rafmacalaba/datause-encoder-data huggingface dataset:
- 18 -

The VAT typically accounts for a thivd<sup>or more of total</sup> national tax revenue in the councries<sup>studied. Although most</sup> countries have chosen the consumption<sup>type of VAT levied on a</sup> destination basis, a few countries,<sup>such as Colombia and</sup> Turkey, have moved closer towards the<sup>GNP type of VAT under</sup> their policy of only partially refunding taxes paid on capital goods. Indonesia recently moved the VAT from<sup>the</sup> manufacturer's to the wholesqler<sup>level of application.</sup> The VAT still operates at the manufacturers'<sup>level in Malawi as</sup> it will in Jamaica if the plans for a VAT<sup>mat3rialize in that</sup> country, but it applies at the retail<sup>level in the rest of</sup> the countries that have adopted a VAT.<sup>Countries with VAT at</sup> the retail level have put in place special<sup>tax regimes for</sup> small retailers for whom some simplified bookkeeping procedures are desirable on administrative<sup>grounds.</sup> Most countries have operated with VATs that ha-t<sup>a single rate and</sup> allow exemptions for basic necessities<sup>ar.d certain hard to</sup> tax sectors. Exports are invariably<sup>zero rated, although in</sup> some countries the poor timeliness<sup>of VAT refunds implies</sup> that exports are subject to some unknown tax<sup>burden.</sup> Multiple rate structures, such as<sup>those found in Mexico,</sup> Malawi and Turkey, appear to have been<sup>the political price</sup> that was paid for raising the baaic<sup>VAT rate significantly</sup> above 10 percent.

2. A number uf countries have turned<sup>to a wide assortment of</sup> measures to create broader personal and<sup>corporate income tax</sup> bases.
the immediate past. All of these sources of error can be considered non-deliberative or unintentional. On the other hand, intentional misreporting can arise if there is perceived social pressure to appear either “wealthier” or “poorer” to the interviewer and thus the social context of the interview may also be relevant. Finally the interviewer and respondent could suffer from fatigue in the latter part of the interview when surveys are long, or lack integrity when supervision is limited.

The evidence on the implications of consumption survey design which we review is fragmentary and typically sheds light on only one aspect of survey design while ignoring resource implications of the variants considered. Thus previous work at best either explicitly or implicitly investigates how only one source of reporting error varies with design. For example, systematic variation in the length of recall can explore the net relative effect of recall and telescoping errors at different reference periods. A challenge to this previous research is the lack of a consumption benchmark, making it difficult to conclude one design is more accurate than another since there are no data on actual consumption to validate the survey estimates. Scanner data may allow validation in certain contexts in developed countries but will be unavailable for many years in developing countries where many goods are either home produced or bought in informal markets. Prior attempts to form a benchmark for consumption surveys in developing countries have not been fully successful. For example, in India’s NSS experiments enumerators visited households every day and gave volumetric containers for measuring food consumption but for some foods less than two-thirds of respondents used the containers and many respondents did not use the daily diary given to them (NSSO 2003).<sup>6</sup>

# _Diary or Recall_

In a non-negligible number of developing countries, including Brazil, China, and many countries of Central Europe and Central Asia (where literacy rates are high), there exists a tradition of diary-based collection of consumption data – at least for certain consumption items such as food. This contrasts with the more common practice in Living Standards Measurement Study (LSMS) surveys and other multitopic survey instruments to base data collection on recall. It is far from clear to what extent diary and recall surveys are substitutes, or if there are biases introduced by switching from one to the other. In theory, entries are recorded in the diary by the respondent household at the moment of consumption or soon thereafter. However, in practice it can often be the case that interviewers assist in completing the

