rafmacalaba/datause-encoder-data huggingface dataset:
Geographically, the EU and the US represent the most important destinations of Pakistan’s exports. The US and European markets absorb 31 percent and 23 percent of Pakistan’s total exports. China represents the third most important destinations, with an 11.5 percent share. UAE, Afghanistan, Oman and Turkey have recently become important destinations.

From a sectoral<sup>5</sup> point of view, Pakistan’s exports are dominated by labor-intensive light manufacturing -- textiles, clothing, footwear and leather. This group’s share in exports in 200709 was 65 percent. The second most important export sector was the agrifood sector with slightly more than 11 percent of total exports. Despite the geo-political and security difficulties posed by the war on terror, all sectors of Pakistani exports appear to have seen a healthy growth during the period 1999-2007. Almost all sectors experienced double-digit annual export growth, with extractive industries, metals, and chemicals growing above 20 percentage points annually. During 2007-09, years coinciding with the financial crisis, however, the manufacturing sectors, especially the textiles and apparel industries saw a drop in demand and annual growth was much slower in all sectors except machinery, electronics and transportation equipment, possibly partly sustained by the vitality of the electronics sector globally.

**Table 2 Composition and Growth of Exports by Sector**

|_Sectors_|Value<br>(US$ ‘000),<br>1997-99|Share<br>(%),<br>1997-99|Value<br>(US$ ‘000),<br>2007-09|Share<br>(%),<br>2007-09|CAGR<br>(%)<br>1999-<br>07|CAGR*<br>(%)<br>2007-<br>09|RCA<br>1999-<br>07|RCA<br>2007-<br>09|
|---|---|---|---|---|---|---|---|---|
|Textiles,<br>apparel,<br>leather,<br>footwear<br>(HS 41-42,<br>50-65)|5860275|81.25|11400000|65.44|9.16|-3.05|9.77|11.59|
|Agriculture,<br>meat and<br>dairy, seafood<br>(HS 1-10, 12-<br>14)|530182|7.35|1980391|11.38|10.94|3.45|2.86|1.89|
|Extractive<br>industries<br>(HS 25-27,<br>68-71)|106941|1.48|1650572|9.49|30.58|20.63|0.18|0.42|
|Other<br>industries<br>(HS 37, 43,<br>49, 66-67, 90-<br>97)|315141|4.37|739045|4.25|11.22|-1.34|0.63|0.74|
|Iron, steel,<br>and other<br>metals<br>(HS 26, 72-<br>83)|68640|0.95|552095|3.17|26.89|-1.55|0.16|0.36|
|Food,|227851|3.16|455360|2.62|3.24|19.40|0.47|0.49|

5 The sectoral classification follows Hanson (2010). Export values reflect mirror data in Comtrade.

6
Figure 2. Did Narrow Incumbent Victories Cause Conflict?

<!-- Start of picture text --> −.05 −.04 −.03 −.02 −.01 0 .01 .02 .03 .04 .05<br>Margin of victory<br>Mean Nonparametric fit<br>−.05 −.04 −.03 −.02 −.01 0 .01 .02 .03 .04 .05<br>Margin of victory<br>Mean Nonparametric fit<br>1.5<br>Casualties 1<br>.5<br>0<br>1.5<br>1<br>Casualties<br>.5<br>0<br><!-- End of picture text --> _Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC). _Note:_ The figure presents the relationship between the incumbent’s margin of victory and casualties experienced in the 12 months before and after the 2007 election. Scatter dots represent the mean of casualties per month and are sized to reflect the number of municipalities in each bin. Dashed lines are linear fits, separately estimated on both sides of the eligibility threshold. Solid lines are nonparametric fits from a local linear regression that uses triangular kernels with a bandwidth of 5 percentage points, separately estimated on both sides of the eligibility threshold.

20
"safety not" for the poor also gets favorable reviews in other recent literature (see for example ILO, l988, World Bank, 1990,<sup>Chapter 6, and UNDP,</sup> 1990). Some other discussions have been somewhat less enthusiastic (s0e, for example, World Bank, 1986).

