rafmacalaba/datause-encoder-data huggingface dataset:
and the Southwest.

To quantify export participation at the household level, we use two definitions, the share of land allocated to export crops and the share of income derived from export crops. The average plot size in the data was 2.57 acres (Table 1). Around 7 percent of household land is allocated to export crops; around 8 percent of household income is derived from the sale of these crops. Participation in export agriculture is in fact limited in Uganda. It is our aim to explore to what extent this is due to high export marketing costs.

Our measure of marketing costs in export agriculture comes from the community module. The community module collects information on community characteristics at the level of enumerator areas (the first stage sampling units). In our regression analysis, we use this information aggregated at the district level, excluding urban areas.

As suggested by the model of section 2, marketing costs are closely related to the extent of intermediation activity, given by the presence of outlets where farmers can sell their export crops. There are three different types of such markets in rural Uganda.<sup>11</sup> First, the most widespread way of marketing export crops is through export intermediaries. These are agents that purchase output from farmers, store the production and then transport the agricultural produce with pick-ups or small trucks to Kampala, the capital of Uganda. Second, typical outlets for cash crop are district markets or stalls along the road to Kampala. Third, an additional channel to sell agricultural produce is through large commercial plantations, particularly of coffee and tea. These plantations often purchase the output of neighbor farmers thereby constituting additional channels of market availability. Most plantations are run by foreign firms.

The community module provides information on whether there is at least one of the three types of outlets available in each community.<sup>12</sup> Since communities are very small and farmers can in principle easily commercialize their products in neighboring communities, we construct a measure of market density at the district level. There are several communities (enumerator areas) in each district, and in each community the availability of markets is a

> 11The survey asks separate questions on availability of food markets where farmers can sell the production surplus of food crops. This means that the availability of agricultural produce markets refers to commodity markets and cash crops like coffee, tea, and cotton.

> 12There is no information on the specific type of outlet or on the number of outlets

11
cost of commuting in City A was also varied across respondents. The nature of the three scenarios dictates the relative magnitude of the baseline risks and risk reductions that appear in table 1: baseline risks and risk reductions are highest in the City scenario, in which risks are attributable to all travel modes. The annual risk reductions delivered by the pedestrian scenario are lower than in the city scenario, and the helmet scenario, which lowers risk of death due to head injuries only, delivers the lowest annual risk reduction of the three scenarios.<sup>5</sup>

# **C.  The Questionnaire**

Because the main beneficiaries of road safety programs are those persons who are most exposed to traffic, our survey targeted commuters.<sup>6</sup> Specifically we required respondents to be between the ages of 18 and 65, to be employed and to commute regularly to their place of work. The questionnaire began by asking respondents about a typical journey to work, including the time and money cost of each leg of the trip. Respondents were also asked about travel they undertook while on the job.

The next section of the questionnaire introduced probability concepts and administered a short probability quiz. This was followed by a discussion of fatal traffic risks faced by Delhiites. Risks were communicated using a grid of 100,000 squares, each 1 mm by 1 mm. Squares were colored in red to indicate risk of death in a traffic accident.<sup>7</sup> The use of a grid of squares has been found to be a successful risk

> 5 A corollary of this is that both the scenario dummies and baseline risk are highly correlated with the size of the risk change (delta risk) presented to the respondent.

> 6 In developing country cities work trips constitute a higher percent of kilometers traveled than in the US. Baker et al. (2005) report that in Mumbai work trips constitute 67.5% of all trips made by adults, weighted by distance traveled.

