rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A6: Base Regressio**|**n Results for**|**Petty Corrupt **|**ion Depth**||
|---|---|---|---|---|
|Dependent variable:_Employment Growth_|**(1)**|**(2)**|**(3)**|**(4)**|
|Petty Corruption Depth (CI avg.)*Financially|-0.218***|-0.233***|-0.230***|-0.228***|
|Constrained (CI avg.)|(0.079)|(0.077)|(0.078)|(0.078)|
|Petty Corruption Depth (CI avg.)|0.117***|0.128***|0.131***|0.129***|
||(0.044)|(0.044)|(0.043)|(0.044)|
|Financially Constrained (CI avg.)|3.076|3.616*|3.663*|3.674*|
||(1.928)|(1.987)|(1.992)|(1.994)|
|Initial Employment (logs)|-5.764***|-5.621***|-5.888***|-5.888***|
||(0.322)|(0.311)|(0.310)|(0.314)|
|Age of firm (logs)||-2.065***|-1.954***|-1.954***|
|||(0.296)|(0.291)|(0.291)|
|Manager Experience (logs)||0.015|-0.086|-0.072|
|||(0.273)|(0.274)|(0.274)|
|Exports (proportion of sales)||1.601|1.394|1.399|
|||(1.802)|(1.828)|(1.840)|
|Foreign Ownership (% ownership)||0.022***|0.021**|0.021**|
|||(0.008)|(0.008)|(0.008)|
|Multi-firm (dummy)||1.900***|1.841***|1.828***|
|||(0.639)|(0.648)|(0.645)|
|Female Manager (dummy)|||-1.328***|-1.327***|
||||(0.436)|(0.435)|
|Power Outage (logs)|||0.204*|0.213*|
||||(0.121)|(0.120)|
|Informal Competition (dummy)|||-0.767*|-0.727*|
||||(0.428)|(0.421)|
|Firm Bought Assets (dummy)|||3.106***|3.134***|
||||(0.412)|(0.423)|
|Time Tax (%)||||0.005<br>|
|||||(0.013)|
|Courts: Obstacle Severity||||-0.033|
|||||(0.198)|
|Crime Losses (% of sales)||||-0.025|
|||||(0.045)|
|Taxes: Obstacle Severity||||-0.171<br>(0.164)|
|Labor Laws: Obstacle Severity||||-0.003|
|||||(0.171)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Number of observations|41,117|41,117|41,117|41,117|
|R-squared|0.167|0.177|0.186|0.186|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country times industry level. Significance is denoted by *** (1%), ** (5%), * (10%). All regressions use a constant term (not shown).

57
pundits, industry analysts openly speculated about the possible end of the domestic automotive industry.

In this environment, congress, supported by a new Obama administration unwilling to preside over the liquidation of the United States largest manufacturing industry, offered several waves of bailouts, but only after a series of humiliating Congressional hearings where Big 3 CEOs made the case for government assistance and were aggressively cross examined about management’s culpability for the crisis. In the aftermath, General Motors’ CEO resigned and the company was forced to file for bankruptcy. Chrysler only avoided bankruptcy because it entered a strategic partnership (with equity stake) with the Italian automaker Fiat. While it is widely believed that Ford has not yet asked for or received government assistance, the company did accept a $5.7B “retooling loan” from the Department of Energy to develop more fuel-efficient cars and trucks in June 2009. In Europe too, bailouts were provided.

The immediate motivation for the myriad government interventions in the auto sector was the unprecedented collapse in sales in almost all countries. In the United States, one of the most hard hit countries, sales of light passenger vehicles in the first three months of 2009 was 38.4% below the level for the same period in the previous year, which itself was already 8% lower than in 2007. The reduction was somewhat smaller in Europe, 17% below the 2008 level, but that was to a large extent due to the success of the scrappage subsidies in Germany. Excluding Germany, European sales were 24.7% lower in the first 3 months of 2009 versus 2008.<sup>8</sup> Even in China, the world’s fastest growing automotive market, 2008 passenger vehicle sales dropped single digit growth for the first time in 10 years. With sales reductions of this scale, any company operating in an industry with high fixed costs would struggle to remain profitable. The losses at Chrysler and GM were staggering, but even the most successful companies ran into trouble. Toyota, for example, recorded its first loss in the more than 70 years it has been manufacturing cars. In the first quarter of 2009, its losses ($7.7 billion) were even higher than GM’s ($6 billion). Ford, even though it has so far avoided bankruptcy, recently “celebrated” the fact that it was “only” losing $1B per month!

