rafmacalaba/datause-encoder-data huggingface dataset:
# **Table** **<u>6: Probits for widowhood</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||A|ll|Urb|an|Ru|ral|
|Age|0.005***|0.005***|0.003|0.003|0.006***|0.006***|
||[3.35]|[3.41]|[1.31]|[1.27]|[3.30]|[3.34]|
|Age squared|-0.000|-0.000|0.000|0.000|-0.000|-0.000|
||[-0.75]|[-0.81]|[0.12]|[0.14]|[-1.14]|[-1.16]|
|Age at marriage|-0.001|-0.001|-0.002**|-0.002**|-0.000|-0.000|
||[-1.21]|[-1.17]|[-2.21]|[-2.10]|[-0.02]|[-0.03]|
|Years of education|-0.001|-0.001|-0.001|-0.001|-0.000|-0.000|
||[-1.29]|[-1.05]|[-1.30]|[-1.34]|[-0.06]|[-0.08]|
|Wealth quintile 1|--|-0.000|--|-0.008|--|-0.002|
|||[-0.04]||[-0.59]||[-0.30]|
|Wealth quintile 2|--|0.001|--|-0.005|--|-0.001|
|||[0.11]||[-0.47]||[-0.24]|
|Wealth quintile 3|--|0.005|--|-0.012|--|0.005|
|||[0.75]||[-1.43]||[0.83]|
|Wealth quintile 4|--|0.009|--|0.005|--|--|
|||[1.34]||[0.83]|||
|Born in urban area|0.004|0.004|0.003|0.002|0.006|0.005|
||[0.80]|[0.89]|[0.56]|[0.28]|[0.76]|[0.73]|
|Height|-0.000|-0.000|0.000|0.000|-0.000|-0.000|
||[-0.63]|[-0.65]|[0.05]|[0.00]|[-0.79]|[-0.80]|
|Bambara|-0.005|-0.003|-0.003|-0.004|-0.004|-0.003|
||[-0.70]|[-0.48]|[-0.34]|[-0.43]|[-0.52]|[-0.40]|
|Malinke|0.001|0.002|0.010|0.010|-0.005|-0.005|
||[0.17]|[0.27]|[0.78]|[0.76]|[-0.50]|[-0.48]|
|Peulh|-0.010|-0.010|-0.003|-0.004|-0.013|-0.013|
||[-1.49]|[-1.35]|[-0.27]|[-0.34]|[-1.46]|[-1.40]|
|Sarkole|0.010|0.011|-0.004|-0.004|0.020*|0.021*|
||[1.20]|[1.31]|[-0.37]|[-0.31]|[1.81]|[1.86]|
|Sonrai|0.008|0.007|-0.005|-0.006|0.015|0.015|
||[0.95]|[0.90]|[-0.50]|[-0.57]|[1.38]|[1.31]|
|Dogon|-0.019**|-0.018**|-0.025***|-0.025***|-0.016|-0.016|
||[-2.30]|[-2.17]|[-2.86]|[-2.87]|[-1.62]|[-1.52]|
|Tanachek|-0.003|-0.003|0.003|0.002|-0.006|-0.007|
||[-0.31]|[-0.31]|[0.23]|[0.14]|[-0.51]|[-0.53]|
|Senoufo|<br>-0.009|<br>-0.008|<br>0.005|<br>0.005|<br>-0.013|<br>-0.013|
||[-1.13]|[-0.98]|[0.37]|[0.35]|[-1.50]|[-1.40]|
|Bobo|0.005|0.006|0.008|0.009|0.003|0.005|
||[0.34]|[0.46]|[0.34]|[0.38]|[0.22]|[0.31]|
|Rural|0.003|0.003|--|--|--|--|
||[0.67]|[0.60]|||||
|Pseudo R-squared|<br>0.113|<br>0.114|0.158|0.161|0.102|0.102|
|Observations|12,212|12,212|3,961|3,961|8,251|8,251|

Note: Dependent variable is a dummy for being or having been a widow. Marginal effects are shown. Robust t-statistics in brackets, clustered at the community level. *** p<0.01, ** p<0.05, * p<0.1. Since few households fall in wealth quintile 5 in rural areas, quintile 4 is also excluded. The household‘s wealth quintile is determined using a principal components analysis of assets owned. It is a household not a per capita level variable. Source: 2006 Mali DHS.

