rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Marginal Impact in Percentage Point - Summary

|All sample<br>|ODT<br>|
|---|---|
|Tab. 6<br>Col. 4<br><br>|Tab. 7<br>Col. 6<br>|
|Foreign presence _per se_<br>2.62<br>Foreign exports value<br>0.04|1.39<br>0.04|
|Figures correspond to the increase in the a<br>ability that domestic frms start exporting<br>|verage prob-<br> in a prod-<br>|
|uct/country pair when foreign frms’ exports|are positive|
|for this product/country pair (frst row) and|when foreign|
|frms’ exports rise by 10% (second row).||

Ultimately, focusing on ordinary trade activities for both foreign and domestic firms, the presence _per se_ of foreign firms exporting product _k_ to country _j_ increases the average probability that domestic firms in the same province will start exporting this product to this country by 1.39 percentage points.<sup>17</sup> This effect is almost four times greater than the effect of a 10% increase in the GDP per capita of the destination country and three times greater than the impact of a 10% increase in the product-destination country total imports. A 10% increase in the value of foreign exports increases the average probability that domestic firms will start exporting by 0.04 percentage points.<sup>18</sup>

# **III Conclusion**

Using panel data from Chinese customs for the 1997-2007 period, domestic firms’ capacity to start exporting new varieties to new markets is shown to respond positively to the export activity of neighboring foreign firms. The results are very robust to the introduction of different sets of fixed effects and to instrumentation strategies that control for the endogeneity of foreign exports. Weak or no foreign export spillovers are detected when other dimensions of the export activities of foreign firms are considered (other destination countries, other products). This result is coherent with previous results obtained by Koenig et al. (2010) for France and indicates

> _x_ is equal to (1 _._ 1<sup>_βx_</sup> _−_ 1); _βx_ is the coefficient of _x_ . The increase expressed in percentage points of probability is equal to (1 _._ 1<sup>_βx_</sup> _−_ 1) _Px_  ̄.

> 17 0 _._ 062 This figure corresponds to [ _e −_ 1] _×_ 0 _._ 217 from column 6 of Table 7.

18 0 _._ 017 This figure corresponds to [1 _._ 1 _−_ 1] _×_ 0 _._ 217 from column 6 of Table 7. 30
- 25 -

# Table 5

# Comparative Analysis of Nominal Transport Costs for Major Carlbbean Food and Agricultural Raw Materials Exports to the<sup>United States</sup>

