rafmacalaba/datause-encoder-data huggingface dataset:
The most striking feature of Figure 6 and Table 4 is the magnitude of the gap between the nonanonymous and anonymous growth rates. For example, in the WID database, the mean difference between the midpoint of our estimated range for the non-anonymous growth rate, and the corresponding anonymous growth rate of the bottom 40 percent is 4.1 percent per year, while for the top 10 percent it is -10.7 percent per year. These differences are very large when compared with the standard deviation of anonymous growth rates across countries, which is only 2.8 percent per year for the bottom 40 percent, and 2.1 percent per year for the top 10 percent. Similar gaps are observed for the PovcalNet sample, where the mean difference between the non-anonymous and anonymous growth rate of the bottom 40 percent is 3.1 percent per year, and for the top 10 percent the gap is 8.1 percent per year. Equivalently, given the extent of mobility that we have inferred from the aggregate data, we can conclude that the anonymous growth rate of the bottom 40 percent understates the actual growth rate of those initially in the bottom 40 percent by 3.1 percent per year on average. And conversely, anonymous growth in average incomes of the top 10 percent overstates the average growth rate of those initially in the top 10 percent by 8.1 percent per year on average.

# **7.  Conclusions**

This paper demonstrates the feasibility of approximating individual-level income distribution dynamics when the researcher only has access to time-series data on aggregate moments of the income distribution such as the mean and the Gini coefficient or top income shares. Our key identifying assumption is that individual incomes follow an autoregressive lognormal process with individual fixed effects. We show that the limited information embodied in the time-series of these aggregate moments is sufficient to place bounds on the extent of mobility in the income distribution, even though we do not observe income dynamics at the individual level. An empirical application using data from the PSID confirms that these bounds generally contain the point estimates that are obtained using the record-level panel data, and moreover are reasonably tight. Encouraged by these findings, we apply our methodology to two large cross-country datasets, namely the WID (including mostly high income countries) and the World Bank’s PovcalNet (including mostly developing countries). Some of the cross-country patterns we observe in estimates of mobility seem quite plausible given our priors. For example, among the high income countries, the Scandinavian countries and much of Europe show relatively high levels of income persistence, while the United States, Singapore and Taiwan rank among the countries with low levels of income persistence. When comparing estimates between the WID and PovcalNet, our estimates suggest 24
An important stylized fact from the existing research on informality is that the education of owners and managers explains a considerable proportion of the variation in outcomes of businesses (La Porta and Shleifer, 2008, 2014). Our data enables inspection of this relationship. Panel B of Figure 8 divides informal businesses from each country into one of three possible levels of schooling completed by the owner: no formal education (including those with partial but incomplete primary schooling), those that completed primary (high school graduates), and those that completed secondary education or college. Within every country, the management practices index is higher for businesses whose owner’s education is higher.<sup>17</sup> On average, the informal businesses whose owners have secondary education or a college degree scored 32.7 out of 100 on the management practices index, compared with 23.7 for businesses owned by those completing only primary education and 18.4 for businesses whose owners have lower educational attainment. As with the link to productivity, establishing a causal relation between the owner's education and management practices is left for future research.

# **Business Relations**

Data on the relations of informal businesses with their customers and suppliers can help researchers and policy makers better understand the extent to which these businesses are integrated into their local markets. One of the simple but important questions included in the Informal Sector Enterprise Surveys in some countries (namely, Lao PDR, Mozambique, Somalia and Zambia) on these topics is whether the business supplies products or provides services to other businesses. Around 13.6% of businesses are integrated into value chains in this rather narrow understanding, varying from 7% in Mozambique and Lao PDR to 20% in Somalia. By comparison, more than 52% of formally registered micro-enterprises sell to other businesses.<sup>18</sup> While within-country variation in informal businesses supplying to other businesses is low in some countries (namely, Lao PDR and Zambia), cross-city variation within Mozambique and Somalia is considerable. For example, in Maputo (Mozambique), 18% of informal businesses supply or provide services to other businesses, compared with below 1% in Beira (Mozambique); and the rates in Mogadishu and Bosaso (both in Somalia) are 21% and 6%, respectively. To

> 17 The difference in management practices between owners without any formal education and those with only a primary school education is statistically significant at conventional levels only in India and Iraq. The difference in practices between college and high school graduates is not statistically significant for Iraq, Lao PDR, and Mozambique.

