| |$ \Delta $|ln(IT budget) . | |$ \Delta $|ln(IT budget/ sales) . | |$ \Delta $|ln(IT budget/ emp.) . | |$ \Delta $|ln(PCs) . | |$ \Delta $|ln(PCs/ sales) . | |$ \Delta $|ln(PCs/ emp.) . | |
|---|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
| |$ \Delta $|Opioid prescriptions | 0.108*** | 0.173*** | 0.122*** | 0.065*** | 0.093*** | 0.029*** |
| (0.039) | (0.044) | (0.038) | (0.019) | (0.021) | (0.008) | |
| |$ \Delta $|ln(Income) | 0.192** | 0.143* | 0.193** | 0.076* | –0.006 | 0.032** |
| (0.083) | (0.081) | (0.076) | (0.042) | (0.040) | (0.015) | |
| |$ \Delta $|ln(Population) | –0.134* | –0.217*** | -0.136** | –0.010 | –0.046** | 0.003 |
| (0.072) | (0.067) | (0.068) | (0.026) | (0.023) | (0.010) | |
| |$ \Delta $|White ratio | 0.001 | –0.004 | 0.002 | 0.004*** | 0.001 | 0.002*** |
| (0.004) | (0.004) | (0.004) | (0.001) | (0.002) | (0.001) | |
| |$ \Delta $|Age 20-64 ratio | 0.011 | 0.023*** | 0.013 | 0.002 | 0.006* | 0.002 |
| (0.009) | (0.009) | (0.009) | (0.004) | (0.003) | (0.002) | |
| |$ \Delta $|Age above 65 ratio | 0.005 | 0.010 | 0.004 | 0.003 | 0.002 | 0.002 |
| (0.010) | (0.009) | (0.009) | (0.005) | (0.004) | (0.002) | |
| |$ \Delta $|Neoplasms mortality | –0.016 | 0.001 | –0.010 | –0.007 | 0.011 | –0.003 |
| (0.019) | (0.018) | (0.018) | (0.008) | (0.007) | (0.003) | |
| Firm-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Industry-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 286,073 | 272,642 | 286,073 | 298,288 | 284,790 | 298,288 |
| |$ R^{2} $| | .390 | .460 | .415 | .450 | .679 | .596 |
| |$ \Delta $|ln(IT budget) . | |$ \Delta $|ln(IT budget/ sales) . | |$ \Delta $|ln(IT budget/ emp.) . | |$ \Delta $|ln(PCs) . | |$ \Delta $|ln(PCs/ sales) . | |$ \Delta $|ln(PCs/ emp.) . | |
|---|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
| |$ \Delta $|Opioid prescriptions | 0.108*** | 0.173*** | 0.122*** | 0.065*** | 0.093*** | 0.029*** |
| (0.039) | (0.044) | (0.038) | (0.019) | (0.021) | (0.008) | |
| |$ \Delta $|ln(Income) | 0.192** | 0.143* | 0.193** | 0.076* | –0.006 | 0.032** |
| (0.083) | (0.081) | (0.076) | (0.042) | (0.040) | (0.015) | |
| |$ \Delta $|ln(Population) | –0.134* | –0.217*** | -0.136** | –0.010 | –0.046** | 0.003 |
| (0.072) | (0.067) | (0.068) | (0.026) | (0.023) | (0.010) | |
| |$ \Delta $|White ratio | 0.001 | –0.004 | 0.002 | 0.004*** | 0.001 | 0.002*** |
| (0.004) | (0.004) | (0.004) | (0.001) | (0.002) | (0.001) | |
| |$ \Delta $|Age 20-64 ratio | 0.011 | 0.023*** | 0.013 | 0.002 | 0.006* | 0.002 |
| (0.009) | (0.009) | (0.009) | (0.004) | (0.003) | (0.002) | |
| |$ \Delta $|Age above 65 ratio | 0.005 | 0.010 | 0.004 | 0.003 | 0.002 | 0.002 |
| (0.010) | (0.009) | (0.009) | (0.005) | (0.004) | (0.002) | |
| |$ \Delta $|Neoplasms mortality | –0.016 | 0.001 | –0.010 | –0.007 | 0.011 | –0.003 |
| (0.019) | (0.018) | (0.018) | (0.008) | (0.007) | (0.003) | |
| Firm-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Industry-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 286,073 | 272,642 | 286,073 | 298,288 | 284,790 | 298,288 |
| |$ R^{2} $| | .390 | .460 | .415 | .450 | .679 | .596 |
This table presents a first-difference estimation using changes in opioid prescription rates over 2002–2006 and 2006–2010 and subsequent changes in establishment IT investment over 2007–2011 and 2011–2015, respectively. The dependent variables are changes in the following: logarithm of IT budget in column 1, logarithm of IT budget by sales in column 2, logarithm of IT budget by employment in column 3, logarithm of PCs in column 4, logarithm of PCs by sales in column 5, and logarithm of PCs by employment in column 6. Controls are measured as changes over 2002–2006 and 2006–2010. Industries are defined by four-digit NAICS codes. All variables are defined in the appendix and winsorized at the 1% level. Standard errors are double-clustered at the county and firm levels and presented in parentheses.
|$ p $| < .1;
|$ p $| < .05;
|$ p $| < .01.
