rafmacalaba/datause-encoder-data huggingface dataset:
**Table A3.4. Balance of payments in 2012 and for base simulation by** **<u>period (% of nominal GDP)</u>**

|||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|---|
|**Outflows **|**Agriculture imports**|0.5|0.4|0.4|0.5|0.4|0.4|
||**Food imports**|9.5|8.8|9.8|9.4|9.2|9.2|
||**Petroleum imports**|15.5|15.8|19.4|16.5|17.5|17.2|
||**Other industry imports**|38.4|38.0|41.3|40.8|38.4|39.1|
||**Non-competive imports**|24.6|5.2|4.6|10.8|5.5|7.0|
||**Service imports**|15.1|15.1|17.3|15.8|16.1|16.0|
||**Factor income mining**|7.0|9.9|6.4|9.1|7.2|7.8|
||**Other factor incomes**|4.3|4.2|6.0|4.4|5.3|5.0|
||**Interest**|0.1|0.3|0.4|0.3|0.3|0.3|
||**Total**|115.1|97.8|105.6|107.5|100.0|102.1|
|**Inflows**|**Agriculture exports**|12.0|13.2|33.3|14.9|23.0|20.7|
||**Mining exports**|18.8|35.8|31.6|26.1|38.2|34.9|
||**Other industry exports**|1.1|1.3|2.6|1.4|1.9|1.8|
||**Service exports**|5.1|6.3|13.4|6.3|9.9|8.9|
||**Private transfers (net)**|5.6|5.3|6.4|5.7|5.9|5.8|
||**Government transfers**|24.3|18.9|12.8|22.1|15.2|17.1|
||**UNMIL transfers**|29.5|0.0|0.0|9.5|0.0|2.7|
||**Government borrowing**|2.2|2.5|1.2|4.3|1.3|2.1|
||**FDI -- mining**|14.0|13.7|0.2|16.0|0.3|4.7|
||**FDI -- other sectors**|2.4|0.8|4.0|1.2|4.3|3.4|
||**Total**|115.1|97.8|105.6|107.5|100.0|102.1|

# **Note:**

**Columns show values for single years or averages for multiple years (as indicated).**

**Table A3.5. Sectoral GDP shares in 2012 and for base simulation by** **<u>period (% of nominal GDP at factor cost)</u>**

||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|
|**Mining**|9.6|19.1|19.9|14.1|22.4|20.1|
|**Non-mining**|90.4|80.9|80.1|85.9|77.6|79.9|
|**Agriculture**|38.2|34.8|44.1|37.7|37.7|37.7|
|**Non-mining industry**|5.8|5.0|2.8|5.4|3.4|4.0|
|**Private services**|34.0|27.8|21.9|29.7|24.0|25.6|
|**Government services**|12.4|13.2|11.4|13.1|12.4|12.6|
|**Human development services**|5.8|5.9|4.8|5.9|5.3|5.4|
|**Infrastructure services**|1.9|2.1|2.0|2.0|2.1|2.1|
|**Othergovernment services**|4.7|5.2|4.6|5.2|5.1|5.1|
|**Total**|100.0|100.0|100.0|100.0|100.0|100.0|

35
moisture growth rate lowers the annualized growth rate of its urban share by about one sixth of the mean growth rate. Moreover, across the range of annualized growth in moisture, moving from the lowest to highest moisture growth rate (in a slightly trimmed sample) lowers the annualized growth in urban share by about 100% of the mean, a huge effect.

Having documented this population effect, we consider a critical related question: does this push raise urban incomes and stimulate development of the urban sector? The answer is theoretically ambiguous and depends on the initial state of the urban sector. When the local agricultural sector is competing for labor with an urban sector engaged in production of goods for export outside the district, declines in moisture encourage local city growth by drawing labor out of farming. However the decline in farming leads to decreased spending by farmers on local urban goods and services, potentially offsetting the first effect. The labor force effect dominates in this case, and total city income rises. If, however, cities only exist to serve agriculture with local services not traded across districts, then a decline in moisture has an ambiguous effect on city population because the two sectors are not in competition for labor for export activity. Since the export potential of the whole district decreases, total city income generally declines.