||1986 Exp|orts<br>|U.S.|Nom|inaol<br>rpesi|ht Rate|
|---|---|---|---|---|---|---|
|||%of|Applied|Caribbean|Other||
|Coountry/Product<br>1/|Value($000)|Total 2/|<br>Tariff 3/|<br>Exporter|Exporters|Dlfference 4/|
|Antigua|||||||
|Bovine Leather (1216105)|1,273|10.8|5.0|3.8|3.9|-0.1|
|Fancy Leather, NSPF (1216500)|743|6.3|2.9|3.9|3.9|0.0|
|Candy and Confection (1571005)|415|3.5|0.0|12.8|9.7|3.1|
|Lmown Julce (1653650)|303|2.6|26.9|12.5|15.5|-3.0|
|Fresh Fish, NES (1103570)|288|2.4|0.2|16.2|20.4|-4.2|
|Subtotals|3,022|25.6|5.5|7.2|7.4|-0.2|
|Behamas|||||||
|Fresh Lobsters (1144530)|8,766|2.0|0.0|2.2|3.3|-1.1|
|Rock Lobster Tails (1144525)|7,445|1.7|0.0|3.5|_2.8_|0.7|
|Subtotals|16,211|3.7|0.0|2.8|3.1|-0.3|
|Barbados|||||||
|Sugar and Sugar Syrups (1553500)|<br>1,446|1.3|0.6|1.7|12.3|-10.6|
|Rum (1691415)|1,054|1-0|0.2|7.0|5.9|1.1|
|Subtotals|2,500|2.3|0.4|3.6|9.5|-5.9|
|Bel**Iz**|||||||
|Sugars and Molasses (1552045)|11,110|22.1|0.0|10.2|6.7|3.5|
|Orange Julce, NSPF (1652900)|6,371|12.7|4.0|6.5|9.3|-2.6|
|Citrus Juices, NSPF (1653680)|4,069|8.1|3.3|5.2|13.3|-8.1|
|Rock Lobster Tails (1144525)|3,179|6.3|0.0|1.5|2.9|-1.4|
|Fresh Lobsters (1144530)|562|1.1|0.0|1.6|3.2|-1.6|
|Subtotals|25,291|50.3|1.5|7.1|7.8|-0.7|
|Dominican Repubilc|||||||
|Coffee, Crude (1601020)|109,972|10.5|0.0|2.6|3.7|-1.1|
|Sugars and Molasses (1552045)|96,936|9.2|0.0|5.1|7.1|-2.0|
|Cocoa Beans (1561000)|57,781|5.5|0.0|8.9|7.4|1.5|
|Beef without Bones (1061060)|25,625|2.4|0.1|5.6|16.3|-10.7|
|Cigars (1707000)|18,190|1.7|0.3|1.8|3,2|-1.4|
|Sugar and Mixtures (1554000)|9,597|0.9|0.0|20.2|25.5|-5.3|
|Subtotals|318,101|30,2|--|5.1|7.0|-1.9|
|Domlnica|||||||
|Cocoa Beans (1561000)|705|4.6|0.0|9.8|7.6|2.2|
|Rock Lobster Tells (1144525)|457|3.0|0.0|3.4|2.8|0.6|
|Citrus Juices, NSPF (1653680)|129|0.9|0.4|12.0|10.9|1.1|
|Subtotals|1,291|8.5|--|7.4|5.9|1.5|
|Grenada|||||||
|Nutmgs (1616300)|494|16.5|0.0|2.7|3.9|-1.2|
|Cocoa Beans (1561000)|254|8.5|0.0|4.4|7.6|-3.2|
|Frozen Fish (1103575)|129|0.9|0.4|29.5|12.0|17.5|
|Subtotals|877|25.9|--|4.1|**5.4**|-1.3|
poverty in a specific dimension may experience more significant changes due to concerted efforts in tackling that specific area.

The second additional explanatory variable is the sum of the headcount ratios of all other indicators in the initial survey year, excluding the indicator under consideration, ∑ ii≠cc the broader context of multidimensional poverty within which a specific indicator is embedded. This HHccii<sup>(tt). This variable captures</sup> could be important because the potential spillover effects from other indicator of poverty can influence the trajectory of poverty reduction in the indicator of interest. High levels of deprivation in other indicators may exert upward pressure on a particular indicator, either through resource constraints or through direct effects of multidimensional poverty. Conversely, in situations where overall multidimensional poverty is relatively low, improvements in a specific indicator may be more readily achieved due to a lower intensity of spillover effects.

By including these variables in our model, we consider the primary factors affecting changes in poverty levels across different indicators, allowing us to isolate the unique contribution of the structure of poverty on multidimensional poverty dynamics.

# Table 1 illustrates the results from our regression models.

In column (1) of Table 1, we present a baseline regression model that includes only the dummy variables, which accounts for approximately 29% of the observed changes in the censored headcount ratio (Adjusted = 0.29). When we add our poverty centrality index (column (2)), we get a significant improvement in the model’s explanatory power (Adjusted = 0.37). More importantly, RR<sup>2</sup> we find that more central indicators usually have larger decreases in the headcount ratio, as suggested RR<sup>2</sup> by our analysis in the previous section.

