rafmacalaba/datause-encoder-data huggingface dataset:
|Log export sales<br>Intermediate products<br>Final products<br>(3)<br>(4)<br>-0.499<br>0.041<br>(0.421)<br>(0.318)<br>-2.764***<br>-1.195*<br>(0.981)<br>(0.654)<br>1.651***<br>1.086***<br>(0.198)<br>(0.194)<br>1.501***<br>1.406***<br>(0.118)<br>(0.180)<br>-0.023*<br>-0.037***<br>(0.013)<br>(0.014)<br>11,375<br>8,973<br>0.639<br>0.688<br>MNF<br>Non-MNF|-0.603*<br>-0.025<br>(0.335)<br>(0.227)<br>-3.141***<br>-2.213***<br>(0.861)<br>(0.455)<br>1.650***<br>1.287***<br>(0.201)<br>(0.137)<br>1.897***<br>1.443***<br>(0.139)<br>(0.090)<br>-0.081***<br>-0.030***<br>(0.015)<br>(0.009)<br>9,230<br>25,097<br>0.537<br>0.585<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>mns (1) to (3) the dependent variable is the logarithm of the number of exported products to country<br> Heteroskedasticity-robust standard errors clustered by frm-country pairs are reported in parentheses.|
|---|---|
|Log number of exported products<br>Intermediate products<br>Final products<br>(1)<br>(2)<br>-0.005<br>0.403<br>(0.300)<br>(0.246)<br>-1.684**<br>-0.664<br>(0.725)<br>(0.541)<br>0.909***<br>0.898***<br>(0.164)<br>(0.167)<br>0.892***<br>0.936***<br>(0.083)<br>(0.148)<br>-0.056***<br>-0.067***<br>(0.010)<br>(0.012)<br>11,375<br>8,973<br>0.601<br>0.656<br>MNF<br>Non-MNF|-0.487*<br>0.023<br>(0.268)<br>(0.181)<br>-2.229***<br>-1.131***<br>(0.673)<br>(0.336)<br>0.714***<br>0.984***<br>(0.148)<br>(0.118)<br>1.009***<br>1.018***<br>(0.098)<br>(0.070)<br>-0.119***<br>-0.076***<br>(0.012)<br>(0.007)<br>9,230<br>25,097<br>0.549<br>0.556<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>ample of frms exporting intermediate and fnal goods (in the bottom part of the table). In colu<br>ort sales in country_j_ by frm_f_ in year_t_. Control variables defnitions are reported in table 4.|
|Dependent variable<br>Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_<br>Extensive margin competition_fjt_(N_◦_countries)<br>Extensive margin competition_fjt_(N_◦_frms)<br>Intensive margin competition_fjt_<br>Observations<br>_R_2|Tariff_fj,t−_1<br>Tariff_fj,t−_1_×_China_j_<br>Extensive margin competition_fjt_(N_◦_countries)<br>Extensive margin competition_fjt_(N_◦_frms)<br>Intensive margin competition_fjt_<br>Observations<br>_R_2<br>Country level controls<br>Firm fxed effects<br>Country fxed effects<br>Year fxed effects<br>_Notes_: The regressions are OLS estimations of equation (3) for the period 1999-2005 for the subs<br>_j_ by frm_f_ in year_t_, and in columns (4) to (6) the dependent variable is the logarithm of the exp<br>_∗∗∗, ∗∗,_and_∗_indicate signifcance at the 1, 5 and 10 percent levels respectively.|

25
**Figure 3: Innovation Effect of an Increase in Regulatory Freedom as Function of Governance and Schooling**

<!-- Start of picture text --> Personal Computers<br>120 90<br>80 60<br>40 30<br>0 0<br>2 2.5 3 3.5 4 4.5 5 5.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5<br>-40 -30<br>-80 -60<br>Governance (ICRG index) Schooling (average years)<br>Internet Users<br>120 90<br>80 60<br>40 30<br>0 0<br>2 2.5 3 3.5 4 4.5 5 5.5 2.3 3.3 4.3 5.3 6.3 7.3 8.3 9.3 10.3<br>-40 -30<br>-80 -60<br>Governance (ICRG index) Schooling (average years)<br> (per 1,000 people) (per 1,000 people)<br>Change in Pers. Computers Change in Pers. Computers<br>(per 1,000 people) (per 1,000 people)<br>Change in Internet Users Change in Internet users<br><!-- End of picture text --> Notes: 1. The solid lines show the effect of a one-standard-deviation increase in the index of regulatory freedom on personal computers and internet users, respectively; 2. The x axis represents the .05-.95 percentile range of, respectively, governance and schooling in the sample; 3. Dotted lines are 90% confidence bands.

