rafmacalaba/datause-encoder-data huggingface dataset:
round compared to similar period in 2019 (Panel A, Column 1) and the size of the food gap by 0.47 month (Column 4). Second, inclusion in the PSNP offsets virtually all of this adverse change. The magnitude of the coefficient 1 is -0.093 in Panel A, Column 1, indicating that PSNP participation reduced the likelihood that the household was food insecure by 9.3 percentage points. ββ Adding coefficients 0 and 1 together shows that the likelihood of becoming food insecure increased by 11.7 percentage points for non-PSNP households and by 2.4 (= 11.7-9.3) percentage ββ ββ points for PSNP households. Looking at the results found in Panel A, Column 4, adding coefficients 0 and 1 together shows that the duration of the food gap increased by 0.47 month for non-PSNP households and by 0.133 (= 0.474- 0.341) month for PSNP households. Third, our ββ ββ finding that the PSNP offset the impact of the pandemic are robust to the definition of access to the PSNP (see panels B and C) and whether we weight or do not weight our data (see columns 5 and 6).<sup>8</sup> We note four additional features of our results. First, our identification hinges on the assumption that in the absence of the pandemic, food security outcomes for PSNP beneficiaries and non-beneficiaries follow parallel trends. We probe this using pre-COVID-19 rounds and trends of food security. As shown in Table A4, before the pandemic, the food security trend of PSNP beneficiaries and non-beneficiaries for the March 2019 and August 2019 were similar. Second, alternative estimators, fixed effects logit and Poisson regressions, give comparable results, see Table A5. Third, we control for a second shock that occurred at this time – the locust invasion. Dropping this variable does not affect our findings (see Table A6). Fourth, to probe the role of other shocks, including conflict and civil unrest, we re-estimate our main specification dropping regions that have been affected by recent conflicts and civil unrest. Oromia was the region that has experienced conflicts and demonstrations the most in the months prior to our June 2020 survey. Excluding Oromia from our sample does not affect our results (see Table A7).

The role of PSNP in protecting food security and smoothing consumption is expected to be higher for poorer households, for whom the share of PSNP transfers in total consumption expenditure is higher. The impact of the COVID-19 pandemic is also likely to vary across households by socio-economic characteristics, with poorer households facing greater adverse

> 8 While the interaction terms in Panels A and B capture actual impacts (average treatment on the treated) on the protective role of PSNP, the corresponding parameter in Panel C represents the role of living in a PSNP covered district (intention to treat). Thus, the latter (ITT) is expected to be higher than the former (ATT).

14
that the bigger market is an attractive export destination. A fact that also precludes firms in the bigger market from exporting and that makes them focusing on the domestic market instead. As corollary it is true that the domestic cut-off level is higher in the domestic market compared to the smaller foreign market.

Even though our model is similar to Melitz (2003) the predictions it yields are different. Most importantly, not the goods that are produced by the most productive firm (low prices) are traded furthest, but the highest quality goods. Furthermore, the cut-off levels are not identical across countries as in other heterogeneous-firm trade models, but depend on the market size. Our model therefore adds important new dimensions to the standard heterogeneous firms’ model. At the same time it contributes to the literature of trade models which include quality aspects.

We find strong empirical evidence for the predictions delivered by the model. The first key result is that the unit price is increasing with trade costs. To the extent to which high unit values mirror high quality, higher quality goods are indeed exported further. The second main finding is that there is empirical evidence for the market size effect as described in this paper. Overall, our model with heterogeneous quality firms seems to fit the data better than the standard heterogeneous firms’ trade model.

The key results of our paper have strong implications for developed as well as developing countries. Our model gives strong emphasis to the finding that, apart from trade costs and market access, the quality dimension is key for the export performance of countries. Especially in developing countries, where trade costs can be still considerably high, looking at quality aspects is an important policy consideration. One interesting extension of our model would be to include the case of trade between dissimilar economies.<sup>33</sup> This would imply an extension of the empirical analysis to a wider range of countries, which might then yield new development-relevant results.

Our model is certainly only one of many possibilities to include quality in a heterogeneous firms’ framework. It might also be worth studying how to add additional dimensions to the model. For example, including dynamics of product innovation might yield further interesting hypotheses. Another shortcoming of our model is that it neglects price competition between firms. A promising approach might be to combine our model with the type of price competition model that Melitz and Ottaviano (2005) propose.

> 33 One possible source of detailed trade data is the data set on Japanese trade used by Okubo (2007).