> 8 Sales statistics are taken from the online databank of Automotive News.

13
- 23 -

asked what they liked most about the money-keeper system, farmers tended to talk of trustworthiness and safety more than of proximity or convenience.22 Since most deposit-taking goes on within villages and among kin, those who save this way are displaying trust not Just in the individuals to whom they are confiding their savings, but also in the networks and groups to which both they and the keepers belong: the mechanisms to which they would turn for support as a last resort if keepers refused to return the money.

An implication for banks in the western Sahel is that if they wish to attract more customers, they may need more and better public relations in the honest sense of the term--not just better interest rates or accessibility, though these would probably interest customers in their services. It may help to recruit some elders where literacy or other qualifications permit, since local practice suggests they are more trusted than juniors.23 But banks and other formal institutions should expect only to supplement rather than supplant the local money-keeping system, since the latter has devoted practitioners. In the 1988 survey, for instance, informants who used the services of "informal" money-keepers were asked whether they were content with this system of saving. 94Z replied yes. Conversely, when asked if there were any problems with it, 98Z replied no.<sup>24</sup> A bank might envy such user confidence.

# Deferred Wages

Wage-earners have a related way of saving, which is to ask employers to withhold wages over short times to let them accumulate. (This is a system familiar to researchers hiring local research assistants. While some assistants in the city or countryside constantly request advances, others ask to defer
- The CTC indicators also correlate with measures of actual cross-border investment and trade. Figure 10 plots the CTC indicators at the economy level against a five-year average of inflows of FDI. The correlation of 0.269 shows that countries with more-open foreign exchange regimes also receive more FDI inflows on average, and this association is statistically significant. Figure 11 plots the CTC indicators against exports of goods and services across economies. The correlation is 0.271 and statistically significant, showing that countries with less-restrictive foreign exchange regimes tend to export more on average. This does not suggest that a more open foreign exchange regime would necessarily lead to more FDI inflows or exports; the size of an economy’s GDP is another determinant of investment flows and exports, and the CTC correlations with FDI and exports disappear once the size of GDP is accounted for in the analysis.

Figure 9: Economies with fewer restrictions measured by CTC are scored as having lower remittances risk <!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 20 40 60 80 100<br>CTC average indicator score per economy<br>Remittances Risk Index<br>(Extreme risk → Low risk)<br><!-- End of picture text --> Note: the Pearson correlation is 0.388, which is highly significant at conventional levels. Source: Maplecroft Global Risk Analytics Figure 10: Economies with fewer restrictions measured by CTC receive more FDI inflows <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>0 20 40 60 80 100<br>CTC average indicator score per economy<br>(Billion USD)<br>Inward FDI flows, 2007-11 average<br><!-- End of picture text --> Note: one economy is omitted as an outlier with more than $200 billion in inflows (the United States). The Pearson correlation is 0.269, significant at the 1% level. With the outlier included, the correlation is 0.236, significant at the 5% level. Source: UNCTADstat for FDI data.

Figure 11: Economies with fewer restrictions measured by CTC export more goods and services <!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>0 20 40 60 80 100<br>CTC average indicator score per economy<br>Exports of goods and services, 2007-11 average (billion USD)<br><!-- End of picture text --> Note: two economies are omitted as outliers with more than $1.5 trillion in average exports (Germany and the United States). The Pearson correlation is 0.271, significant at the 1% level. With the outliers included, the correlation is 0.267, still significant at the 1% level.

Source: WDI for export data.

