rafmacalaba/datause-encoder-data huggingface dataset:
a large-scale CDD project in the Philippines. Under this project, all villages within a municipality prepare proposals for small-scale infrastructure investment, while only a subset of villages ( _i.e._ , those with the best proposals) actually implement those investments. The paper takes advantage of a unique and detailed panel dataset of 2,092 households in 135 villages in 16 municipalities of the Philippines. The sample includes eight municipalities in which the project is implemented and eight matched comparison municipalities.<sup>2</sup> Our paper builds on existing research on participatory projects’ impact to enhance social capital and foster collective action. First, we use household panel data collected both before the project started and after one cycle of subproject implementation in both treatment and comparison municipalities. The survey instrument includes information on a broad range of household and village-level social capital measures. Thus, we can analyze whether _changes_ in social capital indicators can be attributed to the project, controlling for initial village characteristics and time-constant unobserved characteristics.

Second, communities followed very detailed, facilitated participatory processes throughout the project, tailored to Philippine conditions. Since opportunities for participation are clearly defined, we improve upon previous research that used data from CDD projects in which the decision rules within communities and with local authorities were not always clear. However, note that while the subproject implementation cycle is repeated three times in each village, this research summarizes data currently available (collected after the first cycle only).

Our regression and propensity score matching estimates highlight that the project impacts both participation in local governance activities and community social networks. As a result of the project, treatment villages experienced increases in participation in village assemblies

> 2 The selection procedure for treatment and comparison municipalities is described in Section 3. More details are available in Chase and Holmemo (2005).

4
changes are uncertain—rainfall may increase or decrease—but droughts may also become more severe and more common (IPCC, 2014). The combined effects on streamflow in the basins that serve Lima are highly uncertain.

These uncertainties raise important questions for decision makers concerned with water reliability in Lima. Are the capital projects in the Master Plan sufficient to ensure reliability in the face of deeply uncertain future climate change and demand? On the other hand, are all proposed projects necessary to achieving reliability? Many projects are challenging to implement—how should considerations of project feasibility shape the city’s investment strategy? Ultimately, how should projects be prioritized? Which should be implemented now, which can be delayed until they are necessary, and what specific indicators would trigger their implementation?

This study draws upon state-of-the-art methods for decision making under deep uncertainty (DMU) to give SEDAPAL and decision makers in Lima answers to these pressing questions. It draws upon several methodologies including Robust Decision Making, Decision Scaling, and Adaptive Pathways, to prioritize the investments in SEDAPAL’s Master Plan. Together these methods help define an investment strategy that is _robust_ , ensuring water reliability across as wide a range of future conditions as possible while also being economically efficient. This strategy has two key characteristics of a robust plan:

1. It is _no-regret_ . It identifies investments and projects that are useful no matter what the future brings;

2. It is _adaptive_ . It guides decision makers on how to implement future investments and projects as climate, demand, and other future conditions evolve.

The strategy is defined in a decision tree as shown in Figure 1-2. It consists of a set of near-term, no-regret investments that SEDAPAL can embark upon now; signposts of specific project feasibility, streamflow, and demand conditions SEDAPAL should monitor in the medium and long term; and sets of deferred projects that SEDAPAL should implement if the signposts are triggered. Section 7 describes the strategy and decision tree in detail.

Importantly, the strategy prioritizes among the 14 supply-side investments. Yet efficiency and demand management will play a key role in ensuring water reliability in Lima, particularly as they may be more cost effective than some of the supply-side investments. At the time of this project, SEDAPAL sought to focus its analysis on the Master Plan projects as it concurrently explored ideas for demand side and efficiency options. Therefore, while this analysis does not explicitly evaluate efficiency and demand management actions, it highlights the need for such actions by testing the limits of the water reliability that can be achieved with the proposed projects. In Section 8, we recommend evaluating demand and efficiency actions alongside supply-side investments in a follow-on analysis to develop an integrated water resource management plan.

3
10 imports are primary stage items), whiie the Korean average (57.2 percent) is also well above that of other Asian countries. Neither Japan nor Korea figures prominently among important Asian commodity producers (see Table 4). Their combined imports of primary and processed commodities are almost 55 percent of the Asiae total. These facts highlight the importan .e of tariff escalation issues in any multilateral Asian trade liberalization effort.

