rafmacalaba/datause-encoder-data huggingface dataset:
radical step of dropping a survey. However, there are problems we cannot fix. It is known that surveys differ between countries, including how the questions are asked (such as recall periods), survey response rates, whether the surveys are used to measure consumption or income, and in what gets included in the survey’s aggregate for consumption or income. These differences are known to matter to the statistics calculated from surveys, including poverty measures.

The literature on measuring global poverty has dealt with such survey comparability problems in two main ways. The first makes some effort to iron out such problems using the micro data, but essentially ignores the problem beyond that, in the expectation that aggregation across surveys will reduce the problem considerably. Following this method, our past estimates have applied an international poverty line, converted to local currency at PPP, to the distributions from the surveys. This follows the practice in national poverty measurement; essentially the difference is that for global poverty measures the poverty line is fixed in real terms across countries rather than being country specific. However, it is acknowledged that when applying a common line to different national surveys one will obtain different poverty measures even if the same measure would be obtained using the same survey instrument in each country.

The second approach re-scales the survey means to be consistent with the national accounts (NAS), which are assumed to be comparable and accurate. In one version of this method, Bhalla (2002) replaces the survey mean by consumption from the NAS, but keeps the survey-based distribution. (In other words, he re-scales all survey-based consumption (or income) levels by the ratio of NAS consumption to the survey mean.) Bourguignon and Morrisson (2002) and Sala-i-Martin (2006) also re-scale the mean, although they anchor their measures to GDP per capita rather than to consumption.<sup>32</sup> It is claimed by the proponents of the second method that it corrects for survey missmeasurement. It is argued that NAS consumption captures things that are sometimes missing from surveys (such as imputed rents for owner-occupied housing) and that NAS methods are more standardized across countries, since all countries are supposed to follow the UN Statistical Division’s _System of National Accounts_ (SNA), although, in practice, compliance with SNA guidelines appears to be quite uneven across developing countries.

> 32 Given the time span of their study, Bourguignon and Morrisson (2002) had no choice. Sala-iMartin did have a choice, though computationally convenience appears to have dictated his use of national accounts data, combined with aggregate summary tabulations of the relative distribution.

19
the domestic price P moves up to the previous international food prices _P_<sup>_W_</sup> , and domestic import demand moderates from _M_ to _M’_ . In this case the fiscal burden associated with the subsidy remains unchanged, but the domestic food price increases by the amount of the subsidy _s_ .

If the world food price _P_<sup>_W_</sup> falls down to the domestic price _P_ , then the government might decide not to pay the subsidy _s_ per unit of imported food. This will have the effect of keeping the domestic food price stable at _P_ , and food consumption at _M_ . In this case government interventions related to prices subsidies might have an asymmetric effect on domestic prices. Under asymmetric transmission, increases in world food prices will be accumulating and pushing food price levels up. Volatility in global food prices would, therefore, be especially harmful, even in countries with extensive subsidy programs.

In general, the reasons behind the asymmetric transmission of prices are often complex (Meyer and Cramon-Taubadel, 2004), and associated with a number of factors.<sup>14</sup> There is evidence that adjustment issues at the wholesale or retail level of distribution might be causing domestic prices to be downward sticky. Uncertainty of whether food price shocks are permanent or transitory, along with political uncertainty in some MENA countries, might also exacerbate market reluctance to respond to food price signals. Non-competitive practices in the domestic and/or international market will also contribute to the asymmetric response. In fact, Morisset (1998) finds that declines in world commodity prices are either not transmitted or transmitted only imperfectly to domestic consumer prices in developing countries, precisely because of suspected uncompetitive practices.

Thus, it is unclear _a priori_ to what extent world price fluctuations are transmitted to domestic food prices in MENA. Moreover, it is likely that the effects are rather short-run. The main factors that could impact on the pass-through from world to domestic food prices will be exchange rates, transport and distribution costs, as well as domestic market distortions, including food subsidies. The world average pass-through effect are probably in the order of about 0.3, from what we believe are the most credible empirical studies.