Empirically, using intensity of nighttime urban lights to reflect changes in income, we find that decreased moisture leads to an increase in total city income for towns likely to have an export manufacturing base. For the cities most likely to have an export base in arid countries, the elasticity of lights with respect to rainfall is -0.40. However, when cities are likely just to provide services to famers, reduced moisture has little effect on total city income.

Finally, we ask how moisture changes affect a related margin of adaptation: occupational choice in the rural sector. This question is motivated by the little-noticed transformation of the rural sector over the last 20 years in many African countries, signified by a large shift into non-farm occupations. For example, data for Benin, Malawi, and Niger in the period 1987-1996 all showed between 85 and 91% of the rural male labor force working in agriculture. By 2006 to 2008, only 57-72% of the rural male labor force in these countries remained in agriculture.<sup>2</sup> Based on individual-level observations from the Demographic and Health Surveys (DHS), we show more systematically that increases in moisture increase the probability of working in agriculture. For women, a one standard deviation (levels) increase in moisture increases the probability of working in farm activities by about 0.03 from a mean of 0.44, a 7% increase, mostly through decreased probability of not working (-0.027). Increasing moisture across

> 2 We are comparing the 1996 and 2006 DHS surveys in Benin, the 1992 and 2006 DHS in Niger, and the 1987 and 2008 censuses of Malawi.

3
Table 17: Business dynamism and SOE concentration at the sectoral level (2d), 2016-2019. Competitive sectors.

||Entry rate|Exit rate|Share young|SD(growth)|Creation|Destr.|Net change|Realloc.|HHI|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Share of emp. in SOEs|0.0446|-0.150**|-0.357**|1.657|0.738***|-0.830*|1.567***|-0.0920|0.262***|
||(0.0863)|(0.0577)|(0.180)|(2.120)|(0.224)|(0.435)|(0.334)|(0.606)|(0.0403)|
|Average size|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Average age|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector emp. share|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector 2d|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|260|260|260|260|260|260|260|260|260|
|R-squared|0.947|0.934|0.979|0.446|0.897|0.844|0.751|0.913|0.988|

_Note:_ Dependent variables at the 2-digit industry level are, for each column, entry rate, exit rate, share of employment in firms up to 5 years of age, standard deviation of employment growth rate, gross job creation, gross job destruction, net employment change, job reallocation, and the Herfindahl–Hirschman index (HHI). The share of employment in SOEs is the fraction of employment in all SOEs relative to total employment. All models include year and sector-fixed effects. The sample is restricted to competitive sectors. Data includes private sectors from RAIS, 2016-2019. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.
||(0.034)|(0.034)|(0.033)|(0.035)|
|---|---|---|---|---|
|<br>**Observations**|327|327|327|327|
|**Pseudo R2**|0.241|0.254|0.242|0.256|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

Testing how 2003 expenditures affect vulnerability in 2007 (Table 5) show that agricultural spending in 2003 has a statistically significant and positive association with 2007 VAM levels (Column 1). Results also confirm a larger association of restricted vis-à-vis extended spending, both statistically significant and, as found earlier, with the former roughly double in magnitude of the latter. Current spending in 2003 does not appear to have a significant relationship on the vulnerability in 2007, while investment spending continues to have a positive and statistically significant association. With a four-year spending lag, research and extension has a significant but negative effect on vulnerability. The link between research and extension and the reduction of vulnerability to food insecurity is stronger when a longer lag of spending is considered. Department-specific effects remain unchanged with respect to the 2007 contemporaneous effects.

