. The common rule of thumb is however to assume that MCPF increases more or less linearly with the tax rate, so that countries with higher rates of tax also have higher MCPF levels.<sup>16</sup> Table 4.1 sums up a number of studies estimating MCPF for various tax types, in some high-income and some low-income countries, made at various points of time. For HICs with relatively low tax levels such as the United States, Australia and New Zealand, we find estimated MCPF levels largely in the range 1.1-1.4 (implying, roughly, that the efficiency loss related to collecting one tax dollars is 1040 cents). In a high-tax country such as Sweden, MCPF levels are considerably higher.

For the referred studies from LICs here, the numbers are quite dispersed, and have been assessed basically only for sales and import taxes, and are seemingly highly distortive in some major countries (China and India) but less so in others. A weakness of these studies is however that they seem not to be able to separate distinguish between the distortive effects of different taxes; and thus give less of a recipe for what taxes to reduce.

> housing versus other expenditures, and non-taxable versus taxable benefits for wage earners. According to Feldstein, such factors may raise the distortive effects of taxes by a factor of 10.

> 16 This also generally implies that the total distortion loss related to taxes increases with the square of the tax level (as both the tax level and the marginal damage as function of level increase linearly). This is the famous Harberger triangle loss; see Harberger (1964); see Hines (1998) for a wider discussion.

17
18

# **Table 4: The Impact of Liberalization on Resource Discoveries (2SLS)**

|VARIABLES|(1)<br>Number of<br>Discoveries|(2)<br>Number of<br>Discoveries|(3)<br>Number of<br>Discoveries|(4)<br>Number of<br>Discoveries|(5)<br>Number of<br>Discoveries|
|---|---|---|---|---|---|
|SW/Wacziarg Openness, lagged|0.0209***<br>(0.00432)|0.0756***<br>(0.0161)|0.0760***<br>(0.0152)|0.0641***<br>(0.0147)|0.0140***<br>(0.00410)|
|(log) Price, lagged||0.0146**|0.0149***|0.0103**||
|||(0.00678)|(0.00532)|(0.00448)||
|Stock of Discoveries, lagged|||‐0.000541|0.0257*|0.0382***|
||||(0.00500)|(0.0141)|(0.00499)|
|Stock of Discoveries squared, lagged||||‐0.000441** <br>(0.000175)|‐0.000284***<br>(8.24e‐05)|
|Country FE|/|/|/|/|YES|
|Year FE|YES|YES|YES|YES|/|
|Natural Resource FE|/|/|/|/|/|
|Country by Natural Resource FE|YES|YES|YES|YES|/|
|Year by Natural Resource FE|/|/|/|/|YES|
|Observations|157,284|57,976|57,976|57,976|157,284|
|R‐squared|0.279|0.323|0.323|0.335|0.227|
|Estimation|2SLS|2SLS|2SLS|2SLS|2SLS|
||Year by|Year by|Year by|Year by|Year by|
|Error Clustering|Natural|Natural|Natural|Natural|Natural|
||Resource|Resource|Resource|Resource|Resource|

Note: The table reports the estimates of the second stage of a 2SLS regression for the number of natural resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of control variables. Distance weighted average openness and distance weighted growth of closed economies are used as excluded instruments for the SW/Wacziarg Openness indicator. The number of observations in columns (1) and (5) are larger than in the other columns, because columns (2) – (4) include natural resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country- resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

Table 4 shows the second stage of the baseline 2SLS estimates. Across all specifications, regression results consistently show that the coefficient on our measure of market orientation is positive and statistically significantly associated with more discoveries. More open countries are thus more likely to discover new natural resources.<sup>20</sup> More open institutions allow for easier transfer of capital and technology and thus exploration is more attractive in those regions, as predicted by the model in section II. Comparing the magnitude of the IV and OLS coefficients shows that the IV estimates are larger by a factor 2 to 4. We believe this to indicate that the

> 20 Adding the stock of past discoveries (which is not instrumented) could potentially raise identification concerns so we always show a column where they are not included.
- 13 -

also been determined by the availability of data on workers and total population abroad, starting in 1977 and ending in 1989. Subscribing to the framework of the previous section, we also make a preliminary assessment of the relevance of demographic, income and other macroeconomic factors as potential determinants of official remittances.

