rafmacalaba/datause-encoder-data huggingface dataset:
and those with foreign background (basically immigrants' descendants born locally from one of two foreign-born parents)<sup>13</sup> .

- As discussed above, emigration rates are provided for 195 territories in 2000 (191 UN member states, Vatican, Palestine, Hong Kong, Taiwan, Macao minus one Korean country). The world configuration changed between 1990 and 2000. Czechoslovakia split into the Czech Republic and the Slovak Republic, the ex-USSR seceded into 15 countries (7 on the European continent and 8 on the Asian continent), Ex-Yugoslavia seceded into 5 countries, Eritrea and East Timor emerged as a independent countries in 1993 and 2002. On the contrary, Germany and Yemen were unified. Consequently, we distinguish 174 countries in 1990 (the ex-USSR replaces 15 countries, ex-Yugoslavia replaces 5 countries, ex-Czechoslovakia replaces 2 countries). For homogeneity reasons, we aggregated East and West Germany as well as the Democratic Republic and the Republic Yemen in 1990. In 1990, the ex-USSR totally belongs to the European area<sup>14</sup> .

- A related issue concerns the dependent territories. Each dependent territory is linked to a nation. Individuals born in these territories have the unrestricted right to move to and to live in the nation. We naturally consider them as natives of the sovereign nation. Once the concept of foreign born is chosen, it means that they should not be considered as immigrants if they move to the sovereign state (internal migration). They should only be considered as immigrants if they move to another independent state (external migration). This criterion is especially important for U.S. dependent territories (such as Puerto Rico and the US Island Areas such as Guam, etc.), UK overseas territories (Bermuda, Anguilla, etc.), French dependent territories (such as Guadalupe, Reunion, etc.), Denmark (Greenland and Faroe Islands, etc.) or around Australia and New-Zealand (Cook Islands, Niue, Tokelau, etc.).

- For example, in accordance with the US Census Bureau definition, we consider that one million Puerto Ricans living in the United States are U.S. natives but not immigrants. This considerably reduces the total stock of Puerto Rican emigrants. We have computed on the same basis the emigration stock for the other dependent territories, except for Taiwan, Hong Kong and Macao which are assimilated to independent countries. Then, given the small numbers obtained, we have eliminated Northern Mariana Islands and Western

> 13 Data by foreign background are provided in the Netherlands, France and Scandinavian countries. See Alders (2001) for the Netherlands or Ostby (2002) for Norway.

> 14 Note that aggregating appropriated stock data would allow computation of emigration rates for ex-Yugoslavia, the ex-USSR and ex-Czechoslovakia in 2000.

9
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has not contributed much to Congo’s growth—it could contribute much more**

a. Contribution of infrastructure to annual growth per capita, 2001–5 versus 1991–5,% <!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>telecom electricity roads<br>% of GDP<br><!-- End of picture text -->

- b. Potential improvements in growth per capita if infrastructure were improved to level of Mauritius, %

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>-1<br>telecom electricity roads<br>% of GDP<br><!-- End of picture text --> _Source_ : Calderon 2008.

The infrastructure network has the potential to interconnect with the networks of neighboring countries. The power network is already closely linked with that of the Democratic Republic of Congo. In the transport sector, a road corridor—functional but in poor overall condition—links Brazzaville and Pointe-Noire and extends to the borders of Gabon to the west, Cameroon and Central Africa to the far north, and Angola and the Democratic Republic of Congo to the south and southeast. The Republic of Congo also has important rail and river corridors.

This report begins by reviewing the main achievements and challenges in each of the Republic of Congo’s major infrastructure sectors, with the key findings summarized below (table 1). Thereafter, attention will turn to the problem of how to finance Kenya’s outstanding infrastructure needs.

4
back-better” experienced by firms in Germany after a major flood in 2013 was also partially attributed to the availability of government aid, and banks’ willingness to fund re-investments (Noth and Rehbein, 2019). Manufacturing firms, which received the _most financial aid_ post 1959 Ise Bay Typhoon in Nagoya City, Japan, had higher chances of remaining viable as opposed to those in retail and wholesale sectors (Okubo and Strobl, 2021).

