rafmacalaba/datause-encoder-data huggingface dataset:
**Table A4: Impact of PBF on Health Provider Satisfaction**

|**Variable**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**Trend**|**PBF**<br>**impact**|**p-value**|**N**|
|---|---|---|---|---|---|
|Relationship with facility staff|0.944|0.016|0.019|0.973|2126|
|Relationship with district and ministry<br>of health staff|0.905|-0.059|0.018|0.838|2126|
|Relationship with facility management|0.955|-0.023|-0.025|0.607|2126|
|Quality of facility management|0.862|-0.015|0.033|0.848|2126|
|Availability of medicine|0.226|0.052|0.408***|0.000|2126|
|Quality of medicine|0.416|0.033|0.334|0.216|2126|
|Availability of equipment|0.115|0.019|0.318**|0.039|2126|
|Quality of equipment|0.163|-0.020|0.330|0.155|2126|
|Availability of other supplies|0.064|0.048|0.320**|0.044|2126|
|Physical condition of health facility|0.265|-0.042|0.270|0.146|2126|
|Ability to provide high quality of care|0.681|-0.005|0.146|0.182|2126|
|Salary|0.068|0.081|0.124|0.443|2126|
|Overall satisfaction|0.650|0.115|0.088|0.265|2126|

Note: data from interviews of health providers in both rural health centers and health houses. All regressions are the difference-in-differences specification with facility fixed effects. All regressions include controls for facility type, age and gender of providers, position, a binary indicator for experience of over 20 years and an indicator for being born in the district. Standard errors are clustered at the district and survey round level with wild bootstrapping.

For each category, health providers responded whether they are satisfied, unsatisfied or neither. The regressions are run on binary variables indicating the respondent reported satisfaction. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 34
SARS collection costs have consistently been the lowest of the cases examined, ranging from 0.90% to 1.16% of revenues collected (the average over the period 19982001 was 1.00%).<sup>30</sup> SARS’s low cost level put it on par with a number of OECD countries in terms of cost to collections efficiency, though the cost ratio has risen consistently since 1998, reflecting increased allocations.

In spite of the reforms, and the increasing budgets that went along with them in some cases, one could argue that most of the ARAs remain underfunded. The URA, for example, lacked equipment and infrastructure, including an IT system, due to an underfunded capital budget (in some years the URA’s capital budget was nearly zero). The lack of a proper IT system alone seriously undermined the URA’s performance, from registration to assessment to compliance.

The KRA also reported inadequate resources for IT investment and for rolling out the higher KRA salary structure to the entire staff. The KRA operated on a tight budget in comparative perspective. The government, however, provided a modern office building to KRA (Times Tower), which ought to have had the effect of improving the agency’s professional image. Given its very low collection costs, the record of the KRA appeared even more impressive in comparison.

Though SARS has had an increase in investments since 1997, and some of these were quite significant, especially in the IT area, its capital budget was somewhat low. The percentage of SARS capital expenditures to its total budget, which was in line with the other cases, stood low at approximately seven percent (personnel expenditures account for roughly half of the SARS budget).<sup>31</sup> The LTO, for example, reported a lack of basic infrastructure and key items such as laptop computers.

Thus the cases show that these reforms have not generated high recurrent cost obligations in comparative terms. In the best cases increases in salaries were offset by staff reductions. The case studies do suggest, however, that capital expenditure is limited to the point of creating inefficiencies for several of the agencies. The cases also show that the start up costs of the reform—including for retrenchment and purchase of new

> 30 Calculations based on SARS data. Using 2001 budget data ( _Estimates of National Expenditure_ ), the average collection costs ratio rises to 1.10% over the same period and does not show a steady increase. 31 These figures do not include special capital investment projects, financed separately by the MOF, in the IT area.

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# **1.- Introduction**

The knowledge-based view of the firm argues that wealth creation in a firm is a function of its ability to create new knowledge and to exploit it in the market (Teece, Pisano and Schuen, 1997). However, the investment that a firm makes in knowledge-related activities has important implications beyond its boundaries because of its lack of ability to appropriate and exploitat all the benefits. As a result, existing organizations may be characterized as having an abundance of underexploited knowledge (Agarwal et al., 2004).