| |$ \Delta $|ln(IT budget) . | |$ \Delta $|ln(IT budget/ sales) . | |$ \Delta $|ln(IT budget/ emp.) . | |$ \Delta $|ln(PCs) . | |$ \Delta $|ln(PCs/ sales) . | |$ \Delta $|ln(PCs/ emp.) . | |
|---|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
| |$ \Delta $|Opioid prescriptions | 0.108*** | 0.173*** | 0.122*** | 0.065*** | 0.093*** | 0.029*** |
| (0.039) | (0.044) | (0.038) | (0.019) | (0.021) | (0.008) | |
| |$ \Delta $|ln(Income) | 0.192** | 0.143* | 0.193** | 0.076* | –0.006 | 0.032** |
| (0.083) | (0.081) | (0.076) | (0.042) | (0.040) | (0.015) | |
| |$ \Delta $|ln(Population) | –0.134* | –0.217*** | -0.136** | –0.010 | –0.046** | 0.003 |
| (0.072) | (0.067) | (0.068) | (0.026) | (0.023) | (0.010) | |
| |$ \Delta $|White ratio | 0.001 | –0.004 | 0.002 | 0.004*** | 0.001 | 0.002*** |
| (0.004) | (0.004) | (0.004) | (0.001) | (0.002) | (0.001) | |
| |$ \Delta $|Age 20-64 ratio | 0.011 | 0.023*** | 0.013 | 0.002 | 0.006* | 0.002 |
| (0.009) | (0.009) | (0.009) | (0.004) | (0.003) | (0.002) | |
| |$ \Delta $|Age above 65 ratio | 0.005 | 0.010 | 0.004 | 0.003 | 0.002 | 0.002 |
| (0.010) | (0.009) | (0.009) | (0.005) | (0.004) | (0.002) | |
| |$ \Delta $|Neoplasms mortality | –0.016 | 0.001 | –0.010 | –0.007 | 0.011 | –0.003 |
| (0.019) | (0.018) | (0.018) | (0.008) | (0.007) | (0.003) | |
| Firm-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Industry-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 286,073 | 272,642 | 286,073 | 298,288 | 284,790 | 298,288 |
| |$ R^{2} $| | .390 | .460 | .415 | .450 | .679 | .596 |
| |$ \Delta $|ln(IT budget) . | |$ \Delta $|ln(IT budget/ sales) . | |$ \Delta $|ln(IT budget/ emp.) . | |$ \Delta $|ln(PCs) . | |$ \Delta $|ln(PCs/ sales) . | |$ \Delta $|ln(PCs/ emp.) . | |
|---|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . | |
| |$ \Delta $|Opioid prescriptions | 0.108*** | 0.173*** | 0.122*** | 0.065*** | 0.093*** | 0.029*** |
| (0.039) | (0.044) | (0.038) | (0.019) | (0.021) | (0.008) | |
| |$ \Delta $|ln(Income) | 0.192** | 0.143* | 0.193** | 0.076* | –0.006 | 0.032** |
| (0.083) | (0.081) | (0.076) | (0.042) | (0.040) | (0.015) | |
| |$ \Delta $|ln(Population) | –0.134* | –0.217*** | -0.136** | –0.010 | –0.046** | 0.003 |
| (0.072) | (0.067) | (0.068) | (0.026) | (0.023) | (0.010) | |
| |$ \Delta $|White ratio | 0.001 | –0.004 | 0.002 | 0.004*** | 0.001 | 0.002*** |
| (0.004) | (0.004) | (0.004) | (0.001) | (0.002) | (0.001) | |
| |$ \Delta $|Age 20-64 ratio | 0.011 | 0.023*** | 0.013 | 0.002 | 0.006* | 0.002 |
| (0.009) | (0.009) | (0.009) | (0.004) | (0.003) | (0.002) | |
| |$ \Delta $|Age above 65 ratio | 0.005 | 0.010 | 0.004 | 0.003 | 0.002 | 0.002 |
| (0.010) | (0.009) | (0.009) | (0.005) | (0.004) | (0.002) | |
| |$ \Delta $|Neoplasms mortality | –0.016 | 0.001 | –0.010 | –0.007 | 0.011 | –0.003 |
| (0.019) | (0.018) | (0.018) | (0.008) | (0.007) | (0.003) | |
| Firm-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Industry-period FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Observations | 286,073 | 272,642 | 286,073 | 298,288 | 284,790 | 298,288 |
| |$ R^{2} $| | .390 | .460 | .415 | .450 | .679 | .596 |
This table presents a first-difference estimation using changes in opioid prescription rates over 2002–2006 and 2006–2010 and subsequent changes in establishment IT investment over 2007–2011 and 2011–2015, respectively. The dependent variables are changes in the following: logarithm of IT budget in column 1, logarithm of IT budget by sales in column 2, logarithm of IT budget by employment in column 3, logarithm of PCs in column 4, logarithm of PCs by sales in column 5, and logarithm of PCs by employment in column 6. Controls are measured as changes over 2002–2006 and 2006–2010. Industries are defined by four-digit NAICS codes. All variables are defined in the appendix and winsorized at the 1% level. Standard errors are double-clustered at the county and firm levels and presented in parentheses.
|$ p $| < .1;
|$ p $| < .05;
|$ p $| < .01.
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