rafmacalaba/data-use-mentions-extended huggingface dataset:
than that of the ultimate acquirer. Transactions that were included involved either two or more companies pooling their assets to form a new entity (a _merger_ ), or a foreign company gaining a portion of a domestic company (an _acquisition_ ). All completed and partially completed deals were included, as well as intended and pending deals announced after September 1, 2009. The definition of a cross-border M&A transaction used in this paper includes any deal where any equity stake is obtained by the acquirer firm.<sup>8</sup> When no deals were recorded for any country and year, the dependent variable was coded as zero.<sup>9</sup> The compilation resulted in a working database that covers some 10,000 companies from 61 emerging-market economies, over the period between 1997 and 2010.

These were merged with the main independent variables of interest and additional controls, which were drawn from a variety of additional sources. These include macroeconomic conditions from the World Bank’s World Development Indicators (WDI) and the IMF’s International Financial Statistics (IFS); financial factors from Dealogic DCM Analytics, MSCI,

> 8This grouping includes investments where share purchases resulted in acquisitions of less than 10 per cent of a firm’s voting shares, a narrower but commonly-used definition of FDI.

> 9The database also provides historical information on acquirer and target countries (both immediate and ultimate), status, sector, and consideration offered. These were used in the section describing stylized facts in the data, but were excluded from the econometric analysis.

8
Figure 1 Economic and Social Indicators for Nepal <!-- Start of picture text --> Nepal Bangladesh India Sub-Saharan Africa World<br>GNI per capita (Atlas Method) Share of Agriculture in GDP (%)<br>1500<br>60<br>1000<br>40<br>500 20<br>0 0<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Life Expectancy at Birth (Years) Infant Mortality Rate, (per 1,000 live births)<br>70 200<br>60<br>100<br>50<br>40 0<br>1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010<br>Access to Improved Sanitation (% of Population) Primary Completion Rate<br>100<br>60<br>40<br>50<br>20<br>0 0<br>1990 1995 2000 2005 2010 2015 1970 1980 1990 2000 2010<br>Youth Literacy (% of 15 - 24 Years Old) Adult Literacy (% of adult 15 years or above)<br>80<br>80<br>60<br>60<br>40 40<br>20 20<br>0 0<br>1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Source: World Development Indicators (World Bank, 2016). World average not shown for GNI per capita. Sub-Saharan Africa includes only Low and Middle Income countries as of July 1st 2015.

5
not necessarily substantiated by strong _ex-ante_ evidence and hence it cannot be ruled out that a more traditional development financing approach (without explicit incentives) could be more effective, as recent evidence from the health sector demonstrates (Kandpal et al., 2020).

# **6 Conclusion**

Going forward, we recommend, first, that projects targeted at building state capacity invest more resources in learning through policy experimentation within the project, given the lack of established knowledge about how state capacity comes and the existing critiques of donor approaches to transplant formal institutions (Bourguignon & Gunning, 2018; Andrews et al., 2017; World Bank, 2017). Second, we recommend more research to understand forces of change in countries that may be strengthening incentives of local governments to deliver services. From reviews of research available so far, it seems that greater political contestation and demands from citizens for improved governance and service delivery are behind these improving incentives, but nevertheless with several risks and pitfalls (Olken & Pande, 2013; World Bank, 2016c; Dal Bo & Finan, 2016; Bardhan & Mookherjee, 2016; Khemani, 2019). A deeper analysis of administrative data can be an essential asset for both of these recommendations.<sup>28</sup> Project designs going forward can use the recommendations from these reviews to strengthen political incentives for service delivery, and thus enable the emergence of state capacity along similar lines as how such capacity emerged in today’s rich countries (Besley & Persson, 2009; Fukuyama, 2004; World Bank, 2016c; Khemani, 2019). For example, performance assessments could focus on rigorously measuring service delivery performance—e.g., road connectivity, garbage collection, coverage of drainage and sewage systems—rather than on primarily reviewing documents and protocols as is currently being done through the APAs. Project design could focus on the communication of, and deliberation around, these performance assessments, with citizens, especially through mass media whose role in strengthening institutions has been recently
analysis, we compare data from the 2011 WBES with the earlier 2004 WBES. For the firm-level innovation analysis, we compare findings from the latest WBES with a representative sample of enterprises collected as part of the Sri Lanka Longitudinal Survey of Enterprises by de Mel et al. (2009). These analyses provide complementary windows into key levers with which policymakers may improve the efficiency and novel use of resources already available in the economy.

