rafmacalaba/datause-encoder-data huggingface dataset:
used in the literature have usually been the firm revenue deflated by the the industry-level prices. Thus, within-industry price differences (e.g. due to different markups) have been part of the output and productivity estimates of such studies, potentially biasing their results.

# 5.3 Estimation Results

Before moving on to the results, let us first observe the simple correlations of total factor productivity and tariffs. The overall correlation coefficient for log Ait and log τ it in the whole sample of 920 observations is -0.222. This is significant at the 1% level and can be observed graphically in Figure 5 as well. In Table 3, I present the correlation matrix for all the combinations of log Ait and log τ it across years. The two variables again have a relatively small negative correlation for the most part which is insignificant for certain years such as 1992 through 1994. These relationships also appear in Figure 6 which includes plots of TFP versus tariffs by year. In Table 3, it is interesting to note that the two variables can be concurrently and also intertemporally correlated. This is one reason why using lagged tariff rates may not get around the endogeneity problem of tariffs with respect to productivity. Topalova (2004) notes a similar pattern in the Indian data for the 1997-2001 period and excludes this period from her analysis due to her concern about endogeneity.

However, we cannot establish a causal relationship between tariff protection and productivity with these crude observations alone. We need to control for the other important variables as required by the theory and tackle the endogeneity issues. In this section, I show how productivity influences tariffs after I control for the endogeneity of productivity. Later in Section 6, I estimate a system of equations related to this setup and show that accounting for the effect of productivity on tariffs may strengthen the positive impact of trade reform on productivity.

As noted in Section 5.1, the two right-hand-side variables—the inverse import penetration to import demand elasticity ratio, and total factor produtivity—in the tariff regressions are portentially endogenous. I use instrumental variables to address this problem. More specifically, I employ the two-step efficient generalized method of moments (henceforth IV-GMM) estimator with either fixed effects or first differences for my unbalanced panel. This methodology is more efficient than regular instrumental variables in the presence of heteroskedasdicity of unknown form due to its use of an optimal weighting matrix (Cragg 1983). A Pagan-Hall (1983) test confirms the presence of heteroskedasticity in the data and further justifies the use of the IV-GMM methodology.

In Table 5, I present the main estimation results. In column 1, we have the estimates for equa21
Figure 2: Utility and Production Functions <!-- Start of picture text --> Utility Domestic Exports Untaxed<br>D ID E IE U IU<br>A A � � A A � � A A � �<br>A � τD A � τE A � τU<br>W A� A� A�<br>�A �A �A �A<br>�� AA σW �� AA σD �� AA σE �� AA σU<br>�� AA �� AA �� AA �� AA<br>Z C I KD LD KE LE KU i L i U<br>�A �C �C �C �C<br>�� AA σC �� CC σD K �� CC σD L �� CC σE K �� CC σE L<br>�� AA �� CC �� CC �� CC �� CC<br>U D M KD i K D f Li D L D f KE i K E f Li E L E f<br><!-- End of picture text --> that the _untaxed_ good is produced and consumed purely domestically.

Tax revenue received by the Government is transferred lump-sum to consumers. The experiment of increasing a distortionary tax rate and returning the revenue lump-sum can be interpreted in terms of both ‘differential’ and ‘balanced-budget’ analysis. Treating government expenditure as transfers rather than spending on public goods may approximate the reality of much government spending in Africa.<sup>10</sup> But as emphasized in section 2, for purposes of MCF estimates the realism of the modelled public expenditure is not as important as the fact that a consistent experiment is conducted across tax instruments.

The economic relationships in the model can be represented by a rectangular social accounting matrix (SAM) such as Table 2. The entries in the SAM are expressed as percentages of GDP at market value. All rows and columns sum to zero, reflecting a Walrasian equilibrium in which incomes equal expenditures. In the consumer’s column positive entries are endowments or factor incomes, negative figures are expenditures on goods, including investment. In the production columns, positive entries are the receipt of sales revenue or investment, and nega-

> 10In Uganda, Reinikka & Svensson (2002) found that schools received only 13% of the money paid by the central government for schools’ non-wage expenditures. Pritchett (1996) observes that growth accounting data can be reinterpreted supposing that total factor productivity growth is the same across all regions. Actual public investment in Africa could then be as little as 8.4% of the amount spent on capital. Rajkumar & Swaroop (2002) find that the effect on outcomes of marginal public spending on health and education is not significantly different from zero in countries rated as very corrupt or with ineffective bureaucracies. These studies suggest, by different means, that only a small portion of public spending in Africa arrives in projects with positive social rates of return.

