rafmacalaba/datause-encoder-data huggingface dataset:
# **_Figure 1: Participants flow diagram_**

<!-- Start of picture text --> Allocated to<br>Intervention<br>group 1<br>n= 1314<br><!-- End of picture text --> <!-- Start of picture text --> Randomly selected patients from the e-health<br>database in 4 Provinces who were 35 to 68 years old<br>and did not screen for hypertension and diabetes in<br>the previous year.<br>Random pre-assignment to study groups.<br>n=6934<br><!-- End of picture text --> <!-- Start of picture text --> Allocated to<br>intervention<br>group 4<br>n= 1332<br><!-- End of picture text --> <!-- Start of picture text --> Allocated to<br>intervention<br>group 2<br>n= 1318<br><!-- End of picture text --> <!-- Start of picture text --> Allocated to<br>intervention<br>group 3<br>n= 1230<br><!-- End of picture text --> <!-- Start of picture text --> Allocated to<br>control group<br>n= 1740<br><!-- End of picture text -->

# Excluded (n=4887).

- Out of the community (n=1893)

<!-- Start of picture text --> Eligible<br>n=2047<br><!-- End of picture text -->

- temporarily out of reach (n=8)

- not contacted (n=5). (For details by study groups see table 3.)

<!-- Start of picture text --> Final study groups<br>according to random pre-<br>assignment<br>n=2000<br><!-- End of picture text --> <!-- Start of picture text --> Intervention<br>group 1<br>n= 400<br><!-- End of picture text --> <!-- Start of picture text --> Intervention<br>group 2<br>n= 400<br><!-- End of picture text --> <!-- Start of picture text --> Intervention<br>group 3<br>n= 400<br><!-- End of picture text -->

- Will leave for 6 months (n=77)

- Member of the same HH (n= 39)

- Refuse to talk (n=251)

- Out of reach (n=1966)

- Duplicated ID (n=1)

- Screened at pre-selected HF (n=386)

- Screened at another HF (n=255)

- Dead (n=18)

- Wrong age group (n=1) (For details by study groups see table 2)

<!-- Start of picture text --> Intervention<br>group 4<br>n= 400<br><!-- End of picture text --> <!-- Start of picture text --> Control group 15<br>n= 400<br><!-- End of picture text -->
# **III. CHANNELS OF CONTAGION**

# **1. Contagion Syndromes**

The Thai baht devaluation triggered withdrawal of capital from the Association of South East Asian Nations **(ASEAN)** region and several other East Asian countries as a financial panic progressively ensued. Developments in Thailand caused investors to look more critically at vulnerabilities elsewhere in the region that they had previously ignored. In the process, investors discovered new information that amplified their concerns, especially about the health of the financial system and the magnitude of short-term external debt in the region. Market doubts were compounded **by** the lack of transparency about the financial and corporate sectors and, hence, about the magnitude of contingent liabilities. Once investors lost confidence that foreign exchange reserves would cover short-term external debt, both foreign and domestic investors scrambled to get out.<sup>8</sup> Markets became much less forgiving. The lack of a mechanism for orderly workouts of private external debt undoubtedly contributed to the full-scale financial panic that swept Thailand, Indonesia, Korea and to a lesser extent Malaysia.

**A** quick review of its chronology illustrates the dynamics of the crisis, along with its spillover effects.<sup>9</sup> After Thailand devalued the baht, the Philippines allowed the peso to freely float on July **II** and Malaysia abandoned the defense of the ringgit peg on July

> **8** With the possible exception of Thailand, foreign investors apparently did not play a large role in triggering the crises. Data on mutual fund and pension fund holdings of equities and other assets in Asian countries do not suggest a massive outflow of foreign capital during the July-December **1997** period (Kaminsky et al., forthcoming). Foreign investors appear to have reduced their holdings prior to the crisis, particularly in Thailand, and increased their holdings _afterward,_ particularly in the first few months of **1998.** According to Eichengreen and Mathieson **(1998),** hedge funds and other short-term investors played a limited role in triggering the crisis. The massive reversal in capital flows arose mainly from the reluctance of foreign lenders to roll over short-term claims after September **1997** (causing an outflow of about **US$50** billion for the affected countries); the purchases of foreign exchange **by** local corporate institutions to cover unhedged, open positions; and capital flight owing to political uncertainty in Indonesia.