**Table 5. Effects of 2003 Per Capita Agricultural Spending on 2007 Vulnerability to Food Insecurity**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**VARIABLES**|Total exp|Restricted and<br>extended<br>definitions|Current and<br>investment<br>categories|By function|
|**Total expenditure**|0.9888***||||
||(0.338)||||
|**Restricted expenditure**||1.2450**|||
|||(0.562)|||
|**Extended expenditure**||0.6782**|||
|||(0.313)|||
|**Current expenditures**|||-0.0250||
||||(0.564)||
|**Investment expenditures**|||1.3207***||
||||(0.491)||
|**Research and extension**||||-11.1869**|
|||||(4.844)|
|**Infrastructure**||||1.3973***|
|||||(0.531)|
|**Support and development**||||1.8166|

18
case; see Bandaragoda and Rehman 1995), or if the canal becomes over-silted, water may fail to reach the tail outlets. Canal maintenance, which consists largely of de-silting operations conducted during the January canal closures, is the responsibility of the irrigation department, giving it discretion over whether and how a canal gets dredged.<sup>8</sup> When a channel becomes silted up, water level rises at the head, increasing discharge there, while falling at the tail (Van Waijjen et al. 1997). Lack of canal maintenance, therefore, would typically favor farmers at the head outlets (Figure 1 inset), which may give rise to lobbying by farmers at tail outlets to increase maintenance and by those at head outlets to suppress it. Maintenance suppression can thus be seen as one form of water theft.

More blatant forms of theft are well documented in the Indus Basin: Groups of farmers located in the upper reaches of the distribution canals may partly break their outlet or enlarge it in order to increase the discharge delivered to their fields...Farmers offer bribes to irrigation officials to avoid that the outlet be repaired and brought back to its official dimension, but also to avoid that the offense be taken to court. The outlet changes are typically made for a period of 6 months, after which it is repaired unless the farmers pay a new bribe. (Rinaudo 2002, p. 407-8).

Indeed, based on field measurements of 423 watercourse outlets in the Chishtian subdivision of southern Punjab, Rinaudo (2002) reports that 23% show physical evidence of illegal enlargement (see Lashari et al. 1997 for even higher rates of outlet tampering). Other modes of theft along a distributary, also with official connivance, include “the use of flexible siphoning pipes...[and] cuts in the banks,” (Rinaudo et al. 2000). Particularly egregious cases, especially if accompanied by downstream farmer protests, frequently make their way into national newspapers in Pakistan.

Rinaudo et al. (2000) emphasize a higher level contest for water rights, the locus of which is typically the sub-divisional office of the irrigation department:

> 8Farmers are responsible for maintaining tertiary canals; i.e., their own watercourses.

6
identification documents, have low experience in urban jobs that have high local demand, and tend to have lower education relative to the rest of the Colombian population (Ib ́a ̃nez and Moya, 2006), it is plausible that upon arrival at urban centers, IDP take lower tier jobs, which are most likely to be available in the informal sector (Amaral and Quintin, 2006; Perry, 2007; Galiani and Weinschelbaum, 2012; La Porta and Shleifer, 2014a; Meghir et al., 2015), increasing its size.

A larger informal sector may end up hurting formal firms’ performance in several ways. Formal firms, for example, face higher costs relative to informal firms as they pay taxes, fees, and higher wages for their employees (La Porta and Shleifer, 2014a). Informal firms, thus, can usually carry lower prices and costs competing in an unfair way with formal businesses. Beyond reducing the demand for formal firms’ products via lower prices, unfair competition from informal firms could slow down the process in which inefficient firms can be replaced by more efficient competitors and negatively affect the incentives of formal firms to innovate and adopt new technologies (Perry, 2007). Competition with informal firms, consequently, may lead to productivity losses for the formal firms. In addition, since informal firms are able to use public goods but do not pay for them, this may lower the quality public goods and services and crowd out their use by formal firms (Besley and Persson, 2013). At the same time, higher informal competition may force formal sector firms to lower the quality of their products (as proposed by Banerji and Jain, 2007).