> 14 Albers _et al._ (2011) provide evidence of non-linearity of international food price transmission into domestic prices for a number of Mediterranean countries in the MENA region.

-15-
increase with lower incomes is consistent with children playing a different role, perhaps helping in a business with less regard to human capital accumulation foregone.

Again, the sampling for the LIC and LMIC samples for sure are not representative and what we may be finding is simply that people who can afford smart phones behave similarly around the world. Still, either LMIC governments have the capability to, at least, coral the elites, or, again, are simply providing a coordination mechanism.

Cancelling public events never enters significantly with full time fixed effects although the point estimates are often in the -6 to -10 range. The restriction that most robustly reduces mobility among the LICs is closing of public transport, accounting for a massive 16.5 points. In UMICs, and arguably in HICs, the value is a third of that. This would seem the most potent tool of control in the poorest countries.

Public information campaigns curiously enter positively and significantly in LMICs and almost in UMICs with coefficients of roughly 7-10. The intuition is not clear, but it may be the case that guidance on washing hands and wearing masks makes individuals feel more in control and protected and hence, the net impact is to increase mobility.

Restrictions on internal movement have large and significant effects (12, 14.3) in LMICs and UMICs, with much less impact in HICs and virtually none in LICs. In the latter case, this may testify to difficulty in enforcing such shelter-in-place ordinances relative to, for instance, shutting down public transport.

In sum, in HICs, and LMICs, the voluntary component is still as or more important as NPIs. UMICs look quite similar to HICs with the exception of anomalous lack of impact of case incidence, and the large impact of restrictions on internal movement which it shares with LMICs. It may be that in fact, LMICs and UMICs are more effective in enforcing such measures. Overall, for LICs the voluntary component is absent and the only NPI that appears to have any effect is closing public transportation. Again, with the caveat that cell phone coverage in such countries is around or under 20% of the population, this is consistent, again, with limited state capability and more resistance from the population to stop working.

Again, Annex IV presents the complementary regressions on residential mobility and finds patterns that mirror those presented above.

11
liquidity are included.12 Endogeneity of the regressors results also from probable measurement errors and, in any case, one must allow for time invariant firm specific components of the error term. For all these reasons, all the equations are estimated by the Generalized Method of Moments in first differences to eliminate the firm specific effects. Values of the regressors lagged at least twice are used as instruments (Arellano and Bond (1988) and 1992)). All equations contain also year dummies that capture, among other factors, changes in the relative cost of short term versus long term debt.

In both countries there is an element of persistence, in the sense that lagged maturity has a (positive) and powerful effect on maturity today. The most robust result is that the length of maturity is an increasing function of the percentage of fixed assets out of total assets. This confirms the positive prediction of the models by Myers (1977) and by Hart and Moore (1994) that the maturity of assets and liabilities should be matched, just as prescribed by common wisdom. The positive coefficient on the variable ASS also has a collateral based explanation. If fixed assets are the ones more easily collateralizable, and long term debt is the form of debt that is more likely to be backed by collateral, we would expect a positive association between the maturity composition of assets and liabilities.

We allow for a non linear effect of size (proxied here by the log of real sales) on maturity by entering both the log of real sales and its value squared (these results 12 We have also experimented with including only lagged values of ASS and we have obtained results that are similar to the ones reported in the text.

