rafmacalaba/datause-encoder-data huggingface dataset:
of regulation if these firms have obtained control over the regulatory processes. Examining if there are differential effects in who bribery affects among young and old firms will empirically test the regulatory capture view. The business transactions surveyed in Enterprise Surveys also refers to activities involving public workers or officials.

# **4.1 Estimation Results**

Among firms who conducted business transactions, those who were bribed have lower sales growth (0.9%) than those who were not (3.2%). Note that the variation in annual sales growth is high. Among firms who were bribed, sales growth one standard deviation from the mean ranges from -24.3 percent to 30.9 percent. In the sample, less than half of the firms have negative growth rates. Firms who participate in business transactions on average have higher sales growth than firms who do not, and this may be correlated to higher entrepreneurial abilities or plans to expand. In the surveys, bribery data is only available for firms who conducted at least one business transaction in the past two years. The analysis is restricted to these firms, 74 percent of the sample. In the main specification, firms who did not have any transactions are excluded because it is not possible to identify whether bribery was the reason why they refrained from participating in a transaction. Although this sample restriction could lead to a selection bias, the effect of the selection is not clear. Large and efficient firms with growth potential are more likely to apply for permits and have business transactions. Yet they may also be better suited to compensate for bribery distortions. In this case, any negative impact of bribery we find would be the lower bound of negative impact of bribery. On the other hand, increased visibility in the market could lead to more frequent solicitations and higher distortions. We also present results with the sample including firms without any transaction to show whether the relationship between bribery payments and sales growth is sensitive to the composition of the sample. We make the assumption that firms who did not conduct a business transaction were not faced with bribes.

The correlation table for the variables used in the estimation is presented in Table 6. Collinearity between the control variables does not seem to be a concern for the estimation. Table 7 shows IV estimates for the full sample (29 LAC countries). In this table we use the Bribery Indicator ( _BI_ ). The country-location-sector average of the indicator ( ) is used as instrument for the firm’s response. The average is interpreted to represent the likelihood of 16
**Figure 4.31. Ratio of one square meter housing per capita for the bottom quintile to square meters housing per capita for the highest quintile in capital cities and other urban areas**

**Mobility rates are lowest for the poor.** Figure 4.32 shows mobility rates by lowest and highest quintiles for residents in the capital cities.  As expected, the rate of households who have moved during the past five years is substantially higher for upper income households than for lower income households.  The difference is particularly sharp in Armenia, Azerbaijan, Belarus, Georgia and Kazakhstan.

**Figure 4.32. Residential mobility by the poorest and richest quintiles in capital cities**

<!-- Start of picture text --> Poor mobility Rich mobility<br>40<br>30<br>20<br>10<br>0<br>Albania Armenia Azerbaijan Belarus Georgia Kazakhstan Tajikistan Turkmen<br>quintiles of the capital cities<br>Mobility by the pooerst and richest<br><!-- End of picture text --> Source: see Table 2.1 Although analysis of mobility rates in secondary cities generally finds upper income households to be more mobile, the patterns are less clear than those seen in capital cities (Figure 4.33). In Armenia, Azerbaijan, Belarus, Georgia, Tajikistan and Turkmenistan poor households are visibly less mobile than are rich households. However, in Albania and Kazakhstan, poor households are more likely to have moved during the last five years than are rich. In Albania, extensive rural to urban migration may explain the pattern. Rural residents who recently have moved to secondary cities may be poorer than other secondary city residents. In Kazakhstan, large numbers of ethnic Russians, Germans and Ukrainians—primarily living in urban areas--emigrated during the 1990s. They were replaced by ethnic Kazakhs from rural areas, who were likely poorer than the non-moving urban populations.<sup>43</sup>

> 43 For an analysis of population trends in Kazakhstan, see:  Richard Rowland.  2001.  “Regional Population Change

50
111 The nonmarket reservation **wage** of the wife is affected by the fnmily's land, ownership of a business, assets, nonearned income, and the husband's educatioui and experience. These variables **are** added to the wage-status probit equation. The Heckaan (1979) two stage estimate that is less efficient yields an estimate of returns of .16 (Griffin, 1987; Table 3).

