rafmacalaba/data-use-mentions-extended huggingface dataset:
(5+6+7+8-68), chemical products (5), textiles & clothing (26+65+84), machinery & transport (7), and other manufactures (6+8-65-68-84).

Export data in sectoral breakdown is based on 161 countries data available in 2010 from COMTRADE database. Where the aggregate merchandise export data is based on total 192 countries data available in Table 3 from World Development Indicators database. Source: Based on UN COMTRADE Statistics.
|0.779*|
||(0.409)|(0.379)|(0.359)|
|Banking reform index|-0.687||-0.712*|
||(0.391)||(0.387)|
|Banking crises|-0.132||0.035|
||(0.138)||(0.069)|
|Public Banks' Assets Share|0.006*|0.006*|0.005*|
||(0.003)|(0.003)|(0.003)|
|Foreign Banks' Assets Share|0.0004|-0.001|-0.001|
||(0.003)|(0.002)|(0.002)|
|Foreign Exchange reserves/GDP||0.01***|0.01***|
|||(0.001)|(0.002)|
|Country specific trends|x|x|x|
|Year fixed effects|x|x|x|
|Country fixed effects|x|x|x|
|Seasonal Dummies|x|x|x|
|Within R-squared|0.86|0.88|0.89|
|Nbr. obser.|404|400|400|

Note: Robust clustered (at the country level) standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.
: Student (% of Pop.)|0.071|0.088|0.000|
||(0.257)|(0.283)||
|Occupation: Housework (% of Pop.)|0.093|0.104|0.000|
||(0.291)|(0.305)||
|Family size|3.507|3.517|0.134|
||(2.042)|(2.092)||
|Family in Colombia [=1]|0.413|0.473|0.000|
||(0.492)|(0.499)||
|Family in Venezuela [=1]|0.666|0.652|0.000|
||(0.472)|(0.476)||
|Migration Intent: Stay in Colombia|0.889<br>|0.903<br>|0.000|
||(0.315)|(0.296)||
|Migration Intent: Move to another country|0.019<br>|0.021<br>|0.000|
||(0.136)|(0.143)||
|Migration Intent: Return to Venezuela|0.092<br>|0.076<br>|0.000|
||(0.290)|(0.265)||
|Observations|281,307|161,310|442,617|

_Source:_ RAMV census.

57
in advance opens important opportunities to mitigate and avoid the worst. In this paper, we have explored the benefits of using formal statistical models to forecast food crises, using sub-national data provided by FEWS NET for 21 countries since 2009.

As a baseline, it is instructive to compare model-based forecasts with existing early warning systems. In our validation framework, these historical outlooks turn out as quite conservative, with extremely low false positive rates and relatively few warnings when compared with the actual incidence of food crisis outbreaks. As a consequence, existing outlooks have high false negative rates, i.e. many food crisis outbreaks were not anticipated. In addition, the overall forecast performance of existing outlooks deteriorates with longer forecast horizons.

Using standard cross- and temporal holdout validation methods, we have shown that relatively simple statistical models provide good predictive performance up to 12 months in advance. We used verifiable monthly data capturing environmental factors, conflict, and food price shocks as key predictors for this. We find that the model-based forecasts can deliver much lower false negative rates at the cost of only modest increases in false positive rates. Moreover, our metrics of forecast performance are fairly stable as the forecast horizon increases from 4 to 12 months, suggesting good predictive performance over longer forecast horizons.

A key benefit of statistical forecasts is that they can be optimized relative to an explicit trade-off between false positives and false negatives. This trade-off may be different for different policy purposes. When the costs of full-blown food crises are large and preventive measures have high returns, a forecasting framework with a high tolerance for false positives might be appropriate. Conversely, when resources for prevention are scarce, a greater tolerance for false negatives is more suitable. Our validation framework made this trade-off explicit, by tuning the parameters of our models to minimize prediction loss functions that balance false positives and false negatives. Depending on the use case, calibrated predictions can be generated that strike the appropriate balance between false positives and false negatives dictated by the application at hand. We acknowledge,
shaped individuals. The idea that preferences and the mental models that underlie them are fixed can unambiguously be rejected. Of course, a die-hard devotee of fixed preferences could argue, for example: the soap opera has changed individuals’ posterior distributions concerning the consequences of different behaviors; that is, beliefs about the net payoffs from different conduct have changed.

> 40 <mark>The causal impact is based on the arguably random timing when different parts of Brazil obtained access to the TV emissions.  There was, however, no direct test available of a change in mental models.  Similarly, a study in the US by Kearney and Levine (2015) used historical MTV ratings to show that MTV’s</mark> _<mark>16 and Pregnant</mark>_ <mark>, a US reality TV show, caused a 4.3 percent reduction in teen births.</mark>

36
# **4. Pure Premium Rates**

Under mNAIS premium rates are set at the crop/district level and so the pure premium rate is taken to be an actuarially sound estimate of the expected weighted average loss cost across the crop/district, that is, the expected claim payment divided by the sum insured, for the coming crop season. At this stage the reader may find it helpful to review Figure 1, which offers an overview of how the following steps fit together.