> 7 In earlier versions of the questionnaire, we attempted to communicate risks of death by placing black grains of rice in a jar containing 100,000 white grains of rice.  This device was useful in communicating the

- 8 -
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# **ANNEX 3: Robustness Checks and Additional Quantitative Analysis**

We use alternative sources and methodologies of our dependent and independent variables in an attempt to minimize measurement error.<sup>53</sup> The alternative sources as follows:

|**Variable**|**Main source**<br>**Alt**|**ernatives**|
|---|---|---|
|PFMQuality|PEFA<br>CPIA-13||
|Income|GDP per capita, PPP<br>(constant 2005<br>international$)<br>GDP per capita (con<br>|stant 2005 US$)<br>|
|Tax|Tax/GDP (IMF FAD’s<br>database)<br>Total Revenue/GDP|(IMF FAD’s database)<sup>54</sup>|
|Aid|ODA<br>Country Programma<br>Technical Cooperati|ble Assistance and<br>on|
|Political<br>stability/fragi|lity<br>World Bank’s World<br>Governance Indicators<br>(Political Stability)<br>World Bank’s Fragile<br>Foreign Policy’s Fail|States list (fragility) and<br>ed States index (FS index)|
|Regime|Freedom House<br>PolityIV||
|Education|Primarycompletion rate<br>Adult literacyrate||
|The following|additional country characteristics are also analyzed:<br>**Additional variables**<br>Fiscal shocks ma trier efforts to||
|**Fiscal Shocks**|y gg<br>strengthen PFM systems so as to reduce the<br>probability of future shocks<br>Caceres and<br>Kochanova, IMF<br>2012|Discrete variable for number of<br>fiscal shocks experienced (in a<br>five-year period)|
|**Growth**<br>**Shock**|Similarly, growth shocks may trigger efforts<br>to strengthen PFM systems so as to improve<br>resilience and the fiscal response to future<br>shocks<br>World Bank WDI|Dummy variable for all<br>countries that have a ‘growth<br>shock’ (i.e. when the annual<br>percentage growth rate of GDP<br>is less than negative 2%)|
|**HIPC**|Debt relief was provided on the basis of<br>improvements in PFM systems quality in the<br>run up and after countries reached the<br>decision point and donors provided capacity<br>building support for achieving agreed goals.<br>HIPCs may therefore show larger<br>improvements in PFM systems quality than<br>other countries.<br>World Bank|Dummy variable for countries<br>who have passed the decision<br>point under the HIPC and DRMI<br>initiatives|

> 53 Further checks not included in the report but which confirm our findings include different lengths of lags (and no lags) and alternative aggregation techniques for PEFA as well as the inclusion and exclusion of LICs and MICs.

54 If data is available, includes grants and social contributions. See the Methodology in the IMF FAD database for more information.

45
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking water and sanitation indicators**

|||**Low-income,**<br>**nonfragile**<br>**countries**|**Zimb**|**abwe**|**Middle-income**<br>**countries**|
|---|---|---|---|---|---|
||**Unit**|**Mid-2000s**|**1999**|**2005**|**Mid-2000s**|
|Access to piped water|% pop|9.3|34|37|61.1|
|Access to standposts|% pop|17.1|10|5|22.1|
|Access to wells/boreholes|% pop|39.3|49|50|4.8|
|Access to surface water|% pop|34.2|6|7|10.9|
|Access to flush toilets|% pop|4.7|33|36|47.7|
|Access to improved latrines|% pop|18.3|25|14|33.7|
|Access to traditional latrines|% pop|38.5|16|20|6.9|
|Open defecation|% pop|38.3|27|29|11.0|
|||||**2006**||
|Domestic water consumption|liter/capita/day|50.9||202|196.4|
|Continuity of supply|hours/day|18||20|24|
|Revenue collection|% sales|94.1||—|99.3|
|Distribution losses|% production|34.8||46|28.8|
|Cost recovery|% total costs|89.5|||86.3|
|Operating cost recovery|% operating costs|125.2||—|120.8|
|Labor costs|connections per employee|175.9||89|203.4|
|||**Zimbab**|**we**|**Countries with**<br>**scarce water**<br>**resources**|**Other**<br>**developing**<br>**regions**|
|||**2005**|**2008**|**Mid-2000s**|**Mid-2000s**|
|Residential tariff|US cents / m<sup>3</sup>|24|23|60|3–60|
|Nonresidential tariff|US cents / m<sup>3</sup>|39|82|121||

_Source:_ Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures from the 1999 and 2005 Demographic and Health Surveys; — = Not available.

