rafmacalaba/datause-encoder-data huggingface dataset:
**Table (6)** Statistically Significant Impacts of the Program at Endline and Follow-up

|**Outcome**|**TOT**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|**Panel A: Endline**|||||||
|Focus Respondent Childcare & Chores (Hours)|-6.91***|(2.02)|[0.01]|17.76|(18.39)|1,418|
|Total Labor and Chores Hours Last Week|-10.62***|(3.24)|[0.01]|25.54|(23.39)|1,422|
|General Happiness (Scale 1-3)|-0.22***|(0.08)|[0.02]|1.76|(0.61)|1,422|
|At least 30 JD (95 USD PPP) in savings (=1)|0.14***|(0.05)|[0.02]|0.12|(0.32)|1,422|
|Applied but Did Not Take Loan (=1)|0.06***|(0.02)|[0.00]|0.01|(0.10)|1,422|
|IP Assistance, Last 12 Months (=1)|0.08**|(0.03)|[0.14]|0.04|(0.19)|1,422|
|IP Assistance, Last 12 Months (USD PPP)|128.25***|(46.50)|[0.10]|28.74|(198.87)|1,422|
|**Panel B: Follow-up**|||||||
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.01|(1.00)|859|

_Notes:_ The table shows the regression results on statistically significant results from the endline survey. Each row is its own dependent variable. IP refers to the implementing partner. Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have month-by-year fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to various families in the pre-analysis plan. Focus Respondent Childcare & Chores (Hours) was pre-specified in two outcome families; the table reports the lower of the two q-values; the higher q-value is 0.02 .

46
# **Figure 17:** **<u>Cargo Dwell Time, Monopoly-oligopoly versus Competition, 2011</u>**

<!-- Start of picture text --> 12<br>9.66<br>10<br>7.86<br>8<br>6<br>4<br>2<br>0<br>monopoly/oligopoly competition<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

In all countries except South Africa, monopoly-oligopoly situations are likely to keep cargo dwell times lower (Figure 18). However, shorter dwell time does not necessarily translate into lower prices, since the main objective is to maximize profits. Only in South Africa does the high degree of competition play an important part in keeping dwell time lower: since importers in a competitive situation cannot afford to reflect the costs of delays in their prices because they are afraid of losing customers, they protect the customers from price increases due to cost increases. These findings can be explained by the maturity of the South African economy.

**Figure 18: Cargo Dwell Time in Select African Countries, Monopoly-oligopoly versus Competition, 2011**

<!-- Start of picture text --> monopoly/oligopoly competition<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

# **Analysis by C&F Agents’ Professionalism**

# **Level of Information Provided by C&F Agents about the Clearance Progress**

Overall, when C&F agents provide accurate forecasts and real-time information about progress or delay in the clearance process well in advance (even if unexpected events might arise), dwell time is shorter (Figure 20) – one day significantly shorter when real-time info is provided t(184.615) = 2.242; p = 0.013); and less than one day when clearance delays are well documented in advance, though this 27
# **5.2 Role of technology in the gender gap in performance**

We then turn to the interaction between firm performance and technology adoption to examine how technology can mediate the gender productivity gap. Table 8 shows the estimates based on Equation 4. In addition to a productivity gap for firms with FTM, Table 8 shows substantial gender differences in the effects of technology adoption at the intensive margin on firm productivity. These findings suggest that although there is a gender gap in firm productivity, this gap decreases as firms adopt more sophisticated technologies, such that firms with FTM benefit more from advanced technologies.

Table 8: OLS estimates of productivity and technology gaps

||(1)<br>Ln(VAPW)|(2)<br>Ln(VAPW)|(3)<br>Ln(VAPW)|(4)<br>Ln(VAPW)|
|---|---|---|---|---|
|Female Top Manager|-0.298***<br>(0.0977)|-0.282***<br>(0.0966)|-0.283**<br>(0.129)|-0.283**<br>(0.128)|
|GBFs - Extensive margin|0.268***<br>(0.0670)||||
|FTM*GBF EXT|0.104<br>(0.128)||||
|GBFs - Intensive margin||0.290***<br>(0.0943)|||
|FTM*GBF INT||0.428***<br>(0.145)|||
|SBFs - Extensive margin|||0.131*<br>(0.0717)||
|FTM*SBF EXT|||0.0829<br>(0.146)||
|SBFs - Intensive margin||||0.0665<br>(0.0946)|
|FTM*SBF INT||||0.294<br>(0.203)|
|Controls|Yes|Yes|Yes|Yes|
|Sector|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes|Yes|
|Observations|6717|6717|4370|4362|
|R-squared|0.495|0.496|0.501|0.497|

Note: All estimates control for capital stock, employment, sector, country, and additional controls, including formality and the management human capita index. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. Estimates were performed using sample weights. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 9 provides additional results from the 2SLS model estimates. As we include inter19
-48-

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- Anand, R. and S.van Wijnbergen (1988), "Export Incentives, Exchange Rate Policy and Export Growth in Turkey", in process, World Bank.