# _Loss Costs_

# <u>Description of step</u>

First calculate the historical loss costs for each insurance unit with the threshold yields calculated in Section 3. This is the same as the calculation of loss cost histories for the calculation of the indemnity level (see column [4] of Table 7), except that the indemnity limit is no longer necessarily 90%. For example, the indemnity level for cotton in district GJ8 of Gujarat was calculated to be 70% (see column [3] of Table 9). Threshold yields with indemnity level of 70% are reported in column [2] of Table 10 and historical loss costs, based on these threshold yields and using de-trended data, are given in columns [3] to [12] of Table 10.

Table 10. Cotton in district GJ8 of Gujarat: historical loss costs

|**Insurance**<br>|**Threshold**<br>**yield**||||**H**|**istorical**|**loss co**|**st**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Unit**|<br>**(kg/ha)**|*
open dissemination of the novel WSF3Dv2 data and derived results, all outcomes are finally provided in the form of zonal statistics (building area, building height, and building volume) aggregated to a grid of 90m*90m.

We then use the data to investigate the role of economic development in construction. National building stocks per capita increase convexly with income. Small differences are observed between low-income and lower-middle-income economies and it is only when countries become upper-middle-income economies (i.e., reach a per capita GDP of _∼_ $4,000) that space consumption per capita significantly increases with income. Next, we rely on the globally estimated volume-income relation to quantify global construction needs.

Developing nations in Asia and Latin America & the Caribbean (LAC) respectively account for _∼_ 60% and _∼_ 20% of _current_ global construction needs (South Asia explains two-thirds of developing Asia’s gap). African nations do not have large construction gaps because they have low incomes, and little variation in space consumption per capita is observed at such incomes.

Developing Asia then accounts for about 75% of _future_ , growth-driven global construction needs when considering a horizon of five years. In developing Asia, the effects of population growth and income growth compound each other. African nations, having low incomes and low economic growth rates, do not appear prominently in the rankings despite their fast population
<br>Hired labor loading/unloading<br>Council cess|0.11<br>41.40<br>4.00<br>0.0|
||**Sub total**|**45.51**|
||**Total**|**90.53**|

Search costs are extremely common in many markets. As consumers, most of us have experienced this phenomenon, for instance, when searching for the best price for a new computer online or on the high street. Anderson & Renault (1999) provide another informative example of how search costs function is given by: the tourist trap. Tourist areas are often dominated by low quality, high cost restaurants that offer relatively undifferentiated menus, but that nevertheless have full tables. Their good fortune is caused by the high search costs faced by the average tourist, who is unaware of the local alternatives and that values leisure time highly.

> 12 This refers to transaction costs that are normally measured in surveys, which exclude, amongst others, costs related information, search and bargaining.

> 13 Source: World Bank survey carried out in November-December 2008.

7
trusted, or that you can’t be too careful in dealing with people?”<sup>19</sup> To guage how reasonable the use of this proxy is empirically, Figure III explores the country-level relationship between trust and (i) the rule of law index and (ii) contracting enforcement (as measured in the World Bank Doing Business Survey). Trust is strongly correlated with both stronger rule of law and contract enforcement, providing reassurance that differences in trust across regions captures similar variation to legal institutions and the constraints faced by firm owners in delegating decision-making to managers which is emphasized in the model.

# **_III.D. Regional Income and Human Capital Stock_**

To measure regional income and human capital we rely on data on regional GDP per capita and the share of workers with a college education from Gennaioli et al. (2012). We concord their region codes with those in the WVS and BvD datasets to produce a final set of consistent regional identifiers.

> 18We use data from six rounds of the WVS taken between 1981 and 2014.

> 19One concern is whether trust reported in the survey accurately reflects trust by those who make decisions about who manages the firm. As argued by Glaeser et al. (2000), experimental data has suggested that trust attitudes actually reveal trustworthiness, rather than just a belief that others can be trusted. This suggests trust in the WVS should reflect trust between individuals in a particular region, rather than abstract beliefs.

11
survey was done in parallel to the sampling for the RIA household survey examined in Atiyas and Doğanoğlu (2020). Specifically, the national census sample frames were split into urban and rural Enumerator Areas (EAs). Then EAs were sampled for each stratum using probability proportional to size. For each EA, two listings were compiled, one for households and one for firms; these listings served as the sampling frames. From each EA, 24 households and 10 firms were selected randomly. The data contain sampling weights, yielding EA-level representative data. See Mothobi et al. (2020) for a descriptive analysis of these data.