'/Alternative specifications can include **the age** and education of **the** spouse or the predicted wage of the spouse and a marital status duiy variable if these characteristics of the spouse are missing. These identifying variables are believed to be more likely to be endogenous than the land and nonearned income variables. But their inclusion in the model does not change appreciably the estimates of the private rates of return reported in Table 3 (Schultz, 1988c).

Taxes on personal income are a relatively L small portion of government revenues in low income countries. The exception may be Latin America if one includes 'social securityw taxes (World Bank, 1988: 84). In the case of Thailand in 1981, direct taxes on personal incomes amounted to less than 5 percent of household expenditures and only 10 percent of wage recipients in Bangkok (National Statistical Office, undated). Most of government revenues in low income countries are from taxes on companies and conmodities (excise and trade). This adjustment may therefore be of secondary importance in low income countries but could be quite important in industrially advanced countries.
18 References.

Bhatia, R.J. (1985), The West African Monetary Union: An Analytical Review, (Washington, D.C., International Monetary Fund).

Devarajan, S. and J. de Melo (1987a), "Evaluating Participation in African Monetary Unions: A Statistical Analysis of the CFA Zones', Worl Development, Vol. 15, No. 4, pp. 483-96 Devarajan, S. and J. de Melo (1987b), "Adjustment with a Fixed Exchange Rate: Cameroon, Cote d'Ivoire and Senegal", The World Bank Economic Review, Vol. 1, No. 3, pp. 447-87 Engle, R.F. and C.W.J. Granger (1987), "Co-Integration and Error-Correction: Representation, Estimation and Testing", 2Donoetrica, Vol. 55, No.2, pp. 251-76.

Giovannini, A. (1988), 'How do Fixed Exchange Rate Regimes Work?", NSR Guillaumont, P. and S. (1984), Zong franet &Xelo af r.JRgLL (Paris, Economica).

Guillaumont, P. and S., eds. (1988), Strategies de develoggement com2arees: zone franc et hors zone franc, (Paris, Economica). Guillaumont, P., S. Guillaumont and P. Plane (1988), "Participating in African Monetary Unions: An Alternative Evaluation", World DeveloRment, Vol 16, No. 5, pp. 569-76.

Honohan, P. (1983), "Measures of Exchange Rate Variability for One Hundred Countries", Agplied Economics, Vol. 15, pp. 583-602.

Honohan, P. (1988), "Resource Mobilization by the Banking System in Cote d'Ivoire", mimeo. World Bank, CECFP, April 1988.

Honohan, P. and P. McNelis (1989), "Is the EMS a DM zone? Evidence from the realignments", Economic and Social Keyiew, Vol. 20, pp. 97-110.

Leiderman, L. (1987), "Price and Wage Flexibility in the Adjustment Process of franc zone countries: the Ivory Coast", University of Chicago, mimeo.

Leite and Makonnen (1986), "Savings in the BCEAO countries", Savings and Development, Vol 10. No. 3, July-September.

Levy, V. and J. L. Newman, (1989), Wage rigidity: micro and macro evidence on labor market adJustment in the modern sector", World Bank Economic Review, Vol. 3, No. 1, January.

Macedo, J.B. de (1984), "Collective Pegging to a Single Currency: The West African Monetary Union", in S. Edwards and L. Ahamed, eds., Economic Adiustment and Exchange Rates in Developing Countries, (Chicago: University Press) Paraire, J.-L. (1988), "L'instabilite des taux de change", in Guillaumont, P. and S., eds. (1988).
# TABLE IX

