rafmacalaba/datause-encoder-data huggingface dataset:
The estimates also provides evidence of uneven reductions in tariffs among commodities, with the smallest percent reduction in "sensitive" commodities such as sugar. The uneven tariff cuts also appears to result in higher dispersion in protection in the base and final period than in the pre-UR regime.

.Jaian In Japan, a complex system of fixed and variable levies has kept internal prices well above world prices. Protection for all major commodities in Japan is provided largely through administered prices maintained by tariffs and import restrictions. Japanese raw sugar prices, for instance, were eight times the world price in 1985-86, while rice and wheat prices were more than six times the world price during the same period. Estimates of pre- and post-UR border protection for Japan are shown in Table **4*1** Border protection has fallen in recent years, but remains very high in rice, dairy products, wheat, barley and sugar. In contrast, protection in the Japanese beef sector has declined from 70 percent to 60 percent in 1992 and to 50 percent in 1993. Interestingly, Japan appears to have carried out significant reductions in protection in major commodities in the UR as shown by the estimates in Table 4. Japan committed to further reduce the ad-valorem tariff on beef to 38.5 percent. However, the postUR tariff equivalents for milk, sugar, and wheat remain prohibitive. Japan agreed to convert all NTBs to tariffs, except for rice, where tariffication was delayed but a higher minimum access commitment (4-8 percent of domestic consumption over the implementation period) is provided.

> 1 The estimates of pre-UR protection are based on prices used by the OECD in estimating Producer Subsidy Equivalents. The pre-UR ad-valorem equivalent on wheat based on government resale prices results in lower protection (279 percent) than is reported in the Table 4, but remain higher than the post-UR base period estimates. Further analysis will he done to estimate the pre-UR ad-valorem equivalents based on resale prices for other commodities.

31
TABLE 49: SUMMARY OF AVERAGE ESTIMATED SAVINGS PER TENDER ADMINISTERED ELECTRONICALLY FROM FY12 TO FY18 (BASED ON THE NARROW SAMPLE)

||Components|Savings pe<br>BD<br>|r tender (in<br>T)<br>|Savings pe<br>U<br>|r tender (in<br>S$)<br>|
|---|---|---|---|---|---|
|||Lower<br>bound|Upper<br>bound|Lower<br>bound|Upper<br>bound|
||Government A<br>|dministrative<br>|Cost Savin<br>|gs<br>||
|a.|Advertisement Cost Savings|30,424|38,480|362|458|
|b.|Tender Preparation Cost Savings|7,727|7,727|91|91|
|c.|SecurityCost Savings|1,661|1,661|20|20|
|d.|Staff-time savings|4,289|4,289|51|51|
||Administrative Savingsper tender|44,101|52,157|524|620|
||Firm Tran<br>|saction Cost<br>|Savings<br>|||
|a.|Staff-time savings|116|274|1|3|
|b.|Tender Preparation Cost Savings|-8,666|-7,380|-103|-87|
|c.|SecurityCost Savings|14,038|15,295|165|181|
||Total Private Sector Savings|5,488|8,189|64|97|

Note: lower bound estimates are produced by models employing CEM matching within years, while upper bound estimates are based on models using CEM matching within PEs.

Based on the average savings given in Table 49, the savings components for all tenders administered till FY19 electronically is extrapolated,36 For estimating the savings due to higher winning rebates, the average percentage savings on the estimated cost was used. The savings was estimated to be between 6.5% and 7.4% of the estimated cost of a tender. The components of the savings have been given in Table 50.