> 7 Legal requirements for any natural or legal person(s) wishing to exercise a commercial activity in Senegal include registration with the local business registry (RCCM, Registre du commerce et du crédit mobilier) and with the national tax authorities (DGID, Direction Générale des Impots et des Domaines, Senegal’s revenues and customs authority). Although the RIA data also include variables on whether the business pays local or municipal taxes and whether the business is registered for VAT or sales taxes, these are separate legal compliance issues for ongoing operation rather than entry registration: firms with annual turnover

8
10 from simple to more sophisticated products. The third element involves the relocation of industries across countries, from advanced to developing countries as the latter undergo the process of convergence.

Are the Asian geese described by Akamatsu still flying? This pattern does appear to have persisted in Asia over the past two decades. For example, in the early 1990s China was already a dominant player in some light manufactures such as footwear and toys (table 1). Japan continued to be a dominant player in toys but was clearly moving up the technology ladder to more sophisticated games, such as Nintendo and Sony PlayStation. China, a low-income country in the 1990s, also still exported live animals on a large scale. In the 2000s it was able to move up the product ladder to more sophisticated manufactures and overtake Japan in world export shares in plastics, electrical machinery and parts, and television receivers. Korea was a major player in exports of live animals in the early 1990s but has now moved out of that primary sector. India lags in market shares but has gradually moved up in footwear.

**Table 1. Geese Still Flying in Asia:** **<u>Country Rankings in Selected Industries, 1992 and 2008</u>**

||**Live an**<br>|**imals**<br>|**Pharma**<br>|**ceuticals**<br>|**Foot**<br>|**wear **<br>|**Iron **<br>|**& steel **<br>|
|---|---|---|---|---|---|---|---|---|
|**Country**|**1992**|**2008**|**1992**|**2008**|**
share of manufacturing employment in economy-wide employment (EMP). We focus on RMVA and EMP for our substantive statistical analysis since the trend in the share of manufacturing valued added in GDP at current prices derives from movements in both price and quantity that are indistinguishable to the researcher. While the manufacturing share at current prices conflates price and quantity, the real manufacturing output share adjusts for general movements in prices over time, and thus, is more reliable for understanding structural changes in industrialization and the economy. The real manufacturing output shares are at constant 2005 national prices. Also, since WDI does not include data on manufacturing employment, our employment analysis is limited to the aforementioned eleven countries in the GGDC’s Africa Sector Database. To account for possible natural resource boom, we also examine the trends and patterns of the share of manufacturing value added in non-oil GDP (RMVA-NOIL). This could be important in separating the

> 4As of January 2019, the AFRREO Fall 2018 Database is accessible at `https://www.imf. org/external/datamapper/datasets/AFRREO` .

4
>1.33|Sri Lanka (1987)<br>Nepal (1996)|.99<br>1.24|

Notes: A higher ratio indicates that females are MORE likely to die between ages one and four than are males.

What do the data on child mortality ratios show? First, that gender disparities are worse in South Asian countries than in the rest of the world. Figure la shows a map of the world where the shading given each country reflects the female to male ratio in child mortality<sup>9</sup> . The median in tiLe non-South Asian countries is exactly one, meaning that in a typical DHS country outside of South Asia, male and female children are equally likely to die between ages one and five. In Pakistan, India and Bangladesh, however, this is not the case.

A girl is between 30 and 50 percent more likely to die between her first and fifth birthdays The cutoff points between the various levels are determined by pooling all the (country, Indian state, and Pakistani province) data and selecting the values at the 15th, 50th, and 85th percentiles.

8
<!-- Start of picture text --> Urban Pollution Changes in Urban Pollution<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>Urban Bottom Expenditures Changes in Urban Bottom Expenditures<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>Urban Average Expenditures Changes in Urban Average Expenditures<br>Median Median<br>25%−75% Quar 25%−75% Quar<br>01−1999 07−2001 01−2004 07−2006 01−2009 07−2011 01−2014 01−2000 07−2002 01−2005 07−2007 01−2010 07−2012<br>date date<br>Raw data Controlling for for Fixed Effects<br>80<br>30 60<br>25 40<br>20<br>PM25 20 Percent 0<br>15 −20<br>10<br>5 −60<br>40<br>5e+05 20<br>IR 0<br>Percent<br>3e+05<br>−20<br>1e+05<br>−40<br>80<br>60<br>1500000 4020<br>IR<br>Percent 0<br>500000<br>−40
need" or family size desires have often been used to measure demand, but they have recognized disadvantages in that they do not measure intensity nor deal with the family context or the cost constraints on behavior. These measures are normally developed from surveys that may be too broadly based to relate to the particulars of a specific market. New approaches to assessing demand, perhaps drawn from the experience of social marketing or private sector projects, may be helpful in this area.