13
**Figure 5: Changes in the Gini coefficient and in the return to education; 2000-2010** _Annual % change_

<!-- Start of picture text --> Change in Gini Change in returns on primary schooling<br>Change in returns on secondary schooling Change in returns on tertiary schooling<br>4.0<br>2.0<br>0.0<br>-1.30 -1.24 -1.17 -1.03 -0.91 -0.79 -0.74 -0.72 -0.47 -0.39 -0.20 -0.10<br>-2.0<br>-2.05 -1.99 -1.07<br>-2.64<br>-4.0<br>-6.0<br>-8.0<br>Nicaragua Bolivia Ecuador Argentina El Salvador Mexico Venezuela Brazil Peru Dom. Rep. Panama Chile Costa Rica Paraguay Uruguay Guatemala<br><!-- End of picture text --> _Source_ : Authors' calculations, based on data from SEDLAC (CEDLAS and The World Bank), May 2013. _Note_ : The returns to different levels of education are calculated with respect to no schooling or incomplete primary school. Skill categories are determined by level of formal education. Educational levels correspond to completed primary school, lower- and upper-secondary school, and tertiary education. The time periods used to estimate the percentage changes are the same as those used in Figure 1.

The determinants of the decline in non-labor income inequality include: returns to capital —interests, profits and rents—, private transfers —for example, remittances— and public transfers —for example, CCTs and noncontributory pensions. The contribution of changes in returns to capital in Argentina, Brazil and Mexico, for example, tended to be small and unequalizing (Lustig et al. 2013). However, a well-known fact is that household surveys under-estimate income from capital so the unequalizing effect may have been larger than current estimates indicate. Esquivel et al. (2010) show that, in Mexico, remittances proved to be equalizing and became even more so in the 2000s, because they closed the gap between rural and urban household per capita incomes. Cornia (2013) also shows that the increase in migrant remittances in total household income appears to have had an equalizing effect in El Salvador and Mexico; however, in Honduras their effect was unequalizing.

In terms of public transfers, as mentioned above, Azevedo et al. (2012) find that, on average, government transfers account for 14 percent of the decline in overall inequality. The role of noncontributory pensions cannot be disentangled because the 7
contrasting findings. There is, however, evidence that adjustments happen and more investigation is needed to better explain the different patterns observed across different countries.

This work contributes to the literature on pricing behavior in an effort to analyze how exporters in small Least Developed Countries (LDCs) – normally considered price takers - behave with respect to pricing decisions. This paper’s analysis benefits from a novel, firm-level dataset from Tanzanian exporters<sup>2</sup> . By accessing this disaggregated information, this paper is able to examine diverse aspects of pricing behavior across both firms and destinations. The analysis contained herein benefits from the methodology adopted by Manova and Zhang (2012) and corroborates most of their findings.

Specifically, firms setting a higher export price earn greater revenue and sell lower quantities within a destination for a given product. Moreover, exporters that serve multiple destinations seem to have a higher dispersion of prices across markets, indicating that firms charge different prices if they serve multiple destinations. These results represent preliminary evidence favoring quality competition models. The main difference from Manova and Zhang (2012) relates to the result that homogeneous - rather than differentiated - goods lead price differentiation across destinations. This stands in contrast to the expectations and findings of previous studies. However, most of the studies that work to identify evidence of price differentiation examine middle- to high-income countries and focus mainly on the manufacturing sector. By contrast, Tanzania is a LDC, and the analysis in this paper includes all products, rather than just manufacturing.

In Tanzania, as in most Sub-Saharan African countries, the agriculture sector remains the main source of employment and constitutes one-third of Tanzania’s GDP. Tanzania does, however, have a comparative advantage in agricultural goods and, over the years, some donor-driven projects have been successful in developing technologies to add value in agricultural production<sup>3</sup> . For these reasons, the finding that price differentiation relates to homogeneous goods might indicate that the country’s comparative advantage in agricultural production is based on differentiation of products by quality attributes. This might suggest that countries with comparative advantage in agricultural goods, such as Tanzania, can still undertake price differentiation among destinations. In fact, even goods classified as homogeneous can potentially be or become differentiated by their intrinsic quality (i.e. coffee). However, quality is not the only explanation for price differentiation: in fact pricing-to-market can also be led by firms charging different mark-ups.