16
<mark>not lead to equal outcomes unless the factors behind the differential returns are rectified. Moreover, these studies have been largely limited to Africa. This is partly because it is more common for male and female farmers to manage different plots in Africa, facilitating gender differentiated analysis, and partly because high-quality nationally representative household surveys had recently become available in some African countries (Kilic, Winters and Carletto 2015). Research on this issue is surprisingly scarce in countries outside the Africa region, including South Asia.</mark><sup>2</sup> <mark>Sri Lanka provides an interesting case to study the productivity gender gap since the Household Income and Expenditure Survey (HIES) is representative at the national level and separately contains detailed information on agricultural production such as land, inputs, and crop mix at the level of the individual farmer. Initial analysis of the data reveals a stark contrast to the previous literature: it shows that productivity measured by yields (defined as output value per acre) is</mark> _<mark>higher</mark>_ <mark>among female farmers than male farmers.</mark> <mark>This paper explores the sources of this unconditional female productivity advantage using multivariate regression analysis and Oaxaca-Blinder decompositions. We find that the leading contributor to this productivity gap in favor of women is the smaller size of the plots that women cultivate, reflecting the inverse relationship between farm size and agricultural productivity commonly found in the literature. A</mark> nother important source is the gendered pattern of crop mix: women tend to cultivate more high-value, export-oriented crops than men, who are more likely to grow paddy, which suffers from low productivity. <mark>Male farmers, on the other hand, have greater access to agricultural resources, which helps offset partly their initial productivity disadvantage.</mark> <mark>The contribution of this paper to the literature is threefold. First, this is the first study to analyze the agricultural productivity gender gap using nationally representative data in Sri Lanka, thereby contributing to the scarce literature in South Asia on this topic. While the data primarily captures smallholder farmers due to the nature of the household survey, the setting is particularly interesting as it starts out from an unconditional productivity gap in favor of women which, to the best of our knowledge, has not been observed in any other country. Second, a particular focus is on analyzing how the gendered pattern of crop mix would influence the productivity gender gap among smallholder farmers. While paddy, the main staple crop in Sri</mark>

> 22 One exception is Mahajan (2019), which studies the agricultural gender gap using a nationally representative survey in India.

3
The proportion of children and youth is slightly larger in the countryside than in urban areas. A higher share of the working age population live in urban areas, so urban households should be better able to feed their children than those in rural areas. Hence the overall dependency ratio is larger in rural and non-metropolitan urban areas than in Portau-Prince. The average household is slightly larger in the former areas (4.6 and 4.7, respectively) than in the capital (4.5).

Table 2.4: Age distribution, 2003

|Age|Urban(%)|Rural(%)|Total(%)|
|---|---|---|---|
|0-4|10.21|12.86|11.79|
|5-17|33.31|35.45|34.59|
|18+|56.47|51.69|53.62|
|Haiti|100|100|100|

_Source:_ IHSI 2003 _._ Demographic trends have so far not lowered the dependency ratio, and it has therefore contributed negatively to poverty reduction in Haiti. This trend is likely to deepen further in the future if Haiti does not actively implement reproductive health programs.

Table 2.5: Average Household Size by Income Group and Place of Residence, 2001

|Artibonite|Center|Grand-<br>Anse|R<br>North|egion<br>North-<br>east|North-<br>west|West|South|South-<br>east|Metro-<br>politan|Locat<br>Urban|ion<br>Rural|Total<br>Haiti|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||Ind|igent|||||||
|5.0|5.2|5.2|5.4|5.2|4.9|4.9|5.1|5.2|4.8|5.3|5.2|5.1|
|(2.4)|(2.4)|(2.5)|(2.6)|(2.7)|(2.3)|(2.4)|(2.6)|(2.6)|(2.1)|(2.6)|(2.5)|(2.5)|
||||||P|oor|||||||
|4.8|4.9|5.0|5.2|5.1|4.8|4.7|4.9|4.9|4.9|5.1|4.9|4.9|
|(2.4)|(2.4)|(2.5)|(2.6)|(2.7)|(2.3)|(2.3)|(2.5)|(2.6)|(2.2)|(2.6)|(2.5)|(2.5)|
||||||Non|poor|||||||
|2.6|3.5|3.4|4.2|3.8|3.1|3.9|3.8|3.2|4.2|3.6|3.3|3.6|
|(2.0)|(2.2)|(2.1)|(2.7)|(2.1)|(1.8)|(2.8)|(2.3)|(2.0)|(2.5)|(2.4)|(2.1)|(2.3)|
||||||T|otal|||||||
|4.4|4.7|4.7|5.0|5.0|4.6|4.4|4.7|4.5|4.5|4.7|4.6|4.6|
|(2.5)|(2.4)|(2.5)|(2.7)|(2.7)|(2.3)|(2.4)|(2.5)|(2.6)|(2.4)|(2.6)|(2.5)|(2.5)|