For Agarwal, Audretsch and Sarkar (2010), individuals that perceive unexploited opportunities created by knowledge investments by incumbent organizations, may choose to venture out using the human capital/knowledge they acquired during their tenure at the knowledge-generating organization. Entrepreneurs starting a new venture not only create new firms, but also provide a conduit for the spillover of knowledge that might otherwise not have been commercialized and would have remained dormant in the incumbent firm.

The literature that links knowledge spillovers to entrepreneurship capital emphasizes that existing (incumbent) organizations are an important source of new entrants, as they embody knowledge that can be appropriated, and thus facilitate new entry into the sector (Audretsch, 1995; Agarwal, et al., 2004; Colombo, 2005).

The knowledge spillover theory of entrepreneurship suggests that knowledge spillovers serve as the source and create the entrepreneurial opportunities for new firms. This theory suggests that entrepreneurship is the missing link in the process of economic growth because it facilitates the spillover of knowledge from universities and private firms, resulting in commercialization of ideas that otherwise might remain unexploited or uncommercialized.<sup>2</sup> According to Audretsch and Keilbach (2003), by starting up a business, an entrepreneur literally “bet” on the product he offers and thus is willing to take the risk that this venture bears. Acs and Audretsch (2003) observe that, “by commercializing ideas that otherwise would not be pursued and commercialized, entrepreneurship serve as one mechanism facilitating the spillover of knowledge.” The empirical evidence supporting the knowledge-spillover theory of entrepreneurship is based on the analysis of variations in startup rates across different industries reflecting different underlying knowledge contexts. As pointed out by Caves (1998), those industries with a greater investment in new knowledge also exhibited higher startup rates, while those industries with less

> 2 “Although characteristics of entrepreneurial activity differ across countries, the importance of entrepreneurship for economic development is widely acknowledged” (Global Entrepreneurship Monitor (GEM) 2007 Executive report (2008: 12 ))

2
# **References**

- Adian, Ikmal; Doumbia, Djeneba; Gregory Neil; Ragoussis Alexandros; Reddy Aarti; Timmis Jonathan. 2020. “Small and Medium Enterprises in the Pandemic : Impact, Responses and the Role of Development Finance.” _World Bank Policy Research Working Paper_ .

- Ajayi, Kehinde, Aziz Dao and Estelle Koussoub`e. 2022. “The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation in Burkina Faso.” _CSAE Working Paper_ .

- Apedo-Amah, Marie Christine; Avdiu, Besart; Cirera Xavier; Cruz Marcio; Davies Elwyn; Grover Arti; Iacovone Leonardo; Kilinc Umut; Medvedev Denis; Maduko Franklin Okechukwu; Poupakis Stavros; Torres Jesica; Tran Trang Thu. 2020. “Unmasking the Impact of COVID-19 on Businesses : Firm Level Evidence from Across the World.” _World Bank Policy Research Working Paper_ .

- Berge, Lars Ivar Oppedal, Kjetil Bjorvatn and Bertil Tungodden. 2015. “Human and financial capital for microenterprise development: Evidence from a field and lab experiment.” _Management Science_ 61(4):707–722.

- Bertrand, Marianne. 2020. “Gender in the Twenty-First Century.” _AEA Papers and Proceedings_ 110:1–24.

- Bicchieri, Cristina. 2016. “Norms in the Wild: How to Diagnose, Measure, and Change Social Norms.” _Oxford University Press_ .

- Bick, Alexander. 2016. “The Quantitative Role of Child Care for Female Labor Force Participation and Fertility.” _Journal of the European Economic Association_ 14(3):639–668.

- Bolis, Mara, Anam Parvez Butt, Emma Holten, Leah Mugehera, Nabil Abdo and Maria Jose Moreno. 2020. “Care in the Time of Coronavirus: Why care work needs to be at the centre of a post-COVID-19 feminist future.” _Oxfam Briefing Paper_ .

- Clark, Shelley, Caroline W. Kabiru, Sonia Laszlo and Stella Muthuri. 2019. “The Impact of Childcare on Poor Urban Women’s Economic Empowerment in Africa.” _Demography_ 56(4):1247–72.