TABLE 50: SUMMARY OF ESTIMATED SAVINGS FOR E-PROCUREMENT WORKS TENDERS FROM FY12 TO FY19 (275,201 TENDERS, BDT 2.84 LAKH CRORE OR US$ 33.8 BILLION)

||Components|Savings<br>cro<br>Lower<br>bound|(in BDT<br>re)<br>Upper<br>bound|Savings<br>mill<br>Lower<br>bound|(in US$ ion)<br>Upper<br>bound|
|---|---|---|---|---|---|
|1.||Economic Cost Savi|ngs|||

36 This means extrapolating in three dimensions:

- From electronic tenders in district level Procuring Entities of three government agencies to electronic tenders in all Procuring Entities of all government organizations;

- From e-procurement tenders for FY11-12 to FY17-18 to e-procurement in FY18-19;

- From electronic tenders for public works only to all electronic tenders, including for goods and services.

- The assumptions made for the extrapolation are:

- For savings due to increased winning rebates, that the average reduction in procurement costs per tender as a percentage of the official cost estimate due to e-procurement is the same across (a) all PEs from all organizations, (b) works, goods and service contracts, and (c) FYs;

- For government administrative savings and private sector savings, the average reduction in procurement costs per tender in absolute terms due to eProcurement is the same across (a) all PEs from all organizations, (b) works, goods and service contracts, and (c) FYs.

80
**Table S4.E continued**

||(10)|(11)|(12)|(13)|
|---|---|---|---|---|
|VARIABLES|directimport|sellcredit|subcont|pcf_cont|
|supbuy|0.0205|-0.0121|0.121|0.114|
||(0.116)|(0.104)|(0.120)|(0.252)|
|samesup|-0.0197|-0.00127|0.0787**|-0.116|
||(0.0502)|(0.0403)|(0.0367)|(0.0746)|
|samecus|0.0854|-0.0139|0.0720|0.184|
||(0.0566)|(0.0524)|(0.0691)|(0.124)|
|same_sec|-0.0400**|-0.0171|-0.00111|-0.0367|
||(0.0160)|(0.0155)|(0.00881)|(0.0284)|
|ldist|-0.0318***|0.000235|0.0613***|-0.00688|
||(0.0100)|(0.00439)|(0.0178)|(0.0280)|
|ad_fmage|-0.000423|0.000479|-0.00207***|-0.00417***|
||(0.000805)|(0.000556)|(0.000637)|(0.00145)|
|ad_meduc|0.0292|0.000686|-0.0183**|0.0374|
||(0.0199)|(0.00500)|(0.00710)|(0.0281)|
|ad_mexper|-0.00153**|0.000485|-0.00174|0.00457**|
||(0.000780)|(0.000645)|(0.00120)|(0.00209)|
|ad_mfemale|0.0454|0.00392|0.0245|0.0402|
||(0.0297)|(0.00950)|(0.0305)|(0.0420)|
|ad_lemp|0.131***|0.00261|0.0171|0.138***|
||(0.0150)|(0.00473)|(0.0133)|(0.0313)|
|sametown|-0.121|0.00302|0.347***|0.0135|
||(0.0787)|(0.0270)|(0.0787)|(0.179)|
|Constant|0.333***|0.482***|-0.105|0.845***|
||(0.0753)|(0.0337)|(0.0835)|(0.175)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 15
26 Table St Optimal trade and ledustrial policies for the us outo Induasb.?