# **Challenges**

Due to lack of proper maintenance, Zimbabwe‘s water and sanitation network is in dismal condition, constituting a threat to public health. The cholera outbreaks in 2008 and 2009 were a red flag to the poor state. The disease had killed more than 3,000 people and infected around 60,000 by February 6, 2009, according to the World Health Organization (WHO), and spread over most of the country and across the borders into neighboring countries. A significant contributor to the outbreak was the inability of urban water utilities to treat supplied water.

A sign of the network quality deterioration can be seen in the extremely high distribution losses, which amount to almost half of the water produced. In fact between the early 2000s and mid-2000s, unaccounted for water increased by around 15 percent.

Along with technical water supply problems, the quality of service has greatly deteriorated in recent year∫s. Access to wells and boreholes is the prevalent form of water supply covering about 50 percent of the population, significantly higher than for the peer group. But in 2004 around 75 percent of hand pumps were not functioning, and the situation has become worse since. The increase in acess to piped water, 25
The first expression, assumes that all the countries have the same level of TFP, so that _A_ is a constant. The second expression, proposed by Klenow and Rodriguez-Clare (1997), allows countries to have different levels of efficiency, and splits the contribution from the covariance term evenly between _A_ and _yfactors_ .

> We have data on _k_ , _s_ , and _h_ .<sup>13</sup> We use the WDI GNI series to compute output per worker. For the

> factor shares, most of the previous empirical studies take _α_ =1/3 and ignore natural resources as a

> factor of production (i.e. _β_ =0). A notable exception is the study of Tzouvelekas, Vouvaki and Xepapadeas (2007). They introduce the environment, proxied by CO2 emissions, into their computations of TFP. The implicit shares of the environment in output in their analysis can be as high as 14%.  For our analysis we take a more conservative value of _β_ =10%, but the results in

> Table 3 are robust to taking _β_ =0.

The first column of Table 3 shows the results of the estimation of the two measures of success, and their components. The fraction of the variance of income explained by observed endowments ( _k,s, h_ ) is 0.20 if we use the first standard measure of success, and 0.43 if we use Klenow and Rodriguez-Clare's. The Klenow and Rodriguez-Clare measure assigns a greater role to factors of production, but their contribution is still under 50 percent.

Compare equation (5), with equation (7): Equation (5), even including natural resources, is more or less conventional. Only produced, natural and human capital are represented in the equation, so that the TFP factor _A_ picks up the contribution to income of all ‘missing’ assets.

> For equation (7) we have included intangible wealth as the third production factor. If _ic_ actually

> is measuring a wide range of assets (human, social, institutional, etc.), then the contribution of _A_ to explaining the variation of output per worker should be small – zero in the limit.

So one way to bring together the wealth accounting and growth accounting literatures would be to estimate the two success indicators and examine the estimated contributions of _yfactors_ , (with _ic_ instead of _h_ ), and _A_ to see if the contribution of _A_ is indeed small. The second column of Table 3 shows that to be case. When _ic_ is the third factor of production instead of _h_ , both measures of success of the factors-only model are 97 percent.<sup>14</sup>

> 13 We take the estimates of total physical capital and natural capital wealth and divide them by the labor force series, (SL.TLF.TOTL.IN), to obtain estimates of _k_ and _s_ .

