rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Selection of India by ethnic origin of company contacts -- Base traits of initial contracts**

||Iitil|Sample|of initial hourly|contracts mad|e bycompany|Eldi<br>contacts|Ol|Total sample<br>di|<br>Sl f|
|---|---|---|---|---|---|---|---|---|---|
||na|||||xcung|ny|roppng|ampe o|
||contract<br>sample|2008 and<br>prior|2009 and<br>later|High-end<br>contracts|Low-end<br>contracts|employer<br>searches|employer<br>searches|UAE outlier<br>firm|fixed-price<br>contracts|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||Esti|Pan<br>mates include<br>Dependent v|el A: Total sam<br>fixed effects f<br>ariable is a (0,|ple, excluding<br>or year x job ty<br>1) indicator for|Indian compa<br>pe x country o<br>choosing a w|nies<br>f company co<br>orker in India|ntact||
|Probability that hiring contact|0.059***|0.040*|0.072***|0.044***|0.089***|0.030*|0.127***|0.046***|0.042***|
|is of ethnic Indian origin|(0.012)|(0.024)|(0.014)|(0.017)|(0.017)|(0.015)|(0.021)|(0.010)|(0.010)|
|Observations|35,863|10,888|24,975|19,753|16,110|24,005|11,858|156,507|138,315|
|Mean of dependent variable|0.319|0.403|0.285|0.443|0.170|0.330|0.301|0.287|0.234|
|Relative effect|0.185|0.099|0.253|0.099|0.524|0.091|0.422|0.160|0.180|
||||Panel B|: Panel A cond|itional on a w|orker in India a|pplying|||
|Probability that hiring contact|0.074***|0.040|0.087***|0.045**|0.127***|0.049**|0.104***|0.038***|0.068***|
|is of ethnic Indian origin|(0.016)|(0.028)|(0.019)|(0.019)|(0.027)|(0.019)|(0.026)|(0.011)|(0.015)|
|Observations|20,511|6,241|14,270|13,081|7,430|15,224|5,287|70,821|58,302|
|Mean of dependent variable|0.559|0.701|0.498|0.669|0.369|0.520|0.675|0.633|0.555|
|Relative effect|0.132|0.057|0.175|0.067|0.344|0.094|0.154|0.060|0.123|

Notes: See Table 4.
could actually rise, rather than fall.

# **3 Descriptive analysis using panel data**

In this section, we analyze survey data from eight different countries and approximately 140,000 respondents or about 190,000 respondent-waves. We provide descriptive evidence of two previously undocumented facts. First, for most countries, the majority of individuals who are self-employed at home are not self-employed after migrating, even though they are more likely to be self-employed after moving than wage workers or individuals who are not employed at home. Second, migration rates are lower for the than for workers or individuals with no self-employed wage job. This result holds for internal and international migration, for short- and long-distance migration, and conditioning on baseline covariates such as gender, age, years of education, and income.

## _Choice of panel surveys_

Our descriptive analysis uses panel data from seven developing countries: two waves of the China Family Panel Studies (CFPS), the three main waves of the Egypt Labor Market Panel Survey (ELMPS), baseline and follow-up of the India Human Development Survey (IHDS), the latest three waves of the Indonesia Family Life Survey (IFLS), three rounds of the Mexican Family Life Study (MxFLS), three waves of the Nigeria Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), and all three waves of the Tanzanian Kagera Health and Development Survey (KHDS). We benchmark our developing country patterns against those from the United States, using three rounds of the Panel Survey of Income Dynamics (PSID). Collectively, these eight countries contain one-half of the world’s population, and provide large samples from a range of different regions of the world.