# IMPACTS OF PRICE INCREASE IN BILL 0 ON CONSUMPTION IN PERIOD 1, ALTERNATIVE BANDWIDTHS

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Treatment|-15.901**|-15.266**|-16.296*|-14.780**|-18.863*|-15.872*|-16.091|-13.176|
||(6.491)|(5.929)|(8.105)|(6.882)|(10.386)|(8.484)|(13.525)|(11.124)|
|AAC|0.720**|1.004**|0.759|1.443**|1.060|2.403**|0.703|1.898|
||(0.290)|(0.412)|(0.451)|(0.612)|(0.832)|(0.863)|(1.774)|(2.309)|
|AAC* Ttt||-0.619||-1.522*||-3.134**||-3.220|
|reamen||(0.531)||(0.782)||(1.333)||(2.821)|
|Ctt|433.271***|436.134***|433.000***|438.133***|435.086***|441.855***|438.154***|441.205***|
|onsan|(3.299)|(3.992)|(4.042)|(4.647)|(5.287)|(5.386)|(7.297)|(8.397)|
|Bandwidth|20|20|15|15|10|10|5|5|
|N|7190|7190|5417|5417|3679|3679|1946|1946|
|R-squared|0.001|0.001|0.001|0.001|0.001|0.002|0.001|0.002|

_Notes:_ The dependent variable is consumption in Period 1. The bandwidths determine the range of normalized annual accumulated consumption used to select observations for a particular regression. For example, in Columns (1) and (2) only individuals with normalized annual accumulated consumption between [-20, 20] are included. Average of the dependent variable is 425.49. The estimation method is OLS. Standard errors clustered by accumulated consumption in Period 0.*** p<0.01, ** p<0.05, * p<0.1.

34
measures of relative and absolute mobility are affected by truncation because of coresidency restrictions in a survey.

Taking advantage of two exceptionally rich household surveys from India and Bangladesh, we provide evidence on the effects of sample truncation caused by coresidency restrictions on the rank-based measures of relative and absolute mobility. Our analysis focuses on intergenerational schooling persistence, as education has been the main indicator of economic status in most of the recent research on intergenerational mobility in developing countries in the absence of reliable income data (see, for example, Azam and Bhatt (2015), Emran and Shilpi (2015), Hertz et al. (2007), Nimubona, A, and D. Vecatachellum (2007), Behrman et al. (2001)).<sup>4</sup> The evidence reported below shows that truncation, in general, results in _downward_ bias in the estimate of the _slope_ (relative mobility) and _upward_ bias in the estimate of the _intercept_ of widely-used intergenerational persistence equations including the rank-rank regression. When the focus is on relative mobility, the downward bias in rank correlation is small and similar in magnitude to that in IGC (about -10%), but the bias in IGRC is much larger (about -25%). Truncation bias in the intercept estimate is, in general, the largest in the regression specification used to estimate IGC (i.e., Pearson correlation), while the bias in the intercept of rank-rank regression is, in most cases (26 out of 30), the smallest. Children’s expected years of schooling (or expected normalized schooling in the case of the IGC specification) conditional on parental schooling are overestimated in coresident samples, implying that the upward biased estimate of the intercept dominates the downward bias in the slope estimate. When the focus is on absolute mobility, the rank-based measure a la Chetty et al. (2014) outperforms an alternative measure based on

> 4The focus on educational mobility is appropriate for at least two reasons. First, as noted above, data are not available to construct reliable estimates of permanent income, and schooling attainment is likely to be a good proxy for life-time income and economic status in most developing countries. Blanden (2013) in her review of cross-country evidence on intergenerational mobility concludes that “....the results for earnings and education tend to be fairly well correlated; this implies that information on educational mobility is a good proxy for earnings mobility in countries where earnings information is not readily available.” Second, there is a broad consensus among policy makers and economists that education is key to social mobility in an increasingly skill-driven economy (Stiglitz (2012), Rajan (2010), The Economist (2012), World Development Report (2006)).

3
(as well as that of resource and non-resource rich countries) during the period 1978-85, where not only growth per worker was negative but also the contribution of TFP was largely negative. The annual average growth rate of output per worker was -0.7 percent in the period 1978-95 while physical and human capital contributed to an increase in growth per worker of 0.3 and 0.4 percent per year, respectively. These numbers imply that these additional resources were combined inefficiently with the existing technology. In contrast, growth per worker in the region (as well as all country groups) accelerates during the period 1996-2014. The annual average growth rate of output per worker jumped from - 0.7 percent in 1978-95 to 2.4 percent in 1996-2014. Factor accumulation contributes to growth per capita with 1.5 percent per year (0.9 percent by physical capital and 0.6 percent by human capital). In contrast to the other two periods, the contribution of TFP growth is economically significant (0.9 percent per year).