_Note:_ Standard deviations in parentheses.

_Source:_ Own calculations based on HLCS 2001.

The typical extremely poor or poor household has more young members than does a nonpoor household. In Haiti, extremely poor households in rural and metropolitan areas have on average 2.2 and 1.7 household members below 15 years of age, respectively (see Section 3 for a definition of indigent, poor, and nonpoor). This compares to the average nonpoor household, in which only 0.9 and 1.2 members are below the age of 15. (Table 2.6) Extremely poor households therefore have about twice as many children as do 8
transition perspective, both in terms of financial risk and in the potential for CGSs to support those sectors through the energy transition: We furthermore compare the transition risk of individual CGSs _k_ by summing the sectoral transition risk score over all _n_ sectors:

**To assess the vulnerability of CGSs to physical risks, we map spatial exposure data at the second administrative level to vulnerability data from Think Hazard.** We obtain the full data set from Think Hazard, covering wildfire, coastal floods, river floods, extreme heat, drought, earthquakes, and landslides. We exclude earthquake risk since it is not climate related. We then map our data to NUTS 2 by combining administrative regions in the Think Hazard data set. For those regions where there is more than one administrative region per NUTS 2 region, we average the Think Hazard score. Next, we sum the regional exposures in our sample per risk category in Think Hazard (high, medium, low, and very low risk). For missing Think Hazard data we use the no data label. For coastal flood we furthermore introduce a “non-coastal” category representing zero risk.

**We also map sectoral exposures to a set of physical risk sensitive sectors.** We use two classifications for sectoral climate change sensitivity. The first is based on Lazo et al. (2011) who analyze the weather sensitivity of US economic sectors. Agriculture, mining, manufacturing, finance and insurance, and real estate are identified as being highly impacted by annual weather variability. For these sectors year-to-year variations in weather characteristics are found to affect economic performance by 10 percent or more. A second classification is based on a set of sectors identified by Skougaard et al. (2017), who capture sectors that are vulnerable specifically in the European context. Vulnerable sectors include agriculture, mining, utilities, transportation and storage, accommodation and food, professional, scientific and technical activities, and human health. The detailed mapping can be found in Annex 3.

**As a final step, we conduct a stylized stress test to assess potential financial losses that CGSs could incur under adverse climate-related scenarios.** For this we combine (i) scenario variables on increases in the probability of default (PD) per sector obtained from the European Central Bank (ECB); (ii) CGSs’ specific estimates on baseline PD and loss given default (LGD); and (iii) CGSs’ specific exposure data per sector (exposure at default, EAD). We present the ECB scenario variables in Annex 4. Our first scenario reflects a short-term orderly transition scenario compared to a scenario where almost no action would be taken globally to reduce GHG emissions