# **3. A model of plant selection**

We develop a general equilibrium model of heterogeneous production units, vintage capital, and idiosyncratic shocks, based on Hopenhayn (1992), Campbell (1998) and Bergoeing, Loayza and Repetto (2004). There exists a distribution of plants characterized by different levels of productivity. In each period, plant managers decide whether to exit or stay in business. If a plant stays, the manager must decide how much labor to hire. If the plant exits, it is worth a selloff value. Every period the incumbents receive an idiosyncratic productivity shock. In addition, new plants enter every period. The initial technology level of a newcomer is random, although 13
We begin by estimating the impact of the bailout on credit market outcomes, using difference-in-differences specifications of the form where _Cit_ is a credit market outcome for district _i_ (in region _r_ ) and year _t_ , the variable _Eit_ ( _Bailout sharei × Postt_ ) measures a district’s exposure to the bailout program, _αi_ are district fixed effects and _βt_ are time fixed effects, which we allow to vary across the four administrative regions _dr_ , _i ∈ r_ of the Indian central bank to account for heterogeneity in local business cycles. _Xit_ is a vector of observable determinants of credit market conditions, which always includes lagged monsoon rainfall and a full set of electoral cycle dummies.<sup>16</sup> The coefficient of interest _γ_ measures the effect of program exposure on credit market outcomes, and the error term _εit_ captures all omitted factors, including any deviations from linearity. We drop all observations for 2008, the year in which the program took effect, to rule out any mechanical correlation between treatment intensity and observed credit market outcomes, and estimate equation 4.1 using data for 489 districts observed between 2001 and 2012, for a total of 5,511 observations.

Equation 4.1 will consistently estimate _γ_ if _Cov_ ( _Eit, εit_ )=0. This covariance restriction may, however, not hold if exposure to the program is correlated with unobserved trends in credit market outcomes. To address this concern, we rely on an instrumental variables strategy based on the rule that a loan had to be both in default _and_ backed by land collateral of less than two hectares to qualify for unconditional debt relief. This eligibility rule allows us to use two sources of exogenous variation to instrument for program exposure. The first source of variation is the time series of weather shocks<sup>17</sup> experienced by a district prior to

> 16See Cole [2009b] for evidence that lending by Indian state banks co-moves with the electoral cycle for local (state assembly) elections.

> 17We use years in which total monsoon rain between June and September was below 75% of the district’s long-run average rainfall as our definition of weather shocks. This definition is consistent with the threshold

15
water. Some studies have controlled for water supply (Mendelsohn and Dinar 2003 and Fleisher and Mendelsohn 2007). However, water data is not available in this study. This is important since climate change may reduce (or increase) the amount of water that is available to farmers and this effect is not captured in this analysis. Given China’s clear dependence on irrigation water, this is an important omission.

There have also been concerns about the role of price changes (Quiggin and Horowitz 1999). The Ricardian model does not take into account price changes and thus will overestimate welfare effects. Although changes in local supply might be dramatic, prices of food crops tend to be determined by global markets. With the expansion of crop production in some parts of the world and the contraction in others, the changes in the price of crops from global warming is expected to be small (Reilly et al. 1994). Finally, there is a concern that the Ricardian analysis does not take into account the cost of transition (Kelly et al 2005). Although we expect transition costs to be relatively small, the Ricardian method does not measure them.

# **III. DATA AND MODEL SPECIFICATIONS**

The climate data (monthly temperature and precipitation) were gathered from the National Meteorological Information Center in China. The data are based on actual measurements in 753 national meteorological stations that are located throughout China. The temperature and precipitation data were collected from 1951 to 2001. We rely on the mean values of these variables (climate normals) over this time period for each month.

Because we cannot include every month in the analysis because of the high correlation from month to month, we average the monthly climate data into four seasons. Winter is the averaged of December to February, spring is the average of March to May, summer is the average of June to August, and fall is the average of September to November.