# **5 Empirical Benchmarking of Firm Adaptation Progress**

Given the high dimensionality of the adaptation strategy set, how can a researcher using observational data actually detect whether firms are becoming resilient to climate shocks over time? The economic definition of firm level ‘adaptation progress” is that a firm’s willingness to pay to not face a weather shock is declining over time. The ideal measure would be a firm’s willingness to pay to not face a weather shock. The firm’s willingness to pay would reflect the difference between its profits with and without the shock.

Empirical researchers have access to better data. With the rise of nation level geocoded administrative data sets such as the Census of Manufacturers and the Census of Services, there is an increased capacity to track the economic performance of firms over time. By merging in data on the location specific weather events that have taken place, observational econometrics approaches can used to estimate ”climate damage functions”. These are reduced form regressions such as the one presented below.

In this regression, the unit of observation is firm i in sector j located in city l at time t. The dependent variable is a measure of the flow damage (measured in dollars) suffered by a firm. Controlling for firm attributes, the key explanatory variables are measures of the firm’s recent exposure to extreme weather and the firm’s expectations of future weather conditions. Most empirical studies do not include this second term. Assuming that the error term is uncorrelated with the weather realization, this regression yields an estimate of the average effect of a weather shock on a firm specific outcome variable. It is important to note that this approach does not explicitly model the general equilibrium effects of how the weather shock affects market prices for output or labor. In these empirical studies, the prices are taken as given.

Estimates of this equation are useful but up until now they have not explicitly modeled 19
||(75.12)|(101.9)|(78.79)|(76.92)|(85.06)|
|---|---|---|---|---|---|
|KVIP|130.1|66.01|113.8|123.3|138.4|
|Bucket/pan|(133.6)<br>468.6***<br>(99.81)|(121.0)<br>283.6***<br>(96.92)|(129.0)<br>419.4***<br>(113.9)|(150.1)<br>455.4***<br>(96.07)|(133.5)<br>431.9***<br>(104.2)|
|Public toilet|-70.26|5.108|-38.93|9.324|-42.96|
||(60.09)|(68.16)|(60.24)|(68.14)|(59.05)|
|Waste disposal:||||||
|Collected|ref.|ref.|ref.|ref.|ref.|
|Burnt by household|-264.4|-285.2|-209.4|-278.2|-239.0|
||(255.9)|(238.2)|(247.4)|(292.9)|(248.8)|
|Public dump|96.71|44.96|81.63|72.89|67.29|
||(113.8)|(98.42)|(123.7)|(134.9)|(108.7)|
|Dumped indiscriminately|-269.6*|-408.5***|-281.9**|-359.7**|-237.7|
||(142.3)|(132.4)|(139.1)|(151.2)|(149.2)|
|Expenditure quartiles:||||||
|Q1|||||ref.|
|Q2|||||42.94<br>(110.7)|
|Q3|||||131.6<br>(124.4)|
|Q4|||||226.4*<br>(134.5)|
|NEIGHBORHOOD FIXED EFFECTS|NO|YES|NO|NO|NO|
|Constant|670.2|566.4|722.3|-151.6|486.3|
||(515.7)|(561.4)|(526.4)|(461.9)|(545.6)|
|Observations|1,002|1,002|1,002|816|1,002|

# **Appendix 6 - Details on loss calculations**

As for loss due to missed days of work, we combined information from the flood impact module and household member roster to assess the total labor loss. We initially computed two different values based on two strategies. The first strategy divides total annual household expenditure with number of economically active household members (above age of 5) and then we divide that number by number of work days in a year based on 20 monthly work days. This gives us a rough estimate of the value of one missed work day provided all economically active households members work full time and that all the value generated to be able to sustain their expenditure level is generated by labor income. The second strategy we used is to compute estimated salaries by occupation by regressing number of household members in each occupation group on total household expenditure. This strategy estimates the attribution of having a household member working in a specific occupation on the overall household expenditure.

Table 21 contains the result of this regression and the values can be used as estimates of wages for different occupations – assuming a straightforward relationship between labor and expenditure. For the first 46
nearly overlapped with the subsequent crisis and thus complicates efforts to link layoffs directly to the financial crisis. Nonetheless, evidence from quarterly data suggests that shocks associated with the crisis, which hit in the third quarter of 2008, may have had a stronger effect on gross domestic product (GDP) and employment than earlier policy shocks.