8
**Table 10.  Sector Imports as a Percent of Total Imports of the Regional Market**

|Good|Central Fa|r East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|AGR|3|3|2|3|4|3|1|1|2|2|
|AIR|0|0|0|0|0|0|0|0|0|0|
|CHM|9|8|8|13|7|10|9|6|6|9|
|CNM|2|1|1|2|2|2|2|2|1|2|
|COL|0|0|1|1|0|0|0|0|0|1|
|CON|6|8|10|7|7|8|3|3|20|4|
|CRU|1|1|1|2|1|2|0|1|1|0|
|ELE|0|0|0|0|0|0|0|0|0|0|
|FIN|1|1|2|2|1|1|1|1|2|1|
|FME|7|2|2|2|6|4|3|3|4|7|
|FOO|13|14|9|10|13|9|15|26|5|19|
|GAS|0|0|0|0|0|0|0|0|0|0|
|HEA|1|1|1|1|1|1|1|0|1|0|
|HOU|2|3|3|3|3|3|2|1|3|2|
|MWO|25|28|23|13|22|23|38|35|25|27|
|NFM|3|4|4|9|3|4|2|2|3|3|
|OIL|3|2|3|3|3|3|1|1|4|2|
|OTH|1|1|1|2|1|2|1|1|2|1|
|OTI|1|1|1|1|1|1|0|0|1|0|
|PST|0|0|0|0|0|0|0|0|0|0|
|RLW|0|0|0|0|0|0|0|0|0|0|
|SCI|0|0|0|0|0|0|0|0|0|0|
|TMS|1|1|1|1|1|1|0|0|1|0|
|TPP|2|1|3|1|3|1|4|5|0|7|
|TRD|1|1|1|1|1|1|0|0|2|1|
|TRK|0|0|0|0|0|0|0|0|1|0|
|TRO|0|0|0|0|0|0|0|0|0|0|
|CLI|17|20|22|23|21|21|16|9|15|13|
|Total|142|52|35|142|140|268|528|140|81|41|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

46
as the end-period distribution<sup>12</sup> . The corresponding marginal effect is obtained by comparing poverty outcomes under this counterfactual with those observed in the base period. The second counterfactual is obtained by multiplying the level of welfare in the end period by the inverse of the above ratio. The value of the marginal effect associated with this counterfactual is based on the comparison of observed outcomes in the end period with the counterfactual ones. In order to respect anonymity, the Shapley contribution of changes in the mean to change in poverty is equal to the average of these two marginal effects. We refer to this term as the _scale component_ of the Shapley decomposition.

Similarly, the computation of the contribution of changes in inequality to change in poverty, _ceteris paribus_ , is based on transformations that are _size-neutral_ to the extent they hold the mean of the distribution constant while changing the Lorenz function. This computation relies on the same counterfactuals discussed above<sup>13</sup> .

Table 2.4. Shapley Decomposition of Poverty Outcomes, 1996-2007

||Overall|Scale<br>Ine|quality|
|---|---|---|---|
|||1996-2001||
|Headcount|-13.08|-12.57|-0.51|
|Poverty Gap|-6.30|-6.18|-0.13|
|Squared Poverty Gap|-3.45|-3.47|0.02|
|Watts|-9.29|-9.35<br>2001-2007|0.06|
|Headcount|-0.28|-0.12|-0.16|
|Poverty Gap|-0.47|-0.06|-0.41|
|Squared Poverty Gap|-0.53|-0.03|-0.49|
|Watts|-1.27|-0.09|-1.17|
|||1996-2007||
|Headcount|-13.36|-12.32|-1.04|
|Poverty Gap|-6.78|-6.23|-0.55|
|Squared Poverty Gap|-3.98|-3.52|-0.46|
|Watts|-10.55|-9.39|-1.16|

Source: Authors’ Calculations

> 12 See Lambert (2001) and Kakwani and Son (2008) for applications of this transformation.