**Figure 3. Source of Growth, 1961-2014: By sub-periods** _(average annual growth rate, population weighted averages)_

<!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>Sub-Saharan Africa (SSA) SSA Oil Rich Countries SSA Metal Rich Countries SSA Non-Resource Rich<br>Countries<br>Capital Human Capital 1 TFP Output<br>1961-1977 1978-1995 1996-2014 1961-1977 1978-1995 1996-2014 1961-1977 1978-1995 1996-2014 1961-1977 1978-1995 1996-2014<br><!-- End of picture text --> _Note: The human capital index H is characterized by the relationship between h and s as_ φ�s����φ� ∙s�� _for each country i in period t —where the returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014). Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._ The swing in the growth rate per capita of the region over the three different periods is also observed across all country groups, and these fluctuations tend to be sharper among resource rich countries. Growth per worker of oil rich countries decelerates from 1.5 percent per year in 1960-77 to -0.1 percent per year in 1978-96. In both periods, physical capital accumulation contributes positively to growth per worker while TFP growth plays a detrimental role in the growth of labor productivity. Growth per worker then accelerated to an annual average rate of 3.8 percent in 19962014. Factor accumulation explained half of the growth per worker achieved during this period (1.4 percent attributed to physical capital accumulation and 0.5 percent to human capital) while the remainder was attributed to TFP growth.

_page 7_
31 Third, policies to promote technology dissemination and productivity spillovers need to be based on the recognition that MNC affiliates compare the costs of providing support with the benefits of obtaining higher-quality and more cost-effective local inputs in return. Therefore, host economy governments need to devise policies that lower the costs and increase the benefits for MNC affiliates. This can be done by creating programs in which MNC affiliates and domestic firms jointly participate so that performance improvements that result from technology dissemination are used to improve inputs for the foreign-owned firms.

Fourth, regarding host economy conditions, it is important that a clear and transparent FDI policy is in place, helping MNC affiliates with their entry, start-up, and continued operations. Furthermore, governments need to support and continue to institutionalize a liberalized and open economic environment that fosters competition and allows the most productive firms to thrive. In cooperation with MNC affiliates and domestic firms, this also involves investing in areas of the economy that are characterized by indivisibilities and public good dimensions, including education, infrastructure, finance, and research and development.

# **5.2 Suggestions for Further Research**

Our survey of the available evidence suggests several avenues for future research. The voluminous econometric literature on productivity spillovers in general and on the effects of characteristics of MNC affiliates and local firms is still characterized by a substantial bias toward intra-industry spillovers. Given that the available evidence indicates that productivity spillovers between MNC affiliates and local suppliers are much more prevalent in a wider variety of host economy settings, more research is required into the occurrence and the drivers of this type of spillover. Given the strong implications for policy making, research into backward linkages needs to include a further examination of the effects of the technology gap and the geographical dimensions of productivity spillovers. Also, research on backward linkages will benefit from adopting broader definitions of their economic impact on local suppliers, including productivity spillovers, effects on exporting activities, innovation, and product complexity and quality.

Another aspect that needs further attention concerns the decision processes that MNC affiliates apply in determining their use and support of local suppliers. Although it is clear from the available evidence that such decisions involve economic cost-benefit considerations, more detailed research will help further clarify the context and the critical factors that play a role in these decisions. In analyzing these processes, more attention needs to be paid to the impact of continuing processes of international economic liberalization that make it easier for MNC affiliates to source from
Bank, 2012). However, to our knowledge, this is the first study looking at the issue with a regression analysis.

Finally, rainfall shocks experienced at the commune level in 2008 and 2009 are positively associated with welfare. Households experiencing a one standard deviation drop in local precipitation in 2009 have on average a 6% lower consumption per capita (-5% for a similar magnitude shock experienced in 2008).

