rafmacalaba/datause-encoder-data huggingface dataset:
and inconsistent exchange rate policies (Dollar, 1992; Razin and Collins, 1999). A relatively stronger currency tends to raise the cost of imports (among them, intermediate inputs and capital goods) and has a detrimental effect on investment. Moreover the loss of competitiveness associated with the overvaluation could hamper the country’s ability to adjust internationally and reallocate resources more efficiently across the different sectors of economic activity. However, the literature on the growth effects of RER undervaluation is not abundant. As we mentioned above, Hausmann et al. (2005) and Rodrik (2008) have suggested that RER undervaluation may trigger growth.<sup>1</sup> If it is true that real undervaluation of the currency leads to higher growth, the relevant policy question is what type of policy shocks may cause RER undervaluations and how persistent these are.

To accomplish this task, we use RER misalignments based on Kubota (2009). This measure of RER misalignments is as deviations of the actual from the equilibrium RER. We estimate the fundamental RER equation using the following econometric techniques: (a) Johansen time series cointegration methods, and (b) pooled mean group (PMG) for non-stationary panel data.<sup>2</sup> This equilibrium level is derived from a theoretical model that guarantees intertemporal BOP equilibrium and equilibrium in the tradable and non-tradable goods market by solving for the current account dynamics and Harrod-Balassa-Samuelson (HBS) productivities. We calculate the RER misalignment using two different types of estimates for the coefficients of the long-run RER equation: the time series estimates (Johansen, 1998, 1991) and the PMG panel estimates.

The main goal of our paper is to test whether economic policies and regulations undertaken by the authorities affect the likelihood of keeping the RER undervalued and/or determine the size of the undervaluation. This will allow us to test whether the ― _mercantilist_ ‖ view of the exchange rate policy is empirically valid. To accomplish this task we gather an unbalanced panel dataset of 79 countries, of which 21 are

> 1 Recent research on the ―mercantilist‖ view of exchange rate policy suggests that the accumulation of international reserves by some countries such as China and Argentina are aimed at keeping the real exchange rate undervalued; therefore, promote growth through rising exports (Rodrik, 2008). Others suggest that accumulating reserves may soften the blow of adverse financial and real shocks –that is, demand for reserve hoarding is precautionary (Aizenman and Lee, 2007; Cheung et al. 2007). 2In order to compute our theory-based measure of RER misalignment a long-run RER equation from a theoretical model that considers the equilibrium real exchange rate (ERER) as the relative price of tradable to non-tradable goods. The building blocks of the model will follow Balassa (1964) and Samuelson (1964) for equilibrium in the tradable and non-tradable goods market, and Mussa (1984) and Frenkel and Mussa (1985) for the inter-temporal BOP equilibrium.

4
23

# <u>Indicator definitions</u>

Tables 1, 2 and 3 also show the definitions of the indicators. In choosing exact definitions of indicators, we have been guided by the same initiatives that guided us in our choice of indicators (see above), but also by the constraints imposed by the data and a desire to have a common definition irrespective of the data source. Our definitions sometimes differ from those used in reports and online tools derived from the same surveys we have used (e.g. DHS reports,<sup>16</sup> DHS STATcompiler,<sup>17</sup> MICS reports,<sup>18</sup> UNICEF website,<sup>19</sup> the WHS reports,<sup>20</sup> and the World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank,<sup>21</sup> which contains the data from the DHS and MICS reports), and from the WHO’s Health Equity Monitor<sup>22</sup> which also contains summary statistics at the population level and for wealth quintiles based on analysis of microdata from the DHS and MICS surveys. We summarize the differences in definitions between our definitions and others’ in Annex Table A1. Important examples to highlight include:  Generally, when computing percentages of the population covered by certain services (e.g. fully immunized), we exclude cases with missing information from the denominator. The DHS and MICS reports, by contrast, typically do not, and instead treat missing values the same as if the respondent had answered _No_ when asked about having accessed the respective service. As a result, DHS and MICS reports typically have a lower service coverage rates than us.

> 16 <u>https://dhsprogram.com/publications/publication-search.cfm?type=5.</u>

> 17 <u>https://www.statcompiler.com/en/.</u>

> 18 <u>http://mics.unicef.org/surveys.</u>

> 19 See https://data.unicef.org/. Population rates are also available for some indicators and some MICS surveys via the MICS COMPILER tool at http://www.micscompiler.org/.