In column (3), we include the initial censored headcount ratio of the indicator c as an additional variable, while column (4) adds the sum of the headcount ratios of all other indicators, excluding the HH (tt) one under consideration. Lastly, in column (5), we include both control variables in our regression model. Our results demonstrate that the negative and significant relationship between poverty centrality and long-run changes in headcount ratios persists even after adjusting for these controls. Additionally, we find that indicators with higher initial censored headcount ratios display larger decreases over time.

21
servants plus others such as uniformed personnel) made up 6 percent of all employed; civil servants themselves constituted 4 percent of all workers in the labor market, or about one-third of all jobs if selfemployed and unpaid family workers are excluded. Salaried private sector jobs accounted for less than 5 percent of total employment, and state-owned enterprises for less than 1 percent.

**Table 3: Employment in Lao PDR, 2005**

|**Category**|**Number**<br>**employed**|**% of**<br>**total**|**Female**<br>**(%)**|**Male**<br>**(%)**|
|---|---|---|---|---|
|Government Employee|168,388|6.1|31|69|
|Parastatal Employee|11,446|0.4|33|67|
|State Enterprise Employee|19,486|0.7|27|73|
|Private Employee|121,786|4.4|40|60|
|Employer|7,210|0.3|31|69|
|Own Account Worker|1,149,906|42.0|32|68|
|Unpaid FamilyWorker|1,260,671|46.0|71|29|
|Total|2,738,893|100.0|50|50|

Whatever the debate over civil service earnings, the fact remains that white collar, or office professional, alternatives to civil service employment are limited at present since formal salaried jobs are currently a small fraction of the Lao labor market.

_Source: Results from the Population and Housing Census 2005_ The study undertook two approaches to pay comparator analysis in order to gain as comprehensive a picture of the alternative labor market as possible—comparing public sector earnings to those for similar _workers_ in other labor markets as well as to compensation for similar _jobs_ (see Part I). Overall, the analysis confirmed a common perception that—for certain positions and workers—the formal private sector and state-owned enterprises (SOEs) may offer the potential for seemingly higher monetary compensation than the civil service.

For the worker approach, we drew on data from the 2007-08 Lao Expenditure and Consumption Survey (LECS) in order to calculate the mean earnings for workers in relevant comparator industries to the public sector and for a variety of educational levels. [Methodological details are in Box 2 above; and summary statistical analysis is in Annex 3.] The findings broadly indicate that mean monthly earnings for wageworkers in government administration are somewhat lower than in the private sector (excluding agriculture). For workers with some secondary schooling onward, wage-earners in the private sector and SOEs appear to earn around one-and-a-half times the compensation earned by those in the civil service.

For the jobs approach, we interviewed HR managers at four ministries, fifteen private firms, two commercial banks, and five state-owned enterprises (SOEs), asking questions about compensation, benefits, and HR practices. Table 4 summarizes the analysis, showing the calculated averages and ranges of monthly compensation for civil service professional staff, in comparison to the range in monthly compensation for similar jobs in the private sector and SOEs, along with a ratio of private to public pay. [Methodological details are in Box 3 above; and full details of the analysis are presented in Annex 4.] 25
<!-- Start of picture text -->
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<!-- Start of picture text --> 0.8<br>04 poke<br>0.0 i SL eepen, ete<br>-0.8 Pd ‘<br>-1.2<br>2004 2005 2006 2007 «2008 #2009 2010 = 2011 2012<br>—— Beta (Thailand) ------ Upper Cl ------ Lower Cl<br><!-- End of picture text --> <!-- Start of picture text --> 0.0 -—<br>-0.2 ‘<br>-0.4 See<br>-0.6 BE<br>-0.8<br>“1.4 “a<br>-1.6<br>2004 2005 2006 2007 «2008 2009 2010 2011 2012<br>—— Beta (Vietnam) ------ UpperCl ------ Lower Cl<br><!-- End of picture text -->
mechanism of development–access to external finance–-which reduces the potential for reverse causality. Cull and Xu (2005) similarly unbundle the role of institutions in determining reinvestment rates in China, but do not deal with measurement error, and focus on only one country that – however large - is not necessarily representative of the developing world. Our overall conclusion renders support to AJ (2005) that property rights institutions loom more prominently than contracting institutions for development. Our results indicate that a specific channel through which development is affected more by property rights institutions rather than contracting institutions is external financing for firms.