The events of September 2008 raised concerns that China would face a sharp drop in GDP. The global financial crisis changed the growth trajectories of all major world economies and plunged Japan, the United Kingdom, and the United States into deep recession (Figure 1b). By the first quarter of 2009, annual growth rates were negative in major developed economies; and while the growth rates of China and India were still positive, they dropped steeply in both countries. In comparison to other major economies of the world, China experienced one of the largest changes in annual growth rates from 2007 to 2008. From the first and second quarter of 2008 to the fourth quarter of 2008 and the first quarter of 2009, China’s annualized quarterly growth rate fell from more than 10 percent to 6 percent (Figure 1b).

Responsibility for the slowdown in China’s GDP lies squarely with the global financial crisis, given that GDP growth declined well _after_ contractionary domestic monetary and employment policies were implemented and that the timing was consistent with a sharp drop in export demand: the negative export demand shock contributed more to the contraction in aggregate demand that slowed China’s economy. Customs data on China provides useful evidence on the scale of the shock to its exports. The aggregate decline in trade volume shown in Figure 2 is driven by decreased exports to the European Union and the United States of 22.1 and 17.1 percent, respectively, over the same periods in the previous year. Given these sharp declines, it was apparent that China’s economy would be hit hard by the decline in exports if no alternative sources of demand for goods and services emerged.

As shown in Figure 3, the sharpest shock to China’s economy occurred in the manufacturing sector, which is in the secondary sector in China’s industry classification system (the primary sector comprises agriculture and mining, and the tertiary sector includes services and trade).

4
Oddly enough, our model of actual lending (as opposed to capital available), shown in Table 21, is less informative. The fit is weaker, and a formal test rejects the appropriateness of the endogenous switching model, which means that the variables shown in Table 21 apply equally well to VFs that borrow to on-lend and those that do not. The fit is largely driven by two variables: VFs that received more initial capital also lend more, and VFs that borrow to on-lend actually do lend more. Lending is somewhat lower when a higher proportion of borrowers are women; a possible interpretation is that these are areas where loan demand is relatively low.

|**Table 20. Estimation Results of Model of the Det**|**erminants of**<br>Robust OLS|**Additional Fu**<br>Endogeno|**nds Available**<br>us SwitchingRe|**to Lend**<br>gression|
|---|---|---|---|---|
||||No|Probit,|
||Full sample|Onlending|onlending|<br>switch|
|Supply: Fund Capital|||||
|Initial VF capital|0.073**|0.035|0.084**|0.000***|
||_(0.033)_|_(0.038)_|_(0.038)_|_(0.000)_|
|VF allows for saving|154.421***|172.25**|144.43***|0.157**|
||_(22.075)_|_(86.675)_|_(18.483)_|_(0.066)_|
|Supply: Characteristics of VF Committee|||||
|Proportion who have some higher education|-76.750**|-213.362**|-32.955|-0.007|
||_(32.747)_|_(92.138)_|_(29.318)_|_(0.127)_|
|Proportion who have accounting experience|301.128<br>_(198.471)_||||
|Accounts are computerized (yes=1)|118.218***<br>_(28.933)_|268.64***<br>_(96.492)_|81.022***<br>_(23.519)_|0.067<br>_(0.065)_|
|VF would cut loans equally if funds were short (yes=1)|-46.188*|9.594|-44.255**|-0.016|
||_(23.865)_|_(114.351)_|_(18.245)_|_(0.067)_|
|Demand: Village Size and Organization|||||
|Village has a market (yes=1)|70.581**|162.55*|29.789|0.017|
||_(29.269)_|_(89.593)_|_(25.654)_|_(0.064)_|
|Demand: Household Characteristics|||||
|Proportion of households who are self-employed farmers|251.015***<br>_(80.795)_|597.81**<br>_(280.65)_|-20.972<br>_(52.068)_|0.746***<br>_(0.133)_|
|Expenditure per capita|11.171***||||
||_(4.023)_||||
|Loan terms and conditions|||||
|VF would borrow more if funds were short|-|||1.239***<br>_(0.092)_|
|VF could lend out another 1m baht safely|-|||<br>0.538***<br>_(0.086)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||<br>0.529***<br>_(0.067)_|
|VF borrows to on-lend to members|927.096***<br>_(40.860)_|||<br>-|
|Memo items:|||||
|Number of observations|2,528|||2,593|
|R2|0.328||||
|pfor endogeneity||||0.088|

Source: Village Fund Survey 2010 and SES 2009. Robust standard errors in parentheses. Variables included based on OLS backward stepwise with p=0.2 cutoff. * p<0.05, ** p<0.01, *** p<0.001.