> 14 We repeated the calculations in Table 3 for each of the years, 1995, 2000, and 2005, independently, and for the subsamples of OECD and non-OECD countries. The results, not reported, are available from the authors. The relative contributions of _A_ and _yfactors_ are constant over time and similar to those in Table 3. The differences between OECD and non-OECD countries are minimal when factors of production are conventionally defined, but if _ic_ is used

10
**<u>Table 1 Number of deaths by natural disaster categories (1960-2007)</u>** _<u>(thousand)</u>_<sup>**5**</sup>

|<br>**Killed 1960-2007**|<br>**Climatological**|<br>**Geophysical**|<br>**Hydrological**|<br>**Meteorological**|
|---|---|---|---|---|
|**Europe**|**83**|**40**|**5**|**3**|
|**Africa**|**878**|**23**|**19**|**4**|
|**Asia**|**1,532**|**827**|**213**|**692**|
|**Rest of the World**|**6**|**163**|**60**|**63**|
|Oceania|1|1|3|2|
|Northern-America|3|1|2|12|
|Latin America and the Caribbean|2|162|55|50|
|**Total**|**2,499**|**1,053**|**297**|**761**|

Source: EM-DAT

**-** **<u>Table 2 Number of deaths by natural disaster categories (1988 2007)</u>** _<u>(thousand)</u>_

|<br>**Killed 1988-2007**|<br>**Climatological**|<br>**Geophysical**|<br>**Hydrological**|<br>**Meteorological**|
|---|---|---|---|---|
|**Africa**|**2**|**5**|**15**|**2**|
|**Asia**|**19**|**460**|**105**|**209**|
|**Europe**|**81**|**29**|**2**|**1**|
|**Rest of the World**|**4**|**8**|**46**|**34**|
|Oceania|0|1|2|1|
|Northern-America|2|0|1|5|
|Latin America and the Caribbean|2|7|43|28|
|**Total**|**107**|**501**|**169**|**246**|

Source: EM-DAT Excluding droughts, earthquakes account for the largest number of disaster-related deaths. Indeed, over the past 20 years, the number of people killed in earthquakes is approximately equal to the number of people killed in all other types of disasters. The average annual number of deaths due to earthquakes is higher for the past 20 years than for the previous 28. Storms (hurricanes, cyclones and storm surges) account for about a quarter of disaster-related deaths over the past 20 years and hydrological disasters (floods and landslides) for about one-sixth. In terms of annual averages, deaths due to floods have increased over the past 20 years (compared to the previous 28) whereas annual average storm deaths have decreased.

During the past 48 years—and also during the past 20 years—the greatest number of disaster-related deaths have occurred in Asia. This is also true for specific categories of disasters—earthquakes, storms and floods—and remains true on a per capita basis. This is partly explained by geological and meteorological factors. During the past century, earthquakes of magnitude 5.0 or greater on the Richter scale were concentrated along the west coast of North and South Americas, along the east cost of Asia, and the Pacific Rim (Noji, 1997; National Earthquake Information Center, 2008). Cyclones, hurricanes and storm surges are concentrated in the tropics—most occur between 30°N and 30°S. It is, therefore, not surprising that few deaths due to meteorological disasters occur in Europe or Oceania.

> 5 Geophysical: Volcanoes, Earthquake, Tsunamis. Climatological: Droughts, Extreme temperatures, Wildfires. Hydrological: Floods. Wet mass movements (Landslides). Meteorological: Storms (cyclones).

4
# By Shigeru Otsubo and Masahiko Tsutsumi-

_Japan accounts for about 20 percent of foreign aid to developing countries and for 10 percent of their exports, and its economic health is therefore important to developing regions. 7he decline in asset prices in Japan since 1991 has contributed to an increase in bad debts of the country's financial institutions, has slowed credit extension through the erosion of their capital base, and has been an important factor in the long recession. Although the possibility of a systemic financial sector crisis that will shake up world financial market - the so-called 'Japan Problem' - is unlikely, the authorities may have to restructure severely affected institutions, which could result in higher fiscal expenditures or debt stock, a less favorable starting point from which to confront the problems associated with population aging. The impacts of such a restructuring on developing countries are likely to be most serious for indebted primary commodity exporters. Low-income countries are likely to be affected by fiscal pressures on Japanese foreign aid. On the other hand, many Asian exporters of low-cost manufactures to Japan may be relatively unaffected._