Rural public works schemes have proved repeatedly<sup>in South Asia and in</sup> recent times in parts of Africa and Latin America that they<sup>are one of the few</sup> effective policy instruments available for reaching the able-bodied<sup>rural poor</sup> in times of need. The largest and most established<sup>relief work scheme is</sup> undoubtedly the "Employment Guarantee Scheme" run by<sup>the Indian state of</sup> Maharashtra since the mid-1970s, which developed out of<sup>the aforementioned</sup> successful famine relief efforts of that state in the<sup>early 1970s. This</sup> scheme aims to offer unskilled employment on rural infrastructure<sup>projects on</sup> demand, at wage rates on a par with going wages in agriculture.<sup>From the</sup> point of view of this discussion, the striking feature of the<sup>scheme is how</sup> responsive it is to good and bad agricultural seasons. The monthly<sup>attendance</sup> can easily vary by a factor of three within a year. Attendances<sup>peak in the</sup> dry summer period when there is little other work,<sup>and they vary from year to</sup> year, according to the vagaries of the monsoon. For example, severe monsoon failures in Western India led to record attendances at the scheme's<sup>project</sup> sites in 1987, and the employment provided was clearly instrumental<sup>in</sup> avoiding food entitlement collapses and famine.

The reason relief work schemes seem to be so well<sup>targeted is that they</sup> impose a cost (forgone income, transport cost, disutility of<sup>work) on would-</sup> be participants which tends to be lowest for those in greatest<sup>need. Critics</sup> of these schemes have pointed out that this is a deadweight<sup>loss. However, it</sup> can still be well worth incurring, given the costs of the alternative<sup>forms of</sup> 21
Table 9: Firm-Level Productivity

||Population|Panel Firms|Panel Firms|Firm FE|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Final Goods Tariff|-.046|-.037|-.036|-.042|
||(.026)<sup>_∗_</sup>|(.019)<sup>_∗_</sup>|(.019)<sup>_∗_</sup>|(.008)<sup>_∗∗∗_</sup>|
|Input Tariff|-.496|-.547|-.537|-.128|
||(.107)<sup>_∗∗∗_</sup>|(.087)<sup>_∗∗∗_</sup>|(.087)<sup>_∗∗∗_</sup>|(.035)<sup>_∗∗∗_</sup>|
|FDI Reform|.044|.054|.053|.031|
||(.015)<sup>_∗∗∗_</sup>|(.012)<sup>_∗∗∗_</sup>|(.012)<sup>_∗∗∗_</sup>|(.004)<sup>_∗∗∗_</sup>|
|Delicensed|-.006|-.003|-.003|-.002|
||(.016)|(.013)|(.013)|(.005)|
|Public|||-.166<br>(.014)<sup>_∗∗∗_</sup>||
|Young|||-.093<br>(.005)<sup>_∗∗∗_</sup>||
|Small|||-.058<br>(.009)<sup>_∗∗∗_</sup>||
|Midsize|||-.010<br>(.009)||
|Year FE|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|No|
|Firm FE|No|No|No|Yes|
|Obs.|1322463|388155|388155|388155|
|_R_<sup>2</sup>|.065|.057|.064|.002|

Each observation is a firm. Dependent variable is total factory productivity (TFP). Column (1) includes all firms that were part of the state-industry level analysis; Columns (2)-(4) include only firms that appear in the panel for at least two years. Columns (1)-(3) include industry and time dummies, and standard errors are clustered at the state-industry level. Column (4) includes year dummies and firm fixed effects, and standard errors are clustered at the firm level.

46
4 (2) To the extent that non-benefit taxes on mobile economic units are required -- for example, for redistributive purposes -- only higher levels of government should impose them.

(3) If any non-benefit taxes are imposed by lower levels of government, they should be levied only on tax bases that are relatively immobile across local jurisdictions.