<mark>Section II provides a context for the ensuing analyses by reviewing the importance of productivity and innovation as key drivers of sustainable high growth rates. Section III summarizes historic trends pertaining to reallocation of resources across broad sectors of the Sri Lankan economy. Section IV presents our main business environment-related hypotheses. Section V presents the data, methodology and analysis of cluster-level productivity. Section VI and VII then present the data, methodology and analyses of firm-level TFP and innovation. Section VIII concludes. Our empirical findings highlight the importance of connectivity to global knowledge (reflected by one or more of export participation, directly imported inputs, foreign ownership, and access to the internet), availability of skills, and access to finance as key correlates of cluster-level productivity and firm-level TFP and innovation. We also present evidence, under the assumption that the samples are statistically representative, that both allocative and average technical efficiency have improved, with allocative efficiency increasing roughly four-fold between 2003 and 2010, and accounting for the overwhelming share of the aggregate TFP increase over this time period. Most of the improvement in allocative efficiency has occurred among larger firms, and in large rather than small cities.</mark>

# **II. The Growth Benefits of Endogenous Productivity Improvements**

Compared to advanced economies, productivity in developing economies is low because the average firm is less productive and because there are much larger differences in productivity across firms, both across sectors and within sectors, with less efficient firms having disproportionately large market shares. There is a growing body of empirical work documenting large gaps in productivity across firms in developed and
_29_ <!-- Start of picture text --> Benefit Obligations to Be Paid from the Pension Fund<br>under the Civil Service Retirernent Ran<br>(% of concurrent GDP)<br>6<br>5<br>0 Unf unded<br>4<br>Funded 4.01<br>3<br>2<br>1 0.93<br>0<br>THT Pension<br><!-- End of picture text --> Note: These liabilities are 22.5 percent of total estimated civil service benefit obligations. Sources: Staff calculations based on Taspen's data.

**_Budgetary Implications_** The under-funding of the civil service plan is expected to have a major impact on the budget and the domestic financial markets. The budgetary expenses arising from the government's contractual responsibility is projected to rise significantly. Furthermore, substantial new outlays would be needed to cover THT and pension obligations that are now being paid by Taspen. The severity of this impact is due in part to the demographic shifts, which would increase the number of retirees relative to the work force. Since the civil service represents a relatively mature work force (see Chapter 1), the rise in the dependency ratio occurs sooner than the rest of the population.

Based on current practices under the plan, and the standard macroeconomic assumptions as given in the accompanying charts, the benefit payments under Taspen's THT program are projected to rise significantly and to exceed cash inflows (consisting of contributions and investment income) by the year 2006. Thereafter the THT assets would be drawn down to meet the required payments. By the year 2015, the THT assets would be completely depleted. To pay for the THT benefits thereafter, the government would need to make additional cash outlays, which are not required today. (See the charts below.) The size of this extra annual outlay would rise to about the same as the employees' contributions
>|0.91|0.91|0.93|0.92|0.93|0.92|
|**_ADF_**|-6.03***|-6.21***|-7.17***|-7.00***|-7.34***|-6.86***|

**Source:** Author’s estimates based on World Bank price data.

**Notes** _:_ The dependent variable is the logarithm of cotton price. The numbers in parentheses denote absolute _t-values_ while asterisks denote parameter estimates significant at 10 percent (*), 5 percent (**) and 1 percent (***) levels, respectively. ADF is the Augmented Dickey-Fuller (Dickey and Fuller 1979) statistic for unit root and corresponds to the MacKinnon one-sided _p-value_ . The lag length of the corresponding ADF equations was determined by minimizing the Schwarz-loss function. The standard errors and covariance matrix have been estimated in a heteroskedasticityconsistent manner using White’s method.

17
# **4. Descriptive Statistics**

The descriptive statistics shown in Table B5 suggest that 42% of the firms are microenterprises and that the service sector comprises 43% of the observations. The hospitality subsector, which was one of the hardest hit sectors in the pandemic, accounts for almost one-third of the firms in the service sector. Among the firms, 40% are young (less than five years), 13% are maturing (between five and fourteen years), and 47% are what we call established (with fifteen years or more in the market). Only about one-fifth of the firms in our sample are female-led.

The primary outcome of interest, the change in sales compared to the value before the pandemic, has been previously studied in (Apedo-Amah et al., 2020; Cirera et al., 2021). The three rounds of data show that, on average, and without controlling for any other factor, firms exhibit a decrease of 29% percentage points against their pre-pandemic level. This value, of course, includes periods of recovery. We also analyze recovery for the last two rounds, measured as ∆ _Change in salest,t−_ 1. Though recovery is, on average, around 11 percentage points, the median is still zero, indicating that half of the firms have not recovered and that the dispersion of recovery is very high with the standard deviation being around four times the mean, which underscores the need to analyze sources of heterogeneity in recovery after shocks.