36
# **1. Introduction**

One of the oldest debates on poverty concerns whether it is ―absolute‖ or ―relative.‖ The idea of relative poverty has long dominated measurement practice in Western Europe. The poverty line is set at a constant proportion—one half is common—of the country or year-specific mean (or median) income.<sup>2</sup> By contrast, absolute poverty lines have dominated past practice in some rich countries (including the US) and in most developing countries. By this view, the poverty line is intended to have constant real value. An example is the World Bank’s international line of $1.25 a day at 2005 purchasing power parity (Ravallion et al., 2008).

There are two ways we can interpret this difference. One can think of a poverty line as the money metric of an underlying concept of welfare. While not observed, the poverty line in the welfare space can be thought of as a social norm, which may well vary from one setting to another. The poverty measure in any given setting will only have salience and be accepted if it accords reasonably well with prevailing ideas of what ―poverty‖ means in that setting.<sup>3</sup> We can expect norms to differ between a rich society and a poor one, and evolve over time in growing economies. In this sense ―poverty is relative.‖ However, using a lower real poverty line in poorer countries will not then be welfare consistent, in that two people judged to have the same welfare can be treated differently depending on where or when they live. Reasoning along these lines has motivated the past emphasis on measuring absolute poverty using a common real poverty line.

There is a second, very different, interpretation of why richer countries have higher poverty lines. The absolute approach implicitly sees welfare as depending on ―own consumption,‖ though often with allowances for differing needs, depending on (say) household size or demographic composition.<sup>4</sup> By this view, the setting in which a person lives is irrelevant to whether that person is deemed to be poor or not, once one knows the person’s own consumption level. By contrast, a relative line is implied by the presence of certain social determinants of welfare, which naturally vary with the context. Relative lines are seen to reflect

> 2 See, for example, Smeeding et al. (1990), Atkinson (1998), Eurostat (2005), Nolan (2007) and OECD (2008, Chapter 5).

> 3 This can be formalized in the idea of a social subjective poverty line, which postulates that in any given society there is a level of consumption below which people typically think they are poor, but above which they do not. For an overview of this approach and antecedents in the literature see Ravallion (2012a).

> 4 The poverty lines are typically anchored to the nutritional requirements for good health and normal activities. Of course, the use of absolute lines within developing countries does not mean that different developing countries use the same line. Even if there are no differences in the normative nutritional requirements, there are infinitely many food bundles that can attain any given nutritional intake.

2
_PIndexit_ : the indexes representing the efficiency / capacity of the ports of the exporter country _i_ at the year _t_ . Our primary indicator for the regression is the port congestion index, defined as the sum of the loaded and unloaded containers in TEU at the major container ports in the country _i_ in the year _t_ , divided by the sum of the estimated full physical capacity of the major container ports in the country _i_ in the year _t_ .<sup>23</sup> This indicator reflects the ratio of utilization of the ports. The higher value of this index means the higher possibility of physical congestion in the ports. Accordingly, this index represents the supply curve drawn in Figure 2. For comparison purpose, we also test the port infrastructure quality index in _Global Competitiveness Reports_ ( _GCR_ ) and water transportation index in _The World Competitiveness Yearbook_ of IMD ( _WCY_ ).

_α1k_ : the dummy variables for controlling the commodity-specific fixed effects. _a2t_ : the time dummy variables.

_i_ : the exporting countries / regions in Asia Pacific region, consisting of each of ASEAN5 (Indonesia, Malaysia, Philippines, Singapore and Thailand), China, Japan, Korea, Hong Kong, Taiwan, Viet Nam, Australia and New Zealand.

Commodity-specific fixed effects and uniform time-varying factors across the country and commodity are assumed to exist in the regression. For the latter, time dummy variables enter the regression as explanatory variables, absorbing all the time-varying factors, such as changes in fuel prices and technological progress across the sectors and countries. All the independent variables appear to be exogenous, and we do not resort to the instrumental variable method, as is the case in the most of the existing studies.