Most observers believe that if population programs are to be effective in the long run, they will have to incorporate demand generation elements. This need has been recognized in many of the Bank population projects, but, unfortunately, demand side activities seem to have been among the weakest elements in these projects. In Kenya and India, for example, t-here have been 48
analysis of NTMs, the statistics were done only on total NTMs without breakdown by type of NTM.

# **4.3 Defining the “poor” or “excluded”**

In this study, economic “poverty” is measured as a relative phenomenon based on the analysis of income/consumption for the entire distribution by decile group. Consumption expenditure is widely accepted as a proxy for income owing to the difficulty of getting reliable quantitative data on household income through direct enquiries in household surveys. Additional variables in the data enabled us to also look at groups traditionally excluded from the process of economic growth – women, those working in rural areas and those employed in the informal sector.

# **5. Results**

# **5.1 Tariffs faced in the export market are inversely related to income**

Using household level data, the average income (using consumption as proxy) and tariff faced in export markets for each income decile were calculated (Table 1). Households in the poorest decile have an average weekly consumption of only 511 rupees ($9.60), while households in the richest decile consume ten times that amount.

Table 1: Average tariff faced by income decile based on household weekly consumption.

|Income<br>Decile|Household W<br>|eekly Income<br>|Average Tariff<br>Faced (%)<br>|
|---|---|---|---|
||Rupees|US $|Simple*|
|1|511|9.6|24.4 a|
|2|783|14.8|22.9 b|
|3|962|18.2|22.4 b|
|4|1130|21.3|21.5 c|
|5|1305|24.6|20.3 d|
|6|1504|28.4|19.2 e|
|7|1761
respondents may be unwilling to give candid responses to sensitive questions about corruption or other dimensions of governance, particularly in countries where governance is weak.<sup>6</sup> On the other hand, a shortcoming of expert assessments is that they reflect the views of a narrower set of respondents than household or firm surveys. It also is possible that the ratings provided by one expert assessment to some extent reflect the views of other expert assessments, so that each assessment does not bring completely independent information on the underlying governance concept of interest. To guard against this, we do not use expert assessments that are explicitly based on other existing data sources.<sup>7</sup> In Section 5.1 we provide further analysis of this issue.

The WGI include 12 expert assessments produced by NGOs. The data provided by these organizations cover a range of specific topics, including press freedoms (Reporters without Borders), political rights and civil liberties (Freedom House), human rights (Human Rights Measurement Initiative), budget transparency (Open Budget Project), and electoral integrity (Africa Electoral Index). A number of these data sources cover a broader range of topics relating to governance, including the Bertelsmann Transformation Index and the Varieties of Democracy Project. The organizations providing the data are geographically diverse, based in Europe (Varieties of Democracy, Reporters with Borders, Bertelsmann Foundation), Africa (Africa Electoral Index and Africa Integrity Indicators), New Zealand (Human Rights Measurement Initiative), in addition to the United States (e.g. Freedom House, Open Budget Project, World Justice Project).

The WGI also include eight data sources provided directly or indirectly by public sector organizations. Most notable among these are four very similar expert assessments of the quality of policies and institutions produced by the African Development Bank (Country Policy and Institutional Assessment), Asian Development Bank (Country Performance Assessment), World Bank (Country Policy and Institutional Assessment), and the International Fund for Agriculture and Development
Figure 3. Share of Sub-Saharan African exports by sector _Notes: Average exports share of total merchandise exports value by sector in Sub-Saharan African RR countries, pre-boom (1998-2003), boom (2004-2014) and post-boom (2015-2018) years averages. Source: authors calculations using World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division._ The boom created a set of major public policy challenges. First, high prices meant significant revenues would accrue to government, which in turn implied ‘big government’ in some form was hard to avoid. Government, as the constitutionally designated resource owners had to attempt to capture the full value of resources on behalf of citizens, while also efficiently managing state-owned resource companies and production shares. Second, they had to maximize these revenues from the sector in a way that was also consistent with inward investment into the sector, for example to take advantage of the geological potential and new discoveries being made. Third, they had to ensure those revenues were put to good use – fulfilling urgent development needs and longer-term investment goals for sustaining prosperity. Finally, they needed to ensure the economy was protected from the harmful effects of resource dependence – ranging across hazards such as Dutch disease (Corden and Neary, 1982), price and revenue volatility, corruption, conflict, and profligacy.<sup>6</sup>

> 6 These various challenges faced by government are described in documents such as the Natural Resource Charter, which highlights the various public policy choices that must be made along the decision chain, from exploration for resources, through to how to invest revenues from resources for sustainable and diversified development (Cust and Manley 2014).

8