rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> These simulations reveal a sharp immediate impact of the lost labour and remittance incomes for households,<br>with the poverty rate rising by 11 percentage points. While the government assistance mitigates less than half of<br>the initial shock in the early months, as the economy begins to reopen, incomes recover and inflation recedes,<br>the government assistance is able to push the poverty rate near where it would have been without the crisis by<br>the end of the six months of temporary payments. The mitigating impact of social assistance is time bound and<br>poverty may jump-up again when the temporary payments are scheduled to finish and if a second wave of<br>infections, lockdowns and economic restrictions delay the economic recovery in the country.<br><!-- End of picture text --> These simulations reveal a sharp immediate impact of the lost labour and remittance incomes for households, with the poverty rate rising by 11 percentage points. While the government assistance mitigates less than half of the initial shock in the early months, as the economy begins to reopen, incomes recover and inflation recedes, the government assistance is able to push the poverty rate near where it would have been without the crisis by the end of the six months of temporary payments. The mitigating impact of social assistance is time bound and poverty may jump-up again when the temporary payments are scheduled to finish and if a second wave of infections, lockdowns and economic restrictions delay the economic recovery in the country.

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> After social assistance response<br><!-- End of picture text --> <!-- Start of picture text --> + After (de)inflation<br><!-- End of picture text --> <!-- Start of picture text --> Income shock<br><!-- End of picture text --> <!-- Start of picture text --> Counterfactual<br><!-- End of picture text --> Source: Refaqat _et al_ . (forthcoming) Taken together these three reforms, reducing electricity subsidies for higher consuming households, imposing a flat GST tax rate without exemptions and redirecting and expanding existing and planned social assistance spending, would mean increased fiscal space of JOD 115 million while reducing poverty by 1.7 points and reducing inequality by 1.3 points in comparison to the current fiscal system. An additional JOD 110 million of the 2020 bread subsidy compensation budget could either be redirected to povertytargeted programs, further reducing inequality and poverty at no additional cost to the budget or used to close the fiscal gap. This would come at some cost to poverty and inequality, although it would be less than the reductions estimated under the three reform scenarios.
markup premium for large companies is 12.2 percent higher than that of small firms (figure 9). Both size and age effects are still present, even after controlling for productivity differences (see table A1.6 in annex 1).<sup>33</sup>

**Figure 8. Average markup differences relative to young firms, by age category**

<!-- Start of picture text --> 35<br>30<br>22.8<br>25<br>20 15.8<br>15<br>7.9<br>10 4.8 5.4<br>5 2.2<br>0<br>-5 -1.60<br>Mature (between 6 and 15 years old)<br>Old (older than 15)<br>Source: World Bank staff calculations based on SBS data<br>set.<br>% difference<br><!-- End of picture text --> _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for age dummy categories—relative to young firms (1–5 years old), the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**Figure 9. Average markup differences relative to small firms, by size category**

<!-- Start of picture text --> 32.7<br>35<br>23<br>25 16.9<br>12.2<br>15<br>5 0.90<br>-5<br>-15 -8.30<br>Medium (between 51 and 250)<br>Large (more than 250 employees)<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for size dummy categories—relative to small firms (1– 50 employees), the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**By the same token, export intensity also influences markup performance, with firms that**

**export earning higher markups.** Being an exporter is a relevant firm feature to be analyzed when it comes to understanding markup performance. Recent models of international trade predict that exporting firms earn higher markups than non-exporting firms, either because they are relatively more productive and can thus undercut their rivals (see Melitz and Ottaviano 2008), or because they produce higher-quality goods and rely on higher-quality inputs, which allow them to charge higher prices (see Hallak and Sivadasan 2009). In this regard, the results summarized in figure 10 are consistent with the literature and suggest that exporting firms— defined as those whose direct exports represent more than 10 percent of total revenue—earn higher markups than non-exporting firms that operate in the same product market (NACE 4- digit sector); the difference ranges from 12 percent to 41.6 percent, depending on how data are clustered across large sectors (manufacturing, services, and mining).

> 33 See footnote 32 on how to interpret results in table A1.6 in annex 1 (after including productivity as an additional control).

24
Goto, Junichi. "FTAs and Their Economic<sup>Implicationswith Referenceto Asia."For</sup> Presentation at Capacity Building Workshop:<sup>Trade PolicyIssues.Singapore,16-24 April</sup> 2001. Kobe, Japan: Research Institute<sup>for Economicsand Business Administration,Kobe</sup> University, 2000.