Since Table 1 shows that the current structure of Asian imports is heavily biased against processed commodities (whether these shipments originate in Asia or elsewhere), an important related point is how this structure is evolving over time. Table 2 addresses this point by tabulating the value of Asian imports of primary and processed commodity imports at ten year intervals over the last two decades and also by showing the share of unprocessed (stage 1) commodities in this trade.<sup>6</sup> The import performance of the European Community for these same goods over the same period is also reported (see memo item).

Between 1970 and 1990 the share of Asian imports of all unprocessed commodities declined by approximately 11 percentage points (from 63 to 52 percent), which is about the sanm as the decline registered by the EC. In other words, the relative Asian trade bias against processed commodity imports has not narrowed over this 20-year period. This finding accents the -eed for policy action to identify and remove existing constraints to Asian intra-trade in processed commodities.

Aside from the overall regional trend, several different patterns are reflected within the regional sub-groups. South Asia's import shares for unprocessed agricultural raw materials declined sharply betwoee 1970 and 1980, but this was primarily due to special factors not directly connected with a shift to processed products. For example, India all but discontinued its (previously major) imports of raw cotton while its imports of cotton thread and fabrics remained fairly constant in dollu terms- this produced the appearance of a shift toward - fabricated cotton goods. Historically, India was a major market for Egyptian long-stem cotton dia's imports - averaged 25,000 tons per year in the early 1970s but, after developing domestic production of a suitable cotton substitute for spinning fine yams, its imports of Egyptian cotton declined to an average of less than 3,000 tons per 'One important data problem to be noted is that it was not possible to hold the composition of importing Asian countries constant over the 1970-90 period. The Peoples Republic of China did not report imports or exports to the United Nations prior to 1984 so the group of Asian countries included in the 1970 and 1980 statistics is somewhat different from that for 1990. Tables 1 and 2 rely on trade data reported by individual Asian countries (there are<sup>gaps</sup> in some country's reporting) while Tables 3 and 4 show imports reported by their trading partners. For this reason it was possible to report a somewhat larger selection of smaller Asian countries like Bhutan, Nepal and the Maldives in the latter tabulations.
Most of our variation in contrast is cross sectional, across countries, so we use exports of different countries. Suppose a country in our sample were the United States as in the sugar example; then we are using the equivalent of Cuban exports to the world to instrument for price in that country. Compared to the approach of using the neighbor’s imports, using the neighbor’s exports to the world provides us with greater assurance that the instrument is unrelated to local demand, since exports to the world are determined by international demand.<sup>11</sup> A neighbor’s exports of cement are generally either much more or much less than local consumption of cement: the standard deviation of the ratio of the nearest neighbor’s exports to local consumption is 1.4.

Table 2 reports several estimates of Equation (1) using this instrument, with all market characteristics in natural logs. Estimates of the demand curve are broadly in line with industry accounts and existing studies of the industry. In all Columns 1-5 the demand elasticity is greater than 1, consistent with the exercise of market power. The model in Column 1, which includes no market characteristics, yields _η_ = 1 _._ 96 (s.e. = 0.80). The Kleibergen-Paap F-statistic of the first stage relationship is 29.23, well above the heuristic threshold of 10. Column 2, which includes population, GDP per capita, and the construction share of GDP, all of which have positive and significant effects on demand, yields _η_ = 1 _._ 38 (s.e. = 0.26), and has a slightly larger first stage F-statistic of 30.65. The lower standard error and higher F-statistic in this specification motivate retaining these demand shifters in the demand curve. All are plausibly exogenous, with population and GDP per capita determined over the long term. The share of cement in construction investment (13.5 percent in our sample), leads construction investment to be considered exogenous to unobserved cement demand or costs (e.g., Syverson, 2004).