**13**
# **_Figure 1 Percentage point change in distribution of employment by sector_**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>0.0 Agriculture<br>-2.0 Private wages<br>-4.0 HEs and MEs<br>-6.0<br>-8.0<br>-10.0<br>-12.0<br>Population<br>Change in share of Employed<br><!-- End of picture text --> _Sources: see Annex Table 1_ Though growth in private wage and salary jobs has been high, wage and salary employment in private non-agricultural enterprises is still rare in SSA (figure 2) – this sector on average accounts for only 9 percent of the employed population.<sup>5</sup> In the countries where the share of family farming employment as primary employment is lowest (Cameroon, Ghana, and Senegal), the share in private wage employment is still only slightly above 10 percent. The largest category of non-farm employment is NFEs (HEs and MEs) employing 15 percent of the employed population on average. And the majority of those operating NFEs operate HEs (91 percent, see Figure 6).

> 5 In figure 1, private sector wage jobs in agriculture are including in total agricultural employment. They are separated from wage jobs in other sectors as agricultural wage jobs often are of a very different quality, different remuneration level and located in different locations. Furthermore, growth in agricultural wage jobs is generally related to growth or reform in agriculture, while growth in private wage jobs is more dependent on growth in industry and services.

8
hotter world.

Various explanations for why and how heat affects crime have been offered. Early economics work uses a Becker-style model to focus on potential reductions in the likelihood of being caught, because heat increases the costs of police effort, or on the increased relative benefits of crime due to heat-driven reductions in economic payoffs from other activities. Work in psychology, meanwhile, has focused on the role of heat in driving aggressive behavior (Anderson, 1989; Anderson et al., 2000; Baron and Bell, 1976). More recent economics work has examined how the impact of heat on crime varies across neighborhood characteristics, suggesting that heat’s differential effects may be a manifestation of differential investment in early childhood coupled with underlying psychological mechanisms (Heilmann and Kahn, 2019).<sup>2</sup> In this paper, we re-examine heat’s impacts on criminal activity using the most comprehensive data set yet brought to bear on this topic. Using data on the universe of more than 10 million arrests across the state of Texas from 2010 to 2017, we examine how heat impacts the commission of crimes. Our data are unique in providing detail at the individual defendant level across a large geographic region, with meaningful temperature variation across space and over time. The richness of our data on individual defendants allows us to better understand how heat affects human behavior and to conduct the most extensive examination to date of how adaptation could mitigate the effects of future climate change on crime.

Our data contain demographic information on the arrested individual, including their home address, race, and date of birth, as well as information on the charge at arrest. Cru-

> 2A psychological mechanism for heat’s impact on crime is consistent with the rapidly growing body of literature in economics that shows heat has a meaningful impact on cognitive and non-cognitive skills in a variety of contexts. Stress from any source, along with emotions like anger and sadness, adversely affects judgement, decision making, and cognition (for instance, Keltner et al. (1993); McEwen and Sapolsky (1995); Card and Dahl (2011); Lerner et al. (2015)). The weather, in turn, shapes emotions. Sunshine, for example, has a positive effect on mood (Schwarz and Clore, 1983), in a way that measurably increases optimism among stock market investors (Hirshleifer and Shumway, 2003). Heat also exerts a direct impact on human emotion. Higher temperatures have been shown to increase negative affect around the world (Baylis, 2020; Denissen et al., 2008; Kenrick and MacFarlane, 1986). Lab studies have confirmed existing work suggesting that heat increases aggression (Anderson, 2001; Alm ̊as et al., 2019). In other contexts, heat has been shown to decrease cognitive ability and test performance (Wyon et al., 1996; Fang et al., 2004; Seppanen et al., 2006; Cheema and Patrick, 2012; Fesselmeyer, 2019; Park et al., 2020a,b).