Goto, Junichi and Koichi Hamada. "Economic<sup>Preconditionsfor Asian Regional</sup> Integration." Macroeconomic Linkages:<sup>Savings,ExchangeRates,and Capital Flows.</sup> Eds. T. Ito and A. Krueger. Chicago:<sup>Universityof ChicagoPress,1994.359-88.</sup> Goto, Junichi and Masahiro Kawai. "Macroeconomic<sup>Interdependence in East Asia."</sup> Prepared for presentation to the Intemational<sup>Conferenceon "EconomicInterdependence:</sup> Shaping Asia-Pacific in the 21<sup>**St**</sup> Century."<sup>Organized jointly by the Institutefor</sup> Intemational Monetary Affairs, the<sup>IMF andthe World Bank, and held in Tokyo on</sup> March 22-23, 2001. (mimeo).

Hanson, Gordon. "Should Countries<sup>PromoteForeign DirectInvestment?"UNCTAD's</sup> G-24 Discussion Paper Series No.<sup>9. New York and Geneva:United Nations,2001.</sup> Harwood, Alison. "Building Local<sup>Bond Markets:Some Issuesand Actions.Buildine</sup> Local Bond Markets: An Asian Perspective.<sup>Ed. Alison Harwood.Washington,DC:</sup> Intemational Finance Corporation/World<sup>Bank Group,2000.</sup> Hicks, John. A Theory of Economic<sup>Historv. Oxford:Clarendon Press,1969.</sup> Hood, Ron. "Malaysian Capital Controls."<sup>World Bank Working Paper.Washington,</sup> DC: The World Bank, n.d.

Intemational Monetary Fund. Direction<sup>of Trade Statistics Yearbook.Washington, DC:</sup> IMF, 1985.

Intemational Monetary Fund. Direction<sup>of TradeStatisticsYearbook.Washington,DC:</sup> IMF, 1992.

International Monetary Fund. World<sup>Economic Outlook.Washington,DC: IMF, Oct.</sup> 1997.

Intemational Monetary Fund (2000a).<sup>Annual Report 2000.Washington,DC: IMF, 2000.</sup> Intemational Monetary Fund (2000b).<sup>Direction of TradeStatistics Yearbook and various</sup> Ouarterlv Updates. Washington,<sup>DC: IMF, 2000.</sup> International Monetary Fund (2000c).<sup>"Recoveryfrom the Asian Crisis and the Role of</sup> the IMF." Washington, DC: IMF, June<sup>2000. 29 May 2002</sup> <http://www.imf.org/extemal/np/exr/ib/2000/062300.htm#table 1>.

137
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|---|---|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Western Kenya CDD and Flood Mitigation Project|Valens Mwumvaneza|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|Bhutan - Education Development Project|Mark F. LaPrairie|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|AIDS CONTROL PROJECT|Dorothee B. Eckertz|
|Social Assistance Grant|Inguna Dobraja|
|Social Assistance Grant|Inguna Dobraja|
|Social Assistance Grant|Inguna Dobraja|
|Financial Sector Development TA Grant|Alexander Pankov|
|Financial Sector Development TA Grant|Alexander Pankov|
|Financial Sector Development TA Grant|Alexander Pankov|
|Financial Sector Development TA Grant|Alexander Pankov|
-4-

that results inter alia from the adjustment effort. The financing must also reconcile debt implications, including that of additional borrowing (which are not addressed here.) This connection between policy and financing highlights the special role of trade expansion reforms. If imports respond faster than exports, trade reforms would need to be supported by additional external resources in the short term.

Trade reforms have been the most significant component of adjustment lending and Bank advice in the 1980.. About half the Bank's sector adjustment lending in fiscal 1987 primarily supported trade libe.ralization in five large borrowers: Argentina, Indonesia, Jamaica, Mexico, and Nigeria. A wide range of studies quoted in World Bank (1987a) concern the positive relationship between trade reforms for greater outward-orientation and performance. Edwards (1985), Frenkel (1982), and Papageorgiou, Michaely, and Choksi (1986) have examined the phasing and sequencing of outward-oriented trade reforms. Corbo and de Melo (1987), Fischer (1986) and Mussa (1987) have been concerned with the interrelations among trade and macroeconomic reforms during adjustment. Dornbusch (1987) and Sachs (1986) have questioned the wisdom of trade liberalization and the effectiveness of the Bank's conditionality. On the whole, much has been written on the merits and problems of trade liberalization. Needed now is a clarification of the concepts and a synthesis of the evidence and operationally useful results.