> 6 National Accounts (NA) estimates of household food consumption are also not a plausible source of validation data, at least in developing countries where NA data may also suffer from various sources of inaccuracy. For example, in comparisons between survey and NA estimates of food consumption in India, both survey and national account statisticians have concluded that discrepancies more likely reflect errors in the national accounts (Minhas 1988; Kulshreshtha and Kar 2005).
|**Table 7. OLS Regressions across countri**<br>**responses.**|**es for decision-making over large purcha**<br>**Wife and husband agree she is the main**<br>**decision-maker / decision-making is joint**|**ses comparing spouses’**<br>**Wife and husband disagree**<br>**(wife gives herself more**<br>**power than husband)**|
|---|---|---|
||**(1)**|**(2)**|
|**_Women's Status & Empowerment proxies_**|||
|Difference in years of schooling: husband-<br>|0|-0.001**|
|wife|[0.00]<br>|[0.00]<br>|
|Wife not working|-0.031***<br>[0.00]|-0.033***<br>[0.00]|
|Wif k ff f|0|0.026***|
|e wors o arm|[0.00]<br>|[0.01]<br>|
|Wife says she earns more than her husband|-0.008<br>[0.01]|0.112***<br>[0.01]|
||-0009|0065***|
|Wife says she owns land|.<br>[0.01]|.<br>[0.01]|
|Woman condones a husband beatin his wife|-0.046***|-0.010***|
|g|[0.00]<br>***|[0.00]<br>**|
|if i d 11<sup>(b)</sup>|-0.072|-0.021|
|We s age 5-9|[0.01]|[0.01]|
|<sup>b</sup>|-0.031***|-0.003|
|Wife is aged 20-34<sup>()</sup>|||
||[0.00]<br>-0001**|[0.00]<br>-0001***|
|Age difference: husband-wife|.<br>[0.00]|.<br>[0.00]|
|if id bf 20|<br>0.016***|<br>-0.003|
|We was marre eore age|[0.00]<br>|[0.00]<br>|
|Years of marriage|0.001**<br>[0.00]|0<br>[0.00]|
|Total number of children woman has|-0.003***<br>|0<br>|
||[0.00]|[0.00]|
||<br>0005**|<br>0002|
|Total number of children who died|-.<br>[0.00]<br>|-.<br>[0.00]<br>|
||-0.067***|-0.010**|
|Couple in a polygamous marriage|[000]|[000]|
|**_Individual/Household-level characteristics_**|.|.|
|Husband works in agriculture|0.004<br>000|-0.004<br>001|
||[.]|[.]|
|Rural area|0.008*<br>|-0.017***<br>|
||[0.00]|[0.01]|
|L ti i t th t t|<br>-0.001***|<br>0.001*|
|og me (mns) o e neares waer source|[0.00]<br>|[0.00]<br>|
|Household has electricity|0.022***<br>[0.01]|-0.008<br>[0.01]|
|Wealth quintile: bottom 20%|-0.026***<br>|-0.019***<br>|
||[0.00]<br>0013***|[0.01]<br>0011**|
|Wealth quintile: next-to-bottom 20%|-.<br>[0.00]<br>|-.<br>[0.01]<br>|
||0.024***|-0.004|
|Wealth quintile: second highest 20%|[0.01]|[0.01]|
||0.064***|-0.001|
|Wealth quintile: top 20%|<br>[0.01]|<br>[0.01]|
|Observations|61,839|61,839|
|Adjusted R-squared|<br>0.161|<br>0.033|
|Notes:|||
|(a) Robust t-statistics in brackets. *** p<0.01,<br>(b) Country fixed effects are included.|** p<0.05, * p<0.1 .||

46
Table 13: IV estimates of the effect of ICT use on TFP: Manufacturing

||(1)|(2)|(3)|
|---|---|---|---|
|**Dependent variable:** ∆**ln(TFP)**||||
|∆Share of labor that uses computer|0.0152***|0.0293***|0.0186***|
||(0.0055)|(0.0062)|(0.0034)|
|F- frst stage|40.43|38.71|106.26|
|Sargan p-value|0.9763|0.6905|0.7873|
|∆Share of labor that uses Internet|0.0118***|0.0230***|0.0245***|
||(0.0041)|(0.0045)|(0.0047)|
|F- frst stage|73.39|70.25|73.93|
|Sargan p-value|0.7783|0.8617|0.9825|
|∆ln(stock of computer equipment/worker)|0.2606**|1.2524***|2.7092**|
||(0.1159)|(0.3934)|(1.3048)|
|F- frst stage|26.34|11.11|6.69|
|Sargan p-value|0.5403|0.2944|0.5170|
|**Controls**||||
|∆ln(capital/worker)|No|Yes|Yes|
|Age dummies|No|No|Yes|
|Size dummies|No|No|Yes|
|Observations|11,438|11,438|11,438|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