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- Celasun, M. and D. Rodrik (1987),***, Forthcoming, NBER and University of Chicago Press

- Chhibber, A. and S. van Wijnbergen (1988): "Public Policy and Private Investment in Turkey", mimeo, World Bank.

- Cohen, D., (1985), "How to evaluate the Solvency of an Indebted Nation", Economic Policy, vol.I.

- Cohen, D. (1987), "External and Domestic Debt Constraints of LDCs: a Theory with Numerical Applications to Brazil and Mexico", World Bank Quarterly Economic Review.

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ChhibberDisk/chhibber-Turkey Paperlac:07-28-88: 1c
# **Macroeconomic Expectations and Credit Card Spending**<sup>_∗_</sup>

Mikhail Galashin<sup>_†_</sup> Martin Kanz<sup>_‡_</sup> Ricardo Perez-Truglia<sup>§</sup>

**JEL:** C81, C93, D83, D84, E31, R31.

**Keywords:** exchange rate, inflation, expectations, field experiment, spending.

> _∗_ This version: January 22, 2021. We are grateful for comments from numerous colleagues and seminar discussants. Javier Feinmann, Luisa Cefala and Santiago De Martini provided superb research assistance. PerezTruglia was employed at University of California, Los Angeles while the research was being conducted. This project was reviewed in advance and approved by the Institutional Review Board at University of California Los Angeles (IRB #18-000683). We gratefully acknowledge financial support from the UCLA Fink Center for Finance and Investments, the UCLA Center for Global Management, and the World Bank Research Support Budget. The experiment was registered with the American Economic Association registry for randomized control trials (AEA-RCTR #0003717). The opinions expressed in this paper do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent.

> _†_ University of California, Los Angeles, Email: `mikhail.galashin.1@anderson.ucla.edu` .

> _‡_ World Bank and CEPR, Email: `mkanz@worldbank.org` .

- §University of California, Berkeley and NBER, Email: `ricardotruglia@berkeley.edu` .
wide conditions under which corruption may be higher under private ownership, and relate this to popular dissatisfaction with privatizations in Latin America.

# _Political economy_

As mentioned previously, some authors have attributed differences in the estimates of infrastructure returns across geographical units to the fact that some may have exceeded their optimal level of infrastructure stock, while others may still be below it (Canning and Pedroni, 2004; Devarajan et al. 1996; see discussion in de Romp and Haan, 2005). Different conclusions have emerged depending on the econometric techniques used, from simple panel estimations to sophisticated unit root testing. While the question of the optimal stock of infrastructure is obviously an important one (see Gramlich, 1994), what has been missing, however, is a positive theory, and related empirical tests, of the decision to invest in infrastructure that could guide the optimality assessment. In this sense, the most promising area appears to be the political economy approach.

Indeed there is a presumption that decisions to invest in infrastructure, be it directly through public budget or by calling on the private sector through some form of PPI, respond to political motives rather than economic efficiency. Pork-barrel, electoral pandering, etc., provide theoretical channels through which investment may be suboptimal and returns may be affected. Examples of such analysis are in Robinson and Torvik (2005), Maskin and Tirole (2004; 2006) and Dewatripont and Seabright (2005) among others.

These theoretical insights are confirmed by the few empirical papers available. Cadot et al. (2006), specify a simultaneous-equation model, which explicitly considers the political-economy process that drives infrastructure investments. Their results, based on a panel of French regions over the period 1985-92, support the idea that such investments are mostly determined by electoral concerns and interest-groups activities, to the detriment of the maximization of economic returns. In their evaluation of the economic impact of dams in India, Duflo and Pande (2007) conclude that the microeconomic 35
<!-- Start of picture text --> Trade Costs, Barriers to Entry, and Export<br>Diversification in Developing Countries<br>Allen Dennis and Ben Shepherd*<br>The World Bank!<br><!-- End of picture text --> JEL codes: F12; F13; O24.

Keywords: International trade; Economic development; Product variety; Trade policy.

