rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Second Stage IV Regression, Dependent Variable: Tari¤s

|||(1)|(2)|(3)|
|---|---|---|---|---|
||AVEborder|-0.125***|-0.0740***|-0.0874***|
|||(0.0309)|(0.0137)|(0.0284)|
||Observations|253,965|248,215|253,818|
|Importer|Fixed E¤ects|Yes|||
|Exporter|Fixed E¤ects|Yes|Yes||
|Product|Fixed E¤ects|Yes||Yes|
|Importer-Product|Fixed E¤ects||Yes||
|Importer-Exporter|Fixed E¤ects|||Yes|

Notes: Standard errors are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively; AVEnij is instrumented using the average AVE of exporting country j of the product n in the non-i markets.

Table 3: Downward Bias Least Squares Regressions

|Dependent variables|Quan|tity Discrep|ancies|Valu|e Discrepa|ncies|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|AVE of NTM-border|0.403***|0.383***||0.360***|0.348***||
||(0.0663)|(0.0688)||(0.0650)|(0.0675)||
|Tari¤||0.829***|||0.707***||
|||(0.0413)|||(0.0421)||
|AVE of NTM-border+Tari¤|||0.736***|||0.636***|
||||(0.0346)|||(0.0353)|
|SPS/TBT NTMs|||-3.769***|||-3.277***|
||||(0.991)|||(0.965)|
|Other NTMs|||-3.999***|||-4.022***|
||||(0.961)|||(0.955)|
|Constant|95.83***|89.31***|93.25***|98.70***|93.09***|96.72***|
||(0.326)|(0.464)|(0.760)|(0.319)|(0.465)|(0.749)|
|Observations|256,711|250,432|250,432|251,934|245,615|245,615|
|R-squared|0.164|0.166|0.166|0.163|0.164|0.165|
|Importer Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|
|Product Fixed E¤ects|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors in parentheses are clustered by importer-product; *, ** and *** indicate that coe¢ cients are signi...cant at 90%, 95% and 99%, respectively.

41
> - 17 -

total nonfuel exports. In total, the 20 countries listed in Table 3 accounted for $113 billion or approximately 89 per cent of all labor intensive exports from developing countries. 14/ For many of the largest exporters of labor intensive manufactures textiles and clothing were dominant early in the expansion and then declined in importance as countries shifted to other items. For example, from 1965 to 1975 labor intensive manufactures rose from 46 to 79 per cent of Taiwan's total nonfuel exports and the share of textiles and clothing within these items rose from 3 to 28 percent. From 1975 to 1986 the share of all labor intensive products in Taiwan's exports continued to rise (to 85 per cent), yet the share of textiles and clothing in the labor intensive group fell by about 12 percentage points. This same general pattern is evident in many of the developing countries' export profiles. 15/ In the initial stages, textiles and clothing appear to have been a major element or catalyst for the initial export expansion and then were replaced by other labor intensive products.

The question of which labor intensive products replace textiles and clothing as "dynamic" elements in developing country exports is addressed in

- 14/ The subject is not explored in this paper, but there is considerable evidence that the export performance statistics reported in Table 3 are linked to domestic policy measures in developing countries. For example, the rapid 40 point increase in labor intensive products share in total Dominican Republic exports over 1975-1986 can be tied directly to internal measures, such as the establishment of export processing zones, aimed at trade expansion. See World Bank (1988b) for an evaluation of how domestic policies influenced the export performance of the Caribbean Group for Cooperation in Economic Development (CGCED) countries.

- 15/ Among the five largest exporters of manufactures China is an exception as the share of textiles and clothing in the labor intensive group rose throughout 1965-1986 and reached 47 per cent in 1986. This may be explained by a difference in timing as China began its major export expansion to developed countries later than Taiwan, Korea, Hong Kong and Singapore. That is, the current Chinese export profile appears to be similar to that of the "newly industrialized" countries in the mid1970s. See Appendix 2 for related information on this point.
cle, motorcycle, video/VCD/DVD recorder or player, refrigerator, electric fan, electric kitchen or gas stove, cell phone, electric iron, desktop or laptop computer, plough, harrow/rake/hoe/spade/axe, batteries and bed sets (bed mattress).