39
of achievement trends before the pandemic and the dependency of results on regression specifications. Thus, we perform multiple robustness checks. We first estimate equation (1) on the pooled data from all rounds of PISA with comparable achievement data (8 rounds for reading, 7 for mathematics, and 6 for science). Next, we test for non-linear trends using quadratic terms for time. Then, we re-estimate it for shorter periods deleting data from 2000, then from 2003 and 2006, and so on, leaving at the end the 2018 to 2022 comparison only. Moreover, we estimate results for the OECD countries only, and we use different weighting methods: one estimating results for the whole population of 15-year-olds in participating countries and then using the so-called senate weights which weight each country equally, which is standard approach in OECD reports (see, for example, OECD 2009). Finally, we try different regression specifications, including additional control variables. We compare these results to see if the main estimates of the learning loss are robust to different regression and sample specifications.

The pandemic could affect students in different ways. Disruptions could affect health, economic, and social well-being. Thus, after estimating the overall effect of the pandemic on achievement, we use information on the number of weeks of school closures to show how achievement differs from the time trend depending on the country-average school closure duration. We estimate the following regression model: i = ∑ + ∑ ∗t t 2022 2022 w i i (2) nn nn ii ii=1 ii ii=1 ii ii ii ii YY αα ββ + ττDD + ππDD ∗wwt + γγXX + εε The model is specified as before, but the interaction term between a dummy denoting 2022 data and a measure of the length of school closure in weeks provides an estimate of how the departure in 2022 depends on the length of closures. In this case, captures the departure of achievement in 2022 from country-specific time trends assuming no school closures. In a sense, ττ it captures the impact of other factors than school closures that affected students during the 9
2012 _t_ =1 at the time of the endline (August–October 2012) and zero otherwise. When we consider a household-level outcome, _i_ instead indexes households.

In treatment villages, 9.0% of households did not receive treatment—likely due to lastminute changes in community prioritization or household refusal. In control villages, 0.6% of households received treatment—likely due to their proximity to a treatment village. As a result, our intent-to-treat estimates represent a lower bound on the actual impact of receiving transfers. We also estimate the effect of treatment on the treated by using the fitted values from a regression of treatment on assignment to treatment in place of _Ti_ in equation 1.<sup>11</sup>

# **4.2 Heterogeneous treatment effects examined**

We estimate the overall impacts of the CCT program as well as its impacts on several subgroups. First, we examine impacts by age group. As health conditions applied only to children aged 0–5 and elderly aged 60 and over, and given that each of these two age groups has a different set of health issues and faced different conditions under the CCT program, it is instructive to examine program impacts on them separately. Overall impacts include all individuals in the surveyed households, not only all individuals in the two sub-groups.

Second, for two central outcomes likely to be heavily influenced by the quality of available health care—health clinic visits and health during the last month—we examine heterogeneous impacts of the CCT program by baseline health clinic staff (doctors, nurses, and other assistants) per capita. Specifically, we divide villages into two types: those with abovemedian and below-median health clinic staff per capita at baseline. This helps us assess if improvements are sensitive to capacity constraints Finally, for outcomes likely to be influenced by how credit constrained a household is— shoes and slipper ownership, expenditure on insurance, participation in the CHF, whether one treats illness and where (public or private facilities), and how one finances treatment— we examine heterogeneous impacts by baseline household asset wealth. This allows us to

> 11 We use the Stata package –xtivreg– written by Schaffer (2010).

10
# **Appendix Table 5: Savings and Borrowing -- Extensive Margin - IV Specification**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Have a Bank<br>Account|Have a SACCO<br>Account|Belong to a VSLA|Borrowed Money<br>in Past 3 Months|Loan From a<br>Regulated<br>Financial<br>Institution|Loan From an<br>Unregulated<br>Financial<br>Institution|
|Takeup in Autonomous Trainer Selection (T1)|-0.163|-0.005|0.049|0.014<br>|0.030|-0.020|
||(0.117)|(0.018)|(0.107)|(0.096)<br>|(0.059)|(0.102)|
|Takeup in Fixed Trainer Selection (T2)|-0.174|-0.011|0.055|-0.034<br>|-0.022|-0.030|
||(0.110)|(0.017)|(0.096)|(0.078)<br>|(0.050)|(0.080)|
|R-squared|0.001|0.007|0.025|0.045<br>|0.000|0.054|
|N|7885|7885|7885|7885<br>|7885|7885|
|Dependent Variable Mean in Control Group|0.286|0.993|0.731|0.555<br>|0.086|0.499|
|F-test(p-value): T1 = T2|0.921|0.767|0.953|0.629|0.396|0.922|