Table 1 summarizes the tax assignment to different levels of government that may be derived on the basis of these rules.<sup>11</sup>

**Table 1: Tax Assignment in a Federal State: The Standard Approach**

|**Revenues**|**Central**|**Region**|**Local**|
|---|---|---|---|
|Personal income taxes|Yes|Possible piggyback|No|
|Payrolltaxes|Yes|Possible piggyback|No|
|Enterprise profit taxes|Yes|No|No|
|Natural resource taxes|Yes|Limited|No|
|Value-added taxes|Yes|No|No|
|Retailsales taxes|Yes|Yes|No|
|Customs duties|Yes|No|No|
|Excise taxes|Yes|Possible piggyback|No|
|Property taxes|No|No|Yes|

The theoretical public finance literature emanating from Musgrave (1959) and Oates (1972) thus suggests a system in which subnational (regional and local) governments, even if they have substantial control over expenditures, will levy few taxes. The principal reason for this outcome is because subnational governments are essentially viewed in this literature as little more than decentralized service providers with the strictly allocative role of providing sub-central public goods. Furthermore, in the absence of tax differentials, individuals and firms are assumed to make sensible consumption and investment decisions. As a result, subnational taxation of such potentially mobile tax bases as consumption, trade, labor and capital will as a rule be economically distorting and hence reduce national well-being.<sup>12</sup> At most, as Table 1 suggests, _local_ governments may be allowed to impose taxes on land and property in addition to user charges. Interestingly, this is precisely the local finance model that one sees in most Englishspeaking countries, other than the United States (Bird and Slack 2004). In addition, the literature suggests that _regional_ governments may be permitted to impose retail (finalstage) sales taxes and a few excises as well as to “piggy-back” -- that is, impose

> 11 This table (adapted from one in Bird and Smart 2010) synthesizes previous summaries of the literature in McLure (1983a), Shah (1994), Martinez-Vazquez, Rider and Wallace (2008), and Bird (2009). For a much more detailed table along somewhat similar lines, see Shah (1994).  User charges are also a suitable source of revenue role for subnational governments (Bird 2001b) but this interesting subject is not further discussed here.

12 There may of course be some exceptions, as when direct expenditure benefits are offset by well-designed user charges) or when (as with well-designed environmental taxes) negative externalities are reduced, but these possibilities are for the most ignored in the present paper.
20

# **Resources**

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OECD, UNDP, and UNECA. (2012). _African Economic Outlook 2012._

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Collier, Paul. (2010). _“The Case for Investing in Africa.” McKinsey Quarterly_ . June.

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Deloitte. (2012). _2011 East Africa Private Equity Confidence Survey: Promising 2012_ . March.

- Dutch National Committee for International Cooperation and Sustainable Development (NCDO). (2008). _Venture Capital and Private Equity Funds for Development: Index 2008_ . Amsterdam.

East African Community Secretariat. (2011). _East African Community Facts and Figures: 2011._ EBRD. (2006). _Regional Venture Funds Program, Russian Federation_ . London, May.

- Emerging Markets Private Equity Association. (2011). _EMPE Annual Fundraising & Investment Review 2011_ . Washington, DC.

- Ernst & Young. (2012a). _Building Bridges: Ernst & Young’s 2012 Attractiveness Survey_ .

- (2012b). _Private Equity Roundup—Africa_ .

- Fellows, William C. (2011). Financial Services Volunteer Corps. _Entrepreneurship, Access to Capital & SMEs in MENA: Supply? Demand? The Issues_ , February.

- Global Impact Investing Network. (2012). _Diverse Perspectives, Shared Objectives: Collaborating to Form the African Agricultural Capital Fund_ . Washington, DC, June.