An assumption required for identification of any demand elasticity is that shocks to demand for complements and substitutes enter into the error term _ξit_ as a scalar index (Berry and Haile, 2014). While we do not observe consumption of such goods in each economy, Column 2 does include the log of the construction share of GDP as a control that is correlated with demand for these goods. In addition, in Column 3, we include the exchange rate, which may affect demand for imported construction goods, the price of diesel, and the price of aggregate. The latter two prices may affect the ready-mix concrete industry, which is a major consumer of cement. In this specification these variables do not have a significant effect on demand, and the elasticity changes little relative to Column 2, at _η_ = 1 _._ 41 (s.e. = 0.36), though it has a higher standard error. For these reasons, and because the ICP reports the price of aggregate for a smaller set of countries, we prefer the specification in Column 2

> 11Genesove and Mullin (1998) study the period 1890-1914, and it is plausible they did not have access to comprehensive data on Cuba’s sugar exports to the world during that time period.

11
prices, are respectively HBS 2011/12 and NPS 2010/11. And the base year in the HBS and NPS, when adjusting for inter‐year price variations using the CPI, is HBS 2011/12.

**Table 5: Comparison of monthly consumption aggregates in Tanzania Mainland**

|||**HBS**|||**N**|**PS**||
|---|---|---|---|---|---|---|---|
||**2007**|**2011/12**|**Variation**<br>**%**|**2008/09**|**2010/11**|**2012/13**|**Variation**<sup>*****</sup><br>**%**|
|**Mean monthly total consumpti**|**on per adul**|**t equivalent**||||||
|Nominal consumption|32,579|63,153|94%|48,262|55,867|77,846|61%|
|Real consumption|31,578|62,427|98%|49,251|56,578|78,134|59%|
|Real consumption, temporally<br>adjusted (survey price<br>indicators)|59,997|62,427|4%|60,086|56,578|58,309|‐3%|
|Real consumption, temporally<br>adjusted (CPI), base year HBS|52,419|62,427|19%|67,868|65,137|71,536|5%|
|2011/12||||||||
|<br>**(a) Distribution by quintile of n**|**ominal con**|**sumption**||||||
|Poorest Quintile|10,826|24,478|126%|16,719|18,871|24309|45%|
|2<sup>nd</sup>Quintile|17,886|36,621|105%|26,148|30,358|39,769|52%|
|3<sup>rd</sup>Quintile|24,696|48,640|97%|35,506|41,735|56,475|59%|
|4<sup>th</sup>Quintile|34,980|66,722|91%|51,032|59,354|84,592|66%|
|Richest Quintile|74,557|139,343|87%|111,942|129,285|184,304|65%|
|**(b) Distribution by quintile of r**|**eal consum**|**ption**||||||
|Poorest Quintile|10,849|25,191|132%|17,995|20,205|25,346|41%|
|2<sup>nd</sup>Quintile|17,881|37,539|110%|28,079|31,974|41,356|47%|
|3<sup>rd</sup>Quintile|24,526|49,503|102%|37,907|43,679|58,419|54%|
|4<sup>th</sup>Quintile|34,127|66,656|95%|53,197|60,872|86,333|62%|
|Richest Quintile|70,551|133,283|89%|109,161|126,361|179,594|65%|
|**(c)Distribution by quinti**<br>**indicators)**|**le of real co**|**nsumption**|**adjusted by**|**inter‐year**|**price variati**|**on (using s**|**urvey price**|
|Poorest Quintile|21,025|25,730|22%|21,953|20,205|18,915|‐14%|
|2<sup>nd</sup>Quintile|34,503|38,058|10%|34,256|31,974|30,863|‐10%|
|3<sup>rd</sup>Quintile|46,961|49,771|6%|46,247|43,679|43,596|‐6%|
|4<sup>th</sup>Quintile|64,615|66,568|3%|64,901|60,872|64,428|‐1%|
|Richest Quintile|132937|132048|‐1%|133176|126361|134025|1%|
|<br>**(d)Distribution by quinti**<br>**baseyear HBS 2011/12 for all s**|,<br>**le of real co**<br>**urveys**|,<br>**nsumption**|<br>**adjusted by**|,<br>**inter‐year p**|,<br>**rice variati**|,<br>**on (using th**|<br>**e CPI),**|
|Poorest Quintile|18,347|25,730|40%|24,797|23,261|23,206|‐6%|