|Ina|trument\1odal<br>CRTS|Contestable<br>**Market**|Monopollotte<br>Competition|
|---|---|---|---|
|**1.**|Wn9 Subsidy|||
||Weltare<br>gan<br>b<br>167.0<br>Relative<br>**wage**In autos**(wage**subsldy)<br>1.0 (27)|616.0<br>.7 (67)|n0.0<br>16(-3S)<br>-6.0|
|2.|Production<br>Subsid|||
||Welfere<br>gain<br>24.0<br>Production<br>subsidy<br>1.8|977.0<br>10.0|264.0<br>6 4|
|3.|Tariff|||
||Welfare<br>gain<br>4.0<br>Tariff<br>1.2|182.0<br>6.8|80.0<br>8 3<br>0.60|
|4.|**Wage**and Production<br>Subsidioe|||
||Welfare<br>gain<br>b<br>167.0<br>Relative<br>wage In autos (wage<br>subsidy)<br>1.0 (27)<br>Production<br>subsidy<br>0.0|1224.0<br> <br>.67 (40)<br>7.2|066.0<br> <br>1.6 (-63)<br>6 1<br>-46|
|S.|LarIff<br>and Production<br>Subsidy|||
||Welfare<br>gain<br>J40<br>Production<br>subsidy<br>2.1<br>Tariff<br>2.2|1364.0<br>11.0<br>12.2|B37.0<br>6.2<br>604<br>6 0c|
|.<br>b.<br>c.|Welfare<br>gain estimates<br>areIn millions<br>of dollars. All subsi<br>Relative<br>wage In autos under optimal<br>wags subesdy<br>or tax.<br>parenthese. Atax rate<br>has a negative<br>sign. All rates<br>**are cal**<br>a relativo<br>wage In autos of 1.27.<br> Porcentage change Inthe nuaber<br>of firm.|dies<br>and tarifs<br>are In<br> **Wage**subidy or tax<br>**culated**4roa<br>**an**Init|percent.<br>In percentage<br>In<br>ial<br>condition<br>with|
al., 2009). As already mentioned, malnutrition can be an obstacle to adherence (Weiser, 2003). A negative shock on income or food access that may occur in the event of a food crisis, and more generally, of a global economic crisis, could therefore act a strong determinant of non-adherence to treatment.

We use household level panel data from Mozambique collected in 2007 and 2008, to evaluate the extent to which the effects of the food crisis differed between households with identified HIVpositive patients and households with no identified HIV-positive person. The baseline of the survey was fielded shortly before the peak of the food crisis while the follow-up survey was fielded when the food crisis was already winding down. Moreover, we purposely sampled households with and without an identified HIV-positive member. Overall, the timing of the survey and the sampling strategy make our data uniquely fitted for contrasting the effects of large economic shocks on HIV-patients’ households and other households.

The paper makes two main contributions to the literature on the interactions between economic conditions on the one hand, and access to treatment and adherence to treatment regimen on the other hand. First, we show that access to treatment help shield HIV households from the effects of the food crisis in Mozambique. Our analysis uncovered that welfare, measured in terms of income, food consumption and anthropometric outcomes, has deteriorated for both HIV and comparison households between 2007 and 2008. The welfare loss was, however, less severe for HIV households than for comparison households. We find evidence supporting the hypothesis that initiation of treatment and better services in the health facilities have counter-balanced the effect of the crisis by improving the health of patients and their labor force participation.

Second, we find that change in welfare has no significant effect on the frequency of visits to the health facility of patients and on their treatment outcomes, measured by the change in the CD4 count. We find, however, that people who experienced a negative income shock also experienced a reduction or a slower progression in their CD4 counts. This would be suggestive evidence that welfare loss has a negative effect on treatment outcomes, but that negative effect, however, may be working 5
# **Figure B11: Platinum**

<!-- Start of picture text --> Log scale<br>8.0<br>Log price Permanent component<br>7.5<br>7.0<br>6.5<br>6.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 52 —_
during the 1980s. Moreover, since **1998** the flat fee is deducted from the accounts of active contributors only. Unemployed workers and workers who have retired from the labor force do not pay contributions and do not pay any commissions.

# **3.8** Account **Transfers**

**A** fundamental feature of the Chilean system is the right to transfer accounts between AFPs. This stimulates competition in the system and ensures a high level of operational and investment efficiency. Nevertheless, concern is often expressed that AFPs may incur unduly large expenditures in advertising and marketing their services and in trying to induce affiliates to transfer their accounts. It is estimated that as much as **30%** or 40% of operating costs may be absorbed **by** promotional expenses. Moreover, advertising campaigns appear to place undue emphasis on short-term performance, which is inappropriate for accounts of a long-term nature that may span over **60** years.