# _Results of the Trade Cost Regressions of the U.S._

Table 4 below summarizes the results of the regression. As the observations represent the detailed subdivision of the commodities, the estimated parameters do not reflect the variation of composition of the imported commodities among the exporting countries. With the time dummies in place, the regression reflects only the cross-sectional variation. The commodity specific effects are also controlled by the fixed effects. The variables of distance, value/weight

> 23 See Appendix A for the detailed methodology of the estimation of the port capacities.

21
# **Table 5: IV Panel Data Estimation - Impact of changes in public expenditure of key** **<u>government sectors on changes in a child’s nutritional level by area</u>**

|_Dependent variable:_|Z-score (he|ight for age)|
|---|---|---|
||Rural|Urban|
|Total Public Spending in key sectors|-0.079|0.250|
||(0.030)**|(0.139)*|
|Other Controls|YES|YES|
|N|688|1,214|

Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance Budgetary Expenditure for 2000–2006.

Note: each coefficient represents an independent regression. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional-fixed effects, individual-fixed effects, and time-fixed effects and all the controls used in the main regressions. Spending measured in 100 million soles. The instrument for the IV estimations is the amount of unanticipated mining revenues multiplied by the summation of the region-specific share of expenditures given to education, poverty alleviation programs, health and sanitation, transport, and agriculture in 1999.

# **S4.3 Assessing the instrument’s strength for the selected spending categories**

According to the first stage regression parameters (see **Tables 6** ) the instrument is relevant for the whole sample as well as for rural and urban areas. It is significant at a 1% level for all specifications and it displays a positive and non-immaterial effect on the level of expenditure in the most important sectors (health and sanitation, education, poverty alleviation programs, transport, and agriculture). For example, an increase of 100 million soles in the amount of unanticipated mining revenues will increase the level of selected expenditure by 1,142 million soles and 640 million soles in rural and urban areas, respectively. The multiplier effect is especially big in rural areas probably due to the distribution rule inside regions, which favored rural areas<sup>49</sup> . Both specifications have an F statistic that ensures that the IV estimator is strong according to Stock et al. (2002).

> 49 Inside the region, royalties were distributed by the central government according to the following rule: 20% went to the district and provincial governments of the province where the natural resource was located (distributed according to population density but weighing the rural population double the urban population to give preference to rural areas), 60% went to the district and provincial governments of the region where the natural resource was located (distributed simply according to population density), the remaining 20% went directly to the regional government. In July 2005, the intra-regional distribution rules for mining royalties were modified by the law 28322. Yet since the distribution of royalties is done with 18 months of delay it should only affect royalties starting in 2007.

21
“You have all the elements, but you cannot make steel” —Andrew Carnegie<sup>1</sup>

# **1 Introduction**

Carnegie’s taunt to the owners of the Birmingham Steel Company in Alabama highlights the complexity of technological transfer to lagging regions. As Wright (1986) documents, shortfalls in management, learning by doing, and the R&D capacity necessary to adapt technologies to local conditions all impeded the establishment of a dynamic steel (as well as textile and lumber) industry in the American South, and hence slowed catch-up to the North. The issue of differential technological transfer remains central to the more general convergence debate.<sup>2</sup> Most recently Comin et al. (2008); Comin & Ferrer (2013) argue that the diverging measured intensity of use of new technologies (as a share of economic activity) plausibly explains observed TFP differentials and can drive simulations that closely track the magnitudes of the Great Divergence of the last two centuries (e.g. Pritchett, 1997; Galor & Weil, 2000; Galor, 2011).<sup>3</sup> That human capital is a critical ingredient in technology adoption also enjoys a substantial supporting literature.<sup>4</sup> The nature of the relevant human capital has been less clear. Literacy, and accumulated years of schooling or enrollment have received the most attention, although other dimensions figure importantly in the literature as well: Lucas (1993); Young (1993) and Foster & Rosenzweig (1995), among others, stress the importance of accumulated “learning by doing”;

> 1Cited in Wright (1986) p.171.