Socio-economic data come from China’s National Bureau of Statistics (CNBS). The data were collected by a highly trained, professional enumeration staff in 2001 as part of the annual, nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm households in 4365 villages, 533 counties and 31 provinces.

During the survey enumerators from CNBS collected a rich set of information at both the village and household level. The data provide us with a measure for the dependent variable, net 6
This year we have also discarded two data sources that have not been updated, as we try to rely on regularly updated data sources only for the WGI. In the past, we have treated the World Bank's Business Environment Survey carried out in 1999 and 2000, and a special survey done by the World Bank for the 1997 World Development Report, as a single source used in the 2000 and 1998 versions of our indicators. These were one-time surveys that have not been repeated, and in the interest of improving comparability over time in our data sources we have dropped them. We have also dropped the Columbia University State Capacity Survey, an expert assessment first carried out in 2000, with a limited update in 2002, but none subsequently.

We have also made a few changes to the set of individual indicators going into the six aggregate indicators. Our main reason for doing so is to make these individual indicators more comparable over time. In two cases this means that we no longer use certain variables drawn from one-time surveys. In the past we used data from a special World Economic Forum Executive Opinion Survey for African countries in our aggregate indicators for 1998. As this survey was not repeated with a freestanding questionnaire, we had data for this year only and we have decided to drop this source as the questionnaire differed substantially from the World Economic Forum's core questionnaire for other countries. We do note however that the World Economic Forum's core survey has subsequently expanded country coverage and now has significant coverage in African countries. We have also in the past used a variety of distinct one-time household surveys conducted by Gallup International, and combined them as a single indicator with the annual Global Corruption Barometer survey sponsored by Transparency International, and implemented as part of the Gallup International Voice of the People surveys. To make this source more comparable over time we now use data only from the Global Corruption Barometer survey, which has asked a fairly consistent set of questions regarding corruption since 2002.

Two remaining revisions to the underlying data are noteworthy. First, we have obtained data from earlier years that we already had previously from a number of sources (WMO and ADB), and another one of our sources has significantly expanded country coverage starting in 2006 (EIU). Second, we have in some cases revised the timing of our indicators in earlier years. In some cases, this was due to unavailability of 9
P a g e <u>|</u> **11**

households demand a certain amount of space to live and conduct business in the city. When FAR’s are kept low, much more land is required for a given amount of office or residential space. High prices will of course compress the demand for space, but not completely, so low maximum FARs result both in spread out cities and unnecessarily high land prices.

# **Table 2. Scenarios Comparing Impact of Lower and Higher FAR on Costs at Different Land Prices**

|**Parameters**<br>|**Units**|||||||||
|---|---|---|---|---|---|---|---|---|---|
|**FAR**||**1.3**|**4.0**|**1.3**|**4.0**|**1.3**|**12.0**|**1.3**|**12.0**|
|**landpriceper square foot**<br>|**INR.**|**800**|**800**|**4,000**|**4,000**|**800**|**800**|**4,000**|**4,000**|
|**Impacts**||||||||||
|**land required per square foot**<br>**of built floor space**|**square**<br>**feet**|**0.77**|**0.25**|**0.77**|**0.25**|**0.77**|**0.08**|**0.77**|**0.08**|
|**land cost per square foot of**<br>**built floor space**<br>|**INR.**|**615**|**200**|**3,077**|**1,000**|**615**|**67**|**3,077**|**333**|
|**construction cost per square**||||||||||
|**foot**<br>|**INR.**|**700**|**770**|**700**|**770**|**700**|**1,400**|**700**|**1,400**|
|**total cost per square foot of**<br>**built floor space**<br>|**INR.**|**1,315**|**970**|**3,777**|**1,770**|**1,315**|**1,467**|**3,777**|**1,733**|
|**Cost increase for built space**<br>**due to lower FAR**|**%**|**36%**||**113%**||**‐10%**||**118%**||