23
**Figure 7: Sectoral Composition of Urban Informal Enterprises**

<!-- Start of picture text --> Other<br>2%<br>Finance, insurance,<br>real estate<br>2%<br>Hotels and Restaurants<br>3%<br>Wholesale/Retail Trade and<br>Maintenance and Repair<br>93%<br><!-- End of picture text --> Source: UES, 2004 27. (d) <u>Ownership of enterprises: Ownership issues in the enterprises were assessed</u> through questions on nationality, age and sex. The main findings are:

- i. _National Origin_ :  The majority of formal enterprises are owned by Timorese who lived in Timor Leste prior to 1999 (see Figure 8).  However, while they constitute three-quarters of the owners of enterprises whose turnover is less than USD 10,000, their share declines as the turnover of enterprises grows, and foreign firms acquire an increasing (though still not dominant) share.

- ii. _Gender_ :  One of the major conundrums in Timor Leste is that female labor force participation (FLFP) rates seem to be very low as measured by the TLSS, despite high poverty in the country.  When we look at the ownership of enterprises by sex, we find that only 16 percent of the formal, but almost 43 percent of informal, enterprises are owned by women (see Figure 9). This may be due to measurement and response bias.  In most developing countries, women are more likely than men to be self-employed, rather than wage employed.  Moreover, they are also more likely than men to be unpaid workers in household enterprises.  For these reasons and others (such as the often part-time and seasonal nature of their work), their participation in the labor force is often undermeasured.  In the case of Timor-Leste, since the differentials by sex in formal and informal enterprises are so distinct that we may speculate that the TLSS may have under-measured women’s labor force participation.  On the other hand, it is also possible that women are more likely to be in the informal and less in the formal sector.  But ownership is only part of the story of gender differences in the urban labor market.  Women’s participation in wage work in these enterprises is discussed in another section.

14
Table **A2** Regression Results: Manager Only _Qijt_ **_,-QJ,1-1_** _,j,tui,j,i_ Ai **_j.t_ =** a **_PRj,t_** R **_+ 0 2 Rj,t-1 +_** _3 RLA,t_ **_+_** _04RLMJ_ **_+0 5_**<sup>**_RUs_**</sup> _P_ , **_jai_**

|**Independent Variables**|**_AD_Sample**|**Non-Crisis**|**Crisis**|
|---|---|---|---|
|OwnReturn**(i)**<br>T-statistic|0.0042***<br>**5.56**|**0.0052*****<br>**3.76**|**0.0033*****<br>**2.76**|
|OwnReturnLagged**(N)**|0.0049***|**0.0071*****|0.0004|
|T-statistic|**2.78**|4.04|**0.17**|
|LatinAmericaReturn**(N)**|0.0014|**-0.0001**|0.004***|
|T-statistic|**1.17**|-0.44|3.46|
|LocalIndex Return**(34)**|-0.0004|**-0.0011**|**0.0011**|
|T-statistic|**-0.328**|**_-0.55_**|0.48|
|**US**Return**(|3s)**|**_-0.0115***_**|**-0.0063*****|**-0.0196*****|
|T-statistic|-6.54|**-2.90**|**-7.018**|
|_Constant_|**0.0108**|**_-0.0155_**|**0.0970****|
|T-statistic|**0.326**|-0.40|**2.53**|
|Observations|4,942|3,274|**1,668**|
|AdjustedR-squared|**0.03**|0.02|**_0.05_**|

These results are "Manger Only" in that they control for investor redemption effects as in equation **(8).** T-statistics are corrected for heteroskedasticity across funds. Full sample: April **1993** to January **1999.** The crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the sample. For robustness, results in each cell are based only on observations within three standard deviations of the mean.

- Statistically Significant at the 10-percent level

- ** Statistically Significant at the 5-percent level

*** Statistically Significant at the 1-percent level

**33**
One of the main features of the MyGTAP model is that it eliminates the single ‘regional’ household. MyGTAP allows for one or more private households and a government agent whose expenditures are directly related to the income they each receive from endowment factors and taxes respectively. Moreover, the MyGTAP framework augments the standard GTAP model by including income from inter-regional transfers, such as remittances and foreign aid components of household and government income that can be important to developing countries. This framework is capable of implementing these changes for all regions in the GTAP database.

The new specification explicitly defines the government balance in terms of income and expenditures. The government collects income from taxes and foreign aid, which it then spends on government purchases, transfers to households, foreign aid (out) and subsidies. The difference between government income and expenditure then determines the government’s deficit or surplus.