In each sample, we focus on individuals aged 18 to 65, for whom we have information on occupational status and migration rates. Our primary focus is on whether or not individuals are self-employed at baseline, which is typically based on the primary job during the past week, month, or quarter. Online appendix B describes how this is defined for each sample. We drop all individuals whose primary activity is agriculture, forestry or fishing, and then compare those who are self-employed to individuals whose primary occupation is wage work or not working at all. There are several reasons for excluding agriculture, forestry and fishing.<sup>8</sup> The first is that these are not

> 8For completeness, online appendix figure A.1 does compare the migration rates of the selfemployed to agricultural workers for our different samples, and finds agricultural workers to have

7
A second specification issue arises from descriptive studies examining the relationship between migration and poverty in rural China. For example, Du, Park and Wang (2005) examine correlations between household participation in migrant employment and poverty status, and find that the probability of migration first increases with household income, and then begins to decrease. We are concernced with the general equilibrium effects of village migration on household consumption, and it is not necessary for a household to directly participate in the migrant market to benefit from increased migration. Nevertheless, an inverted-U relationship between migration and income may be present and masked by the assumption of a linear relationship in equation (9). If so, migration should have less effect on household consumption at low levels, increase and peak, and finally decrease for higher levels of consumption. We examine this possibility by including the square of the lagged consumption level interacted with the migration variable, and find some evidence of a quadratic relationship (Table 6, columns 3 and 4).<sup>33</sup> The coefficient on the squared consumptionmigrant share interaction suggests a concave relationship between migration and consumption, but the coefficient estimates are not significant after adding household and village controls. Moreover, the overall effects of migration on household consumption are positive at all consumption levels observed in the dataset, suggesting that even if we allowed some non-linearity in the observed effect of migration on consumption, the effect would still be positive for the poor. Perhaps more troubling, the Cragg-Donald F statistic falls dramatically in these specifications, indicating that the larger instrument set is weaker after adding instruments for the second interaction term. Given both concern over weak instrument bias and only modest evidence that a quadratic relationship might be important, we continue to use only the interaction between migration and lagged consumption in further models.

# 5.4 Out-Migration and Income Per Capita

A strong effect of out-migration on the consumption of poor households might simply reflect a higher marginal propensity to consume out of additional income for poor households, or alternatively, may reflect a decline in the precautionary saving of poorer households.<sup>34</sup> In this case, increases in consumption with migration from villages may not reflect increases in household permanent income. To test whether incomes are also rising in response to migration, we re-estimate equation (9) using

> 33 To instrument for the second interaction term, we interact the t−3 and t−4 levels of lagged consumption squared with the eight instruments for the number of village migrants.

> 34 Giles and Yoo (2007) find, for example, that a larger migrant network is associated with a decline in precautionary saving, and that this effect is stronger for households below the poverty line.

23
18 Higher demand for bank deposits reduces the private sectorue willingness to hold government bonds so that the public sector is required to finance a given budget deficit with a larger amount of domestic credit. While the model has been estimated for Argentina, it is quite apparent that this specification can be readily applied to other developing countries as well. The general conclusion that emerges is that the effect of interest rates policies on the demand for capital goods Is weak in the case of Argentina, albeit the total impact might be stronger on the quality of investment than on the quantity16/. The absence of any strong relationship between real interest rates and the quantity of lnvesment does not result from exceedingly small direct interest elasticities of private investment. Instead, it is due to the Interactions of a number of mechanisms allowed for by the model which tend to neutralize the impact of such policies.

Although the model used in this paper could be improved in mny ways, e.g. inflation should be endogenous and it is very unlikely that real interest rate policies can be considered as exogenous even if it rates are controlled rather than market-determined (e.g. Gelb (1989)), the policy implications of the exercise are straightforward. The increase In real Interest rates, which is a typical element of financial reforms, do not necessarily involve a positive effect on private investment unless the authorities are careful to ensure that s (1) Bank deposits are closer substitutes to unproductive assets (cash, gold) and foreign assets rather than to capital goods; (2) The financial sector assures an efficient allocation of domestic creditss (3) The flow of domestic credit to the private sector is not absorbed by the need of the public sector.