- Grupo BDC. (2002). _Acceso de las Pequeñas y Medianas Empresas al Financiamiento_ . Washington, DC.
Table 2: Income share (%) of the global top 10%, middle 40%, and bottom 50%

||2019|202<br>Counterfactual|0<br>COVID-19|Net COVID-<br>19 impact, pp|
|---|---|---|---|---|
|Top 10%|48.84|48.67|49.81|1.14|
|Middle 40%|40.23|40.34|39.79|-0.55|
|Bottom 50%|10.92|10.99|10.41|-0.58|

_Note_ : This table reports the share of global income held by various income groups. The last column reports the net impact on the income share in percentage points due to COVID-19. _Net COVID-19 impact_ is calculated as the difference between 2020 distribution with COVID-19 and counterfactual distribution.

Turning to poverty, we use the same global distribution just focusing on its lower tail. Figure 6 shows the global extreme poverty rate from 1990 to 2020 and the year-on-year change in the poverty rate from 1991 to 2020. We find that global extreme poverty increased for the first time in over two decades in 2020. There have been only two episodes of increases in poverty in the last 30 years. Poverty increased by around 0.2 percentage point (37 million people) in the year following the Asian financial crisis (i.e., 1997-98). We expect 0.8 percentage point (or 71 million people) to have moved into poverty in 2020 compared to 2019 due to the COVID-19 pandemic. The latter increase is not only larger in magnitude, but also much larger in relative terms. This is because the stock of extreme poor 20 years ago was nearly 2.5-times larger than the 648 million estimated for 2019. In relative terms, the change in the number of poor from 2019 to 2020 represents a 10% increase, whereas the increase from 1997 to 1998 was about 0.6%. The net COVID-19-induced poverty impact includes the additional 0.3 percentage points (or around 19 million people) who would have otherwise escaped poverty in 2020 had there been no pandemic. In total, this means we estimate the net COVID-19-induced poor to be 1.2 percentage points (or 90 million people) in 2020.<sup>22</sup>

> 22 The 2019-2020 observed change is equivalent to 0.83 percentage points and the counterfactual change is 0.34 percentage points adding to a net impact of 1.16 percentage points. Any discrepancy from Figure 6 is due to rounding.

20
z Table 3. Restrictiveness of Rules on Resident Holdings of Foreign Currency Deposits Onshore, 2000 (Continued)

||Mean<br>dollar.<br>|Firms|House-<br>holds|Prior<br>ApprovalF|reeswitch|Index<br>||Mean<br>dollar.<br>|Firms|House-<br>holds|Prior<br>Approval|Freeswitch|Index<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Greece|26.8|||||0|Tanzania|25.0|||||0|
|Guatemala|0.8||P|N|I||Thailand|21.3|E|X|P|N|4|
|Guinea|25.4||||N|0|Trinidad&Tobago|21.3|||||0|
|GuineaBissau|26.8|||P|N|I|Turkey|44.3||||N|0|
|Haiti|35.1|E||||3|Turkmenistan|31.4|E|E|P||3|
|Honduras|19.7||||N|0|Uganda|20.6|||||0|
|Hungary|23.7|E||||I|Ukraine|32.4|||P|N|I|
|Indonesia|20.8|||||0|UnitedArabE.|25.4||||N|0|
|Israel|19.0|||||0|United Kingdom|13.4||||N|0|
|Italy|3.8||||N|0|Uruguay<br>|79.8<br>|||||0<br>|
|Jamaica|19.3|||||0|Uzbekistan<br>|11.4<br>||||N<br>|0<br>|
|Japan|6.7||||N|0|Venezuela|0.1||||N|0|
|Kazakhstan|47.5|E|E|P||3|VietNam|39.1|E|E||N|2|
|Kenya|10.9<br>||||N|0<br>|Yemen,Rep.of<br>|35.0<br>||||N|0<br>|
|Korea|1.9|||||0|Zaire|50.6|||||0|