Second, this study is related to the literature on firms’ access to external finance. Numerous studies have established the importance of external finance for development. Financial development is found to be strongly correlated with country-level growth rates (e.g. King and Levine, 1998; Demirguc-Kunt and Maksimovic, 1998; Clague et al., 1999; Levine, Loayza and Beck, 2000; Levine 2005), growth at the country-industry level (Rajan and Zingales, 1998), and the rate of new firm creation at the regional level in Italy (Guisi, Sapienza and Zingales, 2004). Further empirical work establishes that access to external finance is related to the legal origin of a country (La Porta et al., 1997, 1998, 2000), development of banking systems and stock markets, and with the overall level of institutional development (Beck, Demirguc-Kunt, Laeven and Maksimovic, 2006; Beck et al. 2008). The latter two studies also find that small firms have worse access to external finance. We differ from previous studies by “unbundling” various types of institutions, and examine how measurement error affects our key results. We also show that micro firms are quantitatively important, and are especially vulnerable even compared with small firms.

Section 2 describes our data and methodology in greater detail. Section 3 presents the results. Section 4 concludes.

# **2.** **<u>Data and Methodology</u>**

The main data set we use is the World Bank Enterprise Surveys (WBES), which are collected using a standard and rigorous approach across countries. The surveys provide representative samples of each country’s private sector including manufacturing, construction, services and retail and wholesale trade.<sup>4</sup> Observations are technically establishments rather than firms. Many of these establishments

> 4 See http:\\www.enterprisesurveys.org for a more complete description of the data.

7
results shown in table 2, we find that an increase in the risk of polygamy leads to a higher probability for women to save, out of home only. However, this impact is significant only for women living in the poorest households. These results thus give credence to the precautionary motive for savings. Moreover, they allow us to be confident that our results are not driven by our risk variable capturing wealth, as could be argued based on our identification assumption relying on the exclusion of the polygamy status of the husband’s father from our second step equation (see discussion of identification issues in the Identification Assumptions and Exclusion Restrictions section).

## _Consumption, Labor, and Transfers_

Going one step further, we are interested in understanding the trade-offs behind saving decisions in response to a higher risk of polygamy and in particular the way consumption is affected.

Household Food Consumption and Cell Nonfood Consumption Levels As noted above, a unique feature of the PSF data is that information on consumption is available at the cell level, defined as household consumption subunits.<sup>22</sup> Note that the definition of cells has no impact on the measurement of savings, nor on labor or income, since only information on consumption is collected at the cell level.

We thus additionally analyze the impact of the risk of polygamy on the level of household food consumption per capita for all monogamous unions and cell nonfood expenditures, as well as the contributions of both husband and wife to cell expenditures.

# [Table 4 about here]

Table 4 presents the effect of the predicted risk of polygamy on the level of household per capita food expenditures (column (1)), on the level of per capita nonfood expenditures of the wife’s cell (column (2)), on the level (column (3)) and share (column (4)) of the wife’s contribution to the per capita nonfood expenditures of her own cell, and on the level (column (5)) and share (column (6)) of the husband’s 22 The definition of cells used in the survey implies that the household head and her or his spouse(s) are always part of different cells. However, monogamous husbands who do not head their household are part of the same cell as their wives, implying that we cannot separately observe the consumption decisions of the two spouses. In what follows, we show results on the total sample of monogamous wives, either sharing their husband’s cell or being in a separate consumption cell, to maximize our sample size. However, we test the robustness of our results on the subsample of wives in separate cells, that is, wives of the household head, and find similar results (available upon request).