> 13 In particular the contribution of changes in inequality, ceteris paribus, is equal to the average of the following two counterfactual comparisons.  First, poverty outcomes for the distribution defined by the base mean and the end period Lorenz function are compared with baseline poverty outcome.  Second, end period poverty outcomes are compared with those for the counterfactual defined by base Lorenz and the end period mean.

10
2011). We also construct monthly precipitation level variables for the pre-intervention and intervention period (2011-2015).

# **3 Methodology**

This section outlines the empirical strategy to estimate the average impacts of cash transfers, as well as to test whether cash transfers improve resilience by enhancing households’ ability to mitigate the welfare effects of climatic shocks.

# _3.1 Specifications to estimate average impacts_

As discussed in section 2, a first specification derives intent-to-treat estimates between (potential) beneficiaries in the treatment and control groups. Potential beneficiaries in the control group are identified by applying the same ranking procedure that determined program eligibility in the treatment group, based on an estimated PMT score calculated from the baseline survey and the same PMT cut-off (see section 2.2). In this specification, we include all households whose PMT scores (calculated from the baseline survey) are below the PMT selection threshold, whether they are actual beneficiaries or not. This approach provides a benchmark specification.<sup>18</sup> We apply a Difference-in-Differences (DID) estimator to compare changes in outcomes between 2012 and 2015 for households in the treatment and control groups with baseline predicted PMT score below the PMT selection cut-off (P s s ≤ C C C ): ssii i 0 1T TT 2 3T + ε (1) ii ii ii ii ii,ii where T is an indicator variable for households in treatment villages (with predicted PMT score yy = ββ + ββ + ββ FF + ββ ∗FF below the selection cut-off), ii is an indicator variable equal to 1 at follow-up (in 2015), and 3 ii FF ββ

> 18 This strategy is preferred to a full-sample ITT specification at the village level. This is because comparisons at the village level, while well-identified given the randomization, would give imprecise estimates of program impacts since only 40 percent of households were treated on average. Among households in the treatment group with baseline PMT scores below the PMT cutoff, 55 percent are actual beneficiaries (see section 2.2 and Figure 1). Households with baseline PMT scores below the PMT cut-off are also very similar across the treatment and control groups (see section 3.2 and Table 1).

12
failure, the total amount of inputs given to farmers is reduced.

We considered two management scenarios. In scenario A, farmers plant as much area to hybrid maize as their loan can support, up to a maximum of 1.62 ha. The remainder of their farm is assumed to be fallow. In scenario B, farmers again adjust the area under hybrid maize according to the size of their loan, but allocate the remainder of their land to the locally-available traditional maize with no purchased inputs. For both scenarios, we assumed 2006 prices, and the planting density and fertilizer application rates recommended within the 2006 package.

# **4.3 Analyses**

We base the comparisons on an estimate of gross margin in a given year, based on information from the Malawi 2006 contract design process, and maize yields estimated using the CERESMaize simulation model V. 4.0 (Ritchie et al., 1998).

The gross margin for a given year is the difference between gross receipts and variable costs, where gross receipts are maize grain yields multiplied by the farmgate price, plus any insurance payouts in that year. Variable costs include production inputs, the insurance premium and interest on the farmer’s loan. The 2006 cost of inputs for hybrid maize, 9633 MKW ha<sup>-1</sup> , included the seeds and fertilizers for the management package recommended for the 2006 implementation. The loan interest rate was 27.5%. We assumed the same 20 MKW kg<sup>-1</sup> maize grain price that was used for the 2006 contract. We assumed that maize prices are constant, although we recognize that in reality, supply-driven price fluctuations would tend to dampen the income benefits of forecast-based insurance packages. To simplify our presentation, we omitted a small tax that was part of the 2006 implementation. For some comparisons, we calculated the 14
in 2006 to 55 percent in 2016 (see Table 10). Uruguay has increased its offering of local currency instruments including nominal peso bonds and wage-indexed bonds, in addition to its existing issuance of inflation-indexed securities.

