rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:** Impact of treatment on willingness-to-pay

|WTP for air monitor (GEL)|WTP for air purifer (GEL)|
|---|---|
|Leafet treatment<br>-11.19|-103.47|
|(20.93)|(135.06)|
|Outdoor treatment<br>-33.28|-178.79|
|(21.07)|(148.75)|
|Outdoor & Indoor treatment<br>-1.37|-84.80|
|(15.96)|(126.42)|
|N<br>300|314|
|Pre-period outcome mean (GEL):<br>80.61|204.11|

Notes: The outcome in all columns is the difference in the household’s reported willingness-to-pay to pay for the item between the two rounds measured in Georgian Lahi. We drop all households that answered I don’t know in either round or didn’t answer the question. In results available upon request we show that these results are robust to alternative measures of dealing with these non-responses. Heteroskedasticity robust standard errors clustered by census district - the sampling block - are in parentheses (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

21
trades in New York and dollar denominated bonds. This is very important in Latin America. Our data set includes all these trades.

One technique available to managers is using "cash" (e.g., liquid money-market instruments such as **U.S.** Treasury bills) to buffer their portfolios from redenptions. Holding cash allows managers to meet redemptions without the need to sell less-liquid assets. In principle, this can mute the effect of investor outflows on the underlying stocks. However, managers can also reinforce investors' actions if they increase their liquid positions in times of investor retrenchment. For our whole sample, funds kept an average of 4.4 percent of their net asset value in cash. We then split our sample into two sub-samples, one where on average these funds received inflows, and one where on average these funds suffered outflows. In the inflows sub-sample we find an average cash position of 4.6 percent, whereas in the outflow sample we find an average cash position of 4.3 percent. Average cash positions are remarkably stable. Managers' choice of cash position does not appear to either mute or reinforce investor actions.<sup>13</sup>

# **IV.2. Momentum Trading Results**

In our full sample, we find strong evidence of lag-zero momentum trading at all three levels: whole-fund, manager-only, and investor-only (Table **1,** column **1).** Interestingly, contemporaneous momentum trading is especially strong during crises. In terms of attribution, it is investors that account for the lion's share of the contemporaneous momentum trading at the whole-fund level. Significant lag-one momentum trading is present only in the non-crisis portion of our sample, and it is concentrated at the manager level.14 For robustness, we estimate each cell based only on observations of **Mi,,** within three standard deviations of its mean. This is the reason why, within any column of Table **1,** the

> **13 A** natural question is whether these cash positions are stable because managers face some kind of constraint. The reality is that funds are far less constrained **than** our cash-holding results might indicate in any de jure sense. De _facto,_ however, managers are sensitive about departing too much from their benchmarks. The classic example is the hapless manager at Fidelity's Magellan Fund in the late 90s who felt that the stock market was over-valued, switched heavily into cash, watched the market rise further, and was fired for the decision.

> 14 In our estimation, LIM always relates the transacted quantities between t-1 and t with the return over the month preceding t-1. Increasing the length of the period over which lagged returns are measured diminishes explanatory power, in general.

**16**
The LINKAGE model has been developed for use by the World Bank’s global economic projections team (van der Mensbrugghe 2005). It is a relatively straightforward CGE model but with some characteristics that distinguish it from standard comparative static models such as the GTAP model. A key difference is that it is recursive, so while it starts with 2001 as its base year it can be solved annually through to 2015. The dynamics are driven by exogenous population and labor supply growth, savings-driven capital accumulation, and labor-augmenting technological progress (as assumed for the World Bank’s global economic prospects exercise, see World Bank 2004, 2005). In any given year, factor stocks are fixed. Producers minimize costs subject to constant returns to scale production technology, consumers maximize utility, and all markets – including for labor – are cleared with flexible prices. There are three types of production structures. Crop sectors reflect the substitution possibility between extensive and intensive farming. Livestock sectors reflect the substitution possibility between intensive versus pasture feeding. And all other sectors reflect standard capital/labor substitution (with two types of labor: skilled and unskilled). As in the GTAp model there is a single representative household per modeled region, allocating income to consumption using the extended linear expenditure system. Trade is modeled using a nested Armington structure for each product, in which aggregate import demand is the outcome of allocating domestic absorption between the domestically produced good and aggregate imports of that product, and then that aggregate import demand is allocated across source countries to determine the pattern of bilateral trade flows. Government fiscal balances are fixed in any given year, with the fiscal objective being met by changing the level of lump sum taxes on households. This implies that losses of tariff revenues are replaced frictionlessly by higher direct taxes on households. The current account balance also is fixed. For example, if import tariffs are reduced, the propensity to import increases and additional imports are financed by increasing export revenues. The latter typically is achieved by a real exchange rate depreciation. Finally, investment is driven by savings. With fixed public and foreign saving, investment comes from changes in the savings behavior of the domestic household and from changes in the unit cost of investment. The latter can play an important role in a dynamic model if imported capital goods are taxed. Because the capital account is exogenous, rates of return across countries can differ over time and across simulations. The model only solves for relative prices, with the numéraire, or price anchor, being the export price index of manufactured exports from high-income countries. This price is fixed at unity in the base year and throughout the projection period to 2015.