18
**Figure 2.5: Informality Rates**

<!-- Start of picture text --> Informality rates<br>68.8%<br>70% 45%<br>67.5%<br>65.1%<br>66% 41%<br>63.2%<br>62% 37%<br>58% 33%<br>30.2%<br>27.8%<br>54% 29%<br>28.3%<br>50% 27.0% 25%<br>without access to health non_IMSS contributors<br>self employed and others informaility as defined by ENOE<br>contributors<br>firms (INEGI definition)<br>2007/01 2007/02 2007/03 2007/04 2008/01 2008/02 2008/03 2008/04 2009/01 2009/02 2009/03 2009/04 2010/01 2010/02 2010/03<br>Share of workers without access to health/non-IMSS Share of self employed workers / workers in informal<br><!-- End of picture text --> _Source:_ Own calculations using data from INEGI, BIE, and ENOE, 4th quarter 2010.

Analysis of job creation/destruction by economic activity also tells a story of how the crisis evolved. Figure 2.6 shows annual job creation/destruction for each sector of activity by quarter. Agriculture experienced three consecutive quarters of job destruction at the end of 2008 and beginning of 2009, and again shows destruction in the last two quarters of 2010. A similar pattern is observed for manufacturing: almost continuous destruction of jobs in 2008 and 2009, only recovering in 2010. The second quarter of 2009 shows the highest job decrease for manufacturing, a loss of about 672,000. By comparison, the largest job creation, in the third quarter of 2010, was only 382,000.

8
exchange rates with the dollar are rather insulated from the policy announcements.<sup>21</sup> For emerging economies in our sample, it is perhaps the exchange rate with respect to the US dollar that is more important, since a large part of their trade and capital flows are denominated in US dollars.

Equity prices in emerging economies are not sensitive to the policy announcements by the ECB, and the BoJ; while bond yields are not sensitive to the BoJ announcements, but are sensitive to the announcements made by the ECB and BoE. Over all, the impact of the announcements made by the Federal Reserve Board dwarfs those of the other central banks.

**Table 6: The Effect of Monetary Policy Changes in the Euro Area, Japan, and U.K. on Emerging Economies Financial Variables**

||||Exchang <br>|e rates<br>|||E|quity prices|||Bondyields||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||LC/EUR|LC/USD|LC/JPY|LC/USD|LC/GBP|LC/USD|||||||
|EU 10yr yield|0.084***|0.017|||||-0.016|||0.146***|||
||[4.49]|[0.92]|||||[-1.04]|||[3.16]|||
|JP 10yr yield|||0.027***|-0.002||||-0.003|||0.015||
||||[16.03]|[-1.17]||||[-1.58]|||[1.41]||
|UK 10yr yield|||||0.031***|-0.010|||0.042***|||0.045*|
||||||[3.07]|[-0.97]|||[4.70]|||[1.81]|
|Constant|-0.152***|-0.015***|0.095***|-0.069***|-0.124***|0.009***|0.048***|0.218***|0.027***|0.070***|-0.224***|-0.067***|
||[-32.80]|[-4.11]|[39.51]|[-31.72]|[-111.62]|[5.64]|[14.96]|[75.03]|[19.31]|[7.17]|[-15.14]|[-17.54]|
|Observations|1,620|1,660|2,000|2,140|1,800|1,840|1,558|1,998|1,729|1,364|1,749|1,518|
|R-squared|0.026|0.001|0.021|0.000|0.007|0.001|0.000|0.000|0.005|0.019|0.004|0.004|
|# of countries|20|20|20|20|20|20|19|19|19|17|17|17|

_Notes:_ Regressions have been estimated using a specification similar to that in Equation 1. Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of monetary policy announcements of respective countries and 1 day after that (k=1). The independent variable is percent change in the respective 10-year sovereign yield between t and t-1, t being the day of the announcements. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

> 21 As Rey (2016) has pointed out, the dollar is the most important international currency, playing an important role in invoicing, issuance of financial assets, used as a funding currency for banks and asset managers, and as a vehicle currency in foreign exchange markets and commodity trade. To the extent that the emerging countries’ exchange rates with respect to the dollar are mostly unaffected by the policy surprises in other advanced countries, this indicates the weaker spillover impact of the latter.

