rafmacalaba/datause-encoder-data huggingface dataset:
|By outside providers|47.9|29.4|45.7|30.5|44.4|42.7|36.2|
|---|---|---|---|---|---|---|---|
|None|40.5|_55.9_|45.7|33.8|26.0|36.9|35.8|
|Total||||||||
|Percent|100|100|100|100|100|100|100|

_Note:_ The values show the percentage of each category of workers with each level of education. For example, 11.6 percent of the manufacturing workers in the sample who work in administration received inhouse training.

_Source:_ Round Three of the 1994 Regional Program on Enterprise Development for Ghana.

Only 26 percent of support staff do not receive any training, compared with the average of 35.8 percent across all occupations (see table 7). Employees in commercial and sales occupations receive the least training; 55.9 percent of this group does not receive any kind of training (table 7). University graduates receive the most training, 76.9 percent (see table 8).

25
<!-- Start of picture text --> 40%<br>35%<br>30%<br>22% 22%<br>20% 19%<br>- 7<br>0% 7 —<br>Gcc Non-GCc<br>=% of Banks with programs targeted toward women-ownedbusinesses<br>=% of female loan officers<br>=%& of Banks with lower defaultforloans by female loan officers<br>40%<br>31%<br>30% 20%<br>20% eae<br>17%<br>10%<br>-Oo 7 a<br>State Private<br>= % of Banks with programs targetedtoward women-owned businesses<br>oe % of female loan officers<br>= % of Banks with lower defaultforloans byfemale loan officers<br><!-- End of picture text -->
# **Table 2c. Women in SHG Members and Non-Members groups, by house type**

|H|Number of<br>|people in each ty<br>|pe of house*|
|---|---|---|---|
|ouse type|Non-<br>Members|SHG<br>Members|Total|
|AdhKhh|285|114|399|
|r-acca|(71.4%)|(28.6%)|(100%)|
|Cl|15|10|25|
|oony|(60%)|(40%)|(100%)|
|Khh|588|612|1200|
|acca|(49%)|(51%)|(100%)|
|Pkk|471|143|614|
|aa|(76.7%)|(23.3%)|(100%)|

(*) In parenthesis: proportion in Non-Members and SHG Members. n = 2238 (without missing values)

**Table 3. Average time in SHG, and savings, by caste**

|Caste|SHG time<br>(Months)|<br>Monthly saving<br>(Rupees)|s<br>n|
|---|---|---|---|
|General|67,6|583,1|394|
|Minority|60,4|658,3|744|
|Other Backward Classes|58,3|688,5|1709|
|Scheduled Castes|55,9|660,6|1512|
|Scheduled Tribes|47,3|917,4|76|

n = 4435 40
training sessions (Table 3, Panel B).<sup>27</sup> We examine whether firms’ compliance costs, experiences with unofficial payments, and their propensity to evade taxes affect their likelihood of adopting e-filing as outlined in the conceptual framework. Since almost all firms in the treatment group adopt e-filing, we run Equation 2 using only Groups B and C and present the results in Table 4 to show which firm characteristics are associated with e-filing adoption. The first two columns of Table 4 provide results for all firms while the third column covers only legal entities.<sup>28</sup> We find a positive association between e-filing adoption and number of hours spent on tax preparation, supporting the hypothesis that firms with higher compliance burdens under paper filing would be more willing to switch.

In addition, we find that firm size is strongly associated with e-filing adoption: one(wo)man businesses are significantly less likely to e-file and, among firms with employees, e-filing increases with the number of employees, suggesting that firms with more complex taxes are more likely to adopt.

Unlike our priors, we find no significant correlation between e-filing adoption and firms stating that bribe payment is common (our baseline proxy for firms that pay bribes) in Table 4. Since this measure does not distinguish between voluntary and involuntary bribes, we interact it with the risk score, as firms with higher risk scores are more likely to be voluntarily paying bribes in exchange for tax evasion support from tax officials, but find no significant effects (regressions not shown). This could be because this measure of bribes is too noisy (given the potential bias in responding directly to a sensitive question) or because no relationship between e-filing use and bribe payments exists once you control for risk score and other characteristics.