41
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T p p T
nantly between-industry variation rather than within-industry variation. This suggests that any frictions associated with the accumulation of ICT are reflected in changes in industrial composition, rather than in changes in the production structure within industries. This reasoning is consistent with a Heckscher-Ohlin model of trade specialization. Despite this potential source of endogeneity, we find similar results when using predicted values based on industrial composition in 1980, which is at the beginning of the ICT revolution and therefore less likely endogenous to ICT adoption. However, the extent to which industrial composition is endogenously determined by ICT adoption remains an open question, which is beyond the scope of this paper. While industrial composition may be, to some degree, endogenous to ICT accumulation, it is also likely that a number of other factors determine industrial composition, such as the level of development, comparative advantage, and path dependence.

The remainder of this paper is organized as follows: we begin with empirical evidence on the distribution of ICT capital around the world in Section 2, together with the details of the construction of our dataset. Section 3 presents several possible explanations for the observed correlation between ICT abundance and income per capita. In Section 4 we present a simple model that highlights the relationship between ICT abundance and industrial composition and we provide empirical evidence consistent with its predictions. Section 5 illustrates a benchmarking procedure implied by our analysis, which can be used to assess whether ICT is over- or underabundant in a given country. We offer some concluding remarks in Section 6.

# 2. Measuring ICT Capital Around the World

We begin our analysis by estimating the stock of ICT and non-ICT (henceforth, NICT) capital for 70 countries at various levels of development. To accomplish this, we use data on ICT spending from the World Information Technology and Services Alliance (WITSA) as well as the International Telecommunication Union (ITU).<sup>3</sup> WITSA is currently the most widely used source for data on ICT spending on a global scale and is assembled using a combination of various surveys,

> 3 These two datasources are described in detail in Appendix A.

4
The downward trend in the number of deals that involve private financing—documented on the PPI database- shows that governance preferences may also evolve over time according to circumstances, including concerns for commercial, economic or political risk. The 2008 crisis and the large number of failures have increasingly switched the public and private preferences towards management contracts, illustrating, once more, that markets adjust to evolving institutional constraints revealed by regulatory conflicts. Many governments are increasingly reluctant to try to get private operators to provide financing to the sector, focusing instead on their ability to improve the management of the sector and cut costs.

To be fair in the discussion of the regulatory challenges of the sector, it is important to note that similar regulatory conflicts are also arising in many European countries. Investments have not been delivered by regulated companies in spite of increasing average WSS prices justified by the need to finance investment. Over the last 10 years, the British water regulator has had to fine operators who failed to comply with investment obligations, including some who were blamed for similar failures in developing countries (Hall (2015)). In Canada and the US, the most recent deals have seen the public sector take on most, if not all, of the investment risks as part of public private partnerships (PPPs). Contracts are increasingly simply management contracts (i.e. contracts without any investment commitment).

From the partial evidence available on the details of the financing strategies of the sector, a few other insights serve as evidence of regulatory institutional issues. They reveal some of the macroeconomic and firm-specific consequences of the limited ability of the sector to generate revenue from the business. They also reveal the importance of tracking the ways in which firms finance their investment and price their services. They point to underestimated perverse effects of an institutional inability to anticipate the consequences of investment packaging designed to leverage private sector financing. Finally, they show how the limited capacity of regulators to monitor the financial performance of the actors largely explains the other main financial issues.

The first observation is more of a stylized fact on the financing strategy bias of the sector. The WSS industry has been financing its huge capital expenditure needs from public debt rather than from revenue. And this has been internalized by many regulatory decisions. The consequences of this regulatory bias are that it allows the sector to accumulate debt to finance the continuous increases in consumption, including in regions under water stress. Unless revenue eventually catches up, or the consumption continues to be rationed, some countries could be exposed to a debt crisis within the sector.