19
The impacts of inheritance will naturally reflect how the ―dynastic family‖ allocates its resources, given the market and institutional environment. In principle at least, the extended family has the ability to attain any desired distribution of consumption, independently of the formal assignment of ownership rights. Indeed, it is an open question in this setting whether there is any net impact on the inheritor of a land bequest within the family.

Our results suggest that other mechanisms for the inter-generational transmission of inequality—notably related to parental education and children‘s schooling—are in fact more important than land inheritance in explaining interpersonal economic welfare and economic activities. Even in very poor settings, parental background can influence the schooling, expectations and life chances generally of children in ways that matter to the realized living standards of adults. There is evidence consistent with this view in the literature. Estudillo et al. (2001) emphasize the combination of both land inheritance and schooling in the intergenerational transmission of wealth in the rural Philippines. Lesorogol et al. (2011) find that the current wealth of Kenyan pastoralists is correlated with parental wealth and formal education but not with livestock inheritance. Ferreira and Gignoux (2010) find that family background characteristics are an important source of unequal opportunities in Latin America. Dumas and Lambert (2011) find that parental education has a strong effect on child schooling in Senegal. Some degree of intergenerational correlation in occupational choices can be expected, for which we find supportive evidence, though with a (possibly surprising) degree of mobility.

We begin in the following section by discussing the multiple roles of land ownership in this setting. After that we describe our data for Senegal and the methods of analysis, followed by our empirical results.

# **2. Land inheritance in an underdeveloped rural economy**

Land ownership has long played a role in policy making and thinking about economic development. Taxes and transfers are often conditioned on the amount of land owned. Famously, the American political economist of the late 19th century Henry George advocated taxes on the value of land, and (of course) these were to be levied on the designated owner. Taxes on land are found in almost all countries. Also, transfers and various direct interventions are often targeted according to land holding, defined by ownership. These include policies aiming to redistribute land itself. Tenure security is traditionally defined in terms of individual titles of private ownership. There have been many efforts (often supported by external development assistance) to foster individual ownership through land titling, with expected benefits to the government in 4
|**Table 14a. Decomposition of R**|**egional Welf**<br>_Component_<br>Overall|**are Impacts**<br>_effects on utilitari_|**for the Fed**<br>_an SWF_|**eral Okrugs**|**- WTO Scen**|**ario**|||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|Skilled wages|1.1|1.6|0.9|1.0|1.4|1.0|1.0|1.3|
|Unskilled wages|1.4|2.0|1.3|1.5|1.6|1.0|1.1|1.6|
|**Capital Earnings**|||||||||
|Mobile capital|2.9|2.9|3.1|2.8|2.6|2.7|2.6|3.2|
|Regional energy rents|0.2|0.2|0.2|0.2|0.2|0.1|0.1|0.2|
|Specific capital in domestic firms|-2.2|-2.0|-2.3|-2.1|-1.9|-2.3|-2.3|-2.3|
|Specific capital in multinational firms|1.2|1.1|1.2|1.1|1.1|1.2|1.2|1.3|
|**Tax and Terms of Trade Effects**|||||||||
|Change in lumpsum taxes|0.4|0.4|0.5|0.4|0.4|0.3|0.4|0.4|
|**Total Welfare Change**(% income)<br>Source: Authors' estimates|4.9|6.2<br>-9.E-08|4.9<br>-8.E-09|4.9<br>-6.E-08|5.3<br>1.E-07|4.1<br>2.E-08|4.1<br>8.E-08|5.6<br>-2.E-08|

56