> 2 The dataset was previously used by Freund and Pierola (2011) and by Cadot et al (2011).

> 3 Koshuma, K.P. R. and Mmasi, S.M., (Unknown date).

3
# **<u>Table A1: Variable Descriptions</u>**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Log of sales|Log of real annual sales in USD|World Bank Enterprise Surveys|
|Log of labor|Log of total number of full time<br>employees, adjusted for temporary workers|World Bank Enterprise Surveys|
|Log of capital (replacement value)|Log of cost for establishment to re-<br>purchase all of its machinery|World Bank Enterprise Surveys|
|Log of raw materials (costs)|Log of cost of raw materials and<br>intermediate goods used in production|World Bank Enterprise Surveys|
|Number of water shortages per day in a<br>typical month|Number of incidents of water insufficiency<br>per day in a typical month. Water<br>insufficiency is defined as a shortage that<br>affects production. Outages that do not<br>affect production are not counted|World Bank Enterprise Surveys|
|Average duration of water shortage|Average duration of insufficient water<br>supply in hours|World Bank Enterprise Surveys|
|Number of electrical outages per day in a<br>typical month|Number of power outages per day in a<br>typical month. Only outages that affect<br>production are counted|World Bank Enterprise Surveys|
|Average duration of electrical outages<br>(hours)|Average duration of electrical outages in<br>hours|World Bank Enterprise Surveys|
|Losses due to breakage/spoilage shipped<br>domestically (% of value of the products)|Percentage of value of products the<br>establishment shipped to supply domestic<br>markets lost while in transit because of<br>breakage or<br>spoilage over the last fiscal year|World Bank Enterprise Surveys|
|Formal training Y/N|Dummy variable that takes the value of 1<br>if the firm offered formal training<br>programs to its permanent full-time<br>employees, and 0 otherwise|World Bank Enterprise Surveys|
|Firm owns or shares a generator Y/N|Dummy variable that takes the value of 1<br>if the firm owns or shares a generator, and<br>0 otherwise|World Bank Enterprise Surveys|
|Log of age of firm|Age of firm|World Bank Enterprise Surveys|
|Foreign ownership Y/N|Dummy variable that takes the value of 1<br>if the firm has any foreign ownership, and<br>0 otherwise|World Bank Enterprise Surveys|

29
# _Reluctant Private Sector_

The financial modalities of the large-scale infrastructure projects are still unclear. One issue of public-private co-funding is a key aspect. As in most East Asian countries, public sector provision was for a long time the only service provision model. The costs of low tariffs were absorbed by government budgets, quasi-fiscal loans from statecontrolled financial institutions, capital consumption or reducing operations. The bulk of infrastructure spending occurred through state enterprises, many of which established during the 1970s. Some of them are viewed to be efficiently run (Thai Airways, Petroleum Authority Thailand, Airports of Thailand, EGAT), while others are judged to be inefficient and providing poor services (WMA and State Railway Thailand). Only in the late 1980s, the private sector became an increasingly important force. The private sector has invested approximately $190 billion in East Asian infrastructure since 1990. However, this is a minor share of the region’s needs, and a minor share of total infrastructure investment in the past.

Private sector involvement in infrastructure provision in Thailand is limited both sectorally (expressways, electricity generation, water supply, telecommunications, and housing for workers) and spatially (extended Bangkok region, including the Eastern Seaboard, Ayutthaya, and Western Amenity regions) (Kaothien and Webster 1996). In fact, in 2003, private infrastructure spending amounted to no more than 7 percent of public and private spending (Figure 14.C). Increased private sector participation is constrained by political resistance to user charges even in urban areas, and lack of clear regulatory environments both at the national and local levels (Webster and Theeratham 2004).<sup>13</sup> Given the private sector’s caution to engage in large-scale projects, it is not evident that many private companies will be forthcoming with resources. A 2004 perception survey of 50 private companies active or interest in East Asia infrastructure investment shows that private sector sentiment towards infrastructure investment is contingent on policy improvements and reduced risks (ADB, WB and JBIC 2005).