**Construction costs assumed to increase by 10% and 100% respectively for 4 and 12 FAR . Land costs are hypothetical.**

FAR regulations are not alone in forcing high consumption of land and reducing scope for substitution of capital for land. Minimum plot sizes, regulations concerning use of elevators, parking, street width, maximum building coverage, etc. require high consumption of land per unit of usable built space, and thus contribute to the high cost of formal housing. Many of these regulations are normative and not based on any economic assessment of the costs (including the price of land) they impose on consumers of housing in a given city. In some areas, these additional restrictions are such that they prevent a developer from using even all the available FAR. The private formal sector could provide more housing for low-income groups if these regulations were audited to insure that land use 11
-28:-

**Table 5: Simulations of education indicators by level of per capita GNP, 1993**

|Indicatora/|Item in||P|er capita|GNP, 199|3||
|---|---|---|---|---|---|---|---|
||equation (3)|$200|$400|$800|$1,500|$3,000|$10,000|
|Total govt. budget as % of GNP|Gt / GNP|26.2|26.2|26.8|28.6|31.7|40.2|
|Education share of total govt. budget**(%)**|_Ge/_G,|13.4|15.1|16.2|16.7|16.7|15.5|
|Primary ed. as share of total ed. budget(%)|<br>Gp /**Ge**|45.1|43.0|40.8|38.9|36.8|33.1|
|School-aged<br>children's<br>share of population|Psa/ P,|**23.0**|**21.6**|20.2|**18.9**|**17.5**|**15.1**|
|Teacher pay relative to per capita GNP|TS / (GNP/Pt)|**5.4**|**4.3**|**3.4**|**2.7**|**2.2**|**1.7**|

Source: simulated from regression estimates of the relation between each indicator and the per capita GNP, based on data for a sample of some 125 countries in 1993.

a/ The enrollment rates is defined as the proportion of each age cohort that is enrolled at the end of the primary cycle; we chose this indicato **r** ather than the more common gross or net enrollment ratios because it offers a more accurate picture of coverage.

_Comparing countries with per capita GNP of $200 and $400._ For simplicity, we shall refer to these countries as A and B, respectively. Table 6 shows the advantage of B over A in terms of resources available for primary education, as well as in the deployment of those resources to expand coverage and reduce the pupil-teacher ratio. As before, the unshaded rows in the table correspond to the left-hand-side of the accounting equation (i.e. equation (3)), while the shaded rows correspond to the right hand-hand-side.

The results in the table show that there is no difference between the two countries in overall government budget; but in the share of education in the overall government budget in country B is 1.13 times that in country A. However, because primary education's share of the education budget in country B is smaller than in country A, the resources available for primary education relative to the GNP are in fact only 1.07 times (=1.13 x 0.95) as high as in country A. The other two sources of country B's resource advantage are a lighter demographic burden and lower teacher salaries relative to the per capita GNP. The share of its school-age population in the total population is 21.6 percent, compared with 23.0 percent in country A, implying an advantage for country B of 6.0 percent (=(23.0/21.6 - 1) x 100); and average teacher pay is 4.3 times the per capita GNP in country B, compared with 5.4 times in country A, implying an advantage of 26 percent (=(5.4/4.3 - 1) x 100) for country B.
likely that the product-related cues would also benefit respondents when informing about household usage of those services. We would recommend, therefore, that the institutionbased questions used in the financial modules of larger, sometimes multi-purpose, surveys be adapted to include product-based cues that are appropriate to the country context.

Decisions on future questionnaires will also need to take into account the relative costs of implementing the different treatments. We find that the costs of the different treatments (in terms of interview time) administered conform to expectations.<sup>14</sup> The full enumeration using the product list takes the longest to administer. But, full enumeration itself, using either the product or institution questionnaire adds significant time to the interviews compared to using a proxy respondent for the household. In other words, the finding that the head of household is able to provide similar data to that obtained from full enumeration for most products has positive implications for the feasibility of expanding data collection on financial service use to other countries. Finally, however, for survey designers in countries that may have higher levels of financial service use, it is also important to note how much average interview time rises when household use of financial services is higher. For example, the full enumeration product-based format in questionnaire 1 took 20-30% more time to administer when members of the household used banking or insurance services than when they did not (See Table 13.) Lurking throughout our paper is a concern about our ability to generalize beyond Ghana. While we feel that there is a strong undercurrent of common sense to our main

> 14 The time data collected in this survey are, at best, a rough approximation of the actual time required.  No effort was made to record time at the level of the specific product or institution modules.  Only a total for the entire household interview, which includes a roster and further questions on attitudes and knowledge of finance, is available.  Also as Groups 2 and 3 contain two different treatments, it is not possible to really separate out the time costs associated with each one.