> 20 <u>http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/whs/about.</u>

> 21 <u>http://databank.worldbank.org/data/source/health-nutrition-and-population-statistics-by-wealth-quintile. These data can be</u> more conveniently imported into Stata using the Stata module WBOPENDATA (Azevedo 2016).

> 22 Data available at http://www.who.int/gho/health_equity/en/0 and indicator definitions at <u>http://www.equidade.org/resources/indicators.pdf.</u>
This paper is based on company accounts. The analysis of sources and uses of funds has been done by looking at changes in the balance sheet items over time; a summary of this approach is shown in Table 1. The principal reason for adopting the balance sheet-based approach to the study of source and uses of funds is to facilitate the comparison of U.S. and Indian firms. The basic idea behind the balance sheet approach is that the firm's sources of funds come from decreases in assets and increases in liabilities while the uses of funds take place through increases in assets and decreases in liabilities.

As noted earlier, the measure of internal finance used in this paper is reserves and surplus (retained earnings) plus depreciation (table 1). The measure of stock market contribution or external finance (equity) used here is based on changes in the firm's paid-up capital emanating from changes in the number of shares as well as the price of shares.

However, it should be noted that there is another approach in the literature, following Prais (1976), that measures internal finance as retained earnings net of depreciation and compares it to net capital expenditures.<sup>4</sup> This approach is useful if the focus is on studying the financing of the growth of the firm in terms of net capital expenditures. This paper however has a different focus and examines the broader issue of total sources and uses of funds for the firm and therefore considers depreciation as a source of funds for the firm and compares it to the firm's gross capital expenditures.<sup>5</sup> In other words, replacement investment is considered as another use of funds by the firm. As noted by Prais (1976), one important consequence of this differential treatment of depreciation is that internal finance would me much more important if 4 Singh and Hamid (1992) and Singh (1995) among others follow this approach.

Mayer (1988, 1990), Corbett and Jenkinson (1994), and Samuel (1995b) also adopt this approach.

3
the bay. It has been observed that wind induced waves of up to 3.0 m height may occur under unfavorable conditions in the coastal regions (MCSP, 1993).Table 1 presents surge inundation characteristics for cyclones of varying strength in Bangladesh as documented by the MCSP (1993) (see Table 1).

According to the IPCC AR4, storm surges and related floods are likely to become more severe with increases in intense tropical cyclones in future (IPCC, 2007). For the Bay of Bengal, a study using dynamical models driven by RCM<sup>20</sup> simulations of current and future climates have shown large increases in the frequency of highest storm surges despite no significant change in the frequency of cyclones (Unnikrishnan et al, 2006).<sup>21</sup> Hence, from a practical perspective vulnerability of Bangladesh to cyclones/ storm surges may increase even more as a result of climate change.

# **3. Methodology and Estimates for Damage and Adaptation Cost**

In order to estimate potential damage and cost of adaptation to potential intensification of storm surges, this analysis has followed a 5‐step procedure:

1. Demarcation of potential vulnerable zone and projection of inundation depth from storm surges;

2. Identification of critical impact elements: assets and activities exposed to inundation risk; 3. Computation of potential damage and loss for a 10 year return period cyclone/ storm surges out to 2050; 4. Identification of existing adaptation measures and quantification of changes that will be required in a changing climate; 5. Costing of adaptation.

# **3.1 Demarcation of vulnerable zone and storm surge height:**

This analysis has considered a sea level rise of 27cm<sup>22</sup> , increased wind speed (10%)<sup>23</sup> and landfall during high tide<sup>24</sup> to approximate cyclones in a changing climate by 2050.

> 20 Regional Climate Model

> 21 For North Indian Ocean by 2100, a recent projection by Emanuel has indicated an increase in intensity of tropical storms, as measured by percent change in landfall power, for the Model for Interdisciplinary Research on Climate: MIROC (UN and World Bank, 2010).

> 22 Source: UK DEFRA (2007).

> 23 World Bank (2010).
on households without default female ownership, a linear test cannot reject the hypothesis the the two vouchers have an equivalent impact for households with default ownership (Test 2 under column three). Column (4) displays the unconditional, net co-titling outcomes, indicating no substantial differences between households with de facto ownership in either average outcomes nor responsiveness to vouchers.