1 Introduction In this paper, we show that lower export costs, international transport costs, and domestic market entry costs are all robustly associated with export diversification in a sample of 118 developing countries. With elasticities of -0.3, -0.4, and -0.1, these effects are of real economic significance to *The authors are respectively Economist (Doing Business) and Consultant (Trade Team, Development Research Group). This work is part of a project on trade facilitation and development supported through a Trust Fund of the U.K. Department for International Development (http://econ.worldbank.org/projects/trade_costs). We are grateful to the following for helpful discussions and suggestions: Simeon Djankov, Matthias Helble, Bernard Hoekman, Will Martin, Beata Smarzynska Javorcik, and Christian Volpe Martincus. Comments to: bshepherd@worldbank.org or adennis@worldbank.org.

‘The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.

<!-- Start of picture text --> 1<br><!-- End of picture text -->
As argued by others (e.g., Mosley 1985), we assume that the electorate is capable of forming clear opinions about their demand for foreign aid. Previous research has shown that public opinion about world affairs is structured and consistent (Chanley 1999; Shapiro and Page 1988; Page and Shapiro 1992; Hurwitz and Peffley 1987). Indeed, previous work has demonstrated that individuals hold “stable, internally consistent...generalized beliefs” about global issues such as the trustworthiness of other nations (Brewer et al 2004). Support for foreign aid can be seen as part of a general orientation to foreign policy labeled “cooperative internationalism” as opposed to “militant internationalism” (Witkopf 1990; Chanley 1999).

# _Support for Foreign Aid: Individual Characteristics_

Characteristics of individuals are likely to impact support for foreign aid. For example, the **religiosity** of respondents – their attendance at religious services and the importance of religion to them – is likely to increase support for aid. Most religious teachings discuss relief for the poor. Jewish and Christian Biblical teachings, for example, urge individuals and leaders to support the poor, the sick, widows, travelers, and orphans. Proverbs 22:9 states: “Those who are generous are blessed, for they share their bread with the poor.” Certainly, religion has been found to impact altruism of other types such as philanthropy (Wuthnow and Hodgkinson 1990) and trust (Delhey and Newton 2005; La Porta et al. 1997; Fishman and Khanna 1999), as well as a variety of economic attitudes (Guiso et al. 2003). And surveys show that demand for aid is partly altruistic – individuals cite moral or humanitarian obligations to help others (Mosley 1985, citing Bowles 1978). In short, there are a variety of reasons to expect that those who are more religious will express greater support for foreign aid.

In a similar manner, an individual’s **position on poverty** should influence their support for aid. Individuals likely extrapolate from their beliefs about the poor, or their general ideological position, in making decisions about supporting the poor in other countries. If individuals feel that the poor are lazy, or that they can easily escape poverty, then they are unlikely to support aid, either domestically or abroad. The theoretical argument we make here is related to low-information rationality – that citizens use information shortcuts to allow opinions on politics even when they lack expert 3
for hours, creating obviously total disruption of international economic transactions with the US. More recurrently, it is reported that the impact of tighter US visa requirements on migration flows created severe time delays for many companies trading with the US _(Financial Times 2004, June 2_ ).

Whatever the channel, it is important to stress that the level of security measures and expenditures devoted by governments to counterterrorism is indeed endogenous to terrorists’ actions. As a matter of fact, one may reasonably expect that, given expectations on the likelihood of terrorist activities, counterterrorist policies are the outcomes of public decisions taking into account the costs and benefits of such policies.

Formally, one may assume that the government of a particular country _i_ is concerned both by some measure of the economic welfare _Ui_ and the security level Φ _i_ of its representative citizen. As previously discussed, security measures _Si_ decided by that government on its own territory, have fiscal and transaction costs that affect negatively _Ui_ . At the same time though, _Si_ increases the level of security of national residents, Φ _i_ , against terrorism. At the optimum, the government tradeoffs the marginal economic cost of counterterrorist policies against the marginal security gain of such a measure.

Considering this line of reasoning, three elements can then be formally shown. First, consistent with the very aim of terrorist organizations, the security level Φ _i_ , of national citizens of country _i_ depends negatively on terrorist activities _Ti_ =<sup>�</sup> _Tji_<sup>_k_plannedagainst</sup><sup>_i_.Therefore,wemayexpect</sup> _j,k_ that the optimal level of security _Si_ implemented by country _i_ to depend on _Ti_ : the bigger the value of _Ti_ , the larger should be the marginal security gain from counterterrorist policies and the larger the value of _Si_ .