If our argument is correct, then we should observe that those formal firms operating in sectors that are more prone to informality competition should be disproportionately affected by IDP inflows relative to the other firms. To test this hypothesis we use the manufacturing survey firm data and split the sample according to industry codes in two groups of firms according to whether they are part of a sector that tends to have high levels of informality (e.g., retail sales and construction) or low levels of informality (e.g., financial services, machinery production, production of chemical or pharmaceutical products). The results are presented in Tables VIII through XI, and strongly support our hypothesis. Particularly, we only find negative effects of IDP inflows on the intensive and extensive margins of production for the group of firms that operate in sectors that are more 21
Moreover, if poor and extreme poor households in Santiago del Estero and the other three provinces had the same number of members as the nonpoor households in those provinces, only 30.2 and 17.1 percent of households would be below the poverty line as compared to the current 48.7 and 21.6 percent, respectively.

Table 4.3: Median Monthly Household Income in Dispersed Rural Areas of Argentina, 2003 <u>(AR$)</u>

||Mendoza|Santiago<br>del<br>Estero|Chaco|Santa Fe|Total|
|---|---|---|---|---|---|
|Nonpoor households|318.1|270.7|258.2|593.6|392.3|
|Poor households|330.7|381.5|213.2|373.4|300.0|
|Indigenthouseholds|291.5|191.9|222.6|373.4|228.4|

Note: Poverty measured by consumption. Source: Own calculation based on RHS 2003.

# **Income inequality**

Income inequality is part of the reason why the poverty indicators of rural Argentina and those of the nation as a whole are worse than in other countries with similar per capita incomes. Moreover, the country’s income inequality worsened over the past decade. In 2000, the average Gini index for Argentina’s urban areas was 0.45 (Figure 4.4).

<!-- Start of picture text --> 1991 2000<br><!-- End of picture text --> <!-- Start of picture text --> Santa Fe C. RivadaviaMendoza<br><!-- End of picture text --> <!-- Start of picture text --> Cordoba Sgo. del Estero<br><!-- End of picture text --> <!-- Start of picture text --> Jujuy Catamarca<br><!-- End of picture text --> <!-- Start of picture text --> San Juan Santa RosaTierrra del FuegoCdad. de Bs. Aires<br><!-- End of picture text --> Income inequality is significantly higher in dispersed rural areas than in urban areas. Data for the four provinces show a Gini index of 0.74 (Table 4.4). This high income inequality probably reflects the dispersed population’s wide range of livelihood strategies, 22
- Burivalova, Z., Bauert, M. R., Hassold, S., Fatroandrianjafinonjasolomiovazo, N. T. & Koh, L. P. (2015), ‘Relevance of global forest change data set to local conservation: case study of forest degradation in masoala national park, madagascar’, Biotropica 47(2), 267–274.

- Cacho, O. J., Milne, S., Gonzalez, R. & Tacconi, L. (2014), ‘Benefits and costs of deforestation by smallholders: Implications for forest conservation and climate policy’, Ecological Economics 107, 321–332.

- Cameron, T. A. & James, M. D. (1987), ‘Efficient estimation methods for” closed-ended” contingent valuation surveys’, The Review of Economics and Statistics pp. 269–276.

- Carson, R. & Groves, T. (2007), ‘Incentive and informational properties of preference questions’, Environmental and Resource Economics 37, 181–210.

- Carson, R., Groves, T. & List, J. (2014), ‘Consequentiality: A theoretical and experimental exploration of a single binary choice’, Journal of the Association of Environmental and Resource Economists 1, 171–207.

- Costedoat, S., Corbera, E., de Blas, D. E., Honey-Ross, J., Baylis, K. & Castillo-Santiago, M. (2015), ‘Consequentiality: A theoretical and experimental exploration of a single binary choice’, PLoS ONE 10.

- de Janvry, A., Gordillo, G. & Sadoulet, E. (1997), Mexico’s Second Agrarian Reform: household and community responses, 1990-1994, Center for US-Mexican Studies.

- De Janvry, A. & Sadoulet, E. (2006), ‘Making conditional cash transfer programs more efficient: designing for maximum effect of the conditionality’, The World Bank Economic Review 20(1), 1–29.