Columns (5), (6) and (7) repeat this exercise, interacting the head’s share of total household income with voucher values. Households in which women provide a greater share of household income are slightly less likely to purchase a CRO, although this effect is not significant at the 10% level. There is also no concrete evidence that these households respond differently to either voucher. However, column (6) indicates households where women provide greater shares of income are significantly more likely to co-title, conditional on purchasing a CRO and are less responsive to gender vouchers.

In sum, we find that even small price incentives lead to nearly uniform co-titling, with no reduction in demand for titles. One interpretation of this result is that men are easily persuaded to sacrifice long-term bargaining power over household land in exchange for short-term cost savings. An alternative explanation is that co-titling actually has no such long term explanations. We have showed that households where women have stronger bargaining power are more likely to co-title, suggesting that this decision is taken seriously. We see this as suggestive, but far from conclusive, evidence that the stakes from co-titling are real. Ultimately, the resolution to that question will depend on the results of longer-term follow-up to this randomized titling intervention.

# **6 Conclusion**

In this paper, we presented preliminary results from a land titling experiment in Dar es Salaam, Tanzania, where we use targeted subsides to induce random variation in the price that land-owning households faced when purchasing a land title. In addition to these general price discounts, we reported impacts on overall demand for titling and female cotitling from conditional vouchers that required households to include a woman on the land title application in order to apply the full discount.

Willingness to pay for land titles is, on average, between $40 and $50. This is high as a proportion of owners’ incomes, but low relative to the unit costs of cadastral surveying required to produce legal titles. In short, demand is not sufficient to allow the Ministry of Lands to sell titles at a profit in low-income, unplanned settlements in Dar es Salaam. However, once the Ministry incurs the large fixed costs of cadastral survey work for a given neighborhood, our results suggest the Ministry would increase revenues by engaging in price discrimination to offer lower prices to low-income households.

Turning to the gender dimension of land titling, our results strongly suggest that, on average, both general and conditional subsidies have identical impacts on CRO adoption, revealing that households are not deterred by the requirement of co-titling women. Con28
<mark>Glewwe, P., and K. Muralidharan (2016): “Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications,” Handbook of the Economics of Education, vol. 5, 653-743.</mark> Global Education Evidence Advisory Panel (2020). “Cost-Effective Approaches to Improve Global Learning. What does recent evidence tell us are “Smart Buys” for improving learning in low- and middle-income countries?” Goldin, C., and L. Katz (2002): “The Power of the Pill. Oral Contraceptives and Women’s Career and Marriage Decisions,” _Journal of Political Economy_ , Vol. 110(4).

Grunseit, A., S. Kippax, P. Aggleton, M. Baldo, and G. Slukin (1997): “Sexuality education and young people’s sexual behavior: a review of studies,” Journal of Adolescent Research, 12(4):421– 453.

Gulesci, Selim & Meyersson, Erik & Trommlerová, Sofia. (2020). “The Effect of Compulsory Schooling Expansion on Mothers’ Attitudes Toward Domestic Violence in Turkey.” The World Bank Economic Review. 34. 464-484.

Hahn, Y., A. Islam, K. Nuzhat, R. Smyth, and H. Yang (2018): “Education, Marriage, and Fertility: Long-Term Evidence from a Female Stipend Program in Bangladesh,” Economic Development and Cultural Change, vol. 66(2).

Handa, S., A. Peterman, C. Huang, C. Halpern, A. Pettifor, and H. Thirumurthy (2015): “Impact of the Kenya Cash Transfer for Orphans and Vulnerable Children on early pregnancy and marriage of adolescent girls,” Soc Sci Med., vol. 141, 36 - 45.

<mark>Hanna, Rema, and Benjamin A. Olken. 2018. "Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries."</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 32 (4): 201-26.</mark> Heath, R., and A. Mobarak (2015): “Manufacturing Growth and the Lives of Bangladeshi Women,” Journal of Development Economics, vol. 115, 1-15.