# BIBLIOGEAPHYS

> H. BLEJER, 'The Demand for Honey and the Variability of the Rate of Inflation s some Empirical Results', International Economic Review, 20, june 1979 _H._ BLEJER & T. TANZI, "Inflation, Interest Rate Policy and Currency Substitutions in Developing Economies s a Discussion of some Hajors Issues", World Development. vol.10, n.9, sept. 1982 H. BLEJER & H. KWAN, "Government Policy and Private Investment in Developing Countries', IMP Staff Payers, vol.31, n.2, June 1984. C. BRAINARD & J. TOBIN, 'Pitfalls in Financial Model Buildingo, American Economic Review, 1968 E. CONESA, "The Causes of Capital Flight from Latin America', Washington D.Cs Inter-American Development Bank, working paper, 1986. R CUMBY & R. LEVICH, 'On the Definition and Magnitude of Recent Capital Flight', in Capital Flight and_Third World Debt, D. Lessard and J. Williamson (eds), Institute for International Economics, Washington D.C., 1987

26/It could be clearly of interest In some future work to extend the analysis in this direction.
confidence in public institutions feed off each other. This potential for bi-directional causation complicates the interpretation of the partial correlation between corruption and confidence in institutions that we have documented. This is the classic identification problem: the observed correlation between corruption and confidence might reflect causal effects from corruption to confidence that we emphasize. But it could also reflect causation in the opposite direction.

We note first that a unique strength of the corruption experiences question is that it is much less likely to be prone to reverse causation than the corruption perceptions question. To see why, recall that the experience question asks respondents whether they have been solicited for a bribe during the past 12 months. To the extent that the decision to solicit a bribe originates with the public official with whom the respondent is interacting, there should be no problems of reverse causation. It seems unlikely that a public official would even know the respondent‟s confidence in public institutions, let alone base his decision to solicit a bribe on it. This stands in contrast with the corruption perceptions question, where there is a more plausible channel of causation in the opposite direction: individuals who have low confidence in public institutions may precisely for this reason also believe that corruption is widespread in government. This potential endogeneity bias may in part account for the fact that in most of our specifications thus far, the estimated slope of the relationship between corruption perceptions and confidence is larger in absolute value, and typically is also much more significant, than in the regressions using the corruption experiences question. Thus we argue that our results using the corruption experiences question provide a fairly plausible estimate of the causal effect of corruption on confidence in public institutions.

At the same time, we acknowledge that there may still be such endogeneity bias, although to a lesser extent, even in the corruption experiences question. This would occur if respondents expressing a low confidence in public institutions are more likely to interpret an ambiguous interaction with a public official as a request for a bribe than other respondents with higher confidence in public institutions. Such potential endogeneity bias is extremely difficult to correct using purely cross-sectional observational data such as what we have in the GWP. The usual strategy with observational data of identifying 19
35 Table 14. Basic socio-economic indicators for selected countries (1985)