> 27 The other main source of information about e-filing for the control group was business networks (17 percent). In contrast, almost all firms in Group A learned about e-filing from the intervention trainings.

> 28 Since risk score is only available for legal entities, column 2 assigns the mean value for legal entities to individual enterprises.

25
then we need a framework that can allow for such possibilities. Therefore, our empirical strategy involves two steps. First, we follow Ackerberg, Caves and Frazer (ACF, 2006) to structurally estimate a production function. After getting consistent estimates of the production function parameters, we can recover a productivity measure. As explained above, this productivity term will measure technical efficiency as well as any residual factors that cannot be controlled for by observable inputs. In the second step, we regress the estimated productivity term on firm ownership, foreign employment and its interaction with time and conflict intensity, and other control variables to test hypotheses (1) and (2).

# **4.2 Productivity estimation**

Assuming the production function is a Cobb-Douglas function in capital _Kit_ and labor _Lit_ , we estimate its log transformation: where _ωit_ = _Ait_ + _τit_ , lower case letters denote logs and the error term _ηit_ captures unanticipated shocks to output and measurement errors. In this framework, we can estimate the TFP term if _βk_ and _βl_ are known. OLS estimation of _βk_ and _βl_ , however, runs into an obvious simultaneity problem since high productivity firms may also use more inputs.

ACF (2006) address this endogeneity problem by taking a structural approach in which an inverted function of the intermediate input demand is used to control for productivity.<sup>11</sup> The underlying structure is based on a model that derives industry equilibrium with heterogeneous firms. In the model, firms maximize a discounted sum of current and future profits, which are governed by exogenous productivity shocks that follow a first-order Markov process. Formally, the productivity process can be described as: _p_ ( _ωt_ +1 _|{ωτ }_<sup>_t_</sup> _τ_ =0<sup>_, It_)=</sup><sup>_p_(</sup><sup>_ωt_+1</sup><sup>_|ωt_).Hence,currentproductivitycanbe</sup> written as a function of last period’s productivity and a white noise error:

There are two crucial timing assumptions in the ACF estimation approach. First, capital is treated as a dynamic input. That is, investment _iit_ takes place one period before the new capital can be used in the next period: _kit_ +1 = (1 _− δ_ ) _kit_ + _iit_ . Second, labor, unlike intermediate inputs, is allowed to be a not fully variable input. It is decided at time _t − b_ (0 _< b <_ 1) when only part of the current productivity shock has been observed by the firm and before intermediate inputs are chosen.<sup>12</sup> Suppose productivity also evolves as a first-order Markov process between the ( _t −_ 1 _, t − b_ )

> 11It is related to the approaches originated by Olley and Pakes (1996) and Levihnson and Petrin (2003) but includes additional timing assumptions to address collinerarity issues.

> 12In the context of our research, it is a reasonable assumption since formal firms in Cˆote d’Ivoire are subject to the Labor Code. According to the Bureau of International Affairs (1999), those employed in the formal sector are

10
Export intensity is defined as the ratio between firm nominal exports and nominal total output. Real output is defined as firm nominal total output deflated by an industry output deflator taken from Brandt et al. (2012). Real exports are defined as firm nominal exports deflated by the same output deflator. Employment is defined as the firm total number of workers. Labor productivity is defined as firm real value added divided by employment, where real value added is obtained as real output minus real intermediates, defined as nominal expenditures on intermediates divided by an input deflator taken from Brandt et al. (2012). A total factor productivity (TFP) Törnqvist index is obtained following Caves et al. (1982) with firm real output as the output measure and employment, real intermediates, and capital as inputs. Capital is obtained as firm net fixed assets deflated by a regional fixed investment price index from the NBS, following Du et al. (2014). For the TFP index calculation: the elasticity of labor is obtained as the ratio of nominal wages to nominal output; the intermediates elasticity is obtained as the ratio of nominal intermediates to nominal output; and constant returns to scale are assumed so the elasticity of capital is one minus the elasticities of labor and intermediates; all three are averaged at the 4-digit industry level.