**<mark>Table 10 – Uruguay: Composition of Debt</mark>** _<mark>as of end December 2016, USD billion</mark>_

||Amount|Percent|
|---|---|---|
|Domestic Debt|11.7|45.0|
|External Debt|14.4|55.0|
|Total|26.1|100.0|

The amount of Uruguay’s foreign exchange reserves is slightly higher than its external debt and the FX debt/reserves ratio is 0.97 (Table 11).

**<mark>Table 11 – Uruguay: Composition of Foreign Currency Debt and Reserves</mark>** _<mark>as of end December 2016, billions USD</mark>_

|||USD|Other|Total|
|---|---|---|---|---|
||USD bil. eq.|14.7|0.8|15.5|
|Reserves|Percent of total|95.0|5.0|100.0|
||Percent of GDP|27.9|1.5|29.4|
||USD bil. eq.|14.4|0.8|15.1|
|External Debt|Percent of total|94.8|5.2|100.0|
||Percent of GDP|27.3|1.5|28.7|
|Concentration|<sup>Ratio FX debt/</sup><br>reserves|0.98|1.0|0.97|

Source: Ministry of Economy and Finance, Banco Central de Uruguay, IMF.

Uruguay’s foreign exchange reserves and external debt are very closely matched not only in terms of amount but also the composition and both are 95 percent in USD (Figure 8).

**Figure 8 – Uruguay: FX Currency Composition of:**

<!-- Start of picture text --> Reserves Debt<br>Other Other<br>5% 5%<br>USD USD<br>95% 95%<br><!-- End of picture text --> 20
EDD are constructed using exporter-level customs data, which cover the universe of annual exporter transactions.

Information on financial structures, including the importance of the banking sector relative to the stock market and the importance of foreign banks relative to domestic banks, is obtained primarily from the Global Financial Development Database (GFDD).<sup>6</sup> We complement the GFDD with data from Burger et al. (2015) and Yibin et al. (2013) on the importance of bank credit relative to bond market capitalization. Further, we draw relevant information from a variety of other databases and sources, including the analyses in Maskus et al. (2012) and Klapper et al. (2006) to construct industry-level measures of external financial dependence, the World Bank Banking Supervision Survey on the supervision, monitoring, and regulatory restrictions on banks, the database in Laeven and Valencia (2013) on the occurrence of financial, banking, and external crises, and the analyses in Nunn (2007) and Rauch (1999) to obtain measures of contractual and information complexity of products. We provide further details on measurement and data sources below.

To identify the effect of financial structures on export dynamics, we rely on the established strategy proposed by Rajan and Zingales (1998) which exploits heterogenous financial dependence across industries based on technological factors inherent to the production process. Specifically, in the baseline tests for each outcome we estimate the following regression: _Exportist_ = _β_ 1 _FinStructit × ExtFins_ + _β_ 2 _FinDevit × ExtFins_ + _Dit_ + _Dst_ + _εist_ (1) where _i_ is country, _s_ is ISIC 2-digit industry and _t_ is year. _Exportist_ are variables from the Exporter Dynamics Database that capture the size, dynamics and concentration of the export sector at the country-industry-year level, and _FinStructit_ is the measure of financial structure, capturing the relative importance of banks vis-`a-vis capital markets or the relative importance of foreign banks vis-`a-vis domestic banks. _ExtFin_ measures the industry’s external financial dependence. _FinDevit_ is a measure of financial development while _Dit_ and _Dst_ are country-year and industry-year fixed effects, respectively. Country-year fixed effects capture time-varying country characteristics, including endowment differences that can drive trade patterns and country-specific macroeconomic shocks that may affect domestic production and firm exports. In turn, the industry-year fixed effects absorb shocks to industries that are common across countries, including world demand or supply shocks for specific industries. Finally, _εist_ denotes the error term which captures some unobserved factors that may affect export activities, but that we expect to be uncorrelated with the explanatory variables in (1).

> 6 We use the June 2017 version of the database. For an overview of the data please refer to the 2013 Global Financial Development Report and Cih ́ak et al. (2012).

7
20 In sum the current income tax system seems to be progressive and urban bias. Furthermore, it contributes significantly to reduce overall and sectoral (urban-rural) income inequality.