||Popula<br>|tion<sup>1</sup><br>|Energy consumption<br>|<br>|
|---|---|---|---|---|
||Total<br>(million)|Urban<br>(%)|per capitab<br>(kilograms of<br>oil equivalentd)|GNP per capitab<br>Motor<br>(in 1985 dollars)<br>vehicles<br>per 1000 pop.'|
|OECD Countries|||||
|U.S.A|239.3|74|7,278|16,690<br>714<br> <br>|
|Japan|120.8|76|3,116|11,300<br>385|
|U.K.|56.5|92|3,481|8,460<br>345|
|Portugal|10.2|31|506|1,970<br>154|
|Turkey|50.2|46|258|1,080<br>30|
|Yugoslavia|23.1|45|898|2,070<br>152|
|Asia|||||
|China|1,040.3|22|178|310<br>3|
|Hong Kong|5.4|93|424|6,230<br>56|
|India|765.1|25|100|270<br>5<br> <br>|
|Indonesia|162.2|25|91|530<br>12|
|Pakistan|96.2|29|136|380<br>5<br> <br>|
|Singapore|2.6|100<br>|670<br>|7,420<br>189<br> <br>|
|Korea, Rep.|41.1|64|237|2,150<br>26|
|Thailand|51.7|18|80|800<br>23|
|Africa|||||
|Egypt|48.5|32|313|610<br>21|
|Tunisia|7.1|56|170|1,190<br>56|
|Kenya<br>|20.4<br>|20<br>|114<br>|290<br>6<br> <br>|
|Nigeria|99.7|30|34|800<br>12|
|South Africa|32.4|56|1,702|2,010<br>128|
|Latin America|||||
|Argentina|30.5|84|975|2,130<br>172|
|Brazil|135.6|73|286|1,640<br>83|
|Chile|12.1|83|657|1,430<br>77|
|Colombia|28.4|67<br>|269<br>|1,320<br>77<br> <br>|
|Mexico|78.8|69|622|2,080<br>67|
|Peru|18.6|68|403|1,010<br>30|

a) Source: [United Nations 1987] b) Source: [World Bank 1987] c) Source: [MVMA 1987] d) 1 kg of oil equivalent is equal to 0.426216 joules of energy.
Table 1 Vietnam Aquaculture: Main Species by Region

|Region|Freshwater|Main|Species|
|---|---|---|---|
||Aquaculture<br>(share 2002)|Freshwater|Brackish & Marine|
|Mekong|48.2|tra, basa (catfsh)<br>Common carp, tilapia, barb|shrimp<br>crabs, mollusks|
|South East|33.7|common carp|shrimp, mollusks, lobster<br>grouper, cobia|
|South Central|15.7|common carp, grass carp<br>snakeheads|shrimp<br>mollusks,<br>pearls,<br>mussels,<br>scallops<br>grouper, cobia<br>lobster|
|North East|59.6|common carp|grouper, cobia<br>shrimp, mollusks<br>pearls oysters, seaweed|
|Red River|73.9|Chinese and Indian carp|—|
|North Central|66.4|Chinese and Indian carp|shrimp<br>seaweed, clams, bivalves<br>grouper, cobia, red drum|

Note: The table documents the main fish species produced in Vietnam, by region, based on information in World Bank (2005). The share of freshwater aquaculture by region in 2002 is from the Ministry of Fishing, Vietnam ( `www.fistenet.gov.vn` ).

37
7

**Table 1.3.** Ukraine, housing stock by form of ownership (% of total).

|Type<br>of<br>Ownership|1996*|
|---|---|
|Private|65|
|State|12|
|Municipal|17|
|Coerative|6%|

Source: Vaughan and Mamenko, 1996. * = July, 1996.

In **_Russia_** over the years of reform the structure of the housing stock by form of ownership changed quite substantially as a result of privatization and the transfer of enterprise housing to municipal ownership.

Table 1.4. Russia, housing stock by form of ownership (% of total).

|Type of Ownership|1990|1996*|
|---|---|---|
|Private|33|55|
|State|42|8|
|Municipal|24|31|
|Collective|1|6|

Source: Goskonutat, 1996a. * = at year's end.

The year 1991 saw the institutionalization of the transfer of housing into private ownership free of charge. In 1992, large-scale housing privatization began and housing privatization rates, shown implicitly in Table 1.5, reached their peak in 1993. In 1994, the process became less intensive and in the following years the rates of privatization slowed down noticeably.