We also estimate a multilevel random effect model (second column in Table 10) with a cluster level specific effect. The estimated coefficients for the household and the cluster level variables remain in the same order of magnitude. We are now able however to decompose the unexplained variance into a between cluster component and a within cluster component. The unexplained variance in consumption accounted for by the covariate (between cluster variance) accounts for about 28% of the total unexplained variance. The remainder is explained by idiosyncratic risk only (within cluster variance).

16
for 2006 and 2010 (for our period of study). For this reason we use the top-coded data for our main specification. To verify how sensitive our findings are with respect to this choice, we will present regression results obtained using bottom-coded data as a robustness check. As we only have two years of bottom-coded data, which are 5 years apart, we will estimate the parameters from eq. (2) using a difference-in-differences approach. For completeness, the same difference-in-differences regressions are also applied to the top-coded data. Examining how the relationship between changes in market access and changes in lights holds up over this longer term constitutes a valuable robsutness check in and of itself.

# **6 Results**

To set the stage, columns 1 and 2 of Table 2 present first stage regressions using the natural log of market access as dependent variable and the checkpoint donut count, the number of checkpoints located in a radius between 10 and 25km from the localitys center, as the main explanatory variable. Although obstacles captured by this donut count measure are not deployed within the locality but rather in its vicinity, they clearly significantly adversely impact local market access; each additional obstacle reduces market access by approximately 3.81 percentage points according to the estimates presented in column 1. This estimate is not sensitive to controlling for local conflict as is demonstrated in column 2, suggesting that checkpoint deployment outside ones own locality is largely orthogonal to local conflict. Column 3 provides corroborative evidence for this conclusion by demonstrating that our proxies for local economic conflict are not significant predictors of the donut count measure, which now serves as the dependent variable. In summary, the number of obstacles deployed within a 10 to 25 km radius of the locality is a suitable instrument for market access.

## **6.1 Main findings**

Turning to the main specifications, Table 2 presents estimates of equation 2. Lights emissions are clearly strongly and significantly correlated with market access. Simple OLS estimates presented in column 1 imply that a 10 percentage points increase in market access increases lights by 0.6 percentage points. If we assume that the GDPto-lights elasticity is 0.3 (cf. Henderson et al., 2012), this corresponds to an increase in local output of approximately 0.2 percentage points. This relationship is robust to including proxies for violence, as is done in column 2.

When we instrument market access using our donut count measure of obstacles 30
the actual panel comprised those who had been poor in 2004/05 but were non-poor but vulnerable in 2011/12. This compares with an estimate of 16.7 percent in the synthetic panel. Overall, the impression one is left with is that the synthetic panels do a reasonably good job of reproducing the true panel estimates. Certainly, broad-brush qualitative conclusions derived from the synthetic panels would seem to be rather robust. Based on these reasonably encouraging results, we report below our synthetic panel-based estimates of consumption dynamics over the 1987–2012 period derived from NSS data.

# **V. Welfare Dynamics Analysis**

# **V.1. Mobility Levels and Trends**

We begin by enquiring into the extent of consumption mobility and assessing how this has evolved over time. Table 2 reports consumption dynamics across our three population groups between the 1987/88 and 1993/94 survey years. During this interval, overall poverty remained largely unchanged at roughly 46 percent of the population.<sup>15</sup> We employ two different approaches to setting the vulnerability line. The first refers to an arbitrarily set vulnerability line equal to twice the national poverty line. In panel B we reproduce our estimates, based now on a vulnerability line associated with an average risk of falling into poverty by the vulnerable of no less than 20 percent. The two panels differ primarily in that panel A is associated with a somewhat higher vulnerability line, corresponding to a larger share of the population found in the vulnerable group.

In both panels, a majority of the population can be found in the cells along the diagonal of the transition matrix, indicating that they were either poor, vulnerable, or secure in both years.

> line from the data as described in Section 3. Mobility patterns associated with our estimated poverty line are examined when we turn to our description of mobility patterns based on the NSS-based synthetic panels. 15 Recall that our reference population comprises only that population that belonged to households in which the head was aged between 25 and 55 years in the base year. Overall poverty estimates may therefore differ somewhat from conventionally reported rates for the total population. The underlying regressions are provided in Table A.1 in Appendix A.

19