Heckman, J., and C. Corbin (2016): “Capabilities and Skills,” NBER Working Paper <mark>22339.</mark> <mark>Hendren, N., and B. Sprung-Keyser (2020): “A Unified Welfare Analysis of Government Policies.” Quarterly Journal of Economics 135 (3): 1209-1318.</mark> <mark>Hidrobo, Melissa, Amber Peterman, and Lori Heise. 2016. "The Effect of Cash, Vouchers, and Food Transfers on Intimate Partner Violence: Evidence from a Randomized Experiment in Northern Ecuador."</mark> _<mark>American Economic Journal: Applied Economics</mark>_ , <mark>8 (3): 284-303.</mark> Hubacher, D., Olawo, A., Manduku, C., Kiarie, J., and Chen, P-L (2012). “Preventing unintended pregnancy among young women in Kenya: prospective cohort study to offer contraceptive implants.” Contraception, 86(5), 511-517 31
**Table 1. The Incidence of Significant Land Reallocation and Share of Households Affected (1995-2003)**

||**All**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Panel**|**A: Share o**|**f Villages**|**Experienc**|**ing Signifi**|**cant Land**|**Reallocat**|**ion**|||
|All|0.188|0.273|0.109|0.073|0.327|0.200|0.200|0.182|0.164|0.164|
|Anhui|0.167|0.556|0.222|0.000|0.056|0.167|0.222|0.111|0.056|0.111|
|Henan|0.181|0.188|0.000|0.063|0.438|0.188|0.250|0.250|0.125|0.125|
|Jiangsu|0.343|0.182|0.182|0.273|0.818|0.273|0.182|0.364|0.545|0.273|
|Shanxi|0.067|0.000|0.000|0.000|0.100|0.200|0.000|0.000|0.000|0.200|
|Number of villages|55|55|55|55|55|55|55|55|55|55|
||**Panel B: Sh**|**are of Ho**|**useholds A**|**ffected in**|**Significan**|**t Village L**|**and Reall**|**ocations**|||
|All|0.321|0.557|0.313|0.187|0.654|0.271|0.127|0.231|0.233|0.219|
|Anhui|0.319|0.648|0.472|0.000|0.676|0.348|0.110|0.121|0.276|0.180|
|Henan|0.282|0.596|0.000|0.478|0.798|0.282|0.101|0.293|0.224|0.125|
|Jiangsu|0.358|0.010|0.070|0.092|0.522|0.037|0.336|0.219|0.232|0.245|
|Shanxi|0.349|0.000|0.000|0.000|0.808|0.474|0.000|0.000|0.000|0.279|
|Number of households|<br>1858|799|291|273|947|722|796|536|520|581|

Note: Shares in Panel B are calculated conditional on the village experiencing a significant land reallocation during the year. Source: 2004 Village Governance Survey and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 35_
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are not aware of it. Thus in the model, if borrowing from Azteca or traditional banks, households need to have no default incidents in the past with them.

**3) Official proof of income.** This restriction only applies to clients requesting loans to traditional banks, who are required to be employed in the formal sector at the time of the loan request.

**4) Collateral.** Azteca and pawnshops require households to pledge durable goods as collateral to secure the repayment of their loans. The amount that a household can borrow is bounded by a limit that depends on the value of durable goods that households own.

In sum, the set of lenders that households have access to is determined by the following municipality characteristics and decisions that households make: Besides these four constraints that limit households’ access to suppliers, loan size is also bounded differently by lenders. The bounds on loans from Azteca and pawn shops are determined by the value of durable goods pledged as collateral.<sup>16</sup> The bounds on loans from traditional banks and friends and relatives are determined outside the model: Under this setting, households’ problem consists of selecting the optimal set of decisions (Ft<sup>h�; ih</sup> t<sup>�; sh</sup> t<sup>�) that</sup> maximizes their expected lifetime utility, defined as:

> 16The collateral requirements are considered exogenous in the model. Their values were obtained from CONDUSEF, Azteca’s Financial Reports and PROFECO. According to these sources, the average collateral required by a pawn shop in Mexico is three times the value of the loan . Thus �2 is set to 1=3. According to CONDUSEF and Banco Azteca’s reports, Banco Azteca requires a collateral equivalent to the value of the loan, hence �1 is set to 1.

17
7 Henan, Hubei, Hunan, Jiangsu and Shandong.<sup>7</sup> The CHNS income data are already adjusted for cost-of-living differences across provinces (and within provinces between rural and urban areas) through a price index constructed by the CHNS project team. The team’s information was used to deflate the 1997 income data to 1989 prices, distinguishing between different provinces, and within provinces between rural and urban areas. The deflated income data for the panel provinces imply an annual real rate of growth of per capita household income of 6.0%, somewhat lower than the 6.7% figure for China as a whole for this period reported by Ravallion and Chen (2004). The per capita household income Gini for the CHNS panel rises from 0.3954 in 1989 to 0.4191 in 1997, giving a value of _∆ G_ of 0.0237 (Table 1).<sup>8</sup> The levels of the Gini in both years are a good deal higher the national figures reported by Ravallion and Chen, though the annual percentage change in the Gini in the CHNS data (0.7%) is somewhat smaller than the annual percentage change over the period 1989-97 reported by Ravallion and Chen for China as a whole (1.2%).