32
**Figure 3**

# **Major Recipients Of Private Capital Flows, 1991-94**

<!-- Start of picture text --> Rars: Ihzdiidnal :oantYV yihare (f total pnvatc Lime: Cumulative share of<br>capitalle ows to developingl countflc'. tonal private capital fRows.<br>30 (/2, (percent) 100<br>25 8<br>20<br>60<br>is<br>40<br>10<br>20<br>5<br>0 0<br>Note :Thc remaining 16 ,xrcant of wotal priva[s capital flow. is accomr-tad for by other countries not mcntinned hcre. Includcs DRS and<br>non-DRS cournrie,.<br>.Sourre AVeorld Bank's Debtor Reporting Syslem and staff estimates.<br><!-- End of picture text --> In the first half of 1994 one observed corrections in several overvalued emerging stock markets and a sharp cutback in portfolio flows (through bonds and equities) to some developing countries. Even though there has been a slowdown in the growth of private capital flows to developing countries since 1994, certain elements of these flows have been more stable than others. Notably, foreign direct investment has continued to grow. Since 1990 the share of developing countries in global FDI has been increasing sharply.<sup>4</sup> Recent developments in Mexico (with a 40 percent devaluation of the Mexican peso and a doubling of short term interest rates between December 20, 1994 and January 4, 1995) have also shaken investor confidence and led to portfolio outflows. These episodes have renewed concerns about the sustainability of private capital inflows to developing countries over the medium term. Moreover, for individual countries, managing the volatility of these flows brings about additional macroeconomic and financial sector

> _4_ Source: World Bank, _WYorld Debt Tables, 1994-95,_ Volume 1

6
Equation (8) therefore suggests that gender differences in returns to capital may arise from the following factors, which we will next investigate empirically:

- (a) _Differences in access to capital from the external market:_ Ceteris paribus, returns should be higher for owners with less access to external credit. One reason given for focusing microfinance on women is that they typically have more difficulty accessing financial services.<sup>11</sup> If so, then women should have higher returns.

- (b) _Differences in the shadow cost of capital within the household:_ Ceteris paribus, returns should be higher for owners from poorer households, who are less able to finance business investments from own savings. Advocates of focusing microfinance on women also claim that women are more likely to be poor<sup>12</sup> , in which case they should have higher returns. However, another reason for microfinance’s focus on women is the belief that women are more likely than men to spend the proceeds on improving family welfare, and investing in children’s health and education.<sup>13</sup> This may also lead to a high shadow cost of capital within the household, causing women to devote less of the treatment to their business. This could potentially explain why there is less of a mean treatment effect for women than men. Nevertheless, the higher shadow cost of capital for women should also mean higher returns on the capital that is invested.

- (c) _Differences in entrepreneurial ability_ : If ability and capital stock are complements in production, then, conditional on a given set of internal and external resources, individuals with higher entrepreneurial ability will be more constrained and thus have higher returns. If women entrepreneurs have less business ability, this could potentially explain the lower returns.

- (d) _Differences in the sector of production_ : An individual will be more constrained, and thus have higher returns, if their industry has a greater efficient scale. If women select into industries with less scope for growth, then even with the same access to credit and entrepreneurial ability as men, they will have lower returns.

> 11 See e.g. Niethammer et al.  (2007); SEAGA (2002)

> 12 See e.g. Niethammer et al.  (2007); Burjorjee et al.  (2002);  FINCA (2007).

> 13 See e.g. Yunus (2004), Burjorjee et al.  (2002).