Figure 1 shows the normalized values of three key variables--workers' remittances in current US dollars, the average income in the host countries, also in current US dollars, and the number of workers abroad. For each laborexporting country there is a series measuring the (weighted) average income per capita in the host countries.7 The appendix provide more information on demographic and macroeconomic indicators for each of the six labor-exporting countries, and a detailed account of data sources and construction.

Figure 1 suggests that remittances are closely correlated with the income cycles in the host countries and, to a lesser extent, with the stock of migrant workers. The three variables are closely related in the case of Morocco and Turkey. Remittances are closely related to host country income in the case of Portugal and Tunisia and, to a less extent, in the case of Algeria and Yugoslavia. The relation between remittances and the number of workers seems weaker in these four cases.<sup>8</sup> Although the association between remittances and the number of nationals working abroad seems weak on an individual basis, it is substantially 7 For instance, in the case of Morocco this series is the weighted average of income per capita in each of the seven major host countries, the weights being the shares of Moroccan workers in each respective host country.

8 The very close correspondence between the dollar value of remittances and income in the host countries is partly due to the fluctuations of the dollar visa-via the European currencies. The econometric estimation of remittances in section 4 avoids any possible bias by measuring the variables in real German Marks.
Finally, our data set includes information on GDP per capita by country measured in constant US dollars (from the International Monetary Fund – IMF), information on the yearly evolution of education levels by country measured using the percentage of students in post-high school education over the whole population (from the United Nations Educational, Scientific, and Cultural Organization – UNESCO) and information on the share of each economic sector in labor employment (from the International Labor Organization – ILO). We present summary statistics of these data in the appendix.

# **5. Empirical Strategy**

We seek to quantify the causal effect of the increase in Internet use on paper consumption. Internet use is likely to reduce the demand for some paper categories but is unlikely to affect others. For example, as aforementioned, the reading of online newspapers may induce some individuals to cancel their physical newspaper subscriptions. In turn, the decrease in newspaper circulation caused by the adoption of the Internet reduces the demand for the paper category used to produce newspapers.

We empirically examine the relationship between the growth of Internet penetration and the evolution of paper consumption by using a fixed effect model of the following form: ln�CPijt�= αij + βj ln(GDPit) + μjEDUit + ρjPRODit + τjINTit + At + uijt (1) where _i_ represents the country, _j_ represents the paper category (i.e., Newsprint, Printing and Writing, Household and Sanitary, and aggregate paper consumption), and _t_ represents the year. The dependent variable (ln�CPijt�) represents the 16
In Pmxt = Got 4 InOmxt + 2 Inyxt + ¢3 InDistm, + émxt G2 62

> Ln(Umports)mxt = FEm + FE, + aIn Distyy + Blyaxt + 6 MOmxtlNVme + Emxt

Inxt Omxt Ymt 5>0 In Omxt In(mports)myt = FEm + FE, + an Distmy + Blinxt + GMP mxt!OVme + OV xen mt G3lnDistmx|NYme + © pve a' ~~>~~ 1() &' ~~OSs~~ <u>61</u> 63’ ~~os~~ <u>61</u> o mxt’ ~~Sot~~ <u>5G tO41</u> ~~Oo met~~ <u>Fx</u> My np + Emxt
As a robustness test, we also assess alternative explanations. Some of these alternative mechanisms are illustrated in ...gure 3. One concern is spillovers. Spillovers could a¤ect the estimates in two ways. If information about the intervention spread to control areas and, as a result, control communities became more involved in monitoring the providers, the estimated treatment e¤ect would be biased downward. If, on the other hand, households in control communities shifted from seeking care at the control facility to the nearest treatment clinic, it is possible that the estimated treatment e¤ect would be biased upward. This is a potentially serious concern but also a mechanism which we can test. It is also possible that the intervention did not only (or primarily) increase the extent of community monitoring, but had an impact on other agents in the service delivery chain. For example, the various upper-level authorities in the health sector (e.g. the Health Sub-district) may have become more involved in monitoring the providers, or the district government may have increased its administrative or ...nancial support, following the intervention. While this would not invalidate the causal e¤ect of the intervention it would, of course, a¤ect interpretation. Therefore, this alternative hypothesis is also subject to a battery of tests.