Table 9

||Account Transfer|s|
|---|---|---|
||Account<br>Total<br>Transfers<br>Accounts<br>**0008**<br>**0008**||
|**1985**|211<br>**2107**|**10.0**|
|**1986**|**178**<br>2438|**7.3**|
|**1987**|**183**<br>2741|**6.7**|
|**1988**|**300**<br>**3037**|**9.9**|
|**1989**|**310**<br>**3327**|**9.3**|
|**1990**|384<br>3624|**10.6**|

Source: Iglesias **(1990)**

> **A** related concern refers to the expenses incurred in processing account transfers. In systems that lack sufficient computerization and automation this may imply high costs for employers who have to amend their records every time employees transfer their accounts. At present, administrative delays in processing account transfers result in an effective limitation on transfers of no more than 2 a

> year. There are, however, suggestions that account transfers should be subject to tighter limits. In addition, consideration may need to be given to imposing some limitations on the promotional activities of AFPs, especially with regard to the transparency of their advertising campaigns.

Account transfers have grown from **178,000** in **1986** to 384,000 in **1990.** As a proportion of all accounts, transfers fluctuated between **6.7%** in **1987** and **10.6%** in **1990.** In recent years, AFPs have adopted aggressive competitive strategies to attract high income affiliates and this may explain the increasing mobility of accounts.

# **3.9 Investment Profiles**

Reflecting the strict investment rules imposed on pension funds, their assets were initially mostly invested in bank deposits, mortgage bonds and central bank!treasury securities. Thus, in **1981, 62%** of assets were placed in bank deposits, **9%** in mortgage bonds and 28% in state securities. Between **1982** and 1984, the share of bank deposits declined sharply. In **1983,** following the banking 24
# **2.2.1 Uncontrolled development in flood plains**

**A major cause of overexposure to flood is uncontrolled development of flood plains.** Development on flood plains is often seen as necessary, but if it is done in an unplanned fashion, it may lead to devastation. When development is not guided, planned or controlled, the land exposed to flood hazard is liable to be more highly developed due to its lower cost. According to the WMO (2007), choices of development are often made without proper awareness of the risk in the prevailing area. Despite increases in knowledge of where and how frequently floods may occur, resulting in predictive flood maps, the maps fail to be used for development control as other priorities take precedence. Equally, in the developing world the proportion of structures subject to planning or building controls is low. The lack of controls implies that the most sensitive receptors may well be increasingly placed at risk. Uncontrolled development is likely to be denser, especially in informal settlements, and is typically characterized by substandard accommodation with no view towards minimizing risk to others. Uncontrolled development behind existing flood defenses will lead to a larger impact if defenses are breached; outside these defenses it may lead to totally unprotected settlements. The continued expansion of Buenos Aires, for example, has rendered obsolete the old drainage system.

# **2.2.2 Development outside defenses**

**Allied to the lack of control of developments is the increased development of settlements which are outside the protection of any flood defenses.** The pressure of population growth leading to increased need for residential and other buildings leads to development in previously unoccupied areas. If an urban area is subject to flood hazard then the most likely areas to be defended against flood risk are traditional settlements. Newer development is more likely to be in undefended neighborhoods. However, it is also possible that traditional settlements are likely to be closer to the source of flooding than newer buildings and so these may be areas of lower hazard. On the other hand, if riverine defenses push river levels higher downstream the very presence of defenses may increase the hazard to underdeveloped land which is increasingly being pushed into service. Flood defenses may need to be raised based on change scenarios otherwise there will make more people exposed to floods. Restoration areas should be extended away from risk zones to maintain the standard of protection. In any case, the need for defenses must be assessed carefully before further development: when defenses breach, great damage is caused as could be witnessed in the 2010 flood in Poland.