16

-  -
<!-- Start of picture text --> Fig 4 Share of total operated area by holding<br>size: all India, 1953-54 to 1982<br>100%<br>80%<br>0sp X600 ---- *>6ha<br>0 o M~40<br>0 1- 2ha<br>0<br>20% 4°0% 0.4 - 1ha<br>0% ,~~~~~~~~~~~~~< 0.4 ha<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text -->

# LANDLESSNESS AND ABSENTEE LAND OWNERSHIP

There is a widespread perception that landlessness has increased over the last few decades. While this is true in terms of absolute numbers, in relative terms the incidence of landlessness has not changed significantly since the 1960s in terms of ownership or operational holdings (see Table 3). For India as a whole, the share of households not owning any land remained at around 11 per cent in 1982, while the share of households not operating any land fell slightly from 27 per cent to 24 per cent. However, the share of households owning sub-marginal holdings - often simply homestead land - doubled between 1961-62 and 1982 (from 17 per cent to 32 per cent), in part through the redistribution of ceiling-surplus land.

Commentators vary in their interpretations of the significance of these trends. Chadha (1994), for example, recalculates NSS data on the share of households _owning_ no land to include those with only homestead land but no arable land. On this basis he finds that functional landlessness has increased in relative terms since 1953-54 in most states, particularly in agriculturally progressive states (Punjab, Haryana, Tamil Nadu, Gujarat, Andhra Pradesh, Maharashtra), and has fallen only in Assam, Kerala, and Rajasthan. On this view, land reforms have had little net impact, as they tended to redistribute only very small land parcels, while increased land market (sale-purchase) transactions in the 1970s (by recipients of ceiling-surplus land, among others) added to the share of rural households owning no arable land. Others (e.g. Agarwal 1994) draw attention to the fact that households owning even tiny holdings are significantly less likely to experience absolute poverty. A land base, however small, is argued to offer some security, collateral, and opportunities to increase incomes through livestock production or other land-based activities.

Lipton (1985) finds that it is only with respect to reliably watered land that even tiny holdings significantly reduce poverty risk (e.g. in states such as Andhra Pradesh or West Bengal). Using NSS data, confirmed by village studies, Lipton argues that where land is of poor quality or is ill-watered (as in Rajasthan or Gujarat), households with unirrigated, sub-marginal holdings still face a significant risk of transient poverty associated with variable rainfall. The clear implication is that land reform is most needed precisely where it is opposed (i.e. where land is best). Nonetheless, the incidence of chronic poverty among landless farm laborers is still much higher than among average landed persons.

16
restrictions arising from the fear factor. In the simulations, we capture this via the following increases in trade and transport margins: low case 5%, middle case 10%, and high case 15%.

# **V. Results**

## **_Impacts of AMR on the Global Economy_**

The simulated impacts of AMR on global GDP in 2017-2050 are shown in Figure 1. Compared to the baseline case, in the optimistic “low-AMR” scenario, the global economic output is projected to be 1.0% lower by 2030 and 1.1% lower by 2050. In the pessimistic “high-AMR” scenario, it would be 3.2% lower in 2030 and 3.8% lower in 2050.<sup>12</sup> In the “low-AMR” case, the costs, as measured by the reduction in GDP compared to the base case, will be a significant economic burden, while in the “high-AMR” scenario, the costs can be considered severe, especially since the costly impact endures over time.

**Figure 1: Substantial and Protracted Shortfalls in Global Economic Output**

<!-- Start of picture text --> WORLD REAL GDP<br><!-- End of picture text --> <!-- Start of picture text --> Base Low case Middle case High case<br>101<br>100<br>99<br>98<br>97<br>96<br>95<br>94<br>Base = 100<br><!-- End of picture text --> Given that the simulations were done using a general equilibrium model, economies do adjust to price signals caused by the AMR shocks. These adjustments lead to a reallocation of resources and a different pattern of new investments and capital accumulation. These model characteristics explain the flattening out of the trajectories after 2040 in Figure 1; by this time much of the adjustment of the world economy to shifts in relative prices and reallocation among

> 12 The “high-AMR” case presented here is similar to the results of the modeling done for the UK Review on AMR, “Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations, December 2014: including (1) Taylor et al. Estimating the Economic Costs of Antimicrobial Resistance: Model and Results, Santa Monica, CAA: RAND Corporation and (2) KPMG, “The Global Economic Impact of Anti-Microbial Resistance.”

11