9
licensing that took place during the sample years and brings forward two important points. The first is that the reforms of the 1980s were quite significant in terms of the percentage of manufacturing output, employment and capital affected. Cumulatively, 2. . of output and employment had been de-licensed as of **1990.** Hence, studies that ignore the pre-1991 changes in the licensing regime provide misleading estimates of the impact of the **1991** reforms. The second is that de-licensing in **1991** was not across the board as is the common assumption in most studies. After **1991,** 1'. of manufacturing output and **11%** of employment remained under compulsory licensing though some of these industries were gradually de-licensed in **1993** and 1994. The de-licensing measure used **by** Sharma (2006a) is a dummy variable at the 4-digit industry level denoted **by** _Dekt_ that is equal to one in all years greater than equal to year t if industry **_k_** was de-licensed in year t. Since our **EG** index is computed at the **3-** digit industry level, we need to calculate the proportion of employment in the 3-digit industry that was de-licensed as of year t. Hence, our measure of industrial de-licensing is given **by** **_Nj_** _DEL t_ **_= Ek Dcktykt_** where **_k_** indexes the 4-digit industries within the 3-digit industry _j_ and **Ek-1** **_Ykt_** _Y_ refers to employment.

Data on trade policy are obtained from Das **(2003).** The author computes the effective rate of protection _(ERP)_ for 3-digit Indian manufacturing industries in four sub-periods: **1980-81** to **1985-86, 1986-87** to **1990-91, 1991-92** to **1994-95,** and **1995-96** to **1999-00.23** We also use tariff data from Topalova and Khandelwal (2011) which are available for every year but only from **1987** to **1999** therefore reducing our sample size. However, these data have rich cross-sectional and time-series variation **-** which the ERP data lack. We estimate all our main specifications with both measures of trade protection. We use the FDI deregulation variable from Sivadasan **(2009)** which is a dummy variable indicating which 4-digit industries were FDI-deregulated in **1992.24** We combine this dummy variable with the **ASI** data to calculate the proportion of employment in a 3-digit industry that was exposed to FDI. Hence, our **_Nj_** measure of FDI deregulation is given by _FDIjt FDIktyt_ where **_k_** indexes the 4-digit **Ek=1** Ykt industries within the 3-digit industry **_j_** and _Y_ refers to employment.

> 23Das **(2003)** calculates these ERP measures of trade for **72** 3-digit industries. In order to use all industries in our analysis, we use the average of his ERP measure for the corresponding 2-digit industry and in some cases, the economy-wide average. Note that for any given industry, the ERP has a constant value in all years within each **of** the four sub-periods.

> 24 The announcement of this reform was made in August **1991,** but its implementation began only in **1992.**

18
assess how urbanization is linked to poverty and welfare measures. The sample for the HIES 2016/17 is explicitly designed to produce reliable poverty estimates at the three levels, and is implicitly designed to produce an urban sample large enough to understand Bangladesh’s urbanization patterns.

# **3.2. Data used for the sampling design**

The data used for the design of the sample for the HIES 2016/17 come from the latest round of the HIES collected in 2010 and include 12,240 households. The HIES 2010 sample was designed to provide reliable poverty and welfare statistics only at the division level for both urban and rural areas.

Designing a household survey sample often entails using data from a previous household survey to estimate the key parameters needed to determine the sample size and how to allocate it across strata. In the case of Bangladesh, the latest round of the HIES was collected six years ago. Given the rapid growth observed in the country over the past decade, however, it is not clear whether these data provide a good representation of the actual living standards in the country or not. Still, previous household survey rounds usually provide the best source of information to design a sample.

The main goal of Bangladesh’s HIES 2016/17 survey was to estimate reliable poverty rates at more disaggregated levels than the HIES 2010 survey.<sup>7</sup> Standard errors of mean poverty rates estimated at the district-level poverty using the HIES 2016/17 might end up being a little bit higher than what is predicted under the proposed sampling design because reliable districtlevel poverty rates cannot always be constructed based on the HIES 2010. That is, some of the parameters estimated from the HIES 2010 to design the sample for the HIES 2016/17 might not provide accurate information of living standards in 2016/17. To address this concern, we analyze the robustness of the proposed sampling design for the HIES 2016/17 in several ways. The results of this robustness analysis are presented in Section 5.

> 7 The HIES 2016/17 survey instrument (i.e. questionnaire) is very similar to the one used in the HIES 2010 and field logistics are expected to follow similar protocols in terms of the survey implementation.

8