Two remarks should be made about the NBS data. First, the NBS data do not include information on Chinese firms’ use of ICT, such as the internet or computers. This is not necessarily a handicap for our analysis since firms’ decisions to use the internet are correlated with their production and export decisions as well as their productivity, and this endogeneity would bias the estimated effects of the internet on firm outcomes. We thus rely on a subnational data set on internet rollout, described in Section 3.2, to measure firms’ access to the internet depending on their location. Second, the NBS data include information on firm exports to foreign countries, but unfortunately not on domestic sales to other provinces which may also be affected by increased access to the internet.

Table 1 presents some descriptive statistics on the estimating sample, including information on the number of firms per year (Panel A), and the means and standard deviations of our firm-level outcome variables (Panel B). As a check of the quality of our firm TFP measure, we calculated average annual TFP growth for Chinese manufacturing firms over the 1998-2007 period and found that to be about 2.89 percent, which is extremely close to the magnitude of 2.85 percent provided by Brandt et al. (2012) in their paper focused on TFP estimation. Figures 1 and 2 show the number of exporting firms per province and total exports per province (obtained as the sum of exports across all firms) over time. Exporting firms and exports as a share of the population 8
We will largely focus on the first wave of the survey (June-July 2009), and analyze the associations between various demand and credit proxies before the crisis with firms performance during the crisis.

# **3 The relative importance of demand factors**

This section provides descriptive evidence on the relative importance of demand shocks. The first piece is the most direct, but subjective about factors that most affect firms’ operation in the crisis. One question in the first wave of the crisis survey in June-July 2009 asks: “ _Choosing from the following list what has been the_ **_main_** _effect the financial crisis on this establishment?_ ” Among 1,478 firms that responded, 69 percent pointed to the drop in demand for its product and service as the main effect. Increase in the level of debt, increase in input cost, and reduced access to credit account for about 7-10 percent of firms each. The percentages are similar between small firms and larger firms. Among small firms<sup>6</sup> , 73 percent reported the drop in demand as the main effect, whereas 6 percent of them reported the credit crunch as the main effect (see Figure 2).

The demand factor is also reported as the number-one cause for firms’ recovery or further declines. In the following two waves of the crisis, firms were asked a follow-up question “ _What is the main reason that your establishment’s situation improved or worsened?”_ Again, an overwhelming number of firms report changes in demand as the main reason for the improvement or worsening of their performance. In particular, at wave 2 of the survey in April 2010, 61 percent of firms that see some improvement state that an increase in demand for its product and services is the main cause, whereas 72 percent of firms that see further worsening claim that a drop in demand as the main cause. At wave 3 in August 2010, 76 percent of firms that see some improvement report that an increase in demand is the main cause, and 69 percent of firms that see further worsening attribute drops in demand as the main cause.

> 6 We define small firms as those that in 2007 have fewer than 20 full-time workers, which is consistent with the 2009 BEEPS.

6
Crop Yield Convergence across Districts in India’s Poorest State

# Rishabh Sinha<sup>1</sup> The World Bank _Updated November 2020_

## **JEL classification:** O13; Q14; R11

**Keywords:** Crop yield convergence; Time-varying idiosyncratic productivity; India; Bihar districts; Rice; Wheat; Maize

1 Contact: 1818 H St NW, Washington, DC 20433, USA (rishabhsinha@worldbank.org). Thanks to two anonymous referees and the associate editor for their comments and suggestions which led to significant improvements. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. 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. Most recent version available here.
1960 and 2010, in which armed groups in an oil-producing region fought for independence. Country Income is for the year the conflict began, or the closest year for which data are available. Figures are in constant 2000 dollars per capita. Notice that the danger of resourcefueled separatism is far greater for poor countries than rich ones: all 16 conflicts broke out in countries with incomes below $2100 per capita; 11 of the 16 occurred in countries with less than $1000 per capita. Countries with incomes above $2100 per capita – about the level of Jordan or El Salvador – seem to face a much smaller danger of petroleum-related separatist wars.