- _Poverty status_ : a household is considered to be poor when its monthly consumption per capita is below the national poverty line, which includes a food component based on a minimum caloric intake recommended by the FAO, and a non-food allowance. In the case of Cambodia, three poverty lines<sup>8</sup> are used depending on the geographical location of the household. For 2011 the poverty lines were 182,935 Cambodian Riels (CR) for households residing in Phnom Penh, 146,846.94 CR for those living in other urban areas and 134,507.02 CR for those in rural areas. The poverty gap indicator, which measures the average shortfall of poor people, is also used. It is defined as the average distance of household consumption to the poverty line as a proportion of the poverty line (for the non-poor the distance is considered to be zero). The indicator is usually interpreted as the average amount that would have to be transferred to the poor to bring their expenditure up to the poverty line as a proportion of the poverty line.

- _Extreme poverty status_ : similarly, a household is considered extremely poor when its monthly consumption per capita falls below the food component of the poverty line. As of 2011 the food component for Phnom Penh, other urban areas and rural areas was: 118,444.51 CR, 97,986.32 CR and 89,752.28 CR respectively.

- _Food insufficiency_ : it is computed as the percentage share of the last 12 months during which the household reported not having enough food.

- _Children in school_ : it refers to the proportion of children aged 6-14 attending school.

Table 1 presents the summary statistics of key variables used in the analysis, comparing garment households to their non-garment counterparts.<sup>9</sup> Garment households seem to display a lower level of per capita consumption, but the data also suggest they have accumulated more durable goods with an asset index of around 45 percent versus 42 percent for non-garment households. Poverty incidence is also less prevalent at 19 percent, compared to 21 percent for non-garment households. Similarly, the poverty gap index stands at 3.7 and 4.3 percent for garment and non-garment households respectively. The latter report experiencing food insufficiency about 2.68 percent of the last 12 months, while the corresponding figure for households participating in the textile and apparel industry is lower at 1.40 percent. Garment households also enjoy a higher rate of school enrollment for children aged 6-14 years (92 percent versus 88 percent).

> 8Poverty lines were determined in 2011 using 2009 data. A detailed description of the poverty methodology can be found in World Bank (2013).

> 9Descriptive statistics are computed using sampling weights.

9
- Iacus, S. M., G. King, and G. Porro. 2012. “Causal Inference without Balance Checking: Coarsened Exact Matching.” _Political Analysis_ 20(1): 1–24.

- IMF (International Monetary Fund). 2020. _Fiscal Monitor: Policies for the Recovery_ . Washington, DC.

- Lazzarini, S. G., and A. Musacchio. 2018. “State Ownership Reinvented? Explaining Performance Differences between State-Owned and Private Firms.” _Corporate Governance: An International Review_ . https://onlinelibrary.wiley.com/doi/10.1111/corg.12239.

- Megginson, W. L. 2005. The _Financial Economics of Privatization_ . New York: Oxford University Press.

- Musacchio, A., and E. Pineda Ayerbe, eds. 2019. _Fixing State-Owned Enterprises: New Policy Solutions to Old Problems_ . Washington, DC: Inter-American Development Bank.

- Nellis, J. 2005. “The Evolution of Enterprise Reform in Africa: From State-Owned Enterprises to Private Participation in Infrastructure—and Back?” <mark>Energy Sector Management Assistance Programme (ESMAP) Technical Paper 84, World Bank, Washington, DC.</mark>

- OECD (Organisation for Economic Co-operation and Development). 2015. _OECD Guidelines on Corporate Governance of State-Owned Enterprises._ Paris.

- ———. 2016. _State-Owned Enterprises aAs Global Competitors: A Challenge or an Opportunity?_ Paris.

- ———. 2018. _Ownership and Governance of State-Owned Enterprises: A Compendium of National Practices_ . Paris.