Second, as far as security measures affect negatively international transactions between country _i_ and its trading partners, the distortionary costs of security will depend on the volume of trade of country _i_ with other countries. The larger the volume of trade, the larger the distortionary costs of security policies. It follows from the preceding discussion that counterterrorist policies _Si_ are both a positive function of expected terrorism against country _i_ and a negative function of bilateral trading flows between _i_ and its trading partners: The third point to notice is simply that counterterrorist policies by a country _i_ , are expected to increase the cost _ci_ for any terrorist organization to undertake terrorism in that country. Hence, the behavior of a terrorist organization as described in program (2) is negatively affected by _Si_ . In other words, the equilibrium vector of terrorist activities against _i_ , **T** _i_ = � _Tji_<sup>_k_</sup> � _jk_<sup>areaswellendogenousto</sup> the vector of security levels **S** = ([ _Si_ ] _i_ of the various countries in the world (through the fact that the vector of costs **c** = [ _ci_ ( _Si_ )] _i_ = **c** ( **S** ) depends negatively on **S** ) _._ 9
While there are substantial potential benefits from liberalizing key services sectors, these gains cannot be realized by a mechanical opening up of services markets. Governments have an important role to play in putting place the preconditions for an efficient set of service industries, bolstering the case for focusing on key inputs like education and (institutional) infrastructure. Also important is the design of reform programs. It is now widely recognized that a flawed reform program can undermine the benefits of liberalization. For example, if privatization of state monopolies is conducted without concern to creating conditions of competition, the result may be merely transfers of monopoly rents to private owners (possibly foreigners). Similarly, if increased entry into financial sectors is not accompanied by adequate prudential supervision, the result may be insider lending and poor investment decisions. Also, if policies to ensure wider access to services are not put in place, liberalization need not improve access to essential services for the poor. Managing reforms of services markets therefore requires integrating trade opening with a careful combination of competition and regulation. Here we emphasize three elements of successful reform.

# **_Sequencing of liberalization_**

For some time now, it has been established that larger welfare gains arise from an increase in competition than from simply a change in ownership from public to private hands. The role of competition in unleashing productivity-enhancing turnover of firms – the process through which the least competitive firms are forced out of the market, and innovative, new, firms enter markets —has also been noted (Aghion and Howitt, 2007). Fink et al. (2003) analyze the impact of policy reform in basic telecommunications on sectoral performance, using a panel-data set for 86 developing countries across Africa, Asia, the Middle East, Latin America and the Caribbean in the period, 1985 to 1999. It was found that both privatization and competition can independently lead to significant improvements in performance. But a comprehensive reform program, involving both policies and the support of an independent regulator, produced the largest gains: an 8 percent higher level of mainlines and a 21 percent higher level of labor productivity compared to years of partial and no reform.

25
Data from the employment history module in Panel B are consistent with the findings on selfemployment during the midline and endline surveys.<sup>16</sup> Results suggest that small positive impacts on self-employment activities lasted at most up to 2011. The only statistically significant effect is that the treatment group was more likely to be engaged in self-employment compared to the control group in the second semester of 2010. However, the effect is only marginally significant for the LATE estimates (p-value=0.108 for the ITT estimate), and of small magnitude. No statistically significant difference is observed starting with the first semester of 2011. We conclude that participation in the entrepreneurship training did not lead to sustained gains on participation in self-employment beyond the year after the end of the program.

# **6. Mechanisms for impacts on entrepreneurship**

We now turn to analyze mechanisms that can contribute to explain the lack of medium-term impacts on self-employment. We start with nascent and latent entrepreneurship (Section a). We analyze nascent entrepreneurship by studying the process through which individuals attempted to set-up projects. We study latent entrepreneurship based on whether individuals have new project ideas at the time of the endline survey. We then proceed to analyze intermediary outcomes such as skills, networks or access to credit (Section b). Finally, we test for heterogeneity in the impacts of entrepreneurship education across sub-groups (Section c).

# _(a) Nascent and latent entrepreneurship_

To analyze how the intervention affected nascent entrepreneurship, we assess whether the treatment group was more likely to try and fail in setting up projects. Table 3 (Panel A) shows that in 2011, i.e. one year after graduation, program participants were more successful in setting up their own projects and having an active project compared to the control group. These results are in

> 16 The levels of the employment indicators are measured slightly differently in the module on current employment and on employment history. Thus, they are not exactly equal. The results remain consistent.

15