In addition to the firm outcomes affected by the shock, we also consider variables related to the adjustment response by firms (e.g., use of digital technologies) and access to public support. About 50% of the firms started or increased their use of digital technologies, and 61% report having online sales. Only 22% of firms report having received support from the government, which varied widely across the three rounds.<sup>13</sup>

# **5
“Estimating standard errors in finance panel data sets: Comparing approaches,” _Review of financial studies_ , 2009, _22_ (1), 435–480.

- **Portes, Richard and Helene Rey** , “The determinants of cross-border equity flows,” _Journal of international Economics_ , 2005, _65_ (2), 269–296.

- **Seasholes, Mark S.** , “Re-examining information asymmetries in emerging stock markets,” Technical Report 2004.

- **Seasholes, Mark S and Ning Zhu** , “Individual investors and local bias,” _The Journal of Finance_ , 2010, _65_ (5), 1987–2010.

- **Sias, Richard W** , “Institutional herding,” _Review of financial studies_ , 2004, _17_ (1), 165–206.

- **Wermers, Russ** , “Mutual fund performance: An empirical decomposition into stock-picking talent, style, transactions costs, and expenses,” _The Journal of Finance_ , 2000, _55_ (4), 1655–1703.

26
untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” denotes the concentration ratio of the top 5 investing firms. Countries averaging fewer than 10 foreign investors are excluded from these calculations. “HHI” refers to the HerfindahlHirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

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is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts words from EIU reports for 143 countries since the 1950s. A higher “T3” value indicates greater uncertainty. Uncertainty in year is approximated as the logarithm of the average word count in EIU reports over the three years preceding the investment. The _ExchangeRateV olatility_ is calculated from the monthly exchange rate of the local currency against the US dollar, normalized by the whole-period mean and standard deviation. For a given destination-year, _it_ , _ExchangeRateV olatility_ is proxied as the average score in the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). All specifications include world-region by time fixed effects, and countries are grouped into 7 regions based on World Bank classifications; country fixed effects. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

38
supply of other food products such as meat, cheese, milk, and fish. Other affected countries such as Brazil, Ecuador, Malaysia and Indonesia also represent a significant share of world supply of respectively citrus fruit (48.7), bananas (30.4), palm oil (31.8), and coconut oil (46.6) (Appendix Table 1).

**Figure 4. World imports from top-50 most affected by Covid-19**

<!-- Start of picture text --> Meat 82.8% 17.2%<br>Fresh Fruits - Vegetables 81.8% 18.2%<br>Vegetable and animal oils 78.2% 21.8%<br>Sugar 73.3% 26.7%<br>Cereals 69.6% 30.4%<br>Fish 65.9% 34.1%<br>Legumes and pulses 64.8% 35.2%<br>Dairy and eggs 63.4% 36.6%<br>Nuts and derived products 58.3% 41.7%<br>Oil-bearing 51.8% 48.2%<br>Stimulant crops 38.3% 61.7%<br>World imports from 50 worst affected COVID Rest of the world<br><!-- End of picture text --> _Source_ **_:_** <u>Espitia, Rocha, Ruta (2020). “Database on COVID-19 trade flows and policies”.</u> The negative impact of disruptions in food supply as a result of Covid-19 will be potentially higher for import food dependent countries,<sup>11</sup> with high levels of concentration of imports coming from the top-50 most affected countries. Import food dependent developing countries would be particularly vulnerable to supply shocks in key staples such as cereals, meat and fresh fruits, where more than 74 percent of imports on
18,632|29,139|
|Two adults, two children|18,734|14,795|15,422|19,493|39,064|
|Three adults, one child|20,847|15,755|15,225|20,062|38,746|
|Three adults, two children|20,847|15,852|18,885|24,788|48,671|
|Four adults, one child|23,537|17,100|20,098|27,685|48,353|
|Four adults, two children|22,673|16,812|20,311|27,494|58,278|
|Five adults, one child|26,131|18,253|20,201|28,855|57,960|

**Note:** Population weights are applied. Standard errors for poverty rates are adjusted for complex survey design. Poverty line for reference “one adult” is defined as an average of minimum subsistence levels for working-age individual and pensioners in 2017. The level of absolute poverty line is 10899 rubles per month for working-age individual, is 8315 rubles per month for pensioner and is 9925 rubles per month for child in 2017. Relative poverty line is set on 60% of household size-weighted median equivalized income for each household type using RLMS data in 2017. Poverty lines of reference adult are adjusted with weights in Table 2 using BUC model (where baseline elasticity equals 0.407 and every child has a weight 0.048) and using Pooled Ordered Logit model (where baseline elasticity equals 0.636 and every child has a weight 0.078).