- Shirley, M., and J. Nellis. 1991. _Public Enterprise Reform: The Lessons of Experience_ . Economic Development Institute, World Bank, Washington, DC.

- Shirley, M. M., and P. P. Walsh. 2000. “Public versus Private Ownership: The Current State of the Debate.” Policy Research Working Paper 2420, World Bank, Washington, DC.

- Szamosszegi, A., and C. Kyle. 2011. _An Analysis of State-Owned Enterprises and State Capitalism in China_ . Capital Trade, Inc., for the US–China Economic and Security Review Commission.

- Ter-Minassian, T. 2019. “Identifying and Mitigating Fiscal Risks from State-Owned Enterprises (SOEs).” In _Fixing State-Owned Enterprises: New Policy Solutions to Old Problems_ , ed. A. Musacchio and E. Pineda Ayerbe, 49–72. Washington, DC: Inter-American Development Bank.

- Trimble, C. P., M. Kojima, I. Perez Arroyo, and F. Mohammadzadeh. 2016. “Financial Viability of Electricity Sectors in Sub-Saharan Africa: Quasi-Fiscal Deficits and Hidden Costs.” Policy Research Working Paper 7788, World Bank, Washington, DC.

- Wagner, R. A., A. Musacchio, and M. Jara-Bertin. 2016. “Does the Bond Market Discipline State Owned Enterprises?” SSRN Working Paper 2670899.

- Wang, J., D. Guthrie, and Z. Xiao. 2011. “The Rise of SASAC: Asset Management, Ownership Concentration, and Firm Performance in China's Capital Market.” _Management and Organization Review_ 8(2): 253–281.

22
(reduced) stringency of pandemic-control measures and voluntary social distancing trigger a decrease (increase) in consumption expenditures, employment, business investment, and foreign demand for exports. Moreover, a decrease in business investment lowers the level of potential output by reducing the capital stock.

In addition, the impact of COVID-19 on activity is reinforced by the financial and commodity-price channels in the Oxford Global Economic Model. An endogenous reaction of financial conditions, proxied by movements in the CBOE Volatility Index (VIX), captures volatile financial market expectations surrounding the pandemic’s projected evolution (Figure 3.A-B). Specifically, higher risk premia raise corporate borrowing rates, exacerbating the rise in the cost of capital.

In all scenarios, shocks to financial conditions notably affect real activity domestically, and those that occur in major financial centers such as the United States are assumed to spill over to other countries via the model’s financial channels. Lastly, the endogenous response of oil prices to global demand outcomes in the Oxford Model provides an additional spillover channel and dimension of differentiation across all scenarios (Figure 3.C).

# 4. Pandemic Scenarios

# Baseline scenario

In the baseline scenario, following a sharp resurgence that began toward the end of last year, many economies succeed in reducing the daily number of infections in the first half of 2021. The reduction in caseloads is made possible by a combination of stringent lockdown measures as well as less costly pandemic-control policies such as social distancing guidelines and universal masking. In advanced economies and major EMDEs (including China, India, and Russia), inoculation with highly effective vaccines begins in the first quarter of 2021—first to vulnerable groups and subsequently to the general population— becomes widespread in the second half of 2021 (Figure 2.A). Voluntary and mandatory social distancing eases gradually through the remainder of the forecast horizon. The vaccination process is expected to be delayed by two quarters in most other EMDEs and by four quarters in LICs, owing to logistical impediments to vaccine production and distribution.