3
|Dependent variable: Shar<br>services revenue|e of<br>S|ervices, v|alue base|d|Services|, product|based de|scription|Relative|to manuf|acturing|revenue|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Total factor productivity|0.043***|0.057***|0.059*|0.042***|0.025|0.037*|0.047|0.025|0.025*|0.034*|0.024|0.025*|
||(0.015)|(0.020)|(0.033)|(0.015)|(0.016)|(0.021)|(0.034)|(0.016)|(0.015)|(0.019)|(0.031)|(0.015)|
|Loggedage|-0.087|-0.091—|-0.367***|-0.088|-0.119=|-0.123*|=-0.395***|-0.122|-0.011|-0.015|-0.294**|=-0.005|
||(0.073)|<br>(0.073)|<br>(0.121)|<br>(0.073)|<br>(0.074)|<br>(0.074)|<br>(0.123)|<br>(0.075)|(0.076)|(0.076)|<br>(0.122)|<br>(0.076)|
|Small-sized firm|0.144**|0.144**|0.147|0.187|0.152**|0.153**|0.172|0.160|0.144**|0.145**|0.118|0.304*|
||(0.059)|(0.059)|(0.129)|(0.152)|(0.061)|(0.060)|(0.133)|(0.158)|(0.062)|(0.062)|(0.134)|(0.162)|
|Mid-sized firm|0.019|0.019|0.011|-0.089|0.023|0.022|0.010|-0.093|0.015|0.014|-0.004|0.005|
||(0.033)|(0.033)|(0.062)|(0.089)|(0.034)|(0.034)|(0.062)|(0.092)|(0.034)|(0.034)|(0.060)|(0.091)|
|Logged goods exports|0.002|0.002|0.004|0.002|0.001|0.001|0.003|0.001|0.000|0.000|0.002|0.000|
||(0.002)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.004)|(0.002)|
|Logged services exports|0.018***|0.018***|0.018***|0.018***|0.018***|0.018***|0.018***|0.018***|0.017***|0.017***|0.015***|0.016***|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.002)|(0.003)|(0.002)|
|Logged imports|0.007**|0.007**|0.002|0.007**|0.006**|0.006**|0.002|0.006**|0.007***|0.007***|0.008|0.007***|
||(0.003)|(0.003)|(0.006)|(0.003)|(0.003)|(0.003)|(0.006)|(0.003)|(0.003)|(0.003)|(0.006)|(0.003)|
|Exposure to imports|0.063***|0.056***|0.126**|0.056***|0.062***|0.054***|0.147**|0.053**|0.133***|0.127***|0.153***|0.137***|
||(0.019)|(0.019)|(0.057)|(0.021)|(0.020)|(0.020)|(0.061)|(0.021)|(0.020)|(0.020)|(0.053)|(0.021)|
|High productivity firm||0.005||||0.007||||0.006|||
|*exposure to imports||(0.006)||||(0.006)||||(0.006)|||
|Lowproductivity firm||0.015***||||0.015**||||0.012**|||
|*exposure to imports||(0.006)||||(0.006)||||(0.006)|||
|LoggedR&D intensity|||-0.003||||-0.024||||0.040||
||||(0.045)||||(0.047)||||(0.038)||
|LoggedR&D intensity|||0.005||||0.010||||-0.007||
|*exposure to imports|||(0.009)||||(0.010)||||(0.008)||
|Small-sized firm||||-0.013||||-0.003||||-0.044|
|*exposure to imports||||(0.036)||||(0.037)||||(0.038)|
|Mid-sized firm||||0.028||||0.030||||0.003|
|*exposure to imports||||(0.020)||||(0.021)||||(0.021)|
|Number ofobservations<br>|34639<br>|34639<br>|9121<br>|34639<br><br>|34639<br>|34639<br>|9121<br><br>|34639<br><br>|36128<br>|36128<br>|9407<br>|36128<br>|
|Numberoffirms|4128|4128|1097|4128|4128|4128|1097|4128|4314|4314|1138|4314|