The private household on the other hand collects income from factor endowments, net foreign labor remittances, net foreign capital rents, transfers from the government, and transfers from other households. The household’s income is spent on consumption of goods and services and savings.

The MyGTAP framework allows for the private household in Nigeria to be replaced with multiple households, facilitating analysis of the distributional impacts of various policies on particular household groups. In this paper, 12 types of households are introduced based on data from the latest available Nigerian SAM for the year 2006 (Nwafor et al., 2010). The SAM provides detailed information on the income, consumption, and savings patterns of 12 households, classified by geographical zones, and rural and urban categories. Detailed linkages between household earnings and factors (particularly natural resources) were not provided in the SAM, requiring some assumptions discussed in the data section, Section 2.3. It was possible, however, to calculate savings rates of the household groups based on the SAM and the Nigeria Poverty Profile (NBS, 2012) (see Appendix 1). The inclusion of multiple households is useful for our study, as we aim to assess the impact of complementary policies, such as compensating poor households, which accompany the reduction in the petroleum product subsidy.<sup>10</sup>

# **2.2 Model Closure**

In this study, a fixed level of employment of factor endowments and perfect competition (zero economic profits) are assumed through the selected model closure. While these are not characteristics of the Nigerian economy, it permits us to narrow the scope of our analysis.

> 10 Additional technical details on the model features, applied closure, and experiment variables are provided in Appendix 2.

8
countries within the Eurozone (Schmitz and von Hagen, 2010), although this pattern does not characterize cross-country capital flows at the global level (Lane, 2008).

The diverging trends in credit growth and interest rates in the Eurozone periphery and core constitute key stylized facts of this line of diagnosis. Since the early 1990s, growth of credit to the private sector in the periphery surpassed by far credit growth in the core (Figure 2a): Credit to the private sector in the periphery more than tripled in proportion to GDP from 1990 to 2011, exceeding since the mid-2000s levels seen in the core. The credit boom was accompanied by a decline in real long-term interest rates, which was significantly steeper in the periphery than in the core (Figure 2b). As a matter of fact, because inflation remained higher in the periphery, the average real long-term interest rate was _lower_ in the periphery compared to the core for about a decade, from the late 1990s to the onset of the global financial crisis. This was sharply reversed with the outbreak of the sovereign debt crisis in 2009 when real long-term interest rates in the periphery jumped to levels unseen since the early 1990s, well before the introduction of the euro. In contrast, after a spike during the crisis, real long term interest rates in the core reverted to their earlier lows, reflecting to a large extent capital flight to safety from periphery crisis-hit countries to the core.

<!-- Start of picture text --> Figure 2: Credit and interest rates in the Eurozone core and periphery (1990-2011)<br>(a) Credit to private sector (in % of GDP) (b) Real interest rate on long-term government bonds<br>8<br>6<br>140<br>4<br>90<br>2<br>0<br>40<br>Average Core Average Periphery Average Core Average Periphery<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: IFS and WDI databases.

Easier credit and lower real interest rates are expected to stimulate investment while tempering the incentives to save, thus boosting the current account deficit. Consistent with credit market developments and the expectation of real convergence, the investment ratio in the Eurozone periphery grew more than in the core from the early 1990s to the introduction of the euro (Figure 3a). In parallel, the periphery’s savings rate started to continuously decline, in the aftermath of the introduction of the common currency in 1998-99 (Figure 3b). This contrasts with the increase in the savings rate in the core, up until the global economic crisis. Although rising investment may have been, on average, the initial trigger of external imbalances, this factor seems to have receded in the 2000s as the investment ratio in the periphery stabilized before the crisis. The fall 6
28 Elasticities of Demand for Gasoline: All Studies

|Country|Market Demand Elasticity|
|---|---|
|Austria|0.25-0.27|
|Canada|0.11|
|Israel|0.25|
|U.K.|0.1-0.17|
|U.S.|0.04-0.21|
|West Germany|0.25-0.93|
|Multicountry Studies|0.27-0.52,0.2-0.3,0.32-1.37<br>'|
|Note: 'Long-run elasticities.||