Activity is assumed to recover gradually as caseloads decline and social distancing efforts are relaxed, enticing households to increase their consumption of contact-intensive services. Firms grow cautiously optimistic in the face of a recovery in aggregate demand and a decline in pandemic policy uncertainty and take advantage of historically low interest rates to modestly increase the pace of investment and boost hiring. Sustained fiscal support assists displaced workers and cash-strapped firms in major economies and many EMDEs, while EMDEs facing fiscal space constraints manage to avoid harsh austerity. The vaccine rollout, coupled with extraordinarily accommodative monetary policy, further 10
# **1 POINT OF DEPARTURE**

There is an ongoing debate on whether disasters cause significant macroeconomic impacts and are truly a potential impediment to development. A position backed by anecdotal evidence and a fair number of studies holds that natural disasters can set back economic development (Otero and Marti, 1995; Benson, 1997a,b,c; Benson and Clay, 1998, 2000, 2001; ECLAC, 1999, 2002; Murlidharan and Shah, 2001; Crowards, 2000; Charveriat, 2000; Mechler, 2004; Hochrainer, 2006; Cuaresma et al., 2008; Noy, 2009). Then, there is a position suggesting that disasters have no effects on economic growth (Albala-Bertrand, 1993, 2006; Skidmore and Toya, 2002; Caselli and Malhotra, 2004). Most analyses along these lines have focused on aggregate impacts, and here on GDP as the standard economic indicator for measuring changes in economic welfare. There is almost no work on other indicators of welfare, such as consumption, which in economics is usually taken as the basis for assessing changes in individual utility and social welfare. Furthermore, it is well known that GDP or GNI<sup>2</sup> are imperfect metrics for measuring changes in welfare, as those aggregates generally do not account for the depletion of natural resources, the value of household labor or investments in education. Several alternative concepts have been proposed, an important one being _genuine savings_ ,<sup>3</sup> which is an alternative welfare indicator based on concepts of green accounting (see, e.g., Hamilton and Atkinson, 2006). Genuine savings aims at better measuring the “true” national savings by adding investments in human capital and subtracting the consumption of capital stock, the depletion of natural resources and the adverse effects of air pollution. The validity of savings measures is commonly tested by studying their ability to explain variations in consumption changes. Although fraught with measurement problems, genuine savings has gained acceptance and found applications in research and policy. It is also standardly reported in the World Bank World Development Indicators.

In the context of natural disaster risk, an additional problem arises due to the fact that the destruction of assets (capital stocks) is not considered in national accounting (which essentially measures flows only), while the flow variables reconstruction and relief spending add positively to GDP, yet in fact only contribute to a recovery to a prior

> 2 In the following, we use GNI, as savings measures in the WDI dataset are indicated as a share of this aggregate.

> 3 In the WDI _genuine savings_ are referred to as _adjusted savings._

2
suggest that they will have a limited effect on both the decline in measured poverty and the simulated effects of changes to the tax code on poverty.<sup>14</sup> Finally, we should note that for our simulations we choose a time period that is based upon the year after this most recent survey. Also, we have used an 8 period out of sample simulation, since adding more periods would not seem to add additional information. Finally, our results are based upon the single household study discussed earlier. It would, of course, be better if we had access to other household surveys, so as to test the robustness of our results. However, we are limited to this single survey. How do the simulated tax reforms affect poverty in the model? For each of the four tax reform scenarios, Appendix Table 6 presents estimates of the percentage point change in poverty headcount relative to the baseline. In the baseline simulation, the share of poverty in the economy slowly increases over the first 4 years of the simulation and then declines to approximately its original level, the sharp decline in the final year is a result of the closure rule in the model and is not relevant. Rises in the sales tax rate and the tariff rate have similar, and very slight negative impacts upon the shares of poverty, as compared to the baseline case. When the higher corporate tax is imposed, the share of the population in poverty is, on average, 1.15 percent higher than in the base case. The reasons for these differential impacts upon poverty are essentially the same as those that explain the consumption changes. The increase in the corporate tax rate severely lowers the rate of capital formation, thereby reducing the marginal product of labor and thereby causing all sectors of society, but especially the poor, to realize a 14 Although the sample frame is old, it is only used to sample villages in the first stage. Thus, the PSLM samples are only failing to capture new villages, largely in peri-urban areas. With respect to Baluchistan, the recent wild fluctuations in poverty rates have had a limited impact on national poverty trends because Baluchistan only contains roughly 5 percent of the nation’s population. Finally, while the poverty line is old, the measured reduction in poverty is robust to a wide variety of poverty lines, including the $1.25 per day used by the World Bank.