36
_IMPACT OF WEDP LOANS_ _11_

# **VI. Results**

To account for the possible impact of receiving a loan on firm survival, we code firms that are closed as having zero employment and zero profits in our analysis. This enables us to examine the full unconditional impact on these outcomes in a way which is not subject to selectivity concerns present in comparing only firms in operation. We also provide comparisons of treatment and control profits and sales conditional on start-up and survival. The comparison group for the impact analysis is drawn from the firms that received no WEDP loans as measured at time of follow-up.

# _A. PROGRESSION OF EXISTING FIRMS FROM BASELINE TO FOLLOW-UP_

In tables 4 to 7 we report results for five models: ANCOVA, ANCOVA with controls, difference-in-difference with controls (DID with controls), DID with propensity score matching using entrepreneur characteristics (DID PSM Min Kernel) and DID with propensity score matching using entrepreneur characteristics plus firm size proxied by number of employees at baseline (DID PSM Max Kernel).

Tables 4 to 7 present the impact of receiving a loan on average yearly profit, number of employees, hours worked by the owner and hours worked by employees for firms that were in operation at baseline. These are the outcomes for which we have the pre-program estimates based on retrospective recall. Columns (4) and (5) present the DID estimates using propensity score kernel matching that matches a comparison group based on a number of covariates that predict the likelihood of receiving a loan. The included outcomes used to construct the propensity score are: age of the owner, age squared, education, household size, digitspan score (memory recall), entrepreneurial identity index and number of employees at baseline.

Table 4 reports the results for the average treatment effect of receiving a loan on average yearly profits by comparing WEDP borrowers to non-borrowers. Impacts in the 2014 and 2016 survey rounds are presented in the table. Receiving a WEDP loan has a positive significant impact on the profitability of firms when measured 3 years after the WEDP program started issuing loans (see positive significant coefficient on Loan ~~2~~ 016 in the ANCOVA regressions in columns (1) and (2)). The magnitude of treatment effects is similar across the DID regression models; however, the PSM model using kernel matching produces a more conservative estimate that is no longer statistically significant (positive coefficient on Loan ~~2~~ 016*Post2 shows the treatment impact in 2016). The similar effects for the unconditonal and conditional samples suggests that the impact of loans on profitability is not occurring through the extensive margin of allowing firms to survive and earn profits.

Table 5 reports the average treatment effect of receiving a loan on employment. Receiving a loan has a positive impact on the number of employees when measured 3 years after the WEDP program started issuing loans (positive significant
Table 2 shows definitions and sources for all variables used in our subsequent regressions. In Table 3, we provide descriptive statistics. Just over half the firms in our sample have access to a loan, line of credit or overdraft protection. Firms with a loan pay on average an interest rate of almost 14 percent, with an average maturity of over 30 months. While the median firm obtains no fraction of financing from banks for working capital and fixed assets, the mean is 13 percent and 17 percent, respectively. Firms on average have been in business for about 12 years and employ 28 permanent employees. Just over 50 percent of firms in our sample are in the manufacturing sector, 10 percent are foreign owned, 5 percent are government owned, and 21 percent of firms are exporters.

# **III- Empirical methodology**

We estimate the impact of credit bureau reform, and separately credit registry reform, using the empirical specification in equation (1) below: _Finance Indicator_ i,j,t= β<sup>1</sup> _Credit information sharing reform_ j,t+ β<sup>2</sup> Xi,j,t+ β<sup>3</sup> Zj,t+ α j+γt+εi,j,t (1) where _Finance Indicator_ refers to five different measures of firm financing for firm i in country j at time t: (a) _Access to finance_ is a dummy that takes the value of 1 if the firm has a line of credit, loan or overdraft<sup>4</sup> , (b) _Interest Rate_ refers to the interest rate paid by the firm on the most recent loan and, (c) _Maturity_ is the maturity (in months) for the firm’s most recent loan, (d) _Working capital by banks_ refers to the fraction of working capital of the firm that is financed by

> 4 When the dependent variable is _Access to finance_ , we estimate a logit model and report marginal effects in the result tables .

10