> 18 Although the estimated average percentage of informal businesses selling to other businesses is lower than that of formally registered micro enterprises, the difference is not significant at conventional levels, as shown in Table 2.

22
|and conventions on economic, social,<br>and cultural (ESC) rights (including<br>the right to education and to highest<br>attainable standard of physical and<br>mental health) ratified (2)<br>•Percentage of nominal annual budget<br>allocation in line with PRS priorities<br>(4)<br>•Number of formal initiatives<br>supporting free access to information<br>on service entitlements per year (1)<br>•Number of formal initiatives<br>supporting free access to information<br>on government service delivery<br>performance (1)|with PRS priorities (e.g. government expenditure on<br>health and education as percentage of GDP) (4)<br>•Number of public consultations on policy proposals/<br>formulation per year<br>•Number of women attending public consultations on<br>policy proposals/ formulation per year<br>•Number of inclusive platforms for participation in<br>service delivery<br>•Number of formal legal actions upholding ESC<br>rights with respect to government conduct per year<br>•Number of reported cases of corruption among<br>“street level” bureaucrats per year<br>•Corruption Perception Index (3)<br>(transparency/accountability/freedom from<br>corruption among “street level” bureaucrats, public<br>officials, and professional groups, including<br>investment in capacity building)<br>•Percentage of total population unable to access at<br>least one basic service in the previous year due to<br>cost (4)<br>•Percentage of total population unable to access at<br>least one basic service due to physical distance (4)<br>•Percentage of total population unable to access at<br>least one basic service due to social distance (4)<br>_Informal (cultural)_<br>•Number of complaints regarding transparency and<br>equity of operation of informal social transfer<br>systems (e.g. Zakat) per year<br>•Percentage of women able to access public service<br>entitlements during previous year<br>•Percentage of total ethnic/religious minority<br>population able to access public service entitlements<br>during previous year|Rights<br>(3) Transparency International<br>(4) World Bank Country Policy<br>and Institutional<br>Assessment<br>Where no data source is<br>identified, data must be<br>collected using the national<br>survey or in-depth research<br>techniques.|
|---|---|---|

68
<!-- Start of picture text --> eeA. NL underwriting risk eo7,410<br>B. Deafult 2,959<br>C. Diversification effect 1,118<br>E = C- D: Total SCR [| 9252 |<br><!-- End of picture text -->

|e<br>A.|e<br>eo|
|---|---|
|NL underwriting risk|7,410|
|B. Deafult|2,959|
|C. Diversification effect<br>|1,118<br>|
|E=C-D:TotalSCR|[|<sup>9252</sup>||

|Lineofbusiness|E.<br>PMLgross|F=E*(1-B)<br>PMLnet retained |<br>after prop. RI|G.<br> Non-prop XL RI<br>2012|H.<br>Non-prop XLRI<br>Rate on Premium|1=D*H<br>XL RI premium<br>2012|
|---|---|---|---|---|---|
|Motorvehicle liability insurance|10,000|5,000|4,750 xs 250|2.3%|230|
|Othermotor insurance|10,000|5,000|4,750 xs 250|4.9%|124|
|Marine, aviation and transport insurance|50,000|25,000|24,750 xs 250|16.4%|410|
|Fire and other damage to property insurance||||||
|Fire non natural catastrophe per risk|30,000|15,000|14,750 xs 250|18.9%|1,417|
|Fire natural catastrophe perevent (EQ)|100,000|50,000|49,750 xs 250|8.1%|609|
|General liability insurance<br>|5,000<br>|2,500<br>|2,250 xs 250<br>|4.4%<br>|111<br>|
|[t||t|h|e|2.801|

<!-- Start of picture text --> eeA. NL underwriting risk eo6,970<br>B. Deatult 2,989<br>C. Diversification effect 1,107<br>E=C- D: Total SCR 8,351<br><!-- End of picture text -->
labor force, but the development of labor-intensive small and medium-sized enterprises is repressed because they do not have access to financial services. As their growth is repressed, fewer job opportunities will be available, dampening wages in China. For poor people, the only source of income, other than transfers, is their own labor. The financial structure artificially reduces these job opportunities as well as wages, lowering the income of low-income people.