- De Mel, Suresh, David McKenzie and Christopher Woodruff. 2009. “Are women more credit constrained? Experimental evidence on gender and microenterprise returns.” _American Economic Journal: Applied Economics_ 1(3):1–32.

- Delecourt, Sol`ene and Anne Fitzpatrick. 2021. “Childcare Matters: Female Business Owners and the Baby-Profit Gap.” _Management Science_ .

- Devercelli, Amanda E. and Frances Beaton-Day. 2020. “Better Jobs and Brighter Futures : Investing in Childcare to Build Human Capital.” _World Bank Report_ .

32
that were poor in one period between 2009 and 2013 (close to 80 percent of this group), or that were poor in two non-continuous periods.

<!-- Start of picture text --> Figure 2: Various Shades of Poverty in Georgia, 2015<br>90<br>79.0 80.0<br>80<br>70.0<br>70<br>60.0<br>28.4<br>60<br>50.0<br>50<br>9.0 40.0<br>40<br>30.0<br>30 21.0 20.0<br>20 41.6 10.0<br>9.4<br>10 0.0<br>11.7<br>0 -10.0<br>Poor Non-poor<br>Chronic Poor Never Poor Escapes Poverty Other<br><!-- End of picture text --> _Source_ : Own elaboration based on 2009, 2011, 2013 and 2015 WMS. _Note_ : Number on top of the bar represents the poor and non-poor headcount, respectively In the remainder of this section, we will focus on a comparison of the profiles of those in chronic poverty and those who managed to escape poverty in Georgia. This will help shed light on the key drivers of poverty reduction in the country.

The first point to note is that in contrast to other groups, the chronic poor appear to have benefitted only very marginally from the economic growth between 2009 and 2015. Per capita consumption (per day) grew by over 50 percent for the country as a whole but among the chronic poor this was just below 10 percent (Figure 3). Households that escaped poverty appear to have started from a base lower than those below the chronic poor but witnessed a dramatic cumulative increase in living standards (over 200 percent) during this period. Those never in poverty over these four rounds have in general maintained a higher living standard and have also witnessed a healthy growth (33 percent) in consumption. These results indicate that the chronic poor have not been able to take advantage of opportunities to increase their welfare as other groups have had, suggesting the need to understand the reason behind this difference.

The second point that emerges is that chronic poverty is both an urban and a rural phenomenon. More than 40 percent of the chronic poor are located in urban areas (over half of them in Tbilisi) while the rest are in rural areas (Figure 4). The distribution of other groups in urban and rural areas tends to be slightly more skewed towards urban areas, especially for households who have never been poor. While the chronic poor condition is not purely explained by spatial differences in access to public services or employment opportunities, these results suggest that there are 18
# 5 Estimating the Effects of Climate Variability

## 5.1 Reported shocks and coping strategies

We now turn our attention to the second research question of his report, related to the impact of climate variability on income generation. We begin with an examination of household self-reported shocks in the PEN survey. We are scrutinizing now households’ preferred coping strategies, with special attention to Hypothesis 2 suggesting safety net responses based on environmental extraction. The PEN survey included questions on household self-reported experiences of shocks, their severity and cause, and responses to cope with them. Here our interest lies in studying which household and village level characteristics are associated with the decision to extract additional products from forests and other natural environments, in response specifically to shocks that potentially are climate-related. We thus focus on the following shock types:

1. Serious crop failure

2. Major livestock loss 3. Other major asset loss These categories will also come to cover some non-climate related shocks (such as theft or non-climate related pests), but presumably represent a subset with greater climate relevance than the total amount of shocks analyzed in Wunder et al. (2014). Households reporting a shock were also asked to rank their responses by importance. Responses included 21 predefined options, plus an open answer option. We categorize our response variable as 1 = “harvest more forest or wild products” and 0 = “any other action or non-action”. Wunder et al. (2014) show that second and third rank responses exhibit a similar distribution across response categories. Here we thus only analyze the first ranked response.

Since not all surveyed households reported a shock, the sub-sample of households that did could potentially be biased. We address this by using a maximum likelihood Probit model with sample selection implemented, using the Stata command “heckprobit”. The variables used in the regression analysis are reported in Table 8. Repeated observations (when households reported more than one shock) are weighted using frequency weights. Note that households also reported whether the shock was perceived as “severe” or “less severe”.