The second observation is that the debt bias does not change with an increase in the role of the private sector. When the private sector has been involved, public debt has been replaced by private 28
# **Table 3.** ITT effects on adoption of individual ISFM components.

||Ado<br>com|pted<br>post|Ado<br>blended|pted<br>fertilizer|Ado<br>improve|pted<br>d seeds|Ado<br>line s|pted<br>eeding|Ad<br>li|opted<br>me|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|T1 (extension)|0.150***|0.143***|0.097|0.039|0.129**|0.065*|0.157***|0.091***|0.222***|0.214***|
||(0.057)|(0.028)|(0.062)|(0.042)|(0.058)|(0.036)|(0.057)|(0.029)|(0.042)|(0.029)|
|Unadjusted p-value|0.008|0.000|0.118|0.348|0.027|0.070|0.006|0.002|0.000|0.000|
|Adjusted p-value|0.025|0.000|0.334|0.749|0.092|0.225|0.022|0.007|0.000|0.000|
|T2 (extension + video)|0.219***|0.192***|0.111**|0.046|0.129**|0.067*|0.204***|0.112***|0.254***|0.239***|
||(0.054)|(0.025)|(0.055)|(0.037)|(0.058)|(0.040)|(0.057)|(0.030)|(0.042)|(0.028)|
|Unadjusted p-value|0.000|0.000|0.043|0.218|0.027|0.093|0.000|0.000|0.000|0.000|
|Adjusted p-value|0.000|0.000|0.132|0.539|0.092|0.291|0.000|0.000|0.000|0.000|
|Robust to Adjustment?|**Y**|**es**|**N**|**o**|**N**|**o**|**Y**|**es**|**Y**|**es**|
|Test T1 >= T2 (unadjusted p-value)|0.141|0.058|0.421|0.442|0.501|0.467|0.245|0.250|0.226|0.194|
|Test T1 >= T2 (adjusted p-value)|0.382|0.173|0.829|0.848|0.914|0.891|0.638|0.647|0.548|0.488|
|Endline control mean|0.|405|0.5|96|0.5|74|0.6|24|0.|040|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.027|0.361|0.009|0.220|0.014|0.257|0.033|0.388|0.137|0.336|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,382|2,382|1,464|1,464|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Average marginal effects (AME) of binary probit models. For lime, Tigray is excluded since it is not recommended in this region and adoption is zero. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

31
- 3 -

have a *liquidity preference', the research suggests the opposite--illiqzuidity preference--to be at least as important. The best mechanisms for rural finance link credit and savings together, and they combine elements of individualism and collective respornsibility. They take account of both achievement incentives and social risks for savers. Where farmers are involved with financiers, not just capital but also information must flow two ways between them if rural people are to benefit.

The paper is based mainly on a field study of farmers' financial strategies, using both intensive, open-ended interviewing with a few key informants, and broader, structured surveys, in April and May 1987 and from July to October. 1988. For the surveys, five villages were randomly chosen in Upper River Division, MacCarthy Island Division, and Western Division (on both sides of the river). The ethnic distribution of informants, mainly Mandinka, Fula (Peulh), and Wollof speaking in that order of frequency, approximated that of The Gambia as a whole. While I lived and interviewed mainly in two villages with locally known, trusted, and trained research assistants, other such assistants surveyed three further villages, occasionally visited briefly by me. 138 farmers were visited in 1987; and in 1988, these were revisited with others added, for a total of 167 (counted after information discards). In four of the five villages, 95 to 100 percent of the compounds (sometimes identical to dabada 'work groups', sometimes larger) were visited in the second survey.2 One man and one woman were interviewed in each compound, each several times.<sup>3</sup> The ethnic distribution of informants, mainly Mandinka-, Ful_- (or Peulh), and Wolof-speaking in that order of frequency, roughly approximated that of The Gambia as a whole.<sup>4</sup> A word about ethnic variation. The Gambia differs from some
37 To calculate welfare consequences (loss in consumer surplus) of an increase in kerosene prices for different households, we need information concerning the share of energy in tota household expenditure. Expenditure shares can vay across different groups of households depending on income level, urbanrural nature, and geographic location. Thus, it is reasonable to expect the welfare effects of an energy price increase to also vary across households. Table 2.3 gives the expenditure _hares of fiuel and power for different categories of households.

_Table 2.3: Malaysia: CurrentExpenditure Shares on Fuel and Power_

||M|alay|-C|hinese|...JId|ian_|||
|---|---|---|---|---|---|---|---|---|
||Urban|Rural|Urban|Rural|Urban|Rural|Urban|Rural|
|Employer|0.08|0.04|0.02|0.01|0.01|-|0.03|-|
|Employee|0.05|0.04|0.02|0.02|0.04|0.02|0.04|0.01|
|Self employed|0.01|0.03|0.02|0.02|0.02|0.01||-|
|Otds|0.06|0.02|_0.09_|0.02|0.03|0.06||-|