**Figure 49: Total Financial Assets of China**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010<br>Total China less Big 4 Big 4<br>Current RMB, billions<br><!-- End of picture text --> <!-- Start of picture text --> Source: Bankscope.<br><!-- End of picture text -->

**Table 1: Chinese Interest Rates on Debt**

||2001|2002|2003|2004|2005|
|---|---|---|---|---|---|
|State Owned Enterprises|2.46|2.23|2.67|2.86|2.61|
|Private Enterprises|4.84|4.64|4.61|3.81|4.57|
|Difference|-2.38|-2.41|-1.94|-0.95|-1.96|

Source: Ferri and Liu (2009).

103. The World Bank Enterprise Survey conducted in 2004 provides further evidence for the concentration of lending. While among large firms, 65 percent had a loan from a bank or financial institution, only 32 percent of medium-sized firms and 20 percent of small firms had a loan. Reflecting this lack of bank finance, many of the non-state sectors face a high degree of credit constraints and have to resort to the informal lending market. Also, survey data from the IFC for China (Investment Climate Surveys Draft Country Profile-2003) show that SMEs are less reliant on banks for financing and more reliant on informal sources.

104. The highly concentrated financial structure also artificially lowers the cost of credit and capital, acting as a subsidy to those enterprises with access to financial services (Table 1). The subsidy is mainly financed by people who hold deposits in financial institutions but cannot 66
TEUs, driven by the transshipment activity anchored in Puerto Rico, Jamaica, Bahamas, Dominican Republic, among other smaller countries. That is more throughput than the average in the rest of LAC sub-regions. Nevertheless, this average masks intra-regional variations. The smallest Caribbean port in the sample, Vieux Fort, has an annual average movement of about 33,000 TEUs, which drastically contrasts with Kingston, the second-largest transshipment hub of the continent, which moved roughly 2 million TEUs in 2008 and 2009. Similarly, ports in Central and South America show enormous contrasts and disparities in traffic patterns (see Annex 1 for details).

In terms of infrastructure assets, South American ports possess on average longer total berth lengths and larger terminals, averaging 1,262m and 299,502m<sup>2</sup> respectively. Nevertheless, the number of gantry cranes in Central America and the Caribbean is higher due to the transshipment activity, mainly in Panama and in Caribbean islands.

_Table 3: Descriptive Statistics, Averages by Sub-region over the Period 1999–2009_

|**Region**|**Number**<br>**of Ports**||**Annual**<br>**Throughput**<br>**(TEU)**|**Berth**<br>**Lengt**<br>**h (m)**|**Area**<br>**(m**<sup>**2**</sup>**)**|**Mobile**<br>**Cranes with**<br>**Capacity>14t**<br>**(units)**|**STS**<br>**Gantry**<br>**Cranes**<br>**(units)**|
|---|---|---|---|---|---|---|---|
|Central||Average|403,069|722|174,083|0.8|2.6|
|America and|18|Minimum|31,527|150|15,000|0|0|
|Mexico||Maximum|1,235,869|2,205|431,818|5|11|
|||Average|455,102|837|268,405|1.3|3.4|
|Caribbean|14|Minimum|32,969|250|32,400|0|0|
|||Maximum|1,731,039|3,180|1,037,67|5|13|
|||Average|348,328|1,262|299,502|3.0|1.7|
|South<br>America|35|Minimum|27,933|250|15,000|0|0|
|||Maximum|1,847,604|4,485|933,000|37|12|
|||Average|385,345|1,029|259,309|2.0|2.3|
|Total|67|Minimum|27,933|150|15,000|0|0|
|||Maximum|1,847,604|4,485|1,037,67|37|13|

Source: Containerization International Yearbook, various years. See Annex 1 for port-specific data.

As shown in Table 4, in our sample, total container throughput increased 210% in the LAC region at a compound annual growth rate of 12%, despite the fall during the economic international crisis in 2008–2009. The data also show that the sub-region most affected by the crisis was Central America, with an 18% decrease in container throughput from 2008 to 2009, followed by the Caribbean. In turn, South America has been the region with the fastest growth.