36
**Table 6: Political connections and firms’ access to land, industrial zones, and credit**

||**Acq**<br>|**uired Land**<br>**Governme**|**from**<br>**nt**|**Locate**|**d in Indust**|**rial City**|**Obta**|**ined Bank**|**Loan**|
|---|---|---|---|---|---|---|---|---|---|
||**CEO**|**Owner**|**Broad**|**CEO**|**Owner**|**Broad**|**CEO**|**Owner**|**Broad**|
|**No. of PC firms**|-.007|.021*|.015*|.068|.041**|.034**|.139*|.032**|.019**|
||(-0.11)|(1.79)|(1.75)|(0.73)|(2.32)|(2.01)|(1.74)|(2.54)|(2.21)|
|ln(empl)|.337**|.338**|.337**|.269**|.271**|.269**|.195**|.197**|.195**|
||(16.4)|(16.2)|(16.1)|(6.46)|(6.50)|(6.34)|(9.32)|(9.34)|(9.29)|
|Age|-.019**|-.018**|-.018**|-.034**|-.034**|-.034**|-.005**|-.005**|-.005**|
||(-8.93)|(-8.90)|(-8.91)|(-8.84)|(-8.84)|(-9.04)|(-2.66)|(-2.64)|(-2.66)|
|No. of obs|3,015|3,015|3,015|3,009|3,009|3,009|3,003|3,003|3,003|
|Sector dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.193|0.194|0.194|0.187|0.189|0.191|0.070|0.071|0.070|

Source: World Bank Enterprise Survey (WBES) data for Egypt 2004-2008 and sample of politically connected firms. Note: Number of politically connected firms is the number, in our database, in each 4-digit sector. The three different types of connections (ordered here by their restrictiveness) are defined in Table 1. The number of employees (size) and firm age vary at the firm level. All regressions include 2-digit sector dummies. Standard errors are clustered at the sector level. *, ** indicates significance at the 10%, 5% level, t-statistics are reported in parentheses.

20
Comparing Columns 1 and 2 and 3 and 4 respectively, we note that the inclusion of market and time fixed effects does not significantly impact the coefficient of interest. Column 4 corresponds to our preferred specification described in equation (1) above. The results indicate that transactions of female clients are 1.514 (p-value is 0.000) times more likely to be conducted at female agents than the transactions of male clients. We also report the marginal effect of client gender on the probability of transacting with female agents which is 0.074. In Column 4, none of the other control variables (currency, transaction type, customer age) is significant.

In Column 5, we explore how location affects transaction behavior. Since customers make multiple transactions over the course of a year, they may behave differently when visiting agents in markets that they do not typically visit. To explore this, we create a dummy variable called “outside modal market” that takes the value of 1 if the transaction is made in a market different from the one where the customer makes most of the transactions. The results in Column 5 indicate that transactions of customers (of both genders) are 1.232 (p-value = 0.000) times more likely to be with female agents when a customer is visiting a less familiar market. We also interact customer gender and the modal market dummy to investigate whether this behavior differs along gender lines. We find that the increased likelihood of customers transacting with female agents outside their modal market applies to both genders as women are not significantly more likely to transact with females outside their modal market compared to males (odds ratio 1.048, p-value = 0.792).

In practice, when a customer transacts at an agent, she reveals not only the transaction amount but also her account balance. If female customers’ preference for female agents is (at least partially) driven by a desire to conceal financial information from male agents, we would expect female customers to also be more likely to transact with female agents when they have higher account balances. We matched data on account balances at one point in time (end of October 2017) with customer transaction data. For the analysis, we restrict the data to all customer transactions occurring within 10 days after the time point for which the balance data is available and thus posit that the balance information will affect transaction behavior within the 10-day window after the date for which we have knowledge about customers’ balances.

In Column 6 of Table 4, we add the customer balance variable and an interaction term between customer gender and account balance to our main specification. The coefficient on account balance in logs is smaller than 1 (odds ratio= 0.937, p-value = 0.000), indicating that customers (of both genders) with higher balances are less likely to transact with female agents. This is consistent with the results from descriptive statistics showing that customers at male agents had higher balances. For female customers, however, the effect reverses its direction, indicating that female customers are more likely to transact with 9
||1990|**Table**<br>1991|**3.12: A**<br>1992|**nalysis**<br>1993|**of Chan**<br>1994|**ges in t**<br>1995|**he Gene**<br>1996|**ral Go**<br>1997|**vernme**<br>1998|**nt Debt**<br>1999|**to GDP**<br>2000|**Ratio**<br>2001|2002|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Debt Ratio|79.6|82.2|87.8|110.1|107.6|108.7|111.3|108.3|105.8|105.1|106.2|107.0|104.9|
|Change|9.7|2.6|5.6|22.3|-2.5|1.1|2.6|-3.0|-2.5|-0.7|1.1|0.8|-2.1|
|Primary Balance|6.5|3.0|2.1|2.2|-2.7|-1.0|-3.1|-4.2|-5.3|-5.4|-5.1|-4.9|-4.3|
|Net Interest-Growth Effect|-4.9|-7.8|-1.7|-0.4|-1.1|-1.2|0.1|-2.8|-1.2|0.4|-0.8|-1.5|-2.4|
|Stock-Flow Adjustment|8.1|7.4|5.2|20.5|1.3|3.3|5.6|3.9|4.1|4.3|7.0|7.2|4.6|

Source: Bank of Greece, Annual Report for 2002:235.