||**Table 5. Se**<br>|**paratist Conflicts in Oil-Produc**<br>|**ing Regions**<br>|
|---|---|---|---|
|**_Country_**|**_Conflict Years_**|**_Country Income_**|**_Region_**|
|**Angola **|1975-2007|$1073|Cabinda|
|**Bangladesh **|1974-92|$243|ChittagongHill Tracts|
|**China**|1991-|$422|Xinjiang|
|**India**|1990-|$317|Assam|
|**Indonesia**|1975-2005|$303|Aceh|
|**Iran**|1966-|$1053|Kurdistan|
|**Iran **|1979-80|$1747|Arabistan|
|**Iraq**|1961-|$2961|Kurdistan|
|**Nigeria **|1967-1970|$267|Biafra|
|**Nigeria**|2004-|$438|Niger Delta|
|**Pakistan **|1971|$275|Bangladesh|
|**Pakistan **|1974-77|$280|Baluchistan|
|**Russia**|1999-2001|$1613|Chechnya|
|**Sudan**|1983-2005|$293|South|
|**Turkey**|1984-|$2091|Kurdistan|
|**Yemen**|1994|$443|South|

_Source:_ conflict data are drawn from Gleditsch et al. (2002); income data are from World Bank (various), with missing figures taken from Maddison (2009).

place (Indonesia, Yemen). The other eight conflicts broke out in petroleum-rich regions of countries that did not cross the $100 per capita threshold – either because oil had been discovered but not yet extracted (in PakistanBangladesh, Bangladesh-Chittagong Hills, Nigeria-Biafra, and Sudan), or because oil was relatively scarce at the national level, even though it was abundant in the secessionist region (in China, India, Pakistan-Baluchistan, and Turkey).

Interestingly, none of these conflicts occurred in Latin America. In Latin America, oil is statistically associated with governmental conflicts but not separatist ones. This is not because Latin American petroleum has unusual properties, but because the region is uniquely secession-proof: there have been no separatist conflicts in Latin America for over a century. For more on the Latin American anomaly see Ross (2010). Other types of mineral wealth, including copper and gold, have sometimes been linked to secessionist movements in exceptionally poor regions – for example, on the Papua New Guinea island of Bougainville, and in Indonesia‘s West Papua province (M. Ross 2004a).

4
competitiveness. The following regression is estimated to explain the determinants of inventory levels: It is difficult to obtain consistent time series data on inventory holdings for developing countries. The aggregate data reported in the national accounts is the change in inventories rather than the stock of inventories; often this data is based not on an inventory survey but on the difference between production and sales which leads to highly inaccurate data<sup>9</sup> .Most national statistics agencies do have inventory stock data but they do not publish it. In order to report the size of the country's industrial production, the statistics agency typically carries out a firm survey or census, which asks about total inventory holdings at the beginning or end of the year. More detailed surveys break down inventories into three or more categories: raw materials inventory, goods-in process inventory, and finished goods inventory.

Regressions (1), (2), and (3) of Table 6 present the results of regressing raw materials inventory on infrastructure and the presence of a free market, as well as some control variables. The analysis uses two proxies for infrastructure, telephone mainlines per person and BERI's infrastructure quality index, which, although more comprehensive, is available for fewer countries. These proxies for infrastructure are significant at the 1 percent or 5 percent level; the coefficients suggest that a one-standard deviation worsening in infrastructure increases inventories by 27 percent to 47 percent relative to U.S. levels. The proxy for the lack of a free market is transfers and subsidies to private and public enterprises expressed as a fraction of GDP A one-standard deviation restriction on the free market increases raw materials inventories by 19 to 30 percent.

> 9 However, it is worth pointing out that the initials results of the research, using the aggregate inventory levels computed from the National Accounts data, were not inconsistent with the stylized observation that developing countries hold more inventory than developed countries.

21
introduced. Further, Nile perch was introduced to the Ugandan part of the Lake in 1954. It is estimated that over 200 of the original species are now extinct; most were of no commercial importance but are now considered to be relevant to the sustainability of Lake Victoria’s ecosystem.

During the 1960 and 1970s, the Kenyan landed catch from Lake Victoria was stable at around 100,000 tonnes per annum (Abila and Jansen, 1997). At this time, the fishery was exploited solely by local fishers, 80 percent of which derived their primary income from fishing. Although average earnings declined as the number of fishers on the Lake increased (Francis and Hoddinott, 1983), income was distributed fairly evenly within the community. There were considerable investment barriers such that most boat owners had a single vessel and only sufficient gill nets that they could handle themselves.