- Farquharson, R. (1969), The Theory of Voting, Yale University Press, New Haven.

- Ferraro, P. J. (2008), ‘Asymmetric information and contract design for payments for environmental services’, Ecological Economics 4, 810–821.

- Fox, J. (2007), Accountability Politics: Power and Voice in Rural Mexico, Oxford University Press, New York, NY.

45
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure 10:  Estimate of Physical Capital Stock, 1960-2002**

<!-- Start of picture text --> Cote d'Ivoire: Estimate of Gross Real Physical Capital Stock<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Years 1960-2002<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Billions<br>Billions of Constant US$ (1995)<br><!-- End of picture text -->

**_Per capita_ capital stock has also declined dramatically over the past two decades** (Figure 11).  According to these estimates, per capita real output and physical capital grew strongly until 1979, then fell precipitously. From a peak of over $7,000 per worker, capital stock fell to about $2,500 per worker in 2002, estimated in 1995 dollars. Real output per worker fell from a peak of nearly $3,400 to only $2,000 in the same period.

**Figure 11:  Per Capita Output and Physical Capital Stock, 1960-2002**

<!-- Start of picture text --> Cote d'Ivoire: Real Physical Capital (K/L)<br> And Output/GDP (Y/L) per Worker<br>8<br>Y/L<br>K/L<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>-<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br>Years since independence<br>Thousands<br>Constant US $ (1995)<br><!-- End of picture text --> 13
# 1. Introduction

The economic liberalization during the last couple of decades led to impressive economic growth and poverty reduction in many developing countries. This period has also witnessed worsening of income inequality and widening of spatial disparity (World Development Report (2009); Kanbur and Venables (2005); Kim (2008)). The rise in spatial inequality has become an important concern among policy makers in many developing countries. The widening spatial inequality in the course of rapid economic growth may not be economically ine¢ cient if it is an outcome of increasing specialization based on comparative advantage and/or increasing returns.<sup>1</sup> However, spatial inequality is particularly problematic if it is primarily due to inequality of opportunities such as inequality in the provision of infrastructure, education and other services. This inequality can be argued to be of the "wrong" kind on the grounds of both economic e¢ ciency and social desirability. From the perspective of policy making, it is thus important to know the sources of spatial disparity. In this paper, we construct a simple measure to track spatial disparity in welfare overtime. This measure can be decomposed to ascertain contributions of di¤erent factors to spatial inequality. We apply this method to examine the evolution of spatial disparity in welfare as well as its determinants in Bangladesh.

There are numerous studies documenting spatial di¤erences in welfare in developing countries. The existing literature has taken two analytical approaches to examining spatial disparity in welfare. The most common approach is to use regression of per capita expenditure on observable household and community characteristics. The signi...cance of area ...xed e¤ects and/or community variables in this regression is taken as an evidence of the importance of geographical location (Ravallion and Wodon (2001)). Escobal and Torrero (2005) on the other hand performed spatial autocorrelation analysis of the residual from the above regression. While these approaches can

> 1 The spatial inequality may be sub-optimal in the presence of agglomeration externalities even when there is no restriction on factor or goods mobility.

1
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36
**Figure 6: Total Fertility Rate, CIS countries 1950-2010**

<!-- Start of picture text --> 6.00<br>5.00<br>Central Asian<br>Republics<br>4.00 Armenia<br>Azerbaijan<br>Georgia<br>3.00<br>Russia<br>Ukraine<br>Moldova<br>2.00<br>Belarus<br>1.00<br><!-- End of picture text -->

**_Source:_** UN (2013)

21
<!-- Start of picture text --> Basis Points<br>400<br>ame |-\donth<br>350<br>300 — 36 -Month fll4<br>250 i<br>200 |<br>150 !<br>100 . Fl<br>50 a, ia.”<br>oO See<br>Janoy Aproy jJuloy Octo? Janos Apros fulo&’ Octos Jano9 Aproo fJulO9 Octo9 Jan 10<br><!-- End of picture text -->