A third major issue in the process of adjustment is the coordination of domestic and external players (section 4). Consenousbuilding has been receiving much attention as a domestic element In the
# Table 6

# Determinants of air pollution

||**TSP**|
|---|---|
|INSP|-0.0328* *<br>(0.022)|
|LcINSP|0.0032<br>(0.621)|
|Lca|0.0100<br>(0.843)|
|Le|-0.1603<br>(0.168)|
|Lagged dependent variable|0.3423**<br>(0.040)|
|Emp|-0.0002<br>(0.499)|
|State|-0.1381<br>(0.499)|
|Coll|0.0177<br>(0.945)|
|Petrol|-0.4518***<br>(0.092)|
|Coal|-0.1059<br>(0.749)|
|Construction|9.0519*<br>(0.000)|
|Paper|-0.7835*<br>(0.007)|
|Chemical|-0.8535*<br>(0.002)|
|Test Overid. Restr.|7.3977<br>(0.286)|
|# of observations|396|

Note: GMM is the method of estimation. GMM instruments are provided in the text. Variables are defined as in the text. Test Overid. Rest. tests the validity of overidentifying restrictions. Numbers in brackets are p-values. Robust standard errors are used throughout. **(#),** ( ) and ('**) mean significant at the 1%, 5% and 10% level, respectively.

20
- 67 -

farmers are restricted to short-season (75 to 85 days) varieties of commodities such as corn rather than higher-yielding varieties of corn or soybeans, which require 100 days or more and run the risk of late-season flooding at the onset of the wet season. With high labor costs a,d little scope for raising yields, it is difficult for farm households to increase total returns from such crops.

3.26 In the dry season, farmers in some areas of Indonesia have generally found it more profitable to plant an intercrop of corn and soybeans, corn and mung beans, corn and rice, or corn and melons than a monoculture crop. Managing pests, especially those that attack bean crops, is a complicated task in such intercropped systems, but farmers still find the planting system less risky than monocrops in the dry season. Research is needed to develop short-season, high-yielding varieties of commodities with greater resistance to pests and thus increase the flexibility of farmers, lower their risks, and raise total returns.

# UDland Areas

3.27 It is important to recognize that much of the arable land in Asia is already planted to crops other than rice. For example, more than 85 percent of Malaysia's agricultural land is used to grow non-rice crops. Rubber, oil palm, cocoa, and coconuts occupy a vast proportion of the land - about 82 percent. With 14 percent of the land planted to paddy rice, only 4 percent is planted to other field crops. While this pattern represents an extreme in Asia, other Southedst As. n countries have somewhat similar patterns. The Philippines has large estates producing sugar and coconuts. The area in Indonesia's estate crop sector, which includes coconut, oil palm, rubber, and smallholder tea, coffee, and sugar cane, slightly exceeds that planted in paddy rice. The area farmed by shifting cultivation is 70 percent greater than that of paddy rice. While producers of tree crops and plantation crops such as sugar cane cannot be as flexible as other farmers in their response to crop diversification initiatives, the land can be devoted to other crops if economic conditions change.
for some countries arms imports are not accounted for in military expenditures (Brzoska 1995) and a high arms to total imports ratio provides yet another feature of a highly militarized society. These data have their origin in the U.S. Department of State’s _World Military Expenditures and Arms Transfers_ reports and are taken from (World Bank 2003).<sup>5</sup>

# _The main explanatory variables_

Our primary independent variable is ethnic heterogeneity, which has received a great deal of attention in the recent empirical literature in several fields, particularly in political science, sociology, and economics (Alesina et al. 2003; Fearon 2003). As a result, the measures have improved tremendously over the measure of ethno-linguistic fractionalization previously developed by Soviet social scientists in the 1960s and used in early studies. Defined as the probability that two randomly selected individuals from the same country belong to different _n_ 2 ethnic or linguistic groups, heterogeneity is computed as _ELF_ ≡ 1 − ∑<sup>_p_</sup> _i_ , where _pi_ is the _i_ = 1 population share of ethnic or linguistic group _i_ and _n_ is the number of existing groups. This measure of ethno linguistic fractionalization (ELF) mixed ethnic and linguistic characteristics. Recently, however, Alesina et al. (2003), Montalvo and Reynal-Querol (2005), Fearon and Laitin (2003) and Fearon (2003) independently constructed new measures of ethnic, religious as well as, in the case of Alesina et al. (2003), linguistic fractionalization ( _Ethfrac (Alesina et al.), Relfrac (Alesina et al.), Linfrac (Alesina et al.)_ , _Ethfrac (Montalvo & R.-Q.)_ , _Relfrac (Montalvo & R.-Q.)_ , _Ethfrac (Fearon & Laitin)_ and _Relfrac (Fearon & Laitin)_ ). They use the same formula, but improve upon the older data significantly. These data series are based on more current, updated sources, and do not conflate ethnic, religious and linguistic characteristics in a single measure as blatantly as the old ELF measure. They also rely on survey-based studies that have examined several African countries where distinction of groups is not always straightforward. While there are several definitional and operational differences in their methods, Fearon (2003: 196) claims that his measure is ‘broadly similar’ to Alesina et al’s (2003) measure. Both these measures are highly similar to Montalvo and Raynal-Querol’s (2005) measures of 5 There is one data point of more than 100 per cent (Ethiopia 1989), which can happen if there are inconsistencies in the reporting and measurement of arms as well as total imports. Dropping this observation from the sample had little impact on the results.