Overall, Thailand’s infrastructure provision is satisfactory and infrastructure spending compares favorably with other countries in the region. The government is right in attempting to develop and implement a strategic view on infrastructure priorities. The administration has made progress in addressing new infrastructure needs, although the issues of funding and public-private partnerships are still to be solved. As emphasized in the next section, more needs to be done to extend infrastructure to outlaying regions.

> 13 The Thai Supreme Court decided to nationalize the second stage expressway after a toll collection dispute between the Japanese concessionaire and the Expressway Authority of Thailand.

36
# REFERENCE

Adato, M., B. de la Briere, et al. (2003). The Impact of PROGRESA on Women's Status and Intrahousehold Relations. Household Decisions, Gender, and Development: A Synthesis of Recent Research. A. R. Quisumbing. Washington, D.C. , International Food Policy Research Institute.

Amin, S. (1995). The Poverty-Purdah Trap in Rural Bangladesh: Implications for Women Roles in the Family. Working Paper. New York, Research Division, The Population Council. Attanasio, O. and V. Lechene (2002). "Tests of Income Pooling in Household Decisions." Review of Economic Dynamics **5** (4): 720-748.

Basu, K. (2006). "Gender and Say: a Model of Household Behavior with Endogenously Determined Balance of Power*." The Economic Journal **116** (511): 558-580.

Becker, G. S. (1981). "Altruism in the Family and Selfishness in the Market Place." Economica **48** (189): 1-15.

Binder, M. (1999). "Schooling indicators during Mexico's "Lost decade"." Economics of Education Review **18** (2): 183-199.

Chiappori, P.-A. (1992). "Collective" Models of Houshold Behavior: The Sharing Rule Approach. DELTA Working Papers, DELTA.

Djebbari, H. (2005). The Impact on Nutrition of the Intrahousehold Distribution of Power. IZA Discussion Papers, Institute for the Study of Labor (IZA).

‐ Duflo, E. (2003). "Grandmothers and Granddaughters: Old Age Pensions and Intrahousehold Allocation in South Africa." The World Bank Economic Review **17** (1): 1-25.

17
# **VI. ESTIMATION RESULTS AND POLICY IMPLICATIONS**

Six IV regressions are performed; the results are shown in Table 8. First, the hypothesized nesting structure is valid. _σ_ is significantly different from zero, and the hypothesis of _σ_ being one is also rejected. Thus, new consumers are first selecting a favorite telephone operator and then choosing a type of subscription plan.

Second, termination-based price discrimination works effectively to increase the number of telephone subscribers, particularly in the peak and off-peak time zones. In the first column model, for instance, the price differential coefficients are both positive at about - 17, meaning that a 1 percent reduction in the rival operators’ market share would lead to an _additional_ 0.17–0.26 percent increase in the own market share—which includes the outside option—because of _positive_ indirect network externalities.<sup>26</sup> Given discriminated tariffs, consumers have to bear additional costs of making a call which terminates to other networks.

Although pricing flexibility including termination-based discrimination has a positive impact on network expansion, the authorities need to ensure reciprocal and cheap access between telephone operators. Otherwise, there is the danger that telephone operators might overexploit the discrimination effect to increase their own subscribers, as Laffont _et al_ . (1998a) warn. In addition, if an incumbent operator can exploit indirect network externalities (or tariff-mediated network externalities) to reinforce a dominant position, the authorities may have to regulate this type of price discrimination for competition policy purposes. In this regard, there are a number of litigations in European countries. Many Latin American countries also failed to set clear interconnection policies and enforce fair interconnection rates, even though they opened and liberalized their telecommunications markets (World Bank, 2004).

> 26 These indirect network externality effects are evaluated at the sample means of _MSj_ and _Δ_ ln _ck_ . While the indirect effect through off-net price discrimination is estimated at 0.165, the effect through inter-mode discrimination is 0.258.

28