# [FIGURE 2 APPROXIMATELY HERE]

VL Conclusions This study has made use of the most recent household budget survey to determine the distribution of the overall and sectoral (urban-rural) income and income tax burden in Bulgaria. The distributional impact at the national, urban and rurl level of the present income tax has also been examined.

The findings indicate that Bulgaria is characterized by low income inequality, though this is changing rapidly. while income levels vary significantly between urban and rural areas, both the analysis of income shares by decile and Gini coefficients indicate that there is no significant difference between them in terms of income distribution .

The analysis suggests that the present income tax system is significantly progressive as the percentage of income paid in tax rises with income. For instance, the poor (the lowest two income decile) pay only 1.4 percent of their per capita income to the government in income tax, whereas the rich (top decile) pay nearly 6 percent effective tax rate. The distributional pattern of the tax burden remains unchanged when household per capita expenditure rather than per capita income is used as a base for calculating the effective tax rates. This similarity between income and expenditure-based burden estimates cuts across
|**Table 1: Summary Statistics: Charact**|**eristics and**|**Treatment Var**|**iables for Pro**|**gram Watersh**|**eds**||
|---|---|---|---|---|---|---|
|**Variable**|**Total**|**Baskura**|**Enguli**|**Enkulal**|**Kentai**|**Zefie**|
|**Basic characteristics**|||||||
|Area (ha)|85,702|11,116|29,978|20,113|11,132|13,363|
|Micro-watersheds (No.)|160|30|40|37|25|28|
|Elevation (m)||2340|2417|2390|3023|2701|
|Slope (degrees)||10.0|13.8|8.2|11.3|14.0|
|**Total treated area (ha), 2015**|||||||
|Soil & water cons. (SWC)|72,634|9,792|26,851|14,376|10,056|11,558|
|SWC on cultivated land|53,674|7,066|18,990|10,867|7,990|8,762|
|_Percent of total_|_0.626_|_0.636_|_0.633_|_0.540_|_0.718_|_0.656_|
|SWC on degraded land|18,960|2,726|7,862|3,509|2,066|2,796|
|_Percent of total_|_0.221_|_0.245_|_0.262_|_0.174_|_0.186_|_0.209_|
|Area planted w. trees|6,783|1,215|1,565|1,600|905|1,499|
|_Percent of total_|_0.079_|_0.109_|_0.052_|_0.080_|_0.081_|_0.112_|
|**Means seasonal rainfall**|||||||
|Dry season (_Bega_; Oct 1 -Jan 31)|123.6|98.3|154.9|108.1|109.0|108.8|
|Pre-rainy (_Kiremt_; Feb 1- May 31)|214.3|168.6|249.5|189.0|218.4|206.8|
|Rainy season (_Belg_; June 1-Sep 30)|1,109.1|1,165.3|1,081.6|1,123.9|1,089.5|1,119.0|

_Source:_ Project data.

16
Policy Research Working Paper 10599

# **Abstract**

Over nine years of violence and conflict have profoundly altered the Republic of Yemen’s economy. The war has shattered the country’s already fragile socioeconomic equilibria, affecting nearly every facet of life. Since the onset of the conflict, economic diagnostics have focused on descriptions of the deteriorating macro-fiscal and poverty conditions, lack of food security, and loss of capital accumulation. However, relatively little attention has gone toward the development of a forward-looking vision for the country, rooted in Yemen’s current economic structure. This paper helps to fill this gap by presenting and analyzing a set of scenarios for Yemen’s economy up to 2030. The analysis is based on a new version of the Sustainable Development Goal Simulation model, a dynamic computable general equilibrium (CGE) model, which is applied to a new social accounting matrix (SAM) for Yemen. The new social accounting matrix has the virtue of consolidating sparce and often inconsistent Yemeni data from multiple sources (the World Bank, the International Monetary Fund, and the United Nations system) into a coherent framework that reflects the basic structure of the economy, both at the macro and sectoral levels. The simulation analysis is built around three broad scenarios spanning 2022 through 2030. The results suggest that if the conflict subsides, governance is strengthened, and the donor community provides crucial aid, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great.

This paper is a product of the Equitable Growth, Finance and Institutions Practice 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://www.worldbank.org/prwp. The authors may be contacted at gmele@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