> 2See also, Parente & Prescott (1994); Eaton & Kortum (1999, 2001); Caselli (2001); Comin & Hobijn (2004); Keller (2004); Klenow & Rodriguez-Clare (2005); Comin et al. (2008, 2010a); Comin & Hobijn (2010); Comin et al. (2012).

> 3Though not clear on the underlying determinants, recently Acemoglu et al. (2013) argue for the importance of a the shift in industrial composition from those with low to high innovative capacity as critical to growth.

> 4See, for example, Nelson & Phelps (1966); Foster & Rosenzweig (1996); Cohen & Levinthal (1989); Benhabib & Spiegel (1994); Basu & Weil (1998); Temple & Voth (1998); Howitt (2000); Acemoglu & Zilibotti (2001); Caselli (2001); Comin & Hobijn (2004); Benhabib & Spiegel (2005); Aghion et al. (2005); Howitt & Mayer-Foulkes (2005); Ciccone & Papaioannou (2009); Goldin & Katz (2009).

1
curves show, for each of the data year, the predictions from a fractional polynomial regression of the share of top decile pairs on distance. If buildup were distributed randomly across space, we should observe a flat curve. This is not what we see: as in Figures 9 to 12 we find evidence of agglomeration, which is strongest at distances up to around 60 km, which is the range of distances covered in Figures 11 and 12. Correlation in high buildup falls after that, but only gradually – suggesting that agglomeration continues to have an effect on buildup even at long distances.

# **5 Conclusions**

Developing countries across the world experienced strong population growth during the last four decades. With population growth, urbanization also picked up pace with half of the developing world’s population now living in urban areas. The accelerated urbanization has received considerable attention in the Africa region where urbanization has not been associated as strongly with economic growth as observed historically in developed countries. Less is however known about how human settlements expanded geographically along with population growth and increased urbanization. In this paper, we use high resolution satellite data on built-up areas within micro-pixels to study the evolution of human settlements in Ghana over a 40 year period.

The empirical analysis of built-up data produces a couple of substantive findings. First, we find a strong increase in built-up area over time, mostly concentrated in the vicinity of roads, and also directly on the coast. The proportion of fully built cells increased substantially at the expense of un-built cells leaving the distribution of partially built-up cells unchanged. We find strong evidence of agglomeration effects both in the static sense – buildup in one cell predicts buildup in a nearby cell – and in a dynamic sense – buildup in a cell predicts buildup in that cell later on, and an increase in buildup in nearby cells. These effects are strongest over a 3 to 15 km radius. Within the distance windows analyzed (upto 60km), we find no evidence that human settlements are spaced more or less equally either over the landscape or along roads. Overall, expansion of human settlements in Ghana occurred mainly at the edge of existing agglomeration of built-up areas. If a similar expansion pattern continues then our analysis based on transition matrices predict a strong increase in fully built-up areas – i.e., towns and cities – but no similar increase in the proportion of cells that are partially built-up. And this increase will be concentrated in a relatively small 26
<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1905 1912 1919 1926 1933 1940 1947 1954 1961 1968 1975 1982<br>Year of Birth<br>Life Expectancy at Birth<br><!-- End of picture text -->

**Figure 1** : Life Expectancy at Birth across Cohorts, World Average

35
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

|||countries<br>2005<br>**200**|**5**<br>**2009**||countries<br>2005|
|---|---|---|---|---|---|
|GSM coverage|% population|48|**59**|**82**|97|
|International bandwidth|Mbps/capita|6|**8**|**86**|30|
|Internet|subscribers/100 people|<br>0|**2**|**4**|2|
|Landline|subscribers/100 people|<br>1|**1**|**1**|9|
|Mobile phone|subscribers/100 people|<br>15|**32**|**67**|87|
|||**Ghana**<br>**2005**<br>**2009**|With submarine|cable|Other developing<br>regions|
|Price of monthly mobile basket||**7.53**<br>**6.00**|11.12||9.9|
|Price of monthly fixed-line basket||**8.56**<br>**5.00**|13.58||—|
|Price of 20-hour Internet package||**34.53**<br>**12.00**|47.00||11.0|
|Price of a call to the United States|(US$/min)|**0.39**<br>**0.33**|0.48||0.67|
|Price of inter-Africa calls, mean (U|S$/min)|**0.32**|0.57||n.a.|

Notes: Ghana 2005 data together with benchmarks are taken from the AICD Database. Ghana 2009 is compiled from a variety of World Bank sources to give a sense of the progress made during the last five years. Source: Minges and others 2009. Derived from AICD national database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available. n.a. = not applicable.