25
et al., 2016).<sup>36</sup> To minimize potential non-response bias, we adjusted the sampling weights for unit non-response. The non-response adjustment was calculated at the strata level, so that the weighted distribution of our respondent sample across strata (sector, size, region) exactly matches the distribution of establishments in the sampling frame.

More importantly, to check the reliability of the instrument we implemented a series of ex-post tests in the first phase of the survey, focusing on countries we implemented the survey first. First, we study whether, in the sample of contacted firms, there are significant differences between those that responded and those that declined participating or could not be reached. The only information available in all firms we attempted to contact in the three sampling frames is the number of employees. Table A.18 tests whether there are differences in employment between the respondent and non-respondent groups, controlling for characteristics used for stratification. We find no significant differences in firm size between respondents and non-respondents in any of the three countries.

Table A.18: Comparison of establishment size between respondents vs non-respondents

|VARIABLES|Brazil|Vietnam|Senegal|
|---|---|---|---|
|Respondents (FAT)|2.52|52.34|-4.92|
||(22.19)|(80.27)|(6.63)|
|Observations|1,754|1,500|3,075|
|R-squared|0.129|0.172|0.237|
|Controls:||||
|Sector FE|Y|Y|Y|
|Size-group FE|Y|Y|Y|
|Region FE|Y|Y|Y|

*** ** * Note : p _<_ 0.01, p _<_ 0.05, p _<_ 0.1. Data are from the list of establishments contacted by the enumerators. For each country, the level of employment was regressed on a dummy for respondent while controlling for stratification such as sectors, size groups (small, medium, and large), and regions. Estimates for Vietnam are based on the original list of 1500 firms, with 1346 respondents and 154 non-respondents. Robust standard errors in parenthesis.

Second, under the premise that any systematic relationship between firm characteristics and participation is continuous in their reluctance to participate in the survey, we can learn about sample differences between respondents and non-respondents by comparing firms across different percentiles of the distribution of the number of attempts it took for them 36The average response rate for the WMS is around 40 percent.The response rate for MOPS, implemented by the United States Census Bureau, was around 80 percent.

106
# 1 Introduction

Trade in agricultural products is perhaps one of the most promising instruments for poverty alleviation in developing countries. This is because agriculture is a key productive activity in poor countries and because world markets can provide new opportunities for rural producers. However, international markets for agricultural products have long been distorted by the policies of developed countries. Policies of domestic support, such as subsidies to production or exports, and policies of market access, such as tariffs and non-tariff barriers, are examples. Understanding and measuring the impacts of WTO reforms on poor households in developing countries is therefore critical to the current trade and poverty debate.

When price reforms are implemented, particularly in agriculture, households are affected both as consumers and as income earners. The standard proposition of the literature is that, after an increase in the price of a good, net consumers will be worse off but net producers will be better off (Deaton, 1989). In this static context, the welfare effects of a price change can be assessed by comparing budget shares and income shares. This argument, however, fails to consider dynamic household responses. Consumers may respond by substituting away from the more expensive goods. In rural areas, farmers may increase agricultural production, farm employment and wages, and purchases of inputs and services in local markets. Importantly, thus, the net position of the household becomes endogenous: sufficiently large consumption and income responses may cause an ex-ante net consumer to become an ex-post net producer, thus benefiting from the price increase.

The measurement of the total household welfare effect, one that jointly considers first order effects in consumption and production as well as consumption and income responses, is the objective of this paper. I propose a joint estimator of demand price-elasticities and agricultural wage price-elasticities from survey data. The empirical method relies on a two-step strategy, first estimating the responses of household behavior to prices and, second, linking prices to the reforms. The method requires the estimation of structural models: household responses are linked to prices through structural parameters that are in turn used to simulate policy outcomes.<sup>1</sup>

> 1Sometimes, standard methods for policy evaluation, such as natural experiments, are not feasible. Trade

1