Notes: See Table 3.
<!-- Start of picture text --> 400 1 1 1 r<br>@ 350<br>300<br>£<br>$200 [gore<br>5 150 “Increase(Pee in speed:eee<br>2100 4) ——RER line "B"<br>s fr ---- All public transport lines<br>F 50 (iv __..RER line "B" after an increase in<br>0<br>0 500 1000all other public1500 transport2000 lines2500 speed3000<br>Distance to public transport stations (m)<br><!-- End of picture text --> <!-- Start of picture text --> 400 1 1 1 '<br>@ 350<br>= 300<br>£<br>$200<br>5 150 Increase in speed:<br>2100 —RER line "B"<br>s ---- All public transport lines<br>F 50 ___.RER line "B" after an increase in<br>00 500 1000all other1500 public transport2000 lines2500 speed3000<br>Distance to public transport stations (m)<br><!-- End of picture text -->
who received who received the grants. As with enterprise profits, asset ownership in households of female entrepreneurs was unaffected by the treatments.

The April 2007 and April 2008 household modules contain questions about financial assets. Over the two rounds, more females say they have a current account, a fixed deposit account or a savings account with a bank or other financial institution (43 percent vs. 36 percent of males, p=0.04). Households of enterprise owners receiving treatments are slightly more likely to have at least one such account (42 percent vs. 36 percent, p=0.12). However, the treatment effect is again limited to households of male owners: 43 percent of both treated and untreated female enterprise owners report having an account, while 41 percent of treated males and only 29 percent of untreated males say they have an account.<sup>14</sup> The schooling and current expenditure data reveal no significant treatment effects for either males or females. The lack of any impact of the treatment on health and education expenditures may be due to the fact that Sri Lanka has a good, inexpensive, state system of schooling and health care. Schooling is compulsive up to age 14, and over 91 percent of children in our sample of this age are actually attending school. In countries where education and health expenses are larger burdens on households, we might find more of the treatment being spent on these categories. But males show a large gain in both household durable and financial assets following treatment, while females show no gain in assets following treatment.

# _5.2. The Power to Allocate Resources_

The ability of the owner to invest the grants in profitable activities may be affected by her/his power in household decision making. (See Ashraf, 2008 and Duflo, 2005 for a discussion of the effect of empowerment on household expenditures.) We test whether decision making power affects the returns using responses to questions about who makes decisions regarding household expenditures and business investments. We find no significant effects of decision making for either female or male owners. Column 1 of Table 5 reports the effect of including a control for the number of 4 household

> 14 Owners were also asked for the balances in the accounts. The reported balances are highly skewed, but show a similar patter. Treated households have higher balances, but only among household with male owners.

18

-  -
WPS3784

# **1. Introduction**

Rwanda is one of the poorest countries in the world, with a per capita income of $230 in 2003. The vast majority of the poor reside in rural areas so that rural development is crucial to sustained poverty reduction. The majority of the poor are farmers and the poorest are those that produce mainly or solely for subsistence. Farmers involved in the production of commercial crops, primarily tea and coffee, tend to be less poor and have a high propensity to spend on other products produced in rural areas. Given its small domestic market, it is trade that offers the best prospect of sustained growth and poverty reduction. But at present, handicapped by extremely high trade costs, Rwanda’s rural economy is trapped in subsistence activities. Annual exports per capita in Rwanda amount to just $18 compared to an average of $145 in Sub-Saharan Africa.