_Noe.The_ sh of consumption ecpendittres on energy varies acrss diffeent households. In genurl, he shaw are higher for urbn households u compared to _nru_ areas. elizing that the income levels in the urban aeas ar higher than in the ral ueas, 1k differencas we obuyto expectations given that energy is a normal good with income elasticity less Un on. Hwever, the mast Iily cxplnation **forhis** outm lE in **hc** struche of energy consumptioi **n** rurl **arms.** A lag **porlion oftthe ae nymion** in nal area is non-commenial al rwhosc value is likely to be understimated in the data underlying above caculatioL Another important parameter for calcuating welfare effects is price elasdcity. In ihe absence of a reliable ansis of residential demand for kerosene for Malaysia, we can draw some indicative esimates for price elsicity by exniiring ihe actual response to the change in prices during the 1980s. Kerosene conumpIon by the household sector between 1983 and 1985 fell by nearly 35 percent as a result of a 33 percent inrease in prices, implying an arc elasticity of -0.81. We can illusta the welfare effect of the price incease as follows: in a household with a per capita consumption of 35 lite of kerosene per month and a budget sbare for kemsene equal to 4 percent of the total household expenditure, the cnge in consemer surplus due to the 1984-85 kerosene price increase would be appoximately equal to -1.3 percent D Asmption in this scenrio awe that a low income urba houweold b lEimied 1to c souns of energy and an incom of aound MS 400 per month.
**Table 7: Trade Remedy Measures 1995-2011 and in Force 2011 by Developing Country**

||Antidumping,|1995-2011|Countervaili|ng,1995-2011|Safeguards|,1995-2011|Total AD+CV+|SG,1995-2011||Measures in|Force,2011 *||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country (No. of Countries)|No. of<br> <br>Measures|No. of<br>Products **|No. of<br>Measures|No. of<br>Products **|No. of<br>Measures|No. of<br>Products **|No. of<br>Measures|No. of<br>Products **|Antidumping|Counter-<br>vailing|Safeguards|Total<br>AD+CV+SG|
|Argentina|206|1,075|4|32|4|82|214|1,189|96|0|0|96|
|Brazil|119|541|7|53|2|23|128|617|75|1|1|77|
|Chile|9|35|2|85|7|158|18|278|1|0|0|1|
|China|151|378|4|40|1|223|156|641|115|2|0|117|
|Colombia|24|196|..|..|1|7|25|203|12|..|0|12|
|Costa Rica|3|10|1|2|..|..|4|12|2|0|..|2|
|Dominican Republic|1|1|..|..|2|12|3|13|1|..|2|3|
|Egypt|53|69|..|..|4|25|57|94|15|..|0|15|
|India|478|1,465|..|..|13|108|491|1,573|206|..|2|208|
|Indonesia|42|288|..|..|10|60|52|348|16|..|9|25|
|Jamaica|4|15|..|..|..|..|4|15|..|..|..|..|
|Korea, Republic of|72|336|..|..|2|17|74|353|33|..|0|33|
|Malaysia|25|59|..|..|..|..|25|59|6|..|..|6|
|Mexico|85|1,685|8|65|..|..|93|1,750|38|0|..|38|
|Morocco|..|..|..|..|2|23|2|23|..|..|0|0|
|Pakistan|36|294|..|20|..|..|36|314|33|..|..|33|
|Paraguay|2|2|..|..|..|..|2|2|..|..|..|..|
|Peru|49|379|5|35|..|..|54|414|21|2|..|23|
|Philippines|11|51|..|..|7|18|18|69|1|..|5|6|
|Singapore|2|2|..|..|..|..|2|2|..|..|..|..|
|South Africa|128|460|5|20|1|2|134|482|33|1|0|34|
|Taipei, Chinese|15|172|..|..|..|..|15|172|6|..|..|6|
|Thailand|34|318|..|..|1|1|35|319|23|..|1|24|
|Uruguay|1|12|..|..|..|..|1|12|..|..|..|..|
|Venezuela|25|94|1|8|5|109|31|211|..|..|0|0|
|**Memo Items:**|||||||||||||
|Other Developing (3)|9|37|..|38|16|87|25|162|1|..|3|4|
|Developed (13)|1,017|20,963|133|11,460|44|1,728|1,194|34,151|604|74|11|689|
|All countries (41)|2,601|28,937|170|11,860|122|2,683|2,893|43,480|1,338|80|34|1,452|

Notes: .. = 0 or data not available

*  Number of measures in force based on notifications to WTO as of June 30, 2011.

** Total products affected by AD, CV, and SG are based on number of products on raw data files at HS 6-, 8-, 9-, 10-digit level or mixed  together in reporting countries.

Sources: WTO Antidumping database and Trade Profiles data; and Bown, C (2012), Temporary Trade Barriers (TTB) database (product data).

# **_The Overall Use of Non-Tariff Measures_**

The overall use of non-tariff measures by developing countries during the GATT and WTO sub-periods is presented in Table 8. Column one shows country per capita income in 2011. Column two shows an openness index for each country calculated as the ratio of exports plus imports divided by GDP also in 2011. Column three shows the presence of tariff peaks in the latest tariff schedule. Column four shows the total number of non-tariff measures in place as of June 2011. The other columns show total frequency ratios (Tfm ) for 'core' non-tariff measures (see Appendix B for a discussion of the meaning of total frequency ratios) for different periods.

23