**Table 8: Self-reported characteristics of households experiencing shocks: descriptive statistics**

||Unit|mean|SD|Min|Max|
|---|---|---|---|---|---|
|Used forest or<br>wild products|yes/no|0.08|0.27|0.00|1.00|
|Climate-related<br>shock|yes/no|0.37|0.48|0.00|1.00|
|Severe shock|yes/no|0.23|0.42|0.00|1.00|
|Female headed<br>HH|yes/no|0.11|0.32|0.00|1.00|
|Age of HHH|Years|45.78|14.41|14.00|111.00|
|Education of<br>HHH|Years|4.02|4.17|0.00|31.00|
|HH size|Number|6.23|3.28|1.00|46.00|
|HHH belongs to|yes/no|0.74|0.44|0.00|1.00|

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2008. Subsidies increased by 642 percent, whilst the corresponding ...gure for the stock of debt is 109 percent.

As noted in the introduction, it is the role of ...scal policy during periods of slower growth or crises times that should be of relevance to policymakers in emerging market economies. The ...ndings in this paper suggest that a large ...scal stimulus during an economic downturn like the 199/98 Asian ...nancial crisis - precisely when the number of balance-sheet/liquidity constrained agents were on the rise - could have loosened economic conditions and precipitated a faster economic recovery. These ...ndings provide evidence in support of the argument that higher ...scal outlays are particularly e¤ective as a stabilisation tool in a sharp economic downturn. Thus, a recession could be further aggravated if it forces the government to retrench spending in the midst of the downturn.

A key criticism of the International Monetary Fund’s advice, during the 1997/98 ...nancial crisis that ravaged countries in South-East Asia, including Malaysia, was the stress put on ...scal tightening. These countries were seen as sacri...cing long-run growth in order to show signs of ...scal discipline. Tight ...scal policy implemented during the crisis reduced the country’s net worth, by cutting productive government spending and causing a downward shift in their long-run growth trajectory. The implication of the ...ndings is that expansionary ...scal policy during crisis periods may also help prevent long-term growth damage to the economy. The estimates in this paper show that government investment spending has long-run e¤ects on real economic growth in Malaysia. These results appear more consistent with the predictions of (old) Keynesian theory, and hard to reconcile with those of the neoclassical paradigm. Eggertsson and Krugman (2010) recently argued that a ...scal expansion - even through an increase in (public) debt - could help alleviate a problem caused by high (private) debt and a resulting deleveraging shock due to a sharp economic downturn or economic (...nancial) crisis.

# 6 Conclusion

This paper investigates changes in the size of ...scal multipliers over stages of the growth cycle, which is more relevant for emerging market countries, for Malaysia.

20
results. We also fail to reject the null of a unit root in gold prices, consistent with Smith (2002). The model predicts that poaching data have a unit root, which we test using the global poaching data. Here also, we cannot reject that the logarithm of global poaching has a unit root. Table A3 reports the results. However, our identification admittedly hinges on our model being correctly specified. In particular, we assume that beyond prices, other variables are also stationary. Our main specification thus does not include a time trend, although we check the robustness of our results to adding such time trend.

**From global to local prices** Finally, we need to specify the relationship between global prices _pt_ and local prices _pct_ . We assume that:

The local price is thus a function of the global price with pass-through _θ_ , while _δct_ captures the iceberg trade costs from the poaching site to the global markets. This trade cost encompasses transportation costs, differential law enforcement levels across countries, or any other sources of country-level heterogeneity on the supply side. We assume that these are independent of gold prices, i.e. _Cov_ ( _p_<sup>_g_</sup> _, δ_ ) = 0.

We can then substitute for _pct_ in (4) and obtain ̃ with _β_<sup> ̃</sup> = _β · θ_ and _εsct_ = _εsct − βδ_<sup> ̃</sup> _ct_ , where _δ_<sup> ̃</sup> _ct_ is the component of _δct_ not absorbed by site fixed-effects and parametric regressors.