Source: IMF, _Annual Report on Exchange Arrangements and Exchange Restrictions 2001._ For firns and households, _E_ denotes that only the documented proceeds of exports or remittances can be lodged to the account; Xdenotes accounts not permitted or limited to a very narrow category of holder. P denotes prior approval required. N denotes local currency accounts cannot be freely converted into foreign currency accounts. _Index_ is a composite index obtained by assigning _E=P=I, X=2_ and summing.
opposite sign to those reported by Anderberg et al. (2016) for the UK, who find that a 1 percentage point increase in female unemployment (holding male unemployment constant) is associated with an _increase_ in the risk of experiencing IPV of 0.097 percentage points. Like this study they find that male unemployment rates have an effect of similar magnitude but opposite-sign, a 1 ppt increase leading to a decrease in IPV of 0.090 ppt.<sup>32</sup> As discussed in the Introduction, they motivate their results with a model in which, when women have improved outside options (higher employment prospects), and the possibility of dissolution of the marriage union, men have an incentive to tame any tendency towards violence.<sup>33</sup> In section 4.2, the study considers whether the observed associations differ between countries in which women can access divorce similarly to men, and countries in which they cannot.

The study also compares its estimates with estimates from studies of domestic violence that investigate other causes. For instance, it is estimated that availability of alcohol raises intimate partner violence by 5 percentage points in India (Luca et al. (2017)), that public transfers (Mexico’s Opportunidades program) lower incidence of IPV by 8.2 percentage points (Bobonis et al., 2013), and that a one standard deviation in civil conflict intensity in a respondent’s place of residence raises physical IPV by 5.07 percentage points in Rwanda (La Mattina, 2017).

Heterogeneity by an indicator of gender equality in access to divorce

> 32 Indeed, this is consistent with evidence that relaxation of divorce laws can lead to a reduction in domestic violence due to the resulting change in bargaining power within the household (Stevenson and Wolfers, 2006; Brassiola, 2016). However, here, the study argues that social norms in many of the countries in the sample limit the extent to which divorce can act as a margin for women so that men are less likely to be “tamed” by the threat of divorce, explaining the opposite signed result for low income countries.

> 33 In the UK and other OECD countries, the fact that divorce is a real option (and not uncommon) implies endogenous changes in the composition of surviving marriages. In other words, if the most violent partnerships dissolve, IPV will be measured on a lower risk sample. Schaller (2013) and Stevenson and Wolfers (2016) present evidence for the United States that divorce (like marriage) is pro-cyclical. However there is contrasting evidence from the UK, showing that unexpected improvements in financial circumstances lower the risk of divorce (Böheim and Ermish, 2001). If one thinks of divorce as a limiting case for domestic violence, then one might imagine that business cycle variation in divorce proxies business cycle variation in domestic violence. However, the relationship may flip if the costs of divorce are high

14
**TABLE 4: COMPARING ESTIMATES OF LONG-RUN ELASTICITIES FOR METALS**

||_Baffes (1997)_<br>_1971:IV-1988:IV_|_Chaudhri (2001)_<br>_1973:1-1996:5_|_Present Study_<br>_1960-2005_|
|---|---|---|---|
|Aluminum|-0.04|0.11|0.04|
|Copper|0.25|-0.19|-0.07|
|Iron ore|0.17|—|0.24|
|Lead|0.22|0.09|0.21|
|Nickel|—|-0.05|0.12|
|Tin|0.30|0.55|0.56|
|Zinc|—|-0.17|0.06|

**Notes:** Baffes uses quarterly data, Chaudhri monthly, and the present study annual. ‘—‘ means not available. **Source:** Baffes (1997), Chaudhri (2001), and author’s estimates.

**_— 17 —_**
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34
# **Domestic Water Pricing with Household Surveys: A Study of Acceptability and Willingness to Pay in Chongqing, China**

## **Hua Wang**<sup>**1**</sup> **Jian Xie Honglin Li**

> 1 Corresponding Address: Hua Wang, World Bank, MS 3-300, 1818 H St. N.W., Washington, DC 20433, USA. Email: hwang1@worldbank.org. The authors would like to thank colleagues in Chongqing, Wenchao Jiang, Zhi Zhang and Linjun Zhou, for their help in conducting the study and to thank Carter Brandon and Chinese Economists Society’s 2008 Annual Conference participants for their valuable comments. Any remaining errors are those of the authors.
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