This table presents instrumental variable analysis for savings and borrowing outcomes using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
various fossil fuels on firm performance. The authors find that higher prices for all energy types are associated with higher energy efficiency. Cal`ı et al. (2023) evaluate energy prices’ direct and indirect impact on firms’ economic performance for eleven developing countries between 2002 and 2013. Using World Bank firm-level survey data, the authors find that higher energy prices do not necessarily hamper economic performance. They also report considerable heterogeneities, as energy-intensive firms report a smaller performance effect in response to a rise in energy prices than their less energy-intensive counterparts. Cal`ı et al. (2022) focus on medium- to large-sized manufacturing firms in two emerging economies (Mexico and Indonesia). They find that while surges in electricity price harm plants’ performance, increases in fuel prices yield positive outcomes for labor and total factor productivity and profits. The effects are particularly pronounced for capital-intensive manufacturing sectors. In the same vein, Amann et al. (2021) analyze the effects of energy price hikes on manufacturing firms in Oman. They show that increases in fossil fuel prices are causally linked to improvements in productivity and efficiency and lead to notable business upgrading. As Cal`ı et al. (2022), the Oman study only observes such innovation effects for fossil fuel price increases but not for electricity hikes, which are more detrimental to firm performance.

The extent to which energy inputs can serve as _substitutes_ depends on the country and policy context. Rentschler and Kornejew (2018) find that while electricity can be substituted away by a blend of other energy sources, it plays a minor role in replacing fossil-based energy sources. Amann et al. (2021) evaluate the substitutability of electricity and aggregated fossil fuels and find that Omani firms significantly increase their kWh consumption of electricity in response to rising fuel prices.

Firm-level evidence suggests that the effectiveness of the _absorption_ channels depends on firm characteristics and sectoral attributes. Particularly firms in energyintensive sectors respond more strongly to price hikes. Rentschler and Kornejew (2018) highlight that firm-level response patterns of Indonesian micro-firms are very different depending on the analyzed sector. Brucal and Dechezleprˆetre (2021) report that for Indonesian manufacturing firms, energy price increases are causally linked to upticks in plant exit and employment contraction, particularly for energy-intensive and large firms. The authors also report job reallocation from energy-intensive to energy-efficient firms and sectors. Kumar and Prabhakar (2020) analyze how Indian energy-intensive exports react to changes in energy prices and find adverse trade effects, asserting that carbon leakage concerns are not necessarily unfounded. Furthermore, energy-intensive sectors are affected more severely by energy price hikes, most notably the non-ferrous metal and machinery industry. In turn, Cal`ı et al. (2023) do not find statistical evidence that price hikes lead to employment losses 5
# **4.4  Road Network Upgrading and Trade Expansion**

We simulate road network improvement by increasing all link quality indices to a minimum level of 45, which lies between the current index values for Zimbabwe and The Gambia (Table 2). While this would represent a great improvement for many links, we believe that it is a feasible target (see the discussion of costs in Section 5).

# **Econometric Projection**

For Sub-Saharan Africa as a whole, we can use our econometric results to estimate the overall trade impact of road network upgrading. After estimating equation (5) in Table 5, we re-set all road quality indices to the minimum value of 45,<sup>27</sup> project the resulting logarithmic values for inter-country trade, transform the projections to exponentials, and calculate the sum of projections. Table 6 presents the results for current trade and projected trade after the network upgrade. We also include alternative projections, bounded by the projection standard error.

Total trade (as an annual average) in the IMFDOT data is $US 10.1 billion for the period 2000-2003. After we upgrade all network links, projected trade increases by $19.7 billion, to $29.8 billion. Given the projection standard error, we compute low- and highrange projections as $16.3 billion (an increase of $6.2 billion) and $55.4 billion (an increase of $45.3 billion). In all cases, the projected impact of network upgrading is a large increase in overland trade in Sub-Saharan Africa.