The equation used in this analysis is: where, Dt = general government debt PBt = primary balance (deficit or surplus) Yt = GDP at market prices gt = nominal rate of change in GDP it = average nominal lending rate to the public sector SFt = stock-flow adjustment 40
evidence suggests that moves to unfreeze the economy will fail unless there is confidence that, in fact, the risk has passed.

Several recent papers suggest that NPIs have had an impact in the United States. Engle at al (2020) use daily average changes in distance traveled in every U.S. county as a proxy for reduction in exposure to COVID-19 and find that an official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Brzezinski et al (2020), also using cell phone data, find that a lockdown increases the percentage of people who stay at home by 8% across U.S. counties. Painter and Qiu (2020) show that the introduction of shelter-in-place policies is associated with a 5.1 percentage point increase in the probability of staying home (see also Andersen (2020)).

However, voluntary de-mobilizing behavior that intensifies with prevalence of the disease is also an important driver and affects the effectiveness of official measures. Auld (2006), for example, finds that during the AIDS epidemic, an average respondent decreased risky behavior by about 5% in response to a 10% increase in AIDS prevalence. Further, the 1918 Spanish Flu epidemic suggests that the predisposition of the population to demobilize drove both the incidence of official restrictions and their effectiveness. On the one hand, as Crosby (2003) details, that restrictions were binding is revealed by the fact that in San Francisco “The places of amusement opened first, to huge crowds starved for entertainment (p. 99)” and in Philadelphia “The long thirst was over, and arrests on drunken and disorderly charges bounded back up to and beyond normal levels” (p. 85). However, it is also true that while the San Francisco Department of Health could request that people to smother coughs and sneezes, only when enough fatalities were registered were “San Franciscans...scared enough to accept drastic measures to control the epidemic” (p.95)—and ex post, “Fear had been the enforcer of the Board of Health’s policies.”(p. 108) not the authorities themselves. When schools in San Francisco were opened, many parents kept their children home out of continuing fear. This resonates with the reports from Wuhan today of the anemic rebounding of the small restaurant sector when restrictions were released.

Viewed through this lens, restrictions may often function more like a coordinating device among increasingly predisposed individuals than a repressive measure- if we are all working from home, then I will not be viewed badly if I do; whether schools are online or in person requires a decision that individual concerned parents cannot effect. This, in turn, raises the question of the whether the impact of lock-down measures per se and their subsequent removal is overstated.

# **II. Data**

**Mobility and Economic Activity:** Using data from the Maps application on smartphones, Google generates COVID-19 Community Mobility Reports<sup>8</sup> that use aggregated, anonymized data to construct an index of how visits and length of stay at different places change compared to a baseline.  They can then follow

> 8 <u>https://www.google.com/covid19/mobility/</u>

3
land transfers in response to changed circumstances or competitiveness (Deininger and Byerlee 2011), key aspects of the legal and institutional framework such as the cost of registering transfers and roles of notaries need to be addressed as well (AGREX 2011).

While Eastern Europe‟s experience provides institutional lessons, it gives little guidance on the productivity effects of land fragmentation, an issue that has been explored in great detail in China, a country where the number of plots is well above the levels commonly encountered in Eastern Europe. Most studies use stochastic frontier production functions with the number of plots as a proxy for fragmentation. Results are ambiguous, though: some find a negative impact (Wan and Cheng 2001), others a relationship that is insignificant (Tan _et al._ 2008, Wu _et al._ 2005), or that follows an inverted U-shape (Chen _et al._ 2009). One reason may be that existing arrangements to transfer land at the local level, through market and non-market mechanisms, appear relatively effective in reducing fragmentation over time: the number of plots per household decreased in all of the country‟s regions, from a national average of almost 9 in 1986 to 6 in 1999 (Tan _et al._ 2006). This suggests that, despite a large number of plots, the operation of land markets implies that land fragmentation in China does not pose critical constraints for productivity.