One of the key explanations for Ghana’s relatively high service coverage has been its relatively low tariffs. In 2005, the price of a standard basket of mobile services was $7.53 in Ghana compared with $11.12 in other comparable African countries. At that time, Ghana also had access to the SAT3 submarine cable through competing international gateways, which can be expected to bring down the costs of international voice and Internet services. The prices of dial-up Internet service and international phone calls in Ghana were significantly lower than in other countries in the peer group with access to a submarine cable. Due to intensifying competition both in the domestic mobile market and on the international gateway, prices have fallen significantly since 2005. The price of a mobile basket is now around $6 per month, and the price of a monthly Internet subscription has fallen substantially from $35 in 2005 to $12 in 2009. Competition in Ghana’s ICT market is set to intensify further as landing rights have been granted to two new submarine cables, and international bandwidth could increase to 2,040 bits per second.

# **Challenges**

The recent rapid expansion of Ghana’s mobile telephone networks has come at the expense of service quality. While no official statistics are available, a high incidence of dropped calls is being reported. To some extent, there is a tradeoff between investment in network expansion and investment to upgrade the service on the existing network. With the recent award of new licenses, the current focus of the operators is on securing maximum coverage. Nevertheless, the current situation suggests the importance of establishing a more explicit regulatory framework for quality of service so that these aspects of performance can be monitored and appropriate standards and incentives can be established.

31
<!-- Start of picture text --> 0.0%<br>-2.0% industry<br>S [| chm<br>5 | | is<br>2<br>3 LJ om<br>io -4.0% | | nmm<br>8O Lo<br>|_| pep<br>-6.0%<br>LSR OBR ABR_ IBOR_ IBER<br><!-- End of picture text -->
Although WII was designed as individual producer insurance for small-scale farmers, it could be argued that Agroasemex in fact insures federal and state governments’ budgets. In other words, Agroasemex's WII serves as a state government budget risk management tool since it allows annual budget planning to minimize the risk of catastrophic expenditure should severe droughts occur. Nevertheless, Agroasemex's WII affects the individual producer's behavior: even when farmers pay nothing to get insurance coverage (premiums are paid through a direct government subsidy), they become automatically insured and get informed about their coverage status through officials at the Program for Direct Assistance in Agriculture (PROCAMPO) regional offices (Rural Development Support Centers (CADER) or in the “Ventanillas Autorizadas” depending on plot location and county).

Evidence of farmer’s program awareness was provided by the Ministry of Agriculture in 2009 through WII's program external evaluation written by a local based University. The document describes that a subset of randomly selected farmers were surveyed and asked about their awareness and knowledge of WII. Among those who were interviewed, 98% knew about WII’s existence, and over 80% said they would be willing to pay in order to get insurance against droughts if the government did not provide it.

To be more explicit about the way in which weather index insurance works, we use two counties of the state of Guanajuato and for the case of maize production in Figures 1.a. and 1.b. Agroasemex offers the following contract for insuring maize in the selected counties (Apaseo el Alto and Salamanca): the first period, also known as the sowing period, runs from May 15 to July 5; the second period goes from July 6 to August 20; and the third, or harvesting period, from August 21 to October 31. The minimum amount of accumulated rain above which Agroasemex does not provide indemnity payments --known as the trigger threshold-- equals 43, 80 and 60 millimeters for the first, second and third periods, respectively. There were no indemnity payments in Apaseo el Alto, since accumulated rainfall was higher than the minimum thresholds in every period of 2005. However, indemnity payments were provided in 2005 for maize production in the county of Salamanca as accumulated rainfall was lower than the sowing period’s minimum threshold. To get this information, Agroasemex takes advantage of existing and publicly available rainfall information from weather stations of the National Water Page **6** of **29**