**Table 6: Current and Projected Trade:  Econometric Results ($US Million)**

||Avg. Trade,<br>2000-03|ProjectedTrade<br>After Upgrade<br>(Baseline)|Change|Projected Trade<br>After Upgrade<br>(- 1Std. Error.)|Projected Trade<br>After Upgrade<br>(+1Std. Error)|
|---|---|---|---|---|---|
|Total|10,121|29,809|19,688|16,357|55,463|

> 27  We retain the higher values for South Africa (100), Botswana (87.5) and Zimbabwe (50.0).

19
A Labor Freedom index was added recently, but we do not use it because of the lack of a long time series. The Empowerment rights include a measure of worker rights to organize.

There were 5 countries that were not covered by the Heritage Foundation Index. To correct for the missing data, we projected the Heritage Foundation measures on the 6 World Bank Governance Indicators: Voice and Accountability; Political Stability; Government Effectiveness; Regulatory Quality; Rule of Law; and Control of Corruption (Kaufmann, Kraay, and Mastruzzi (2008)). We then used the projected values based on the 6 World Bank indicators to replace the missing Heritage values for the 5 countries.

Empowerment Rights: We used the Empowerment Index from Cingranelli and Richards (2005): Human Rights Dataset, 2005 The data are available for all countries from 1981-2004. For data sets of vintage 2005 or later, we used the 2004 data. The Index is composed of 5 subindexes:

- Freedom of Movement: Indicates the extent to which domestic or foreign travel is unrestricted;

- Freedom of speech: Indicates the extent that speech and the media is free from government censorship;

- Worker’s Rights: Indicates the extent to which workers have the right to bargain collectively and that internationally recognized worker rights are protected;

- Political Participation: Indicates the extent to which political participation is free and open;

- Freedom of Religion: Indicates the extent to which the government allows free religious practices.

Globalization Index: Dreher (2006) developed the KOF Index of Globalization.which is available for most countries between 1970 and 2006. The index is a weighted average of Economic Globalization (based on trade and capital flows and restrictions on those flows); Political Globalization (based on participation in international organizations and embassies hosted); and Social Globalization (access to telephones, Internet, tourism and books within the country). We generated a predicted index for the 11 cases where the overall index was missing, using a regression of the KOF Index of Globalization on the Political Globalization subindex and the Heritage Foundation’s Trade Freedom Index.

Life Expectancy at Birth. Available from the World Development Indicators, for all countries and for various years between 1960 and 2006. When an exact match of dates was not available, we used interpolations of preceding and subsequent year’s data.

Democracy: Freedom House reports an Imputed Polity indicator between 1972 and 2007 for all countries. The measure ranges from 0-10 with 0 being least democratic and 10 being most democratic.

Percent women in legislature: The Inter-parliamentary union reports the percentage of women in the lower house of parliament for all countries between 1997 and 2008. Melander (2005) reports a similar series from 1965-2002 except that it is the percent of women in the upper house of parliament. To fill in the missing IPUW data, we regressed the IPUW series on Melander’s series 29
collected from July 2006 through April 2007, and a tenth and eleventh waves collected in October 2007 and April 2008. In order to compensate firms for the additional burden of staying in the study longer than we had anticipated, we gave 2,500 LKR (~$US25) in cash to each of the remaining untreated firms after round five of the survey.

Attrition in the data is relatively low. Of the 209 baseline firms, 186 report profits in round five and 173 in round 11 (83 percent of the initial sample). However, only 197 firms report profits in the baseline survey, and firms move in and out of the sample. One hundred thirty-five firms report profits in all 11 rounds, and 182 report profits in eight rounds or more. We restrict our analysis to the 200 firms reporting profits in three rounds or more.<sup>12</sup> Appendix 1 compares the characteristics of the treated and untreated firms among these 200. The randomization was done by computer, so any differences can only be due to chance or to the elimination of these nine firms that report less than three waves of profits. The two groups appear to be balanced on the key observable characteristics, but we will also include individual fixed effects to account for any baseline differences in levels remaining.

# _The Impact of Grants on Profits_

We begin by estimating the mean impact of the grants on real profits of tsunamiaffected firms, via the following fixed effects regression for firm _i_ in period _t_ : Where _AMOUNTi,t_ is an indicator of the amount of treatment received by firm _i_ at time _t_ , coded in terms of 100 LKR. Firms receiving 2,500 LKR after round 5 will thus have _AMOUNT_ of 25 in rounds six through 11 (and 0 before this). The δs are wave dummies.

> 12 This eliminates six control firms and three firms assigned to receive the 10,000 LKR treatment.

18