_Table 1: Per capita income growth and inequality changes_<sup>9</sup>

||China 1989-97|Vietnam 1993-98|
|---|---|---|
|Growth of per capita income/consumption p.a.|6.0%|6.6%|
|Initial Gini coefficient:_G_0|0.3954|0.3223|
|Final Gini coefficient:_G_1|0.4191|0.3346|
|Change in Gini coefficient:_∆G _|0.0237|0.0123|

Note: Computed from panel data from 1989 and 1997 waves of China Health and Nutrition Survey (CHNS) and the two (and only) waves of the Vietnam Living Standards Survey (VLSS). CHNS is not nationally representative, but the 1993 wave of the VLSS was. Figures for China refer to income, but those for Vietnam refer to consumption. For further details of sampling and variable definitions, see text.

> 7 Liaoning was sampled in the CHNS in 1989, 1991 and 1993, but was dropped in the 1997 round, being replaced by Heilongjiang.  Neither is therefore represented in the 1989-1997 panel.

> 8  The Gini and all the various concentration indices were computed using the convenient covariance method (cf. e.g. Jenkins 1988).

> 9  Gini coefficients are computed on household-level data, but households are weighted by household size.  The Ginis therefore indicate the extent of income inequality _between individuals_ .
# **Appendix Tables**

**Appendix Table 1: The Classification of Food and Agricultural Products in SITC Revision 2**

|All Agri|culture(0+1+2+4-27-28)|SITC|Product Name|
|---|---|---|---|
|Raw Food|Meats and Dairy Products|00<br>011<br>0223<br>0251|Live animals chiefly for food<br>Meat, edible meat offals, fresh, chilled<br>Milk & cream, fresh, not concentrated<br>Eggs in shell|
||Grains and Cereals|041<br>042<br>043<br>044<br>045|Wheat and meslin<br>Rice<br>Barley, unmilled<br>Maize (corn), unmilled<br>Cereals,unmilled,others,rye,oats etc.|
||Vegetables and Fruits|054<br>0571-0575<br>0579|Vegetables, fresh, chilled or frozen<br>Fruits, citrus etc.<br>Other fresh or dried fruits nes|
|Cash Crops|Figs and Nuts|0576<br>0577|Figs, fresh or dried<br>Nuts,edible,fresh or dried|
||Tropical Products|0611<br>0616<br>0711<br>0721<br>074<br>075|Sugars, beet and cane, raw, solid<br>Natural honey<br>Coffee, green, roasted or sub<br>Cocoa beans, whole or broken, raw or roasted<br>Tea and mate<br>Spices|
||Feeds, Oilseeds and Tobacco|0811<br>121<br>22|Hay and fodder, green or dry<br>Tobacco, unmanufactured; tobacco refuse<br>Oil seeds and oleaginous fruits|
|Other Food|Processed Food|012<br>014<br>0224<br>023<br>024<br>0252<br>046-048<br>056<br>058<br>0612<br>0615<br>0619<br>062<br>0712<br>0722<br>0723<br>073<br>0812-0819<br>09<br>111<br>112<br>122<br>41-43|Meat & edible offals, salted, smoked<br>Meat & edible offals, prep. & preserved<br>Milk & cream, preserved, concentrated<br>Butter<br>Cheese and curd<br>Eggs not in shell<br>Meals and flour of wheat, other cereal preps. nes<br>Vegetable, roots & tubers, prepared or presv.<br>Fruit, preserved and fruit preparation<br>Refined sugars and other products<br>Molasses<br>Other sugars, sugar syrups, artificial<br>Sugar confectionery and other sugar prep.<br>Coffee extracts, essences/concentrated<br>Cocoa powder, unsweetened<br>Cocoa butter and cocoa paste<br>Chocolate & other food prep. products<br>Bran, oil cake, meal fodder and other food wastes<br>Misc. edible products and preparation<br>Non alcoholic beverages nes<br>Alcoholic beverages<br>Tobacco manufactured<br>Animal/vegetable oils and fats, processed|

19