_Table 4: Throughput Growth by Sub-Region_

||**Growth 1999–2009**|**Compound annual**<br>**growth rate**|**Throughput**<br>**decrease 2008–2009**|
|---|---|---|---|
|Central America<br>and Mexico|205%|12%|-18%|
|Caribbean|89%|7%|-12%|
|South America|328%|16%|-8%|
|Grand Total|210%|12%|-12%|

Source: Own calculation based on Containerization International Yearbook (1999–20009) 10
with the new climate. Using the fraction of cropland and the net revenue per hectare of each AEZ, we then calculate new amounts of cropland and net revenue for each district.

The difference between equations (6) and (4) reflects the change in cropland for each district and the difference between (7) and (5) reflects the change in crop revenue in each district. These results are then aggregated to different scales, namely country, region, and continent.

To test the above empirical framework, we make use of information on 16 AEZs identified and complied by the FAO for Africa (FAO 2003). Two of these AEZs, high elevation semi-arid and mid-elevation semi-arid, occur in only two small locations in Africa and so were dropped from the analysis. Two additional AEZs were combined into a single category because they are similar and again there are not many observations: high sub-humid forest and mid-elevation sub-humid forest. The analysis consequently evaluates 13 distinct AEZs across Africa as listed in Table 1. As can be seen in Table 1, one third of Africa is desert. Lowland dry savannah and lowland humid forest make up another third of Africa. The remaining third is broken into the ten remaining AEZ types.

We obtain cropland estimates across Africa from Lotsch (2007). These estimates are based on remote sensing-based land cover maps measured by the Moderate Imaging Spectroradiometer on the National Aeronautics and Space Administration (NASA) satellites. From this data, land was categorized into discrete uses (e.g. croplands, cropland mosaic, forest, shrublands etc.) at the scale of 1km<sup>2</sup> . There are 197 land cover categories for the African continent in this data set. The information on agricultural land use was then compiled by the International Food Policy Research Institute (Wood, Sebastian, and Scherr 2000) and checked against national statistics. Additional estimates of cropland in Africa by the Oak Ridge National Laboratory (Dobson et al. 2000) and by the University of Wisconsin (Ramankutty and Foley 1998) yield similar results.

Using GIS methods, we overlay the cropland and AEZ data sets and calculate the average fraction of cropland in each AEZ _j_ .

_Fj=CRj/Lj_ (1) where _CRj_ is the amount of cropland in AEZ _j_ and _Lj_ is the amount of land in AEZ _j_ . For each AEZ, we assume that the amount of cropland changes proportionally with the amount of land in that AEZ. That is, the fraction of cropland remains constant for each AEZ. Land simply moves from being in one AEZ to another as climate changes.
Some implications are discussed briefly. Suppose that {Ai}ni=1 are historically given. An increase in w(h) promotes renting-out of land from small farmers (becoming wage earners), if market or rental prices of machines and land rent are both sufficiently high. If family time endowment for i = 1 tends to be m smaller than total labor used, an increase in w(h) decreases π1 . Note also that shadow prices of family and hired labor differ by the margin of σ. On the other hand, if A = ∑ni=1 Ai is large, the per-land unit cost of machine can approach zero (thus, we can ignore the term). Therefore, the introduction of machine creates scale economies. Due to scale economies, the social planner’s profit can be greater than the sum of uncoordinated individual farm profits.

Some remarks follow. First, accumulation of human capital potentially plays an important role in the above dynamics. Since human capital (education) augments wages (assume that w<sup>′</sup> (h) > 0), education policy h also affects the transition. Second, an increase in r can deter the above mentioned transition by increasing rental costs for i = 1. When non-agricultural land use has high returns, an increase in r means that small farmers sell or rent out their land, and farmer 1 will buy or rent in to equalize marginal product of land and land price, which implies reduction of agricultural land (A will change).

Similarly, it is easy to introduce transaction costs in creating the above contractual arrangement. If there is a transaction cost that deters land reallocation, it is also possible to construct multiple equilibria: small farm traps and high-productivity equilibria. In an inefficient equilibrium, land reallocation is difficult, keeping many small holders. In an efficient equilibrium, land can be consolidated among large farmers, and small farmers enjoy relatively high-wage non-farm work.