# **2.2.3 Land use change and increased urban density**

**Changes in the use of land within urban settlements can increase the exposure of receptors to the risk of flooding.** This may be the result of increasing land prices dictating that

29
**Figure 16: Stock, Bond, and Insurance Market Indicators - Median Values Over Time (19952007)**

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Stock Market Capitalization / GDP<br>Stock Market Total Value Traded / GDP<br>Stock Market Turnover Ratio<br>No. of Listed Companies per 10k Pop<br>Private Bond Market Capitalization / GDP<br>Public Bond Market Capitalization / GDP<br>1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> 34
# Figure A.7: Ricardian and Interest Rate Channels: Impulse Reponses and Multipliers

<!-- Start of picture text --> Output Real Effective Exchange Rate<br>before Private Consumption before Current Account Balances<br>A. Impulse Responses<br>Private Consumption Private Consumption<br>0.4 0.3<br>Weak fiscal position Strong fiscal position<br>Weak fiscal position Strong fiscal position 0.2<br>0.2<br>0.1<br>0.0<br>0.0<br>-0.1<br>-0.2<br>-0.2<br>-0.3<br>-0.4 -0.4<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br>CDS CDS<br>80 80<br>Weak fiscal position Strong fiscal position Weak fiscal position Strong fiscal position<br>60 60<br>40 40<br>20 20<br>0 0<br>-20 -20<br>-40 -40<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text -->

- B. Multipliers

<!-- Start of picture text --> On Impact On Impact<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>-0.4 -0.4<br>23.1 32.0 38.5 42.8 47.4 54.6 63.8 76.7 23.1 32.0 38.5 42.8 47.4 54.6 63.8 76.7<br>Government debt (percent of GDP) Government debt (percent of GDP)<br>2 Years 2 Years<br>1.8 1.8<br>1.5 1.5<br>1.2 1.2<br>0.9 0.9<br>0.6 0.6<br>0.3 0.3<br>0.0 0.0<br>-0.3 -0.3<br>23.1 32.0 38.5 42.8 47.4 54.6 63.8 76.7 23.1 32.0 38.5 42.8 47.4 54.6 63.8 76.7<br>Government debt (percent of GDP) Government debt (percent of GDP)<br><!-- End of picture text --> 34
**Table 9. Drivers of migration to urban or rural areas**

||(1)<br>|(2)<br>|
|---|---|---|
||Mover to rural|Mover to urban|
||(1=mover to rural, 0=stayer)|(1=mover to urban, 0=stayer)|
|Baseline variables|First stage|First stage|
|Log WRSI maize x Aged 15-24|0.020|-0.052*|
||(0.020)|(0.028)|
|Log Kilometers from regional capital x Aged 15-24|0.006**|0.002|
||(0.003)|(0.004)|
|Log number of fatalities x Aged 15-24|0.008***|0.002|
||(0.003)|(0.003)|
|Share of one's ethnicity living in urban areas x Aged 15-24|-0.067*|0.068|
||(0.037)|(0.049)|
|Head or spouse|-0.011***|-0.006|
||(0.003)|(0.004)|
|Child of head|0.012***|0.020***|
||(0.004)|(0.005)|
|Male child of head|-0.001|-0.012|
||(0.005)|(0.007)|
|Male|-0.005*|-0.002|
||(0.003)|(0.004)|
|Unmarried|-0.003|0.006|
||(0.004)|(0.005)|
|Unmarried male|0.005|0.004|
||(0.004)|(0.006)|
|Age category (reference: aged 10-14)|||
|Aged 15-24|-0.101|0.227*|
||(0.087)|(0.123)|
|Aged 25-34|0.017***|0.019***|
||(0.004)|(0.007)|
|Aged 35-49|0.009**|0.018***|
||(0.005)|(0.007)|
|Aged 50-65|0.005|0.010|
||(0.006)|(0.007)|
|Aged 66 plus|0.003|-0.003|
||(0.006)|(0.007)|
|Number of effective years of schooling completed|0.001|0.001|
||(0.000)|(0.001)|
|Sanderson-Windmeijer - F test of excluded instruments|5.401|3.335|
|Sanderson-Windmeijer - (p-value)|0.0000|0.0041|
|Observations|10,783|10,824|
|Number of households|2,319|2,290|
|Linear probability models; Initial Household Fixed Effects|||
|Robust standard errors in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|<br>Source: Authors’ calculations with UNPS.|||

30
The MOCA county‐level data show substantial variation in dibao eligibility thresholds. Figure 1 is a graph of the distribution of county dibao thresholds for the CHIP sample counties in each of the three sample years. In 2007 and 2008 the county dibao thresholds ranged from less than 500 yuan per capita per year to more than 3,000 yuan. In 2009 the lowest thresholds had risen above 500 yuan, and the highest to more than 4,000 yuan.