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literature on modeling savings behavior.

# **i) Recent Developments in Literature on Savings Behavior in Emerging and Developing Countries**

Kwakwa (2013) examines the key determinants of national savings using the Johansen cointegration technique as well as the error correction model for Ghana over 1975-2008. The empirical results show that in the long run, income and terms of trade have a positive and significant impact on savings while dependency ratio, political instability and the real interest rate have a negative impact on savings. In the short run, however, only terms of trade positively affects savings.

Awan et al. (2010) analyzes the long run and short run association among the real rate of interest on deposits, financial liberalization, economic growth, terms of trade, real remittances by Pakistani emigrants, and domestic savings behavior in Pakistan, using annual time series data for 1973-2007. The “Auto Regressive Distributed Lag Bounds Testing” approach is utilized to determine cointegration. The results reveal that real interest rate, financial liberalization, and economic growth positively affect domestic savings in Pakistan in the long run. The coefficient of the liberalization dummy is also positive and statistically significant, suggesting a need for increased liberalization and deregulation of interest rates for mobilization of savings. Conversely, the terms of trade and real remittances by emigrants show a negative relationship with domestic savings, thus supporting the Complementarity Hypothesis which was developed separately by Mckinnon and Shaw in 1973.

> 5 Modigliani & Brumbreg, 1954; Modigliani & Ando, 1957.

> 6 See Ortmeyer, 1985; Koskela and Viren, 1982; Schmidt-Hebbel and Servén, 2000; Beneston and Kauffman, 1990.

3
The upper- and lower- bound estimates for mobility derive from two extreme cases of the assumed relationship between the error terms. It is important to note that upper bounds of mobility correspond to the lower bound of immobility, i.e., remaining poor or remaining non-poor, and vice versa. Throughout the paper, upper-bound estimate and lower-bound estimate refer to upper and lower bounds of mobility.

of mobility. At one extreme, we consider the case of , which corresponds to the upper-bound mobility estimate. In this scenario, the errors are uncorrelated and mobility, given household characteristics, will ρρ= 0 be at a maximum, both out of poverty and into it. Because we assume that errors in fact are positively correlated, on average the upper bound will be strictly larger than actual mobility. Predicted upper-bound round 1 income for household surveyed in round 2 will be: ii 1′ �, 2UU ii1 ii2 ıı1 yy� = ββ�xx + εε̂ where � is a residual randomly drawn from the actual distribution of residuals resulting from estimating the income model on the round 1 data. Based on the predictions thus obtained, we can estimate the ıı1 εε̂ probability of each poverty transition for each observation. Aggregating these quantities at the population level, we obtain the country-level poverty dynamics.<sup>11</sup> Due to the random nature of this process, these steps are repeated R times, with R = 100 in this application, and averaged to obtain a robust estimate of the upper bound of mobility.

Analogous to the upper-bound scenario, but without the repetition, we obtain the lower-bound population-level poverty dynamics estimates.<sup>13</sup> Conditional probabilities are subsequently computed

> 11 In our analysis the regressions are at the household level taking log per capita household income as the dependent variable. Transition probabilities for each household are population-weighted when we aggregate, so that the country-level figures represent population proportions.

> 12 We use a scaling factor ⁄ to adjust for the difference in error
out of poverty in 1996, compared with 4.5 percent in 1994 – suggesting little improvement in the scale or targeting of income transfers.

Actually, the Mexican case alerts us to just how severe are data problems on inequality from standard sources. Even as straightforward a question such as: “did the Gini coefficient increase in Mexico between 1994 and 1996” produces conflicting answers in the literature. Table 2 and Figure 1 show the trend in the Gini coefficient 1992-1998 as reported in different studies mostly based on the _ENIGH_ . The apparently sizable increase in the Gini index in 1992-95 reported by the World Bank’s _Povcal_ is not confirmed by any of the other studies for the intervals 1992-94 and 1992-96.<sup>13</sup> (This needs to be borne in mind in considering the findings of Section 5, which is based on _Povcal_ data, though it does not use the offending 1995 Mexico observation.)<sup>14</sup>

# _Russia, 1998_

Lokshin and Ravallion (2000) review the experience with the Russian crisis of 1998. Their major interest is in examining the role of the safety net in favorably impacting the distributional and poverty impact; they conclude that changes in the safety net had a

> 13 Although included in _Povcal_ , this 1995 estimate is annotated as “less reliable” in the WIID2 database. 14 Using household panel data, Fields and Sanchez (2004) provide details on the Argentina case of 2001-2 suggesting that, although the Gini coefficient worsened, it was not those who had escaped from poverty pre-crisis that fell back.

10