6
additional variables to capture the government’s ability to enforce and facilitate tax collection as well as local economic and political conditions that could influence tax outcomes. We then use matching to isolate the remaining variation to better estimate our treatments’ impact on tax outcomes after accounting for local tax facilitation and enforcement capabilities, economic conditions, and other confounding variables. All dependent variables are in constant, 2010 Brazilian _reais_ .

We also present the results of hypothesis tests using various lags between the participatory institutional treatment and tax outcomes. For example, we know when PB programs began, with many emerging during the timeframe of the study. We can therefore assess different arguments surrounding when tax outcomes might be expected to appear, if they are indeed connected to PB. We begin with one-year lags, but also develop models using two and five-year lags of our dependent variables as well as a three-year moving average of tax outcomes. Unfortunately, we do not know when municipalities adopted policy councils and therefore estimate their relationship with tax outcomes based on the councils’ presence, with no lag in terms of potential impact.

# **Dependent Variables**

_Local Tax Share of Municipal Revenue_ We collect data on the local tax share of municipal revenue as our first indicator of tax compliance. The local tax share of revenue reflects the community’s willingness to fund the government as well as the municipal government’s ability to collect tax revenue. The tax share of municipal revenue is distinct from federal transfers, which comprise a large percentage of local budgets in Brazil. The measure has a mean of 5% and a standard deviation of 5.

# _Per Capita Local Tax Revenue_

This indicator provides a broad view of the local tax environment. Annual, per capita, local tax revenue also reflects the relative willingness of municipal residents and firms to fund the local government and the municipal government’s ability to collect revenue. We use the base 10 logarithm of this indicator, whose mean is 3.80 with a standard deviation of 1.13.

# _Per Capita Property Taxes (IPTU)_

The tax collected on the value of land and improvements is another locally-collected tax that reflects individuals’ and firms’ willingness to fund the government. Municipal governments assess property values on an annual basis and then levy taxes at identical rates across municipalities. We use the base 10 logarithm of this indicator whose mean is 1.28 with a standard deviation of 2.08.

15
are aware, has taken this fairly obvious feature of the original Putnam argument seriously. It did nothing to foster these types of interactions and indeed, as our network survey suggests, may have even crowded some of them out.

These results will undoubtedly come as a disappointment to those who had hoped for increases in the program communities’ stocks of social capital but there are several points to keep in mind. First, CDF had several goals besides increases in social capital and this study has not assessed the impact of the program on those other goals. Second, perhaps five years is too short a time to expect such fundamental changes in people’s attitudes. Third, regardless of the impacts of the program on social capital or other outcomes, CDF has already made a contribution to our understanding of development programming by agreeing to a rigorous randomized impact evaluation. The findings from this impact evaluation combined with findings from other programs in other countries will together make development planners smarter so that scarce development funds can be allocated more efficiently. In that way CDF has already had a positive impact on development programming not only in Sudan but also around the world.

36
(Schellenberg et al., 2001). In 2004, a national voucher scheme was created (Njau et al., 2009), but comprehensive national coverage was not achieved until the mass free distribution campaigns of 2008-2011.

This paper focuses on one of these mass distribution campaigns: the 2010-11 Universal Coverage Campaign (UCC). After several years of a voucher program which enabled subsidized access to nets for pregnant women and infants (the Tanzania National Voucher Scheme or TNVS), ITN coverage was still not reaching the policy goal of universal access, leading policymakers to consider free distribution, for which Tanzania won funding for from the Global Fund for AIDS, TB, and Malaria. In the first free distribution campaign, nets were given to all children under 5 (the 2008-09 under 5 catch up campaign). Subsequently, in 2010-2011, additional nets were distributed to cover all household members, by giving one net to each household for every sleeping space not already covered with an ITN.<sup>8</sup> Demographic and Health Surveys show that these campaigns were associated with increases in ITN coverage, as Tanzania reached over 80 percent ITN ownership by 2011.

The procedures for national net distribution campaigns in Tanzania were first established during the 2008 under 5 “catch up” distribution campaign. Using donor funding, the Tanzanian government contracted NGOs to manage implementation. The NGOs trained local government officials, who in turned supervised community volunteers, who registered all households and then distributed the nets (Bonner et al., 2011). The nets themselves were manufactured by A to Z Textiles Ltd., a domestic Tanzanian manufacturing firm. The country was divided into zones, which were covered progressively starting in regions with the highest malaria prevalence. The manufacturer, A to Z Textiles, distributed the nets to the village level, where they were stored temporarily. Village Executive Officers then were in charge of selecting, training, and managing four village volunteers in identification, registration, and distribution of free net vouchers for all under 5s in the 8Technically the nets distributed in these campaigns are long lasting insecticidal nets, or LLINs, rather than ITNs 10