Traditionally, the fishery consisted of wooden crafts propelled by sail. Very few had motorised propulsion. Throughout the 1960s and 1970s there was very little improvement in technology. This served to constrain the effectiveness of the fishing effort and thus limit the level of exploitation at sustainable levels (Geheb and Binns, 1997). Further, fisheries resources were managed judiciously and in a sustainable manner, with indigenous rules and regulations safeguarding fishing activities.

A vibrant artisanal processing and trading sector co-existed with the fishery, based on or near to landing beaches. This was dominated by small-scale operators, mostly women, from the local community. Fish that was not sold fresh was processed by smoking or sun-drying and carried to local inland markets. In general, the wholesale sector was relatively small and traders had little market power. Most fishers sold fish to a limited number of fishmongers with which they had a long-standing relationship (Abila and Jansen, 1997).

Prior to 1980, the Lake Victoria fishery existed largely without outside interference. Whilst the fishery was subject to regulation under the Fisheries Act, this was largely un-enforced (Abila and Jansen, 1997). Whilst the Fisheries Department collected catch statistics though Scouts, these were largely employed from the local community. Instead, the fishery was regulated by local norms and customs covering who was entitled to fish, in which seasons, which areas, types of fish gear that could be used, size of fish that could be landed etc.

Nile perch first appeared in Kenyan catch statistics in 1973 (Geheb and Binns, 1979). However, it was not until the 1980s that the Nile perch came to dominate the Lake Victoria fishery (Figure 3). Landings increased rapidly from 23,000 tonnes in 1981, to 50,000 tonnes in 1985 and 123,000 tonnes in 1991<sup>21</sup> . Subsequently landings stabilised, although with significant annual variations. Thus, a tri-species fishery has become established, dominated by Nile perch, _Omena_ and (to a much lesser extent) tilapia (Othina and Osewe-Odera, 1996).

> 21 Landings increased at an even faster rate in Tanzania and Uganda.

14
9 of health system performance (see e.g. Hakkinen and Joumard 2007). Throughput measures— such as inpatient admissions, and ambulatory care visits—are just that, and need not necessarily capture the ultimate outcome of interest, namely better health. But health indicators such as mortality (or life expectancy) and disability reflect many factors beyond the control of the health sector: deaths from road accidents likely reflect road safety improvements more than health spending, and many deaths are from causes that are still not amenable to medical intervention. Arguably a more compelling approach when assessing the efficiency of a country‟s health spending is to link health spending to deaths from conditions that _are_ amenable to medical care (Nolte and McKee 2008). This is what we do in this study.

Our data come from the OECD‟s Health Data. We have data for 29 countries over the period 1960-2005. Not all countries have data for every year. Health spending is defined as total health spending (i.e. public plus private) measured in 2000 prices in international dollars. Mortality is measured through potential years of life lost (PYLL) among people below the age of 70 who die from nine causes of death that are „amenable‟ to medical care, i.e. causes where timely and effective medical care can result in a death being avoided. We select the conditions from Nolte and McKee (2008) who identify a longer list of conditions but only nine are included in the OECD PYLL database. Deaths among older age groups are excluded by the OECD on the grounds that they are less easily amenable to medical care. We aggregate some of the conditions so we are left with a more manageable six PYLL „outputs‟.<sup>11</sup> Our measure of health sector output—while preferable to a throughput measure and better than all-cause mortality among all age groups—is not without its limitations, of course. It focuses on length of life rather than quality of life and does not capture success in reducing mortality among the over-70s from conditions that are amenable to medical care. Countries that disproportionately target spending at the over-70s, or at patients whose length of life cannot be extended but whose quality of life can be improved will appear in our analysis as inefficient.

# **Hospitals—Vietnam**

Hospitals absorb the bulk of health spending in most countries, and there has been much discussion of the scope for lowering health spending by reducing their inefficiency.

> 11 We aggregated PYLL‟s from three types of cancer (colon, breast and cervical) into an aggregate cancer PYLL, and aggregated PYLL‟s from pregnancies/deliveries and perinatal causes into an aggregate maternal and child health (MCH)) PYLL. The remaining four amenable causes were diabetes, ischemic heart disease, cerebrovascular disease, and influenza/pneumonia.