# **References**

Benjamin, D. and L. Brandt, 2002, Property Rights, Labour Markets, and Efficiency in a Transition Economy: The Case of Rural China, _Canadian Journal of Economics_ , 35: 689-716.

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47

-- and for all but two in 1970.34 When other proxies for the gap were used such as income per capita -- the re, vs obtained were not altered in any significant way.

# V.3 Trade Restrictions and Cross Countra Differences in Growth: Some New Evidence

The question we want to address is whether there is a relation between trade regimes and the rate of growth of output in the developing countries. More specifically we want to know if differences in trade orientation help to explain the observed differences in economic performance across countries. Ideally, an analysis on this type should capture the evolution of the relevant variables during several years, however, our index of trade orientation r is only available for one year (1982). This problem was tackled in the following way. First, it was assumed that r captured the cross-country differences in trade orientation for a period longer than 1982. Regressions were then estimated using that index togeth-er with 197082 averages for output growth, the investment ratio, labor force growth and the level of educational attainment. These data were obtained from Worl Development Report 1984. Since the assumption that the intervention index applies to the whole 1970-82 period is not fully satisfactory, regressions were also estimated for the year 1982 only.35 In this case, the data on labor force and education still came from the World Development Report 1984, while those for growth and investment ratios were obtained from the IMFs 34Alternatively one could use the.Harbison-Myers index, that include enrollment rates in higher education. A limitation of this index, however, is that it is a flow index only.

35 Notice, however, that what is required is that the distribution of the r's across countries captured by the 1982 index reflects its behavior along the whole period and = that the absolute values of the index are maintained.
_Index of Stress_ comes from the 7-item scale in Pearlin et al. (1981) and is the first component of a PCA for the following questions on attitudes towards own life that have been coded in a way that a positive answer receives a score of 1 (0 otherwise): (i) There is really no way I can solve some of the problems I have; (ii) Sometimes I feel that I am being pushed around in life; (iii) I have little control over the things that happen to me; (iv) I can do just about anything I really set my mind to do; (v) I often feel helpless in dealing with the problems of life; (vi) What happens to me in the future mostly depends on me; (vii) There is little I can do to change many of the important things in my life.

_Index of Knowledge of Competition_ is the first component of a PCA of two questions about knowledge of competition: (i) “Suppose 10 traders set up a market right outside your village to sell ghee. These traders never talk or cooperate with each other in any way. They have the capacity to sell as much ghee as they want and their only cost of supplying one tin of ghee is Rs 100/kg. The current price of ghee is Rs 200/kg. What do you think would happen to the price of ghee over time?” and (ii) What would happen if these 10 traders were able to cooperate with each other in setting the price? What would be the price of ghee? For each question is answer correctly, a value of 1 is assigned, 0 otherwise.

_Index of female mobility and No purdah index_ are also the first component of a PCA of several variables with negative values indicating less mobility (or observing more types of purdah). For female mobility, the questions are (i) Would you be willing to travel outside your settlement if your work required it? And (ii) Would you be allowed to travel outside your settlement if your work required it?.

The “No Purdah” the questions are (i) Do you observe any type of purdah? and (ii) When you are within your settlement do you [...], (iii) When you are outside your settlement do you [...], (iv ) When you are working in the field, do you [...], which accepts as answers a) Cover your head only; b) Cover both your head and your bosom; or c) Cover your whole body, including your face.

_Aversion to risk general_ is measured on a 0-10 scale where 0 indicates the most risk averse and 10 the most risk-tolerant/lover.

_Trust in Formal System_ , scores of component 1 of a PCA for the response on considering six different institutions as useful or not to resolve payment disputes.

_Months as member_ , number of months as member of NRSP group.

_Holds Office in Group,_ takes value 1 if member has or has had in the past a leadership position in group.

_Business owner_ equals 1 if the member had a business at baseline, 0 otherwise. _Fraction of Members of same Zaat (caste),_ is a percentage of members in the group that share the same cast of the member.

.

_Eligibility for Loan Lottery_ , dummy takes value of 1 if an individual is eligible to apply for a loan under the lottery following NRSP protocols.

_<u>Household</u>_ _Household size_ , number of people living in the household (excludes migrants, students living away).

36