Table 1.5. Privatization of housin in Russia and se aratel in Moscow, 1990-96(cumulative).

||1990|1992|1993|1994|1995|1996|
|---|---|---|---|---|---|---|
|Russi<br>........|||||||
|Number of privatized apartments,|||||||
|thousands|53.3|2,789|8,593|10,963|12,495||
|Privatized housing stock (% of total<br>subject to privatization)|0.2%|9%|24%|32%|36%|38%|
|...<br>x ,<br>, .....<br>.<br>..<br>,<br>.<br>... ,<br>, ,<br>,<br>,,,,,, ........ .<br>.<br>..|.<br>,... .||||.<br>..|.|
|Number of privatized apartments,<br>thousands|2.8|369|985|1,103|1,199||
|Privatized housing stock (% of total<br>subjct<br>t prvatization)|0. 1%|14%|32%|3841||47|

Sources: _Social andEconomic Situation in Russia,_ 1996, No. 12, p. 198, Goskonistat of Russia; Russian Annual Statistical Handbook, 1996, p. 1123.

Privatization of housing was especially active in the North Caucasus region, where as of October 1, 1996, more than half of the housing stock subject to privatization was privatized: 64% in Karachai-Cherkessia, 63% in the Ingush Republic, 59% in the Kabardino-Balcar Republic, 58% in North Ossetia, and 55% in Stavropol Krai. High privatization rates were also found in some other parts of the country: in the Altai
resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e.g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4,145 battle events for the 1960–2004 period. In the present analysis, we use 2,530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events.

The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960–2004 period according to the Uppsala/PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. The remaining 8 did not have a conflict: Cameroon, Central African Republic, Equatorial Guinea, Kenya, Malawi, Tanzania, and Zambia.

# **_3.3 Handling temporal and spatial dependence_**

Both the squares and the conflicts events are obviously not fully independent – all events within one conflict are related to each other as an action in one location leads to a later retaliation by the opposing party or to further advances in proximate locations. Events in one conflict may also affect the likelihood of other conflicts, such as the spillover of the conflict in Rwanda into Eastern DRC.

The statistical model employed to analyze these data must handle the dependence between observations. We will do this by explicitly modeling the probability of an event in a location as a function of preceding events in the same and in adjacent squares. We can do this since we know both the precise date and the precise geographic location of each event.

We use an adaption of the calendar-time Cox regression model presented in Raknerud & Hegre (1997) for this purpose. In Cox regression, the dependent variable is the transition between `states of nature' -- the transition from peace to conflict in a square. A central concept is the _hazard function_ , λ( ) _t_ , which is closely related to the concept of transition probability: λ( ) _t_ Δ _t_ is approximately the probability of a transition in the 15
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE inefficiencies with reasonable developing-country benchmarks, and executed the full amount of its infrastructure budget.

After adding existing spending to required spending, and subtracting the potential savings from improved efficiency, a substantial funding gap of $460 million per year remains. By far the largest portion of this is to be found in the water supply and sanitation sector.

Niger has the potential to close this funding gap by tapping alternate sources of financing or adopting lower-cost technologies. There is plenty of room for private sector participation in Niger’s infrastructure sectors, in particular ICT. Meanwhile, the adoption of alternate lower-cost technologies in the water supply, power, and road sectors would reduce the financing gap by almost a half ($219 million).

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Niger. The results have been presented in reports covering various areas of infrastructure—ICT, irrigation, power, transport, and water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Niger, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Niger is a poor but stable country, two sets of African benchmarks will be used to evaluate Niger’s situation: low-income countries (LICs) and middle-income countries (MICs). Detailed comparisons will also be made with immediate regional neighbors in West Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, indicators were standardized so that analyses were done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why does infrastructure matter?

Niger’s economic growth is below the desired level for reducing poverty. Between 2001 and 2009 Niger’s grew at 4.8 percent per year, below the annual Sub Saharan growth at 6.2 percent. Niger’s economic performance is below the 7 percent mark needed to make a significant impact on poverty reduction.

The poor condition of Niger’s infrastructure hardly contributed to economic growth. The overall contribution of telecommunications, electricity, and roads infrastructure to Niger’s per capita growth between 2000 and 2005 was only 0.3 percentage points, among the lowest on the continent. At this level, Niger—along with Chad—is one of the worst performers in infrastructure development in the region.

2