This paper shows how opening up rural Rwanda to commercialization and trade could have a substantial impact on poverty. Three major interrelated routes by which trade can enhance rural incomes, unleash the potential for specialization and reduce poverty are analyzed. First, reducing rural transport costs is central to initiate a strong growth impulse through higher farm gate prices for exportables and more specialization in commercial products. We highlight this issue by concentrating on coffee, the principal agricultural export. Transport costs facing producers in Rwanda are very high and many farmers are effectively delinked from national and international markets. High transport costs depress farm gate prices and represent an implicit tax on exportable producers. Second, a key element of the Rwanda government’s strategy for the coffee sector, as well as for other commercial crops, is to increase quality through a fully washed strategy. Fully washed coffee has been commanding prices that are more than double that of standard coffee. Finally, reducing transport costs and the introduction of quality-enhancing techniques will increase the incentives for rural households to participate in the production of exportable products. We assess the impact on incomes and poverty of successful switching of the poorest farmers into higher return economic activities.

Our analysis, based on information from the 2001 household survey, suggests that all three mechanisms have a very strong potential for reducing poverty in Rwanda, with 2
27 54. There is no gainsaying this argument - in the long run: the economist's long run defined as the time it takes for factors of produiction to be redeployed. Much of the capital stock can only be redeployed as it is renewed. This is self-evident for specialized machinery. It is also the case for much infrastructure: even if roads can carry all sorts of traffic and houses provide dwellings to all sorts of people, they need to be in the right places.

55. Investment levels, through which capital is renewed and expanded, were particularly low in the 1980s, as is well illustrated by Chart 2.26 Data on sectoral shares are more difficult to come by, but, like the share of manufacturing production in GDP, the share of manufacturing investment in GDP seems in general also to have fallen within the lower total, in keeping with the supposed _services revoilon_ of the 1980s.

Figure 2 INDUSTRIAL COUNTRIES: SHARES OF INVESTMENT IN GDP.

<!-- Start of picture text --> CHART I I.<br>INVES'IoENT RPArI0. 1970 - 1990.<br>0.49I<br>0.34<br>0.2428<br>0.228<br>0.28 - ,9711'4.1878.<br>0.19<br>195 197 199 1s1 193 t9b t197 1s<br>YEAME<br>JAPM + US O EC<br><!-- End of picture text --> 56. Of the lower manufacturing investmen., more had to go to meet the double challenge of new competition from imports, and new technologies. Much investment aimed at reducing the costs of producing the same volume of products, i.e. producing

> 26 Source: _Worl Tabls_ and _Imemadonal Financial &a&dscs,_ op. cit.
The World Bank has an independent Operations Evaluation Department (OED) that reports directly to the shareholders (not the management) and that makes an ex post assessment of each project financed by the Bank. OED makes a number of different evaluations; we use its most fundamental “thumbs up, thumbs down” measure of whether a project was satisfactory in meeting its stated objective. We take as our universe all of the countries in which the Bank carried out at least three projects in the 1990s (taking the view that if there were only one or two projects in a country then we do not have a very reliable estimate of the expected success rate of donor-financed projects). For each country, we thus calculate the proportion of World Bank projects, conducted between 1990 and 1999, that were determined to have been successful by the OED. In practice, this success rate varies considerably: in the largest borrowing country, China, 90% of projects succeeded; contrast this with Pakistan’s 67% success rate or Nigeria’s 47%.

We consider a number of hypotheses about why this success rate varies across countries. First, in line with the discussion in the introduction, we consider that the overall quality of the recipient country’s institutions and policies may affect the likelihood of project success. We use several different measures of institutions/policies. From ICRG we take the Rule of Law Index. This variable "reflects the degree to which the citizens of a country are willing to accept the established institutions to make and implement laws and adjudicate disputes" (Knack and Keefer 1995). Higher scores indicate: "sound political institutions, a strong court system, and provisions for an orderly succession of power." Lower scores indicate: "a tradition of depending on physical force or illegal means to settle claims." We take this as a key economic institution that indicates whether agents are secure in making transactions. It has been found in other work to be an important determinant of growth and to be correlated with economic policies such as macroeconomic stability and openness to trade.