Figures 2a and 2b show the distributions of dibao transfers in the CHIP sample counties for 2008 and 2009. The distributions based on the county‐level averages from MOCA data and on the village‐level averages from CHIP are similar, although variation is wider at the village level (because averaging at the county level eliminates variation within counties). As is the case for the thresholds, variation in the dibao transfers is substantial. In 2009, for example, county average dibao transfers ranged from less than 500 to more than 3,000 yuan per capita.

Dibao participation increased along with dibao thresholds and transfer amounts. Calculated using the CHIP data, the rate of participation in the rural dibao program increased from 1.9% in 2007 to 3.0% in 2009 (Table 4). Dibao participation rates in the CHIP data are lower than national participation rates implied by the MOCA statistics, which increased from 5.0% of the rural population in 2007 to 6.9% in 2009. Possible reasons for discrepancies between the CHIP and official dibao statistics include those discussed earlier.

Table 4 reports dibao participation rates by province calculated using the CHIP data and also the official data. Based on the CHIP data, dibao participation rates in 2009 ranged from less than 1% in Hebei and Zhejiang provinces to 5‐6% in Guangdong and Chongqing. Similar variation is evident in the official data. Such variation reflects differences across locations in dibao thresholds, financing and implementation, as well as in household incomes and thus eligibility.

Is dibao participation higher for lower income households? Using the CHIP data, we calculate dibao participation rates by deciles of ex ante income (Figure 3). In general, dibao participation rates are higher for poorer income groups. In all three years the participation rates are highest for individuals in the poorest decile of the income distribution. Dibao participation drops sharply for the second poorest decile, and thereafter tends to decline further as one moves to higher income groups. In all years, however, less than 10% of individuals in the poorest decile 13
The growth deceleration would have been much steeper without any fiscal stimulus, especially in China and to a lesser extent in India. Private consumption demand proved very resilient in Brazil; as a result, aggregate demand was less of a binding a constraint than the availability of credit.

During the pre-crisis boom period of 2003–07, economic growth in both China and India was driven largely by investment demand and export demand. By contrast, the leading driver of economic growth in Brazil was private consumption demand. In such conditions, the expansion of public consumption, with increases in salaries of government employees spurring private consumption, was effective in spurring a quick recovery in economic growth.

Table 4 compares real per capita growth in states’ own revenues before and after the global crisis. In all three countries, growth seems to have become more volatile, reflected in an increase in the distance between minimum and maximum rates of growth among states/provinces.

**Table 4 Annual Changes in States’ Own Real per Capita Revenue in China, India, and Brazil, 2003–10**

**<u>(Percent)</u>**

||_Chin_|_a_|_Ind_|_ia_|_Bra_|_zil_|
|---|---|---|---|---|---|---|
|_Change _|_2005–07_|_2008–10_|_2003–07_|_2008–10_|_2005–07_|_2008–10_|
|Maximum|37|41|15|17|20|25|
|75thpercentile|26|24|10|9|13|<br>15|
|Median|22|20|8|8|8|10|
|25thpercentile|18|15|6|6|3|5|
|Minimum|11|5|0|0|–10|<br>–7|

_Sources_ : IMF 2012 for China and Brazil; authors’ estimates for India.

The median level fell by a few percentage points in China, remained unchanged in India, and rose by a few percentage points in Brazil. Comparison of the interregional distribution hides more than it reveals, however. The distribution may look similar in two periods during which 19