Given the wealth of information, we report the main results and tables in the text and refer the reader to appendix for additional ...ndings.

# 6.3 Statistical framework

Given the randomized assignment of the Citizen Report Card project, we expect the 2004 pre-data in the treatment areas to be similar those in the control areas. We have both facility-speci...c data (on utilization, for example) and household-speci...c data (on waiting time, for example). Denoting yijdt the outcome variable of household i (when applicable), health facility j in district d and period t, we start by checking that there is no di¤erence between treatment and control facilities/communities prior to the intervention: where t = PRE denotes the pre-intervention period, Tjd is a dummy indicating whether health facility j is in the treatment group and "ijdPRE is the error term. In regressions using household data, the disturbance term is adjusted to allow for correlations within catchment areas (communities).

To estimate the causal e¤ect of the program, we then run the same regression in the post-period (t = POST ): We also estimate an extended version of equation (2): 14
_Figure 12 Typical Results of Cross Validation Analysis for Ghana 2012/13 data_ Average absolute values of differences between actual poverty rates and projected poverty rates <!-- Start of picture text --> Average MSE<br>actual poverty rates and projected poverty rates<br>0 .02 .04 .06 .08 .1 0 .02 .04 .06 .08 .1<br>pe pe<br>.263 .013<br>.0125<br>.2625<br>.012<br>mmse .262 absdiff<br>.0115<br>.2615 .011<br>.0105<br>.261<br><!-- End of picture text -->

**_Source_** : Results of cross validation analysis using GLSS 2012/13 data.

# _Finalizing the Model_

After the optimal p-value is selected, a stepwise OLS regression procedure is carried out with a full sample of data to estimate a model. To ensure that the coefficients are stable, an OLS regression with the set of variables is carried out for all 10 testing data sets to see whether the coefficients of the select variables do not change signs or are dropped due to collinearity. If some variables are dropped due to collinearity or some signs of the coefficients change, then these variables will be dropped from the final model. After dropping these variables, an OLS regression is carried out to estimate the coefficients and variance of the coefficients and error terms. In addition to the statistical tests, it is recommended to check whether the signs and values of all estimated coefficients make sense to those who know a country very well. If a sign of a variable is the opposite of an expert’s intuition, this can be an indicator of multicollinearity and can be very unstable; therefore, it is strongly recommended to reconsider inclusion of such variables.

# _Simulation and Estimation of Poverty Rates_

The final model is used to project household expenditure or income for all households 20 times following the procedure presented above. Poverty rates are estimated for each round of simulation and the average is taken as the estimate of the poverty rate. The variance of the poverty estimate is calculated using the following formula (Rubin, 1987 and Schafer, 1999): where m refers to the number of simulations, �<sup>�</sup> refers to the poverty estimate in round l of the simulation, �<sup>∗</sup> refers to a mean of ��<sup>�</sup> � and the final estimate of the poverty headcount rate, and ���<sup>�</sup> � is an estimate of the variance of the poverty estimate in round l of simulation. The first bracket presents the between simulation variance, while the second squared bracket presents the within simulation variance. Consequently, the variance of the final poverty estimate is a weighted average of the within and between simulation variances.

36