5
|**Table A7: Interacti**|**on term cont**|**rols and Over**|**all Corruptio**|**n**||
|---|---|---|---|---|---|
|Dependent variable:_Employment Growth_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Overall Corruption (CI avg.)*Financially<br>Constrained (CI avg.)|**-1.160****<br>**(0.478)**|**-1.333*****<br>**(0.496)**|**-1.432*****<br>**(0.512)**|**-1.642*****<br>**(0.528)**|**-1.206****<br>**(0.503)**|
|Overall Corruption (CI avg.)|1.008***|1.388**|1.536**|1.625**|1.974|
||(0.322)|(0.578)|(0.595)|(0.631)|(1.395)|
|Financially Constrained (CI avg.)|-2.605|0.474|1.744|2.215|-27.505|
||(5.018)|(6.296)|(6.195)|(6.261)|(18.112)|
|Initial Employment (logs)|-5.876***|-5.521***|-5.581***|-5.574***|-5.510***|
||(0.328)|(0.739)|(0.751)|(0.755)|(0.776)|
|Age of firm (logs)|-2.030***|-1.748***|-1.710**|-1.759***|-1.822***|
||(0.294)|(0.675)|(0.676)|(0.671)|(0.685)|
|Manager Experience (logs)|0.025|0.031|0.025|0.013|0.029|
||(0.274)|(0.274)|(0.274)|(0.273)|(0.274)|
|Exports (proportion of sales)|1.072|1.024|4.684*|4.768**|4.524*|
||(2.198)|(2.200)|(2.399)|(2.407)|(2.548)|
|Foreign ownership (% ownership)|0.022**|0.023**|-0.010|-0.010|-0.015|
||(0.009)|(0.009)|(0.017)|(0.017)|(0.018)|
|Multi-firm (dummy)|1.741**|1.759**|1.796***|1.792***|1.902***|
||(0.709)|(0.706)|(0.691)|(0.690)|(0.685)|
|Female Manager (dummy)|<br>-1.227***|<br>-1.225***|<br>-1.223***|<br>-1.221***|<br>-1.218***|
||(0.443)|(0.442)|(0.440)|(0.440)|(0.442)|
|Power Outage (logs)|0.201|0.197|0.194|0.362|0.253|
||(0.122)|(0.123)|(0.123)|(0.273)|(0.263)|
|Informal Competition (dummy)|-0.846*|-0.835*|-0.864**|-0.740|-0.637|
||(0.435)|(0.437)|(0.433)|(0.969)|(0.990)|
|Firm Bought Assets (dummy)|3.047***|3.055***|4.163***|4.099***|3.964***|
||(0.455)|(0.455)|(0.918)|(0.930)|(0.953)|
|Time Tax (%)|0.004|0.004|0.004|-0.011|-0.008|
||(0.014)|(0.014)|(0.014)|(0.028)|(0.028)|
|Courts: Obstacle Severity|0.011|0.007|0.005|0.069|0.052|
||(0.213)|(0.212)|(0.213)|(0.394)|(0.401)|
|Crime Losses (% of sales)|-0.013|-0.011|-0.010|0.094|0.080|
||(0.046)|(0.046)|(0.047)|(0.105)|(0.106)|
|Taxes: Obstacle Severity|-0.160|-0.159|-0.160|-0.151|-0.168|
||(0.166)|(0.167)|(0.167)|(0.166)|(0.166)|
|Labor Laws: Obstacle Severity|<br>0.004|<br>0.003|<br>0.012|<br>0.004|<br>0.003|
||(0.174)|(0.174)|(0.173)|(0.173)|(0.174)|
|Square of Overall Corruption (CI avg.)|-0.021|-0.017|-0.015|-0.014|-0.016|
||(0.017)|(0.017)|(0.016)|(0.013)|(0.014)|
|Square of Financially Constrained (CI avg.)|3.844|3.607|3.236|3.067|2.575|
||(4.863)|(4.853)|(4.775)|(4.690)|(4.763)|

58