Equations (4) and (11) can be estimated to assess the poaching elasticity with respect to ivory prices in Africa and China, respectively, while (10) estimates the pass-through rate. In all cases, the endogenous price is instrumented with gold price (note that the exclusion restrictions are not mutually inconsistent as we assume that gold prices affect ivory prices in China, which in turn affect ivory prices in Africa). We will also consider commodities such as silver to test the robustness of our results to alternative choices of instruments.

# **4 Data**

One of the contributions of this paper is to combine for the first time price and quantity data on African elephant ivory.

10
**Table 1.2: Distribution of Income by Socio-Economic Groups**

||Size of<br>Groups|percent|Mean pey|Median<br>pcy|S.D. pcy|Poor<br>percent|
|---|---|---|---|---|---|---|
|Govt.|1,536|10.9|84,906|41,885|443,527|18.3|
|NTG|2,064|14.6|44,977|34,237|44,030|26.6|
|Manu.|575|4.2|43,751|36,598|37,255|25.2|
|Traders|5,241|37.1|43,705|35,554|35,465|26.2|
|Cocoa|2,227|15.8|52,210|39,460|64,605|21.0|
|Food|9,166|54.9|45,122|31,916|1,182,188|32.7|
|TOTAL|14,130|||46,404|34,289|151,202|

pcy = per capita income in cedis for Ghana A Household is described as poor if it falls into the poorest 3 per cent capita income deciles.

_50urce: Table derived by Appleton and Collier(l990) from the SDA Survey of 1988, p.83._ The figures are of interest in giving an indication of broad differences between the income per capita of households in different groups after the Economic Recovery Program had gone some way. They will be referred to again at appropriate points in the paper when we focus on the changing relative position of specific groups at different points in the country's changing economic regimes.

One point of general importance might be emphasized here. It is seen that the median incomes per capita are much closer together than the means. This implies that in some sectors there are considerably greater dispersion of incomes, with a lager proportion of households with relatively high incomes. This is particularly true of the government sector, and to a smaller extent the food sector. (The latter probably reflects regional diversity in the peasant sector in Ghana).

# **1.3 The Impact of Government Policies on the Labor Markets**

A narrow view of government interventions in labor markets would confine the discussion only with those aspect of policy which have a direct impact on these markets. In the formal sector public sector wage and employment policy obviously is an important agent in Ghanaian context. Furthermnore, because of the importance of public employment in the formal wage sector of the economy, these policies could be expected to have ripple effects on its private subsector. In particular, the public secure could be expected to act as a leader in the wage determination of the large-scale private firms.

4
where θ1 is expected to be positive and θ0 is a constant as suggested by equation (25).

# 3.2 Data

The estimation of equations (27) and (28) requires information on U.S. importers and Latin American exporters’ political organization, and data on elasticity of export supply and tariff preferences by industry and by Latin American country. Data on MFN and preferential tariffs as well as exporters’ political organization cover the years from 1997 to 2000. The paper employs data on importers’ political organization from the period 1991-2000. By calculating the average of the data over these years, we perform cross-section regressions to estimate the coefficients of equations (27) and (28). All the variables are organized at the HS 8-digit and ISIC 3-digit levels. Several issues related to the data set are discussed at length next.

MFN and Preferential Tariffs MFN and Preferential tariffs are calculated using customs data provided by the United States International Trade Commission (USITC). The original data is available at the tariff line level (HS 8-digit level) and covers the period between 1997-2000. Before proceeding with the calculation of the MFN and preferential tariffs, we average out the observations for these four years by HS 8-digit code and by country. U.S. MFN tariffs are determined by the ratio between duties paid on U.S. imports and the value of U.S. imports from all over the world that entered under the MFN regime for each HS 8-digit line. Similarly, preferential tariffs are determined by the ratio between duties paid on U.S. imports and the value of U.S. imports by Latin American country and by HS 8-digit line.

Using actual duties paid and the value of imports to calculate tariffs allows the determination of actual tariff preferences (i.e., those actually granted at customs and not ‘on paper’).<sup>35</sup> The data on MFN and preferential tariffs has been aggregated at the ISIC 3-digit

> 35The data on MFN and preferential tariffs used in this paper is the same one used in Kee, Olarreaga and Silva (2003). The interested reader can obtain information about the products and countries which were granted the highest and lowest preferential tariffs in that paper.

22