## l. Introduction - Background and Issues

Japan's business cycle downtum, which started in 1991 with a burst in the asset price bubble, produced one of the longest recessions in its post-war history before it showed signs of recovery toward the end of 1993 (see Figure 1).' The recovery, although weak, lasted for more than a year before coming to a virtual standstill toward the end of the second quarter of 1995, when there were signs of renewed recession with mounting risks of deflationary process (see Figure 2). The deepening problems within Japan's financial sector and the strengthening of the yen in the first half of 1995 were the main factors that diminished hopes for recovery. Most alarming is the fact that a recovery in the employment situation has never been observed; in fact, as Japan's Economic Planning Agency (EPA) has stated, it remained severe and continued to deteriorate during the recent gradual recovery (see Figure 2).

Shigeru Otsubo is an Economist with the International Economics Department, the World Bank. Masahiko Tsutsumi is an Economist with the Economic Planning Agency of the Japanese government. Analyses provided here were based on data available at the end of 1995. The findings, interpretations, and conclusions are the authors' own and should not be attributed to the World Bank or the Economic Planning Agency. Research assistance by Yoshiaki Igarashi and Tetsuo Umemura is gratefully acknowledged. Editorial assistance was provided by Deborah Davis and Sarah Crow. ' According to the Economic Planning Agency's reference dates of business cycles, the bubble cycle had a 53-month expansion phase (from November 1986 to April 1991), followed by a contraction phase spanning 30 months (from April 1991 to October 1993). The average duration of Japan's post-war business cycle expansion has been 33.5 months, while contractions have averaged 16.9 months.

1
**Table 9. The Effect of PMT on the Probability of Stunting Among Children 6-60 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poo<br>Linear Pr<br>(1)|led<br>obability<br>(2)|Linear Pr<br>w/ Comm<br>(3)|obability<br>unity FEs<br>(4)|
|---|---|---|---|---|
|**_A. 6-12 Months_**|||||
|Year Dummy (2000=1)|-0.043|-0.049|-0.054|-0.049|
||(0.046)|(0.046)|(0.100)|(0.103)|
|_Exposed Share_*Year|0.005|0.010|-0.062|-0.081|
||(0.051)|(0.052)|(0.130)|(0.133)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.09|0.10|0.52|0.53|
|**_B. 12-24 Months_**|||||
|Year Dummy (2000=1)|-0.065|-0.072*|0.001|0.003|
||(0.039)|(0.040)|(0.053)|(0.056)|
|_Exposed Share_*Year|0.026|0.028|-0.145*|-0.143*|
||(0.047)|(0.048)|(0.082)|(0.084)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-squared|0.08|0.09|0.38|0.38|
|**_C. 24-60 Months_**|||||
|Year Dummy (2000=1)|0.002|0.001|-0.003|0.001|
||(0.023)|(0.023)|(0.027)|(0.028)|
|_Exposed Share_*Year|0.045|0.047|0.069|0.058|
||(0.046)|(0.046)|(0.068)|(0.069)|
|Time Varying Community Characteristics|No|Yes|No|Yes|
|R-Squared|0.11|0.11|0.21|0.22|

Note: There are 4788 observations. The dependent is an indicator for whether the child has a height for age z score less than -2. _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. Other covariates (with coefficients reported in Appendix Tables A9.1, A9.2 and A9.3) include: gender (boy=1), age (months), mother and father’s education and height, an indicator for male head of household, farm household, household access to private toilet, access to sanitation and free health services, distance of village to bus station, community’s access to sewerage and piped water, number of village midwives, presence of a _posyandu_ in the community, and an indicator for whether the community is urban. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5% and 1%.

35