To see whether fragmentation, proxied by the number of plots, affects agricultural production efficiency, we use LSMS data to estimate stochastic frontier production functions for crop, livestock, and total output. In line with the literature (Coelli 1998), let production follow a Cobb-Douglas functional form where, _i_ indexes households, ln( _y_ i) is the value of output in logs, _xi_ a vector of conventional inputs, β a vector of parameters to be estimated, νi is a two-sided white noise error term assumed to be normally distributed with mean 0 and variance σν2 and _u_ i a one-sided non-negative error term with unknown mean 2 _m_ i and variance σν that proxies for technical (in)efficiency. Assuming _u_ is half-normally distributed, maximum likelihood methods can be used to jointly estimate the frontier production function and determinants of (in)efficiency. Explanatory variables for the former include standard factors of production such as amount of land (of different types) cultivated, purchased inputs (including fertilizer, machinery, and pesticides), expenses on hired labor and the number of family workers as well as their education. Efficiency, in turn, is assumed to be affected by the area of land owned (which can serve as collateral for credit), the number of plots (as a measure of potential fragmentation-induced inefficiency); the head‟s age level of education (as a proxy for farm-specific experience), access to crop extension and veterinary services (to proxy for access to new technology), the share of non-agricultural income (which could either alleviate capital constraints or divert attention away from farming), whether land was inherited (as a 9
Policy Research Working Paper 10086

# **Abstract**

The literature suggests that one of the main factors behind the interstate inequality in economic development in India is the variation in the level of infrastructure development. However, unequal infrastructure development across the Indian States is less understood. This study empirically analyzes various factors (economic, fiscal, demographic, social, institutional, and political) to explain interstate infrastructure inequality using a panel data set for 18 states in India between 2004 and 2020. Employing the principal component analysis technique, three separate infrastructure indices are developed for physical, social, and financial infrastructures. The relationship of each index with its explanatory variables is estimated using System Generalized Method of Moments. The results show that economic factors— including economic performance, financial development, investment, and economic structure—are more influential on physical infrastructure. For social infrastructure, in addition to economic factors, fiscal and demographic factors are more influential. Meanwhile, economic and demographic factors are found to drive financial infrastructure. Financial development fosters physical infrastructure, while its impact on social infrastructure is weak.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**_4_**

comparable to FDI incentive systems of other East Asian countries (see Sec. V, Table 5.1). Despite these developments, however, Viet-Nam's FDI remains constrained by an uncertain legal system and by complicated regulations regarding investment sanctioning. Foreign investment has been most active in two subsectors: oil and gas and hotel construction. Project implementation is slow (Table 1.2) with about 25 percent of the total US$6.7 billion net (excluding licenses revoked or expired) foreign investments approved through February 1994.3 In manufacturing, only a small number of projects have been implemented of the total US$1.7 billion approved. Most of these are joint-venture projects (mainly with Korea and Taiwan) with short gestation periods involving garment subcontracting, consumer electronics, and scooters. An improved regulatory and legal framework may encourage other foreign investors to start implementing projects that take longer time to develop.

**Table** 1.2

|**Fore**<br>||**ign Inves**<br>~~~~~|**tment,**1<br>~~~~(U<br>**million)**|988-1993<br>S$||||
|---|---|---|---|---|---|---|
||1988|1989|1990|1991|1992|June<br>**1993**|
|Cumulative Capital Registered<br>(FDI approved)|364|903|1,503<br>I|2,726<br>lI_I|4,663|6,214|
|Cumulative Inflow of FDI<br>(Investment<br>implemented)|||400|600|900|1,500|

Source: State Committee for Cooperation and Investments (SCCI), and Viet Nam:

**A Developmental Perspective,** prepared for the (Paris) Donor Conference, Government of Viet-Nam, Hanoi, September 1993.

# **Relative Roles of Private Sector and State Sector**

One of the chief issues with Viet-Nam's legal framework for the emerging private sector is the focus on control rather than on promotion. Despite the broad policy to promote a mixed economy ("multi-component economy"), the current laws attempt to circumscribe private sector development without encouraging its growth. Ideology still influences the legal system which is biased against the private sector. A weak legal infrastructure, including institutions unwilling or unable to implement the laws, adds to an uncertain, unstable legal environment. Domestic investors in the private sector have to operate in a more restrictive regulatory framework than foreign investors do, in part because the government continues to struggle with the philosophical issue of how far a liberated economy and an expanded private sector can be compatible with a socialist political structure. Viet-Nam's policymakers do not

> **3** Implementation seems accelerated somewhat in 1994 but overall implementation over five years is still slow. According to provisional data $3.5 billion or